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不同工况下的能量管理

不同工况下的能量管理
不同工况下的能量管理

INTRODUCTION

In the last few years, a motivated interest in reducing fuel consumption and exhaust emissions as well as advanced

technologies of hybrid vehicles without sacrificing the drivability and driver comfort has increased worldwide. Due to the

multiple energy sources, driving modes, environmental effects and complex configurations, the hybrid vehicle control

strategies have become more complicated. In order to deal with these challenges, it is necessary to optimize the energy management strategy.

A parallel hybrid vehicle model is used to approximate the main performance characteristics and vehicle efficiency for a given driving cycle. The main aim of this work is to improve both the powertrain efficiency and thus the fuel consumption and to improve the climate comfort.

This leads to an increase in the hybrid vehicle range capability. The effect of ambient conditions on fuel economy is also included.

For a cold ICE, the coolant temperature is below an acceptable value and the emissions are relatively high since the catalysts are not reaching their operating temperature. To reduce the cold-start emissions, methods for fast engine warm-up are

needed. Unfortunately, fuel consumption increases in the warm-up phase, because the ICE sometimes operates in its low efficiency range.

ICE cold start-up and the catalyst heating function are

modeled. By allowing driving at higher engine load during the warming-up phase, the ICE exhaust gas becomes hotter and can heat the catalyst more rapidly, thereby also the warm-up phase and the fuel consumption are decreased.

There are several publications on “Energy and Thermal

Management” in hybrid powertrains in different environmental conditions. Serial hybrid drive concepts, plug-in hybrid vehicles as well as parallel hybrid vehicles, which are very popular in Europe, were introduced and evaluated energetically from the point of increasing powertrain efficiency, see e.g. a series of publications [25, 26, 27, 31].

In this paper, however, a complex and advanced thermal HEV model for a comprehensive study in this research area was used, where not only individual measures are evaluated, but also possible combinations are optimized. Three different driving cycles and different ambient temperatures were taken into account to analyze the climate comfort in the passenger cabin and the resulting interaction with the energy balance of the vehicle.

Energy Management in a Parallel Hybrid Electric Vehicle for Different

Driving Conditions

Mirko Schulze, Rashad Mustafa, Benjamin Tilch, Peter Eilts, and Ferit Kü?ükay

Technical Univ. of Braunschweig

ABSTRACT

Hybrid electric vehicles (HEVs) are facing increased challenges of optimizing the energy flow through a vehicle system, to enhance both the fuel economy and emissions. Energy management of HEVs is a difficult task due to complexity of total system, considering the electrical, mechanical and thermal behavior. Innovative thermal management is one of the solutions for reaching these targets.

In this paper, the potential of thermal management for a parallel HEV with a baseline control strategy under different

driving cycles and ambient temperatures is presented. The focus of the investigations is on reducing fuel consumption and increasing comfort for passengers. In the first part of this paper, the developed HEV-model including the validation with measurements is presented. In the second part, the combined thermal management measures, for example the recuperation of exhaust-gas energy, engine compartment encapsulation and the effect on the target functions are

discussed. Simulation results show potential of reduction fuel consumption together with increasing the comfort for the passenger cabin.

CITATION: Schulze, M., Mustafa, R., Tilch, B., Eilts, P . et al., "Energy Management in a Parallel Hybrid Electric Vehicle for

Different Driving Conditions," SAE Int. J. Alt. Power. 3(2):2014, doi:10.4271/2014-01-1804.

2014-01-1804

Published 04/01/2014

Copyright ? 2014 SAE International

doi:10.4271/https://www.sodocs.net/doc/2a14444065.html,

193

An important part of this paper is the modeling of the integrated exhaust manifold (IEM) of the ICE, which is integrated in the hybrid powertrain, and the resulting effect on fuel consumption and climate comfort - as an individual measure as well as in combination with further energy management measures.

According to the current state of knowledge, such a detailed and in-depth energetic consideration and the associated effects on the thermal efficiency and therefore on the losses of the hybrid powertrain has never been provided before.

Part of the high level of detail of the HEV model mentioned above are, for instance, the special features to represent the mechanical losses in the ICE - the implementation of engine friction. What is distinctive about the model is that the coolant temperature and the engine load are taken into account in addition to the typical correlation variables engine speed and engine oil temperature. According to the current state of knowledge, it has not been used yet to evaluate the energy management in a thermal HEV simulation environment. A correlation of the coolant temperature, for example, is definitely required in order to evaluate the thermal management measures and their effects on mechanical losses correctly. Measurements showed that the coolant temperature correlates significantly with the friction of the pistons, which in turn play a significant part in engine friction. If only the oil or coolant temperature is assumed as thermal correlation variable - as often found in the literature [28, 29, 30] - the ICE friction calculated during the warm-up phase is too high since the coolant temperature has an higher temperature level as the engine oil.

The paper is organized as follows: at the beginning, the simulation models are described followed by validation. Then, the implementation of energy & thermal management measures is presented, especially the integrated exhaust manifold (IEM). Finally, the results are analyzed and discussed based on the simulation results of different driving conditions. MODELING

The HEV-powertrain, components and vehicle properties were modeled using co-simulation programs consisting of a set of empirical models, test bench maps and key data. Details of HEV components such as the ICE, EM and transmission are represented as modules, as can be seen in Figure 1. The powertrain block contains the components ICE, clutches, transmission, EM and wheel. The map based chemical energy from tank to the ICE effective torque is a nonlinear function of ICE speed and indicated mean effective pressure (imep) of the ICE. The mechanical torque generated from the combustion process (indicated torque) is divided into two main parts: the first part goes to the mechanical powertrain for vehicle propulsion, while the rest goes in the ICE mechanical losses including the auxiliaries units.The vehicle is modeled with a 7-speed dual clutch transmission. Driving resistance characteristics such as frontal area, rolling resistance, vehicle mass and drag coefficient significantly affect the requirement of the powertrain. Using a co-simulation, HEVs were driven over prescribed driving cycles in the normal case scenario of about 60% battery SoC. Three standard driving cycles were used in the HEV simulation:

FTP-75, US06 and NEDC with different ambient temperatures (20°C and ?7°C). The following rules were applied for the HEV simulation:

? At the end of the driving cycle, the battery SoC must have the same level as at the beginning of the cycle. During the simulations, the SoC must not exceed the upper or lower limit to avoid damage of the battery.

? Allowed difference between the desired speed and simulated speed of the driving cycle: 1km/h.

Compared to the most conventional vehicle, there is additional a low temperature coolant system in the HEV. The ICE and EM have a higher temperature level and the inverter, intercooler and turbocharger have separate coolant system with a lower temperature level. The radiator cooling fans are powered

electrically.

Figure 1. Modules of the hybrid powertrain model

Thermal Network - ICE

The ICE simulation model is divided into a small number of substitute masses based on [15]. By connecting substitute masses with thermal resistances, a thermal network can be generated. Every thermal mass represents a node of the network and is defined by its mass and its specific heat capacity. The number of network nodes defines the complexity of the thermal network. Every thermal mass is able to receive or emit heat. By balancing the acting heat fluxes, thermal masses can be warmed up or can be cooled down. Depending on a defined start temperature, the thermal network allows a calculation of time-depending temperature curves of each substitute mass. The model is able to simulate transient ICE behavior for different load cases.

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Figure 2 shows the configuration and structure of the thermal network including all thermal resistances and substitute

masses. It consists of the sub-models crankcase (CC), cylinder liner (CL), cylinder head (CH), piston (Pi), crankshaft and exhaust turbocharger. Each sub-model is divided in different metal substitute masses. The thermal masses of these components are taken from technical drawings and are therefore representing the real ICE masses. The fluid

substitute masses inside the engine are divided into the four nodes coolant in the crankcase, coolant in the cylinder head, direct and indirect oil. The thermal network is able to exchange heat with the ambient on surfaces of crankcase, cylinder head,

oil sump and turbocharger.

Figure 2. Thermal network of the ICE

Heat Transfer - Combustion Chamber

Figure 3 shows the determination of the combustion heat map by cylinder pressure analysis (CPA) of experimental data. Figure 3a and 3b show the measured cylinder pressure and the calculated burn rate. With the measured cylinder wall temperature and the calculated mean global gas temperature (Figure 3c ) the heat flow rate through the combustion chamber boundary can be estimated under consideration of the heat transfer coefficient (

Figure 3d).

(1)

By the integration of (1) the cumulated heat for one combustion cycle Q W can be calculated (Figure 3f). Figure 3h and 3g show

the resulting heat losses for the complete ICE operation map.

Figure 3. Derivation: Combustion chamber side heat losses map

Engine Friction Model

The heat input caused by engine friction is calculated by using an adjusted friction model, which is based on extensive strip measurements. What is distinctive about the model is that the coolant temperature and the engine load are taken into account in addition to the typical correlation variables ICE speed and

ICE oil temperature.

(2)

(3)

The friction mean effective pressure consists of the individual friction mean effective pressures of the components or units - the coefficient is calculated for every component (crankshaft, piston + connecting rod, valve gear ).

