Experimental validation of a type-2 fuzzy logic controller for energymanagement in hev

Experimental validation of a type-2fuzzy logic controller for energy management in hybrid electrical vehicles

Javier Solano Mart?′nez a,b,Je′r?ome Mulot a,b,Fabien Harel a,b,Daniel Hissel a,b,n,

Marie-Ce′cile Pe′ra a,b,Robert I.John c,Michel Amiet d

a University of Franche-Comte′,FEMTO-ST Institute UMR CNRS6174,Belfort,France

b FCLAB,FR CNRS3539,Belfort,France

c De Montfort University,Centre for Computational Intelligence,Leicester,Unite

d Kingdom

d Direction Ge′ne′ral

e de l’Armement(DGA),Bagneux,France

a r t i c l e i n f o

Article history:

Received8October2012

Received in revised form

12December2012

Accepted18December2012

Available online24January2013

Keywords:

Hybrid electrical vehicles

Energy management

Type-2fuzzy logic systems

Fuel cell system

Ultracapacitors

Experimental validation

a b s t r a c t

The aim of this paper is to present experimental validation results of an energy management system for

hybrid electrical vehicles based on type-2fuzzy logic.The energy management system(EMS)is

designed by extracting knowledge from several experts using surveys.The consideration of interval

type-2fuzzy sets enables modeling the uncertainty in the answers of the experts.The validation of the

EMS is performed on a real-scale heavy duty vehicle equipped with different energy sources such as

batteries,fuel cell system and ultracapacitors.Experimental results are strong evidence that type-2

fuzzy logic is wide adapted for performing the energy management in hybrid electrical vehicles.

&2013Elsevier Ltd.All rights reserved.

1.Introduction

One of the effects of the globalization is that people travel

more covering longer distances,live far from their work place and

consume goods from all around the world.Therefore,it is no

coincidence that the transport of people and goods represents

more than25%of the energy consumption worldwide.Several

efforts must be done to reduce the oil dependence,the energy

consumption and the environmental impact of transport systems.

In this perspective,the French Army(DGA)has support the design

and construction of the Electrical Chain Components Evaluation

vehicle(ECCE).It is a mobile laboratory to evaluate under real

conditions the components of hybrid electrical vehicles that

reduce the energy consumption and the pollution emission of

conventional vehicles.The principal objective of the research

presented in this paper is to present experimental validation

results of the type-2fuzzy logic based energy management

supervision system implemented in ECCE and presented in

Solano Mart?′nez et al.(2011).

Recent researches have shown the interest of using type-2

fuzzy logic systems in different engineering applications such as

control of non-linear systems(Al-Khazraji et al.,2011;Lin,2010),

traf?c management(Balaji and Srinivasan,2011),control of

DC/DC converters(Solano Mart?′nez et al.,2012a)or forecasting of

electrical load(Lou and Dong,2012).Different techniques have been

proposed to design the rules and membership functions of the fuzzy

controllers(Kumbasar et al.,2011;Cazarez-Castro et al.,2012;Wu

and Wan Tan,2006).The controller considered in this paper

is designed using knowledge engineering as proposed in Mendel

(2001)and presented in Solano Mart?′nez et al.(2012b).

This paper is structured in the following sections:Section2

presents the ECCE test bench;Section3introduces the energy

management strategy as well as the design of the fuzzy logic

controller used to de?ne the output of the fuel cell system.

Section4presents experimental results and Section5presents

the conclusions and outlooks of the research.

2.ECCE test bed HEV

The Electrical Chain Components Evaluation(ECCE)vehicle is

a mobile laboratory used for researches under real conditions

on technological solutions for Hybrid Electrical Vehicles(HEVs).

Contents lists available at SciVerse ScienceDirect

journal homepage:https://www.sodocs.net/doc/184419972.html,/locate/engappai

Engineering Applications of Arti?cial Intelligence

0952-1976/$-see front matter&2013Elsevier Ltd.All rights reserved.

https://www.sodocs.net/doc/184419972.html,/10.1016/j.engappai.2012.12.008

n Corresponding author at:Fe′de′ration FC LAB rue Thierry Mieg,F-90010Belfort,

France.Tel.:t330384583621.

E-mail address:daniel.hissel@univ-fcomte.fr(D.Hissel).

Engineering Applications of Arti?cial Intelligence26(2013)1772–1779

The ECCE mobile laboratory,which was driven for the ?rst time

on 2003,is presented in Fig.1.

ECCE is designed for real-world evaluation of electrical com-ponents such as electrical machines,power converters or energy sources.Its traction chain is composed by four independently controlled electrical machines.ECCE mobile laboratory can be equipped with various energy sources such as fuel cell systems (FCS),internal combustion engines (ICE),ultracapacitors (UCS),batteries and/or Flywheel Systems (FWS).

Phase I of ECCE project (1997–2005)was devoted to design,construction and evaluation of the vehicle.It focused on a con?guration based on batteries and ICEs,the study of energy management (Pusca et al.,2002)and the study of the batteries and electrical machines (Espanet et al.,2006).Phase II of ECCE project (2008–2012)focuses on the integration,control and management of different energy sources available for transport applications (FCS,UCS,batteries,ICE and FWS).It is supported by the French Army (DGA)and managed by the University of Franche-Comte and the FEMTO-ST laboratory.It counts with the participa-tion of Panhard General Defense and Helion Hydrogen Power.

The hybrid source retained in this paper,integrates a fuel cell system (fuel cell and power converter),an ultracapacitor bank (and its power converter)and a batteries bank which imposes the voltage to a 540V DC bus.The energetic con?guration of the vehicle is illustrated in Fig.2.