In the empirical approach described in [23], the load impact, in this case for a DI (Direct-injection) gasoline engine, is added to the term of the piston group. The load-dependent friction

directly correlates with the break mean effective pressure (3). The proportional coefficient C (bmep ) is given as a function of

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bmep. It should be noted that the load-dependent term of the piston friction includes already the engine pumping losses, which normally are not part of the classic mechanical losses.Figure 4 and Figure 5 show the validation results of the friction

model.

Figure 4. Validation of the ICE friction; Depends on engine speed, different oil and coolant temperatures

Figure 4 shows the first part of the verification of the friction model based on the strip measurements. The special characteristics of the measurements are the different

temperature levels of coolant and engine oil. Based on this data set, the engine friction could also be modeled depending on the coolant temperature.

The results for the represented temperature levels comply very well with the measurement data. Furthermore, the comparison also shows a plausible extrapolation in terms of high engine speeds and low temperatures. Further verification calculations

for the friction of the ICE are given in Figure 5.

Figure 5. Validation of the engine friction; Left side depends on engine speed, oil and coolant temperature; right side: Depends on engine speed, oil, coolant temperature and engine load

On the one hand, they show a plausible extrapolation towards low temperatures (left-hand diagram) and, on the other, that the impact of the correlation variable bmep is represented correctly. This is clearly shown in the right-hand diagram.

With regard to the verification of the model in terms of

extremely low temperatures, it should be mentioned that strip measurements unfortunately were not available for this

temperature range. An overview about the calculation of the remaining powertrain losses gives Table 3 in the appendix.

VALIDATION

This section deals with the validation of the kinetic and thermal models of the HEV. The following figures show simulation and measurement results, performed on the NEDC. First, it is ensured that the load spectra match with the measurements -for the ICE and the EM. This is an important step for the

correct modeling of the baseline control strategy. After this first comparison, the simulated temperatures of the powertrain fluids and components, in particular the EM and ICE, are compared with the measurement - for a cold and warm start. Finally, the fuel consumption and the state of charge of the traction battery are compared with the measured data. When the HEV validation is successfully completed, the developed model can be used as baseline for further investigations. Figure 6 shows results of the simulated load spectrum of the

ICE in the NEDC.

Figure 6. Validation NEDC (load spectrum - ICE)

As can be seen, the ICE speed and the ICE torque matches with the measured data. The same good results were achieved in the load spectrum of the EM (Figure 7). The first step to calculate the correct warm-up behavior of the powertrain has been successfully carried out: The operating points of the powertrain components are calculated correctly within the

baseline control strategy.

Figure 7. Validation NEDC (load spectrum - EM)

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The next figure shows the fluid-temperatures during the warm up phase of the ICE (coolant in the cylinder head, oil sump), the transmission (oil sump) and a component temperature of

the EM - in this case the iron stack.

Figure 8. Validation NEDC (Powertrain - cold start)Figure 8 clearly demonstrates that the simulation results match very well with the measured values - also under the conditions of frequent operating mode changes between the ICE and the EM.

The thermal behavior of the electric drive in the HEV is very important for the resulting losses or for the efficiency of the

complete powertrain system.Figure 9. Validation NEDC (electric machine - warm start)

Figure 9 shows different component temperatures of the EM in the NEDC cycle, in this case a warm start. Also in this case, the transient behavior is very well reproducible and the small deviations from the measured values are acceptable.Figure 10 shows the comparison of the fuel consumption and the state of charge of the battery with the measurement for a

cold start (NEDC).

Figure 10. Validation NEDC (SoC and fuel consumption)

The engine fuel consumption is provided by steady-state maps

that depend on the imep and ICE speed:

(4)

When observing the results in Figure 10, the thermal and

kinetic HEV-model has also the correct behavior in calculating the SoC and fuel consumption during the warm-up phase. This, of course, depends on factors such as the mechanical losses in the transmission and in the ICE, as well as the electrical or thermal losses of the complete powertrain. These factors

depend heavily on the temperature level, which sets due to the load spectrums of the powertrain components.

CONTROL STRATEGY

The essential task of the control strategy in a HEV is to meet the driver power demand while achieving the desired torque required for the EM and ICE as well as the desired gear to achieve minimal fuel consumption and drivability of the HEV.Additionally, the SoC should be kept at a sufficient level to be able to provide the needed power for driving the vehicle over a wide range of driving situations [3].

Control strategies have of course a significant impact on costs, component sizing, powertrain or vehicle efficiency and

customer acceptance [2]. The needed variables for the control strategy are collected through sensors together with the driver desired load. As can be seen in Figure 11, the control strategy is made up of five main functions: operating mode, gear selection, SoC-control, thermal management and brake

distribution. The developed basline control strategy is scalable and can be used with any parallel hybrid vehicle under different driving conditions.

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Figure 11. Structure of the control strategy as implemented in the co-simulation environment

Operating Mode

The operating mode determines the time and duration of each driving mode. The baseline control strategy which is a rule based control strategy is implemented in the HEV. This control strategy requires some information for offline optimization in order to tune the parameters of the control strategy. The HEV has several operating modes, which can be selected according to driving conditions and driver demands. The main operating modes are illustrated in Figure 12 and divided as follows:a. Electric mode: The EM alone is used to provide the entire driving torque when the power demand P D is below ICE power threshold (P ICE,MIN ). The clutch between ICE and EM is disengaged. The electric mode is also used for launching the vehicle from a stop and for turning on the ICE. Being in this mode has an advantage because no fuel is consumed. b. ICE mode: when the vehicle power reaches P ICE,MIN and the battery SoC a maximum limit SoC high at the same time, only the ICE turns on. c. Hybrid mode: depending on specific driving conditions, both the ICE and EM are operating in this mode to propel the vehicle. Two variations are given below: I. ICE/motor-mode: the EM is asked to provide torque to assist the ICE. II. ICE/generator-mode: the ICE is asked to provide more torque to charge the battery to achieve the desired SOC. III. Regeneration mode: converting the kinetic energy during deceleration by allowing the EM to work as a generator charging the battery. d. Coasting mode: it is a transition state between driving (where the vehicle power is positive) and conventional braking/recuperation mode (where the vehicle power is

equal or less than zero). The battery is not being charged in this mode. e. Conventional braking: The battery is not being charged since kinetic energy is converted into friction.

As can be seen from Figure 12, the control strategy is mainly power and SoC-based; when the HEV requires a low level of power, e.g. when the vehicle torque demand (P D ) is lower than P ICE,MIN , the ICE is turned off and the EM/motor provides the needed drive power. When the P D vehicle torque demand is higher than P ICE,MIN , the ICE will be turned on and the vehicle driven by the hybrid or ICE mode. If the SoC is below the SoC small limit, the hybrid mode is activated in order to provide the torque demand and/or to charge the battery. The power demand P D generated during a given driving cycle is used as

an input and expressed as:

(5)

The parameters are rest powertrain efficiency ηRest ,

transmission efficiency ηT , vehicle mass m ; rolling resistance coefficient f r , air density ρ, front area A v and aerodynamic drag coefficient c w . The ICE and/or EM should be able to supply sufficient power to overcome the driving resistances and driveline losses.

When optimal power train control is applied, the ICE starts and stops frequently. This rapid on/off changing of the ICE may probably reduce the vehicle drivability. In addition, the required power is not instantaneously available from the ICE after the start since a certain amount of time is required to bring the ICE up to the transmission rotational speed to overcome the ICE friction and rotational inertia. Therefore, the ICE should turn on

at least for 5s.

Figure 12. Simplified illustration of the rule-based strategy

The overall structure of the baseline control strategy is shown in Figure 13. The baseline control strategy has many

parameters, such as load shift power P LPS , actual SoC, P D , vehicle speed, cylinder head water temperature, etc., which have to be determined in order to make the control strategy work properly. The parameters of the baseline control strategy

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are derived from the control strategy of the tested HEV. These parameters have to be optimized in order to achieve the best

reduction of fuel consumption.

Figure 13. Structure of the control strategy [32]

SOC Control

HEV has to be controlled by an intelligent control strategy to meet the vehicle requirements such as drivability and fuel consumption due its complexity and multi functionality

structure. The SoC controller is designed to optimize the torque distribution for the HEV when the hybrid mode is activated. In addition, it guarantees the SoC balance at the end of the driving cycle and keeps the SoC in the allowed ranges. The conflict of the control objectives can be solved by using some

optimization techniques [32].

Figure 14. Optimal ICE torque curve

The outputs of the SoC controller are instantaneous load shift of the ICE and desired SoC level. Now it is more complicated to determine the input variables. The auxiliaries are chosen as an input variable describing the ambient temperature conditions where, for example, driving in cold conditions

requires more auxiliary power compared to normal conditions due to e.g. the positive temperature coefficient (PTC) power. The coolant temperature is selected as an input describing ICE efficiency together with the catalyst temperature. The actual vehicle torque demand compared to the optimal ICE torque curve is chosen as an input variable. Thus, the difference

between the actual torque (M actual ) and optimal efficiency curve (M optimal ) is determined based on the characteristics of the ICE

as can be seen in Figure 14.

(6)

The optimum efficiency curve describes the best torque and speed combination for a given power. Here, load leveling can be used to move the actual operating point closer to the

optimal working area to improve the ICE efficiency. The SoC controller should also consider the SoC of the battery in the set of the input variables.

Gear Selection

The vehicle speed and driver desired load are used by the gear shifting logic to determine the suitable gear number of the dual clutch transmission. In this way, the ICE speed can be

controlled by the current shifting gears. The modes for the gear selection are categorized into the following: pure ICE mode, hybrid mode, recuperation and pure electric machine mode.

Brake Distribution

The brake distribution controller works to maximize the

regenerative braking power through an optimal distribution of the braking power in the front and rear wheels and to recover as much kinetic energy as possible during the deceleration phase to electric energy. In addition, it is to reduce the vehicle speed while keeping the vehicle driving direction controllable.

Heating and Air Conditioning

A heating and air conditioning system with extensive control possibilities was implemented in the HEV model. The control is based on a number of different virtual sensors and is primarily intended to control the amount of heat delivered to the passenger compartment. The model of the passenger compartment was developed using a standard template in

GTSuite? for the HEV. The implemented PTC unit, which is fed by the high voltage electrical system and connected in series with the exhaust gas heat exchanger, is additionally taken into account in the control loop.

The following correlations between the input of the PTC unit and the comfort variables for the passenger compartment (air outlet temperature and mass flow) show how complex the controlled system is, but, due to the length of the paper, it will

not be explained in detail:

(7)

With the comfort controlled variables cabin airstream , the cabin air temperature T CA , the cabin airstream temperature T CAS

ANDd the defined heating stage HS:

(8)

(9)

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(10)

Based on a series of publications [21, 22] and given data, the state of the art in the field of vehicle air conditioning was represented to make sure that the interactions between the thermal efficiency of the ICE and the passenger compartment are close to reality as possible, but also to represent the impact of the power needed for the auxiliaries on the entire energy chain in the vehicle correctly. Furthermore, the control of the radial blower (air mass input to the passenger compartment) e.g. depending on vehicle speed and resulting dynamic pressure, was also demonstrated; cf. [21].

Energy Management

The potential of energy or thermal management measures in terms of fuel reduction and increased comfort is dependent upon many factors. These factors include, among other things, the used powertrain (diesel engine, gasoline engine, wet or dry transmission) as well as environmental conditions (driving cycle and ambient temperature) - and presented in different publications [8, 9, 10].

For this reason, the ambient temperatures and the driving cycles were varied, so there are six combinations for the

estimation of the potential of the fuel consumption reduction in this research “Energy management for the HEV”.

It is not only important that the cycles differ in load and speed characteristic, but also for example in the cycle duration - ratio “warm to cold powertrain”. For this reason, the following cycles were selected for the researches, as shown in Figure 15:? New European Driving Cycle (NEDC) ? Federal Test Procedure (FTP-75)

?

US06 - a high acceleration aggressive driving schedule

Figure 15. Evaluated driving cycles

Of course it is also possible to evaluate other driving cycles, for example the future type-approval cycle WLTP .

Using the HEV model, the following potentials for reducing the fuel consumption for different cycles and ambient temperatures (Table 1) were calculated.

Table 1. Limited potential of reducing fuel consumption (T Cond : 90°C; T Amb

.: ?7°C/20°C)

For this analysis, the temperature conditioning of the

powertrain was set to 90°C and the ambient temperature was maintained: ?7°C or 20°C.

The results show that the greatest fuel saving of 27% were reached in the NEDC-cycle, followed by the FTP-75 (24.5%) and US06 (16%) - at an ambient temperature of ?7°C. As expected, at higher ambient temperatures, the potential is reduced accordingly. Among others, the factors, which are responsible for this behavior, are the following:? Cycle distance

? Ratio “warm to cold powertrain” ? Load level (Resulting heat losses)

? Level of the mechanical losses in the powertrain at cold temperatures ? Electrical machine, transmission and inverter losses ? HEV control strategy

?The share of fully electric driving, for example ? Catalyst heating function ? Interior climate control

?Electric auxiliary for increasing the passenger comfort (PTC heating element)Different baseline control strategies (Share of fully electric driving, level of load point shifting, etc.) have been

implemented for these cycles. For this reason, these potential savings are difficult to compare. These results can be used only as a benchmark for the evaluation of simulated energy management measures.

The results, shown at different ambient temperatures and driving cycles in Table 1, are based on a purely thermal

analysis. For example, improved aerodynamics was not taken into account.

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Figure 16. State of charge (warm start vs. cold start) from the different driving cycles

When evaluating the fuel consumption of a hybrid vehicle, the SoC level at the end of the driving cycle should be the same as at the beginning. In order to increase the efficiency of the optimization, a 5 % deviation is considered. The correction assumes that a decrease in the SoC during the driving cycle can be compensated by using extra fuel; on the other hand, an increase in the SoC at the end of the driving cycle can save some fuel. In this fuel consumption correction during the optimization, the correction term can be expressed using equation (23), which expresses the correction of fuel

consumption as a function of the average efficiency of the

powertrain components and the deviation of the SOC.

(11)

where m Eq is the equivalent fuel consumption, , , ,

, , are the mean ICE, battery, transmission, EM, inverter and rest powertrain efficiency over the driving cycle. In both cases, the equivalent fuel consumption is added to the fuel consumption obtained over the given cycle. Figure 16 shows the resulting battery SoC curves of the simulation results for the given driving cycle.

The following energy and thermal management measures for potential savings are based on extensive measurements and HEV simulations. There are two main ways to reduce energy consumption of the HEV: Energy and thermal management. In this work, both of the management measures are investigated. The following measures were analyzed:

? Capsulation of the engine compartment, combined with a grille shutter control ? Conventional exhaust gas heat exchanger ?Heat input (coolant) before cab heating ?Heat input (coolant) before oil cooler

? Integrated Exhaust Manifold (IEM) ? Volume-flow control oil pump ?Low and high pressure level ? Different combinations

A special focus of this study is on the integrated exhaust

manifold. In the following subsection, the modeling of this “new technology” will be explained in detail.

Capsulation of the Engine Compartment

That is a combination of energy and thermal management measure, because not only the heat losses to the environment will be reduced, but also the air resistance due to the improved drag coefficient c w , as can be seen in the detailed description in [1]. The effect of reduced aerodynamics resistance is of course also considered in the HEV model.

Conventional Eexhaust Gas Heat Exchanger

Energy losses of the exhaust gas can be recovered and

supplied to the ICE for reducing the mechanical losses and the engine warming-up phase - in particular the heating up of the three-way catalyst for converting the limited emissions.Under these conditions (catalyst heating function) the ICE operates in a bad efficiency area due to the late ignition. For the calculation of the fuel consumption it is absolutely necessary to take this function into account. In the virtual controller, these effects are considered by load dependent factors from a test bench of the ICE.

The engine exhaust mass flow and exhaust temperature are provided by steady-state maps that depend on the imep and

engine speed:

(12)

(13)

The exhaust gas heat exchanger is positioned behind the three-way catalyst. In the HEV model, the complete exhaust system has been developed as a thermal network and

matched with measurement data (Figure 17). In addition, the heat transfer is taken into account by the conversion of exhaust emissions. The physical background has been implemented according to [11, 12, 13].

The focus was to assess the effects of the measure “exhaust gas energy recovery” on the mechanical losses within a certain range. In addition, the effects on the engine control unit with regard to catalyst heating function should be modeled.

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Figure 17. Validation: Temperature of the exhaust gas behind the

exhaust aftertreatment exemplarily for a conventional powertrain in the NEDC at a cold start

In this research, a standard template (map based) was used for modeling the heat exchangers from GTSuite? and

integrated in the cooling circuit. The geometric dimensions and material characteristics of the real measured heat exchanger were implemented, for considering the heating process of the material after the cold start.

The recuperated energy of the exhaust gas is transferred to the coolant or indirectly to the oil by the engine oil cooler. Two versions are examined to show more clearly the effects:

transferring the energy directly before the cabin heating or in the next “setup” before the engine oil cooler.

The detailed configuration of the cooling system will not be discussed further at this point. A similar configuration for the dual-circuit cooling system is shown in [8].

Due to the complexity of the study and the resulting data volume, only the results of the heat input before the exhaust gas heat exchanger are taken into account in the following evaluation.

Integrated Exhaust Manifold

Another type of exhaust gas heat exchanger (ExhHE) is presented by the IEM. Currently, many developers and

automobile manufacturers are involved in the development of this technology. This new development offers according to various publications [11, 12, 13, 14] significant advantages in warm-up behavior and fuel consumption - mainly in the in full-load operation.

In this section, the technology will be developed from the view of the issue “Thermal Management”. The fundamentals for IEM into the existing thermal network (Figure 2) will be described in detail before the results are discussed under the different

environmental conditions in the next chapter.

Figure 18. Configured cylinder head from the one cylinder engine model

The modified cylinder head is shown exemplary in

Figure 18.

Figure 19. Thermal Network (cylinder head)

Figure 19 shows the updated thermal network of the cylinder head, which can be compared with Figure 2. The extra thermal mass m th 5,CH of the IEM has the following heat transfer relations:

? Convective heat transfer

?Environment (engine compartment air) ?Exhaust gas from the combustion chamber ?Coolant in the cylinder head ? Heat conduction

?Exhaust port m th 2,CH ExPo

?The rest of the cylinder head m th 3,CH rest

Firstly, the calculations of the needed masses and surfaces for the IEM are described. For the derivation and implementation of the IEM, technical drawings were provided. After this, the resulting thermal resistances and the component capacities of

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the IEM are explained. Finally, the external heat transfer between the exhaust gas, coolant and the structure mass is described.

The next equations ((14), (15), (16), (17), (18), (19), (20), (21), (22) (23)) describe the geometric data of the IEM and the surrounding coolant.

The coolant mass and volume in the IEM are calculated by using the cylinder head coolant mass and a constant

coefficient:

(14)

f 1 = const. [-] (Derived from technical drawings

)

(15)

The calculation of the heat exchange area for the coolant side

also uses the cylinder head geometry as reference:

(16)

f 2 = const. [-]

For the length of the pipe of the IEM, the length of four exhaust

channels (cylinder 1 and 2) were averaged:

(17)

The following assumption applies for the diameter D IEM

:

(18)

The surface for heat exchange between the surrounding air

and the IEM is calculated as:

(19)

f 3 = const. [-] (Derived from technical drawings )The heat transfer surface between exhaust gas and the

structure is calculated by equation (20):

(20)

The volume of the IEM metal mass V 5,CH for the one cylinder

model results as:

(21)

f 4 = const. [-] (Derived from technical drawings )

The thermal mass of the IEM m th 5,CH is calculated with the density ρCH and the specific thermal capacity c p,CH for

aluminum by using equations (22) and (23):

(22)

(23)

For the conduction resistance, it applies generally:

(24)

L : Characteristic length [m]λ: Thermal conductivity [-]A : Heat transfer surface [m 2]

The following applies for the characteristic lengths:

(25)

(26)

Heat exchange surface A 25,CH and A 35,CH

:

(27)

(28)

f 5 = const. [-] (Derived from technical drawings )

Finally, the external thermal resistances R 25,CH and R 35,CH :

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(29)

(30)

For the exhaust gas side, it applies the following correlations

for the calculation of the heat transfer coefficient α

Exh

[16]:

(31)

(32)

l

Exh

: Length of the exhaust channel [m]

d

Exh

: Diameter of the exhaust channel [m]

(33)

v

Exh

: Kinematic viscosity of the exhaust gas [m2s?1]

v

Exh

: Velocity of the exhaust gas [ms?1]

Exhaust gas side heat transfer coefficient followed by the

resulting heat transfer rate:

(34)

(35)

The heat transfer coefficient for the coolant side α

Coolant

is the

sum of the heat transfer coefficients for free α

Coolant,free

and

forced convection α

Coolan,forced

[16]:

Free convection is calculated by using the following equations:

(36)

Gr

Coolant

: Grashof number [-]

l

Coolant

: Characteristic length [m]

v

Coolant

: Kinematic viscosity of the coolant [m2s?1]

g: Gravity [ms?2]

β

Coolant

: Thermal expansion coefficient [K?1]

Heat transfer coefficient for 106 ≤ Gr · Pr ≤ 109

:

(37)

Heat transfer coefficient for 109 ≤ Gr · Pr

:

(38)

Forced convection is calculated by using the following

equations:

(39)

(40)

(41)

(42)

v

Coolant

: Kinematic viscosity of the coolant [m2s?1]

v

Coolant

: Velocity of the coolant [ms?1]

d

Coolant

: Diameter of the coolant ring surrounding the cylinder

[m]

(43)

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204

Coolant side heat transfer coefficient followed by the resulting

heat transfer rate:

(44)

(45)

The heat transfer rate for the heat exchange between IEM and its surrounding air consists of a term for convection and a term for radiant heat transfer [16]:

The radiation heat transfer is calculated as:

(46)

εEnv : Stefan-Boltzmann constant [W/m 2K 4]

The convective heat transfer is calculated as:

(47)

(48)

(49)

(50)

v Air : Kinematic viscosity of the air [m 2s ?1]v Air : Velocity of the air [ms ?1]

l Air

: Characteristic length of the IEM [m]

(51)

(52)

The heat transfer rate for the heat exchange between IEM and

its surrounding air results in:

(53)

Volume-Flow Control Oil Pump

The modeled oil pump operates on the principle of volume-flow control, meaning that it always generates pressure and volume flow as needed, thus minimizing the pumping capacity.

Extensive measurements have shown that not only the

hydraulic power is reduced, but also the friction of the engine components. These characteristics are also explained by

various publications [17, 18, 19, 20]. Of course, these effects of lower mechanical losses are taken into account for the calculation of the indicated engine power as well as fuel consumption.

RESULTS

Based on the large number of simulations carried out during the study for the present thermal HEV model, an extensive database was set up. On account of the fact that over 130 simulations were conducted and analyzed, the detailed

explanation is related only to the main measured results of the NEDC, FTP-75 and the US06. The results will be presented and the main focus will be on the resulting fuel consumption. Specific characteristics in terms of climate comfort are seen as a marginal issue due to the length of the paper, but

nevertheless explicitly used to evaluate some of the measures.

Figure 20. Results: Fuel reduction (NEDC, ?7°C and 20°C)

Figure 20 shows the results of the reduction in fuel

consumption of the different energy management setups for the NEDC. Generally, the setup #4 achieves the highest potential: Almost 7% for T Amb . = ?7°C.

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205

The potential of fuel reduction is mainly much higher for low temperature levels, as previously discussed in section “energy management”. However, in the setups #1 and #2 the savings for T Amb .=?7°C and T Amb.=20°C are the same. How can this behavior be explained?

In setup #1, the benefit is mainly due to the improved drag coefficient, which, in turn, is the result of the closed grille

shutters over the complete cycle. This fact explains the same level of fuel reduction for the different ambient temperatures. The implemented limit temperatures for controlling the grille shutters have not been achieved in both cases. The advantage of this measure is quite high because about 2/3 of the fuel is consumed in the EUDC (Extra Urban Driving Cycle). Only at this cycle phase of high velocities, the improved drag

coefficient reduces the air resistance significantly, as can be

seen by the following equations [24]:

(54)

(55)

In setup #2, the volume-flow control oil pump modeled as

typical friction-reducing measure, has a benefit of about 1% for both temperature levels. Compared to the base HEV

calculations, the advantages of the improved frictional energy have the same level for the both calculations - about 8% for the complete NEDC.

Before discussing the results of the exhaust gas heat exchanger, it should be noted again, that the rise in the exhaust backpressure and its consequences for the gas

exchange (deterioration of ICE efficiency) has been calculated for the important ICE operation points. These results were taken into account in modeling the bypass controller.

The conventional heat exchanger has a quite low potential for an ambient temperature of 20°C - only 0.5%. This has several reasons:

? Low level of exhaust energy due to the low engine operation points in the four ECE cycles. ? The consumption benefit in the ECE is compensated by the drawback in the EUDC due to the lower battery SoC and the resulting increase of the ICE's shifting load point for the balanced battery SoC.In detail: Although, a benefit was calculated of about 6% over the baseline configuration due to the shortening of the warm-up phase - including also the catalyst heating process - and the higher share of fully electric driving in the ECE, but with the result that for the demanded balanced SoC the increase of the shifting load point is responsible for the disadvantage in the EUDC of about 1%. The comparison of the battery SoC is shown in Figure 23 (a) in the appendix.

In the case of ?7°C, the benefit in fuel consumption was calculated with about 3%. In the ECE, the fuel consumption benefit of 8.5% can be achieved. The higher engine operation points do not compensate this benefit for charging the battery in the EUDC. Note: of course, also the positive influences on the electrical auxiliary (PTC heating element) are included in the calculation of the fuel consumption due to the higher temperature level. Especially in cold conditions, this has an influence on the performance of the ICE.

A combination of Setup# 1-3 presents setup#4. In this case, the advantages of these configurations are summed. For the cold case, it results a potential of about 7% (20°C: 4.5%). This results not only from the improved aerodynamics, but also from the shortening of the warm-up phase.

Setup #5: For interpretation, see the reasons for setup #3. These thermal management measures have basically the same level in reducing fuel consumption for the modeled HEV.The potential at an ambient temperature of 20°C as well as ?7°C in setup #6 is slightly lower than in setup #4. The 20°C case is basically derived from the behavior in setup #1 and setup #4, which shows that most of the reduction is due to the improved drag coefficient (comparatively low exhaust energy flow).

An interesting behavior can be identified at ?7°C, which means that setup #4 has significant advantages over the integrated exhaust manifold - a comparison reveals a difference of 1.5% between both measures. The difference results from different effects, which will be explained in the following:

1. Effects of the thermal engine compartment encapsulation on the heat dissipation to the ambient air. Impacts on the increased thermal capacity of the modified cylinder head (setup #5) compared to the basic cylinder head and the thermally encapsulated ExhHE.

2. The temperature level of the coolant in the cylinder head during the warm-up phase, in particular during the phase in which the catalyst warms up. The “heat catalyst” function is controlled by the current coolant temperature in the cylinder head. During this phase, the engine runs at a lower level of efficiency, which results from the late ignition for increasing the exhaust gas enthalpy flux.

3. Effects of the coolant temperature level in front of the heat exchanger (interior heating) on the electric input power of the PTC heating element.On Point 1: Setup #4 shows a higher coolant temperature in the cylinder head during the first 200 seconds. Compared to setup #6, this is more effective on the catalyst heating function and with this on the consumption-relevant indicated engine operating point. This results in a reduction of the catalyst heating and subsequently in the reduction of the mixture enrichment factors (very low percentage in the entire fuel

Schulze et al / SAE Int. J. Alt. Power. / Volume 3, Issue 2 (July 2014)

206

turnover in the cycle), which is also controlled through the coolant temperature in the cylinder head by the virtual engine control unit.

On Point 2: Because of the increased thermal capacities in the cylinder head and the changed coolant mass flow due to the modification of the cooling system (inclusion of the ExhHE

including pressure loss on the coolant side), advantages on the part of the conventional ExhHE (setup #4) were calculated in this case: improvement of the fuel consumption during the warm-up phase (effective driving power incl. load point increase + consideration of the “heat catalyst” function)

compared to the basic HEV model by approx. 11%. In setup #6 this is only around 6%.

On Point 3: Advantages can be determined in the conventional ExhHE, which “feeds” the regained heat quantity directly from the interior heating into the coolant and with this reduces the electric input power for the PTC heating element (see also subchapter “Air conditioning” for this). This in turn leads to the fact that at the end of the driving cycle a lower load point increase to compensate the battery SoC has to be

implemented. Additionally, the interior comfort temperature is reached more quickly: approx. 100 seconds.

An excerpt of the simulation results of the mentioned points can be seen in Figure 23 (b) in the appendix. In summary, the described effects result in a lower ICE energy turnover, especially in the ECE cycles and with this also in the entire NEDC, in setup #4 compared to setup #6:

? Setup #4: reduction of 6.5% compared to the basis in the NEDC. ? Setup #6: reduction of 5% compared to the basis in the NEDC.Figure 21 shows the results of the measures for the FTP-75, already discussed in the NEDC (Figure 20). The level of the resulting economization is approximately in the same range. This can already be seen from the analysis in Table 1.Despite the longer cycle length, a slightly increased potential can be seen in the recovery of the exhaust gas energy (setup #3 & 5) for the 20 °C case compared to the NEDC.The differences include the increased exhaust gas energy available, especially at the beginning of the cycle (compare Figure 15), due to the differing operating strategy and the increased load profile. On the other hand, the longer cycle duration also has an effect, which generally weakens the

potential of the measures applied over the entire cycle.

Figure 21. Results: Fuel reduction (FTP-75, ?7°C and 20°C)

A further significant point is, as already mentioned, the

modeled basic operating strategy, which determines the share of the combustion engine and with this the resulting fuel

consumption. The comparison in the next table highlights the differences in the implemented ICE energy amount in the first 800 seconds ECE cycles in the NEDC.

In the FTP-75, the ICE in this cycle phase shows an increased energy turnover of the combustion engine by around 30%. During this phase of critical friction states, due to the lower temperature level and the high load of the auxiliary equipment, the highest economization potentials are present, and thus the ExhHE has a corresponding positive effect on the warming-up behavior, also because of the increased exhaust gas energy stream available in the FTP-75. This also explains why there is an overall higher recuperation potential of exhaust gas energy in the FTP-75 compared to the NEDC.

Of interest are also the results of the engine compartment encapsulation in combination with the grille shutter control (Setup #1) for the 20 °C case. Although a slightly higher average speed is present in the FTP-75 compared to the

NEDC, the advantage is distinctly lower here, as can be seen in Figure 21. This is first of all due to the fact that, because of the high temperature in the ICE, the control unit of the shutters signals the release to open the grille shutters and thus after approx. second 1300 after the start of the cycle the improved drag coefficient is not effective anymore and the calculated consumption advantage is correspondingly lower.

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207

Figure 22. Results: Fuel reduction (US06, ?7°C and 20°C)

Basically the calculated economization in the US06 is higher than in the other test cycles. In particular regarding the improved aerodynamics in setup #1 among others, significant advantages can be achieved.

Setup #1 shows significant consumption advantages due to the improved aerodynamics. This is because of the very high average speed, compared to the other cycles, which is around 78 km/h. As a comparison: the average speed in the NEDC is only 33.6 km/h. The link between the speed and the air drag

power P

Air,res was already explained in equation (55) and

illustrates that improved aerodynamics yields a significant advantage regarding the required driving power at higher speed, which results directly in lower fuel consumption.

Setup #2: The potential of the oil pump regulated by volume flow rate is very low in the US06: economization of around 0.5 % is the result of this measure. This low value is due to several reasons. One of the main reasons is the fact that the low temperature levels of the fluids critical for the engine friction (engine oil and coolant) can be overcome relatively quickly with the higher ICE loads.

Due to the significantly higher ICE loads in the US06, which is also down to the basic operating strategy with the parameter settings in the operating mode “load point increase”, a higher exhaust energy is available compared to the other cycles. This is of course noticeable and leads to significant economization in setup #3. With an ambient temperature of ?7°C, the reduction in consumption is around 4% compared to the HEV basic configuration. For example, it was possible to deactivate the PTC element after approx. 350 seconds since the defined passenger compartment comfort temperature had already been reached. Because of this, the load point increase could be lowered significantly to maintain the balanced battery SoC at the end of the cycle (see also Figure 23 (c) for this). At 20°C only around 1% can be saved, comparable to the IEM in setup #5.As can already be seen in the other driving cycles, setup #4 is

the measure of all things here, too. In the “cold case”, economization of up to 7% can be achieved, at 20°C up to

5.5%. Here the improved aerodynamics is very efficient on the driving resistance and the recuperated exhaust gas energy to support the climate comfort incl. the unloading of the HV onboard power supply and to reduce the warm-up phase. When comparing setup #5 with setup #3, the same consumption economization of around 1% emerges, similar to the other cycles. The IEM loses out compared to the conventional ExhHE at ?7°C, since the heat supply via the coolant directly in front of the interior heater reduces the required electric output of the PTC element more quickly and with this subsequently lowers the load point increases to balance the SoC. The detailed links have already been discussed based on the other cycles. The resulting economization of setup #6 follows from the previous explanations. At 20°C they are approx. 5%, and at ?7°C they are around 6%.

Table 2. Calculated passenger comfort indicators for the measure IEM

at ?7°C

Table 2 shows exemplary the effects of the measure IEM on interior comfort and the electrical energy consumption of the PTC. Considered are the heat supplied into the cabin Q

cabin [Wh], the average cabin temperature T

Cabin,mean

[°C] and the

consumed electric energy of the PTC E

PTC

[Wh] for the investigated cycles.

The consumed electrical energy of the PTC can be significantly reduced in all cases. It is even approx. 50 %, which can be saved for the US06. For the NEDC, it is calculated a reduction of about 10 % and for the FTP-75 14 %.

The supplied heat into the cabin increases by about 10 % for the NEDC to 17.5% for the US06 - for the FTP-75 the result is about 13,5%.

Schulze et al / SAE Int. J. Alt. Power. / Volume 3, Issue 2 (July 2014) 208

However, the decisive comfort indicator is the average cabin temperature. Compared to the basis, the cabin temperature can be increased by about 4 K to 17.5 °C for the US06. In the NEDC and FTP-75, the cabin temperature is about 2.5 K higher than in the basis.

Generally, the effects on passenger comfort and consumed auxiliary energy in the US06 are more pronounced than in the other cycles. Given to the fact of higher ICE load in this cycle, logically more exhaust gas energy is available for heating up the ICE and, therefore, also for heating up the passenger cabin und due to the climate control unit for reducing the consumed electrical energy.

CONCLUSION

For the existing HEV technologies, many improvements can still be made with the aim to decrease fuel consumption. These improvements can be applied to the control strategies, ICE, transmission and electric systems. Many of these improvements share the characteristic of their electric energy consumption and their impact on the engine operation points.

In this contribution, energy management measures were carried out and evaluated with the help of a complex thermal HEV vehicle model. These can indeed achieve significant advantages in terms of climate comfort and in particular in terms of fuel consumption.

In doing so, the “new” technology - the integrated exhaust manifold - was implemented in the thermal engine model and was evaluated regarding the additional heat input into the cooling system and its effects on the heat balance of the ICE and with this also on the engine control and the climate control.

Besides the degree of freedom of the individual “energy management measure”, with the thermal HEV model the previously discussed measures were evaluated under different driving cycles and ambient temperatures.

In summary the results show clearly that:

? The economization level or the level of the effective comfort improvements first of all depends on - besides the additional energy-related measures - the limiting conditions “basic

operating strategy”, “cycle characteristic”, “environmental temperature”, “configuration of the powertrain” and

“regulation of the auxiliary consumer”.

? The resulting economization potentials of the individual measures do not necessarily have to add together when

combined.

? One of the most effective energy-related measures is the engine compartment encapsulation including a grille shutter.

The grille shutters are more decisive for the resulting fuel consumption because of the reduced drag resulting from it than the reduced heat losses to the environment.

? At an environment temperature of 20°C, the recuperation

of exhaust gas energy plays a minor role, at least for

the examined HEV model. It becomes interesting when

a request for heating the passenger cabin comes up, in

which the recuperated thermal energy warms up the ICE

more quickly and with this can support the interior heating, which in turn leads to a reduced electricity requirement of the on-board power supply - in particular the requirement of the PTC element. Then the advantages in the consumption reduction are on the level of the engine compartment

encapsulation if the grille shutters are closed during the

entire cycle.

? The use of engine oil pumps controlled by volume flow as

a measure to reduce friction makes perfect sense. The

potential is sometimes on the level of the exhaust gas

energy recovery in the case of temperatures at 20°C.

? The “integrated exhaust manifold” technology is certainly an option purely from the point of view of thermal management in the field of hybridized drive concepts, if the results are

considered in cold ambient temperatures. This technology can be an effective means for heating the ICE more quickly and with this to support the climate comfort in turbocharged gasoline engines, which are increasingly improving from

an energy-related point of view and with this dissipate less heat through the combustion chamber walls. This of course only applies against the background of complying with

component protection, non-impairment for reaching the light-off temperature of the catalyst and maintaining the dynamics in exhaust gas turbochargers during highly dynamic load

changes.

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CONTACT INFORMATION

Mirko Schulze

mirko.schulze@tu-braunschweig.de DEFINITIONS/ABBREVIATIONS

Amb. - Ambient

Bat - Battery

bmep - Break mean effective pressure

CA - Cabin air

CAS - Cabin airstream

CC - Crankcase

CH - Cylinder head

CL - Cylinder liner

CPA - Cylinder pressure analysis

Cond. - Conditioning

cylwall - Cylinder wall

Cyl - Cylinder

DI - Direct-injection

Dis - Desire

ECE - Urban Driving Cycles

EM - Electric machine

Env - Environment

EUDC - Extra Urban Driving Cycle

ex - External

Exh - Exhaust gas

ExhHE - Exhaust gas heat exchanger

ExPo - Cylinder-head exhaust port

FD - Fire deck

FL - Fuzzy Logic

FTP-75 - Federal Test Procedure

HEV - Hybrid electric vehicle

HS - Heating stage

HV - High voltage

ICE - Internal combustion engine

IEM - Integrated exhaust manifold

InPo - Cylinder-head Intake port

NEDC - New European Driving Cycle

Pi - Piston

PTC - Positive Temperature Coefficient

ref - Referenz

rest - Rest of the cylinder head

SoC - State of charge

T - Transmission

US06 - Supplemental Federal Test Procedure

V - Vehicle

vs - versus

WLTP - World-Harmonized Light-Duty Vehicles Test Procedure

Schulze et al / SAE Int. J. Alt. Power. / Volume 3, Issue 2 (July 2014) 210

NOTATIONS AND PARAMETERS Latin letters

A - Area [m 2

]

A v - Vehicle front area [m 2

]a v - Vehicle acceleration [m 2s ?1]a - Weighting factor [-]b - Weighting factor [-]

bmep - Break mean effective pressure [bar]c - Weighting factor [-]C - Factor load [-]

C Bat - Battery capacity [As]c w - Drag coefficient [-]

c p - Specific thermal capacity [Wkg ?1K ?1]D - Diameter [m]D Bore - Bore [m]

D Liner - Outer diameter of the liner [m]d - Characteristic diameter [m]

E - Energy [Wh]f - Factor (constant) [-]

F Air, res - Drag force [N]

f r - Rollin

g resistance coefficient [-]

f mep - Frictionk mean effective pressure [bar]Gear - Actual gear number [-]Gr - Grashof number [-]

g - Gravity [m/s 2]H - Height [m]

H U - Lower heating value [Jkg ?1]i - Index: Number of elements [-]

imep - Indicated mean effective pressure [bar]j - Index: Number of terms in a element [-]L - Length [m]

L Cyclinder - Distance between the center of two parallel cylinders [m]

I - Characteristic length [m]M - Torque [Nm]m - Mass [kg]

m Eq - Equivalent of fuel consumption [kg]m fuel - Fuel consumption [kg]

- Fuel consumption rate [kg/s]m th - Thermal mass [JK ?1]Nu - Nusselt number [-]

n ExPo - Number of exhaust ports [-]n - Speed [rpm]

P Air, res - Air resistance [W]P D : - Vehicle power demand [W]P ICE,MIN - ICE power threshold [W]

P LPS - Load power shift [W]Pr - Prandtl number [-]Q - Heat energy [Wh] - Heat transfer rate [W]

- Heat transfer rate through the combustion chamber [W]Q W - Cumulated heat for one combustion cycle [W]R - Thermal resistance [KW ?1]Re - Reynolds number [-]T - Temperature [K]t - Time [s]

U Bat - Battery voltage [V]V - Volume [m 3]

v - Velocity [kmh ?1] or [ms ?1]

Greek letters

α - Heat transfer coefficient [Wm ?2K ?1]β - Thermal expansion coefficient [K ?1]λ - Thermal conductivity [-]ν - Kinematic viscosity [m 2s ?1]

ε - Stefan-Boltzmann constant [Wm 2K 4

]

- Mean driving efficiency of the battery over the cycle [-]

- Mean driving efficiency of the EM over the cycle [-]

- Mean driving efficiency of the ICE over the cycle [-] - Mean driving efficiency of the inverter over the cycle [-] - Mean driving efficiency of the rest of the powertrain over

the cycle [-]

- Mean driving efficiency of the transmission over the cycle [-]

ρ - Density [kg/m 3]

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APPENDIX

Figure 23. Results: (a) Battery SOC for the base HEV configuration and the configuration with the exhaust heat exchanger (NEDC; 20°C); (b) Setup #4 vs. setup #6 (NEDC; ?7°C): Effects on temperature levels and the electrical power consumption of the PTC; (c) Setup #3 vs. Basis (US06; ?7°C): Effects on temperature levels and the electrical power consumption of the PTC

Table 3. Calculation rules/ Dependence: Thermal losses and friction losses of the remaining powertrain components

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Positions and opinions advanced in this paper are those of the author(s) and not necessarily those of SAE International. The author is solely responsible for the content of the paper.

Schulze et al / SAE Int. J. Alt. Power. / Volume 3, Issue 2 (July 2014)

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安全管理制度上墙

安全管理制度 一、各级领导及广大职工必须认真贯彻执行国家关于安全生产的方针、政策和上级指示、严格执行《劳动法》有关劳动安全卫生和劳动保护及铁道部铁路施工安全规程的条文规定,做到管生产必须同时管安全;切实履行自己的职责,强化安全意识,克服官僚主义和盲干行为,在施工生产及安全管理工作中,采取有效措施,保障国家财产和职工的安全与健康。 二、认真贯彻“安全第一、预防为主”的方针,牢固树立“安全第一”的思想。 三、保证各种生产建筑物、技术设备和工具等,符合安全技术规程和劳动卫生标准。 四、对职工进行安全生产和宣传教育,重点检查新工人的“三级”教育情况,经考试不合格者不得上岗操作。 五、对临时设施、机械设备、小型机具、劳动保护设施等,使用前应组织有关部门检查、验收,未经验收合格不准使用。建立管理使用制度,定机、定人、定责,加强维修保养,不准无证上岗和带病操作,确保安全。 六、严格执行特殊工种持证操作制度,对无安全合格操作证的人不得分配工作。 七、开工前必须对工地、工具等防护措施情况进行检查,

提出安全注意事项,进行安全交底。 八、严格各种易燃、易爆及危险有毒物品的管理,固定专人领取、使用并管理易燃、易爆有毒物品。做好防火、防爆、防盗的工作。 九、严禁将设备交无证者操作。在未熟悉设备性能和操作规程前,不得上岗操作。禁止酒后开车、驾驶机械和进入作业场所。 十、高处作业、起重、打桩等有物体坠落的施工现场,必须带安全帽,在高处、悬崖、陡坡等处作业时必须系安全带。 十一、经常组织检查工地宿舍、环境、食堂、浴室、厕所等卫生,预防食物中毒和煤气中毒发生。 十二、经常对职工进行安全技术教育和尊章守纪教育,搞好劳逸结合,防止超劳,促进安全生产。 十三、要坚持按规范施工,按规程操作,施工现场要标准化管理,要有明显的安全标志、警示和标语牌,规章制度操作规程要到现场到岗位,工程项目要制定有效的安全防护措施,严禁违章指挥、违章作业。 十四、严格坚持工前安全交底、工中安全检查、工后安全讲评的“三工制度”及安全分析会制度。 十五、要建立完善安全生产激励约束机制,加大安全奖惩力度,以促进安全生产工作的落实。 中铁十九局集团广州地铁项目部

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企业规章制度框架 【篇一:公司框架图及管理制度】 公司管理制度 经理法则: 1.总经理必须带头执行公司的各项规章制度。 2.领导部门总管不得越级指挥下一级员工。 3.领导部门总管应明白自己的工作岗位,不得越级进行操作,错指挥、乱指挥下级人员。这样会导致指挥的不明确,制约和影响公司 的发展。 员工守则: 1.员工必须遵守公司的各项规章制度。 2.坚决服从上级的管理,杜绝与上级顶撞。 3.禁止员工议论公司的制度、处理问题的方法和其他一切与公司 有关的事情。员工对 公司有意见和建议,可通过书面形式向公司反映。也可以要求召开 专门会议倾听其陈述,以便公司做出判断。 4.员工必须做到笔记本不离身,上级安排的任务和客户的要求同 事的委托均需记录, 并在规定的时间内落实、答复和回话。自己解决或解答不了的问题 应立即向有关人员汇报,不得拖延。杜绝问题石沉大海、有始无终。 5.公司实现打卡制度。迟到10分钟以内扣款5元,10至20分钟 扣10元,迟到30分 钟以上按旷工半天计算。 6.员工有事外出必须请假,未获批准,不得擅自离岗。因自然灾 害或直系亲属的婚殇 嫁娶等急事需请假时,须将自己的工作交接好,经上级批准后方可 离开。 7.除每月正常休假外,如需请假须填写好请假条,同时将自己的 工作交接好,经上级 批准后方可离开,如因病假须有医院证明,否则公司将做出处罚。 8.员工在工作时必须衣冠整齐,不得穿拖鞋,一经发现罚款20元。 9.员工上班不得一边工作,一边聊与工作无关的事,不得影响别 人工作。

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复合载荷工况下特殊螺纹油套管接头三维有限元分析

DOI:10.3969/j.issn.2095-509X.2017.08.008 复合载荷工况下特殊螺纹油套管接头三维有限元分析 刘一源1,纪爱敏1,李一堑1,樊鑫业2,许才斌2 (1.河海大学机电工程学院,江苏常州一213022) (2.江苏常宝钢管股份有限公司,江苏常州一213018) 摘要:考虑螺旋升角,应用SolidWorks 建立某特殊螺纹油套管接头的三维有限元模型并利用AN-SYS 软件进行有限元分析,分析不同复合载荷工况下油套管接头的应力分布情况三分析结果表明:在一定的内压范围内,管体的应力随内压的增大而增大,但对油套管接头的连接强度影响不大;在一定的轴向拉力范围内,轴向拉力的增大不会引起油套管接头螺纹牙两端的应力超过材料屈服强度,但可导致两端螺纹牙发生断裂失效,影响螺纹连接强度;复合载荷工况下,随着内压的增大,油套管管体和接箍出现向外扩张的趋势,密封面上的接触压力不断增大,可以起到提高油套管接头密封性能的效果三 关键词:ANSYS ;油套管接头;复合载荷;应力分布 中图分类号:TH131.3;TE319一一文献标识码:A一一文章编号:2095-509X (2017)08-0041-03一一油套管接头的作用是通过螺纹将多根油套管连接起来形成数千米的密封管柱,从而可以开采到贮藏在地表以下的石油三日益复杂的石油开采环境,对油套管接头的性能要求更加苛刻三为了提升油套管接头的性能,使特殊螺纹油套管接头能够在苛刻的环境下保证较好的密封性能与足够大的连接强度,模拟油套管接头的受力状态,对其进行性能分析是很有必要的[1-3]三对油套管接头施加不同工况下的复合载荷,通过ANSYS 有限元分析软件进行计算,然后根据计算所得的应力云图以及接触压力曲线图可以对油套管接头的性能进行合理的分析三目前,对油套管接头进行的分析研究,普遍采用二维轴对称模型进行有限元分析,由于忽略了螺纹升角[4-5],无法模拟准确的上扣过程,对螺纹二台肩和密封面处发生的塑性变形二粘扣现象也无法得到合理的控制三此外,油套管柱在井下工作时由于受到复杂载荷的作用,可能会导致管柱发生屈曲变形[6-7],在变形段会有弯曲载荷的存在,而弯曲载荷为非轴对称载荷,因此利用二维轴对称模型进行有限元分析就会产生较大的误差[8-9]三三维油套管接头模型是通过油套管与接箍间的螺纹 啮合形成复杂的空间螺旋曲面,因此采用三维油套管接头有限元模型进行计算得到的结果和实际情况比较相符三为了提升油套管接头的连接强度和密封性以及使用稳定性[10],本文建立了考虑螺纹升角的某特殊螺纹油套管接头的三维有限元模型, 通过施加复合载荷来模拟实际工况下的受力,对油套管接头进行有限元分析三 1 特殊螺纹油套管接头有限元模型的建立 本文以某钢管有限公司生产的?177.80? 9.19mm HQSC 特殊螺纹油套管接头为研究对象,利用三维设计软件SolidWorks 分别建立油套管二接箍几何模型,再装配为一体,如图1所示三该油套管接头采用改进的偏梯形螺纹,承载面角度为-3?,导向面角度为10?,螺纹锥度为1?16,密封 面采用锥面/锥面密封,扭矩台肩为逆向角15?,可以起到较好的辅助密封的效果,该特殊螺纹油套管接头的内二外螺距均为25.4mm /(5牙)三 特殊螺纹油套管接头有限元模型如图2所示,该模型采用八节点六面体单元,节点数为195835,单元数为169326三运用Hypermesh 软件划分好网格, 收稿日期:2017-07-03 作者简介:刘源(1991 ),男,河南商丘人,河海大学硕士研究生,主要从事数字化设计方面的研究三 四 14四2017年8月一一一一一一一一一一一一一一一一机械设计与制造工程一一一一一一一一一一一一一一一一一Aug.2017第46卷第8期一一一一一一一一一一Machine Design and Manufacturing Engineering一一一一一一一一一一一Vol.46No.8万方数据

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公司工衣管理制度 为规范公司工作服的管理,提高员工素质和公司整体形象,特制定本制度。 第一条本制度规定了公司工作服的定制、发放、领用及折旧标准,着装要求等。 第二条行政办公室负责公司工作服的归口管理,包括发放及折旧标准、工作服的购买及验收、领用核签、检查考核等工作。 第三条工作服的发放标准 1.公司的工作服包括冬装和夏装。 2.公司人员发放工作服冬、夏装各两套。 3.新员工进入公司十天后发工作服。期间发放旧的工作服。 4.旧的工作服采取免费优先使用。 第四条工作服的定制及领用 1.工作服使用年限:(冬、夏)工作服各两年。两年内实行冬装公司统一回收,两年后的回收属自愿行为。 2.后勤仓库要定期盘点工作服,并根据员工进出情况和库存情况,及时进行定制申购,备足数量以便使用;并根据使用年限、季节变化及时组织更换。 3.后勤要本着货比三家的原则选择好定制厂家,在定制前应签订合同(或协议),明确数量、材质、尺寸规格、交期、价格、发票税点、验收标准、商标使用规范以及其它定制要求等。工作服做好到厂时由办公室组织进行验收工作,合格后交由后勤保管。 4.办公室依照各部门员工事先自行申报的衣服尺寸,通知各部门统一到后勤仓库领取,仓管员按办公室的要求如数发放, 第五条工作服的折旧标准 1.员工的夏装工作服实行免费使用,冬装工作服收取折旧费用。 2.员工自动离职的应全额收取工作服费用;正常辞职流程办理的员工按在公司工作时间长短收取相应的折旧费。 3.员工在辞职办手续时其工作服应事先洗干净叠好再退还给仓管员,仓管员视工作服新旧情况进行分类保管,以便下次优先发放使用。 4.员工服装在规定使用期限内因丢失、损坏等原因造成无法穿着上班的,可由本人直接向办公室申请购买,经审批后方可到后勤仓库领取工作服,其费用从工资中扣除。若属故意损坏的除加价赔偿外,还将视情节严重情况进行处理。 5.员工因岗位或作业特殊性需增发冬、夏装的,可由员工所在部门申请,经办公室审批后由后勤仓库发放,并要做好相关登记工作。 6.折旧标准: 工作服折旧分类工作3个月内(含)折旧费为50%;工作3-6个月(含)折旧费为40%;工作6-12个月(含)折旧费为20%;一年以上少件、丢失按原价赔偿一年以上按半价赔偿。 第六条着装的要求 .生产及辅助性员工凡在上班期间必须穿公司工作服进行作业。1. 2.员工着装要整洁,并不得自行转借给非本公司人员。

公司管理制度框架

公司管理制度框架第一章管理总则 第二章员工守则 第三章公司行政管理制度 一、办公事务管理 1)、办公设备管理制度 2)、办公用品管理制度 3)、办公文书管理制度 4)、公司档案管理制度 5)、往来信件管理制度 6)、公司印章管理制度 7)、图书资料管理制度 二、办公事务管理流程 1)、办公设备购买流程 2)、办公用品管理流程 3)、办公文书管理流程 4)、公司档案管理流程 5)、往来信件管理流程 三、办公事务管理方案 1)、办公设备采购方案 2)、客户档案管理方案 3)、办公费用控制方案 四、行政人事管理 1)、员工考勤管理制度 2)、员工出差管理制度 3)、公司会议管理制度 4)、日常纪律管理制度 2、行政人事管理表格 1)、员工考勤登记表 2)、月、季度、年度考勤汇总表格 3)、员工请假申请单 4)、员工加班申请单 5)、员工出差申请表 6)、差旅费报销清单 7)、公司会议记录单 8)、员工奖惩记录表 9)、员工违纪处理表 行政人事管理流程

1)、出差管理流程 2)、会议管理流程 五、保密管理 第四章岗位职责 第五章企业招聘管理 1、招聘管理制度 1)、员工聘用管理制度 2)、管理人员录用办法 3)、招聘面试管理制度 4)、招聘与录用管理制度 2、招聘管理表格 1)、招聘工作计划表 2)、应聘人员登记表 3)、人员面试记录表 4)、面试成绩评定表 3、招聘管理流程 1)、招聘计划管理流程 2)、招聘与录用管理流程 3)、员工试用管理流程 第六章培训管理 1、培训管理制度(根据公司岗位设置来安排相关岗位的培训) 1)、新员工培训制度 2)、岗前人员培训制度 3)、在职人员培训制度 4)、外派员工培训制度 5)、销售人会培训制度 2、培训管理表格 1)、新员工培训计划表 2)、新员工培训评定表 3)、员工培训申请表 4)、员工培训评估表 5)、员工培训档案表 3、培训管理流程 1)、培训需求调查流程 2)、员工培训管理流程 4、培训管理方案 1)、新员工培训方案 2)、在职员工培训方案 3)、员工外派培训方案 第七章绩效管理

“三工制度”

施 工 现 场 三 工 制 度 记 录 本 广乐T3标一分部

前言 何谓“三工制度”?那就是要做到工前安排、工中检查、工后讲评,做到警钟长鸣。要求员工每人每天工作结束后,项目施工负责人都要做个小结,或肯定当天的成绩,或指出当天不足、点评到个人、到事,言不在多,点到为止。 为保障工程施工任务安全、优质、高效的完成,项目必须坚持“三工制度”。由现场副经理和施工班组负责人对参建员工每日进行分工,布置当天的工作任务,交代当天施工计划、现场重大危险因素、安全、质量卡控要点、工程施工难点,施工技术等具体要求及注意事项,并将其记录在当天三工记录本上,由分别领导、挂点责任人及上级领导对当日工作情况进行指导监督检查,总结验收,发现问题当天解决,不拖延,解决不了的及时上报,对工作中好的做法和经验及时总结和推广,保证各项工作的顺利完成。同时,每日对架子队工人进行工前安全讲话,安全操作规程、注意事项和安全防护用品是否备齐;工作中安全检查,每个架子队设一名安全员检查各项安全工作,检查是否按照操作规程作业;工后安全总结,是否存在安全隐患,及时处理或报告现场值班人员。 每日点评结束后,无论是受到表扬还是受到批评的员工心里都是亮堂堂的,干起活来也更起劲。通过每日的点评,项目部及架子队能及时发现和指出存在的问题和安全隐患,让问题透明化,让隐患暴露出来,并制止措施,亡羊补牢。 安全工作是项目安全生产的重要环节,项目安全工作抓得不好,将直接影响生产安全,提倡和开展“三工制度”,是对职工进行安全教育最直接、最有效的方法,可以面对面的教育员工,使他们在一种轻松的氛围中接受安全教育,增强安全防范意识。 通过“三工制度”的落实,不断强化安全生产教育,提高员工预防事故的能力,及时排除安全隐患,从而避免事故的发生。有效促进安全生产管理,维护施工生产安全稳定,更好地发挥了员工的进取心、责任心。

综合部分负荷性能系数(IPLV)的计算与限值

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多联式空调(热泵)机组IPLV 采用多联式空调(热泵)机组时,其在名义制冷工况和规定条件下的制冷综合性能系数IPLV(C)不应低于表4.2.17 的数值。 表4.2.17 多联式空调(热泵)机组制冷综合性能系数IPLV(C) IPLV的适用范围

员工工服管理制度

员工工服管理制度 为了树立公司良好的整体形象,规范服装制作流程,特对公司工服的制作、领用、更换、费用负担作出规定。 一、适用范围 公司所有员工(包含实习生),均属统一制作工作服对象。 二、管理部门 质管部为工服的管理部门,负责工服的申报、制作、领取、更换、费用、保管等工作。

、工 作服 基本 规定 五 、工服制作 1、总部管理人员及店长工作服一律由制作商裁缝量身定做。 2、其他人员工服是按国际标准码批量制作,工服型号有:加大码、大码、中码、 小码四个套码。批量制作的工服应备有一定库存量,以便员工及时领取。 六、工服领用 1、工服制作完毕后,质管部入库,运营部根据不同的门店,打出库单,然后领 用配送到门店,工服配送到门店后由店长组织对其进行检查,是否有破损,现任岗 位与服装是否符合要求,若不符,配送后的三个工作日内及时向质管部反馈,否则, 视为认可,质管部拒绝受理调剂。 2、门店员工到岗后即可到人力资源部申领应季工作服。 七、费用与更换 1、员工申领新工服时,不须承担服装制作的成本价。自申领新工服之日起服务 满2年者,可免费更换。如不满两年离职者,需承担工服剩余价值,按月抵扣。

2、驻店药师、营业员工服使用年限为2年,费用按24个月折旧分摊来计算;总部管理人员、店长工服使用年限为3年,费用按36个月折旧分摊来计算,根据员工为公司服务的时间来决定最终所扣工服款金额。员工更换新工服后仍参照工服管理制度第七条第一款规定。(举例:某套服装单价90元,某营业员自领取服装到离职为公司服务了9个月,公司将为其承担费用为:90元/套÷24个月×9个月=34元,即员工个人应承担56元。 3、营业员及药师工作服穿着年限原则为2年,如超过2年仍可穿着者,需本人申请,由直接上级确认同意,方可延长,但第4年必须重新申领新工服。 4、两年以内因保管不当,毁坏、污渍、毁色需要更换的,费用由员工自己承担。 5、工服免费更换时间为每年的1月和7月。旧工服一律上交质管部。 附件:1、工作服领用流程 2、工服延期使用申请单 3、工服以旧换新审批表 工服延期使用申请单 附件二

企业管理制度框架最新版

企业管理制度框架 一、总则 二、行政日常工作规范 1.行政协调工作规范 2.行政沟通工作规范 3.行政经费管理工作规范 三、企业行政办公室事务管理 (一)行政办公室事务管理规范化制度 1.办公室布置规范 2.办公室用品管理制度 3.办公室用品、文具管理制度 4.工作服管理制度 (二)行政办公室事务管理实用表单 1.文具用品一览表 2.办公室用品需求计划表 四、企业员工行为规范管理 (一)企业员工行为规范管理化制度 1.员工守则 2.企业员工仪容仪表规范 3.企业员工礼仪举止规范 4.企业员工言行规范 5.企业员工电话规范

(二)企业员工行为规范管理实用表单 1.自我报告表 2.自我评价表 3.目标工作单 4.主要计划表 5.工作计划6W2H分析表 6.工作记录表 五、企业人事行政管理 (一)企业人事行政管理规范化制度 1.企业人事管理刚要 2.人员招聘管理制度 3.员工培训管理方法 4.员工工作调动管理办法 5.员工辞职审批管理办法 6.劳动合同 (二)企业人事行政管理实用表单 1.企业人事规划表 2.人员补充申请表 3.应聘登记表 4.员工培训计划表 5.工作调动申请表 6.辞职申请表

7.辞退通知单 (三)企业人事行政管理规范化细节执行标准 六、企业会议管理 1.会议管理工作要点 2.会议管理规范化制度 3.会议管理使用表单 4.会议管理规范化细节执行标准 七、企业提案管理 1.企业提案管理规范化制度 2.企业提案管理实用表单 八、企业文书管理 (一)企业文书管理规范化制度 1.企业公文收发规定 2.企业邮件、函电收发制度 3.图书管理制度 (二)企业文书管理实用表单 九、企业档案管理 (一)企业档案管理规范化制度 1.人事档案利用制度 2.文书档案归档制度 3.文件、材料收集归档制度 (二)企业管理规范化细节执行标准

多联机IPLV 测试与负荷组合的关系[改]

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