3.Energy management strategy

The objective of the energy management strategy in ECCE,is to ?nd the UCS,FCS and batteries power references to supply the vehicle power consumption (motor drives and ancillaries)as de?ned by Eq.(1).However,as the batteries are directly con-nected to the DC bus (see Fig.2),the power ?ow of this source cannot be directly controlled.The EMS is thus,in this case,limited to ?nding the UCS and FCS power references P reference ?P FCS tP batt tP UCS

e1T

3.1.Global EMS objectives,inputs and outputs

The proposed Energy Management Strategy (EMS),only considers real-time information such as electrical power,current or voltage,the state of charge of the storage sources or the speed of the vehicle.As the batteries are directly connected to the DC bus,their power is indirectly controlled by the FCS and UCS.The UCS is considered as the priority source to supply the power.A fuzzy logic controller is selected to perform the local energy management of the FCS.

The ?rst step to design the EMS is to de?ne a global EMS regarding the characteristics and constraints of both the vehicle and the hybrid source.The second control level of the EMS is de?ned without considering the energy sources separately.The global objectives of the EMS are de?ned as

To guarantee the general power balance and to recover the maximal amount of braking energy (Eq.(1)changes if mechanical braking is applied.)

In steady state,the power is totally provided by the fuel cell system (fast and easy to recharge).It is also the only real ‘energy’source on the vehicle.

The UCS ensures the dynamic answer and guarantees enough energy to accelerate the vehicle and enough capacity to recover energy.

To minimize the battery (DC bus)voltage variation and to minimize the use of this source because it is less ef?cient and presents a lower durability than the UCS.

Currents,states-of-charge,powers and/or voltages (and change rates)must remain within predetermined and constrained

limits

Fig.1.ECCE mobile test bed.

Battery pack

540V DC-Bus

In-wheel motor

Fuel cell system

Power converter

Power inverter

Motor

Ultracapacitors

Power inverter

Power converter

Fig.2.ECCE energy architecture.

J.Solano Mart?′nez et al./Engineering Applications of Arti?cial Intelligence 26(2013)1772–17791773

The EMS enables degraded operation(i.e.failure of any of the power sources).

The EMS is realized without knowledge of future driving conditions.It only uses real-time information to compute the output power references

Reference power:the propel power(to drive the motors) depends on the actual speed of the vehicle and on the accelerator and brake pedals positions.The auxiliary power depends on the ancillary implemented on-board(as for exam-ple compressors or pumps).

Fuel cell power:the value of the output power of the FCS (measured after power electronics,because this is the net contribution of the FCS to the power sharing).

Batteries SOC:estimated batteries state-of-charge.

UC SOC:estimated ultracapacitor state-of-charge.

UC voltage:measured voltage at the ultracapacitor bank (before power electronics).

DC bus voltage:measured voltage of batteries.

Vehicle speed:measured vehicle speed.

The EMS has the following two outputs:

UCS output reference power:the power(or the current) supplied by the UCS.

FCS output reference power:the power(or the current) supplied by the FCS.

3.2.Local energy management strategies

As the control of the sources is independently realized,the next step is to identify local objectives for each source EMS(?rst level control).This is done by comparing the characteristics of the sources with the global EMS objectives as presented in Table1.

Using Table1it can be de?ned that:the UCS supplies the dynamic power required by the load and is considered to perform recovery braking.The FCS ensures the autonomy of the vehicle, and regulates the state of charge of the storage sources.The battery power?ows are minimized to reduce the voltage varia-tions on the DC bus and also to increase the durability of the battery.The whole strategy is presented in Solano Mart?′nez et al. (2011).

3.3.Fuzzy logic controller

As previous knowledge of the driving cycles is not considered, human expertise is used to design the Fuzzy Logic Controller(FLC) which controls the FCS.An energy management survey was performed among experts in energy management of HEV.Type-2fuzzy logic MFs(T2-FLC)are retained because they consider the uncertainty in the answers and the fact that different experts think different and de?ne different rules.This survey-based FLC has been presented in Solano Mart?′nez et al.(2012b).A non-optimized Sugeno fuzzy logic controller is retained,it considers a singleton fuzzi?er and is defuzzi?ed using center-of-sets technique.

The?rst input of the fuzzy system is the difference between the power consumption of the vehicle and the power supplied by the fuel cell(D P).The second input is the difference between the reference and the estimated value of the UCS state-of-charge (D SOC).The inputs are represented by seven linguistic labels: Negative High(NH),Negative Medium(NM),Negative Low(NL), Zero(Z),Positive Low(PL),Positive Medium(PM)and Positive High(PH).The output of the fuzzy system is the change rate of the power supplied by the fuel cell system(D P FCS).This output is represented by seven linguistic labels:High Decrease(DH), Medium Decrease(DM),Low Decrease(DL),Hold(H),Low Increase(IL),Medium Increase(IM)and High Increase(IH).The input MFs have been selected to be triangular for the labels NM, NL,Z,PL and PM and trapezoidal for the labels NH and PH.The MFs are selected to be symmetrical around the zero axes.The MFs that represent D P and D SOC are,respectively,presented in Figs.3and4.

The experts were asked to de?ne the49(7á7)rules of the fuzzy logic system using the speci?ed linguistic labels.These answers are used to de?ne the rules of the fuzzy system.The processed rules are summarized in Table2.

To avoid real-time calculation issues,the type-2fuzzy logic controller is mapped off-line.The control surface is presented in Fig.5.

Table1

EMS objectives—compatibility with the sources.

Objective FCS UCS Batt

Supply energy to the loadttààtMaximise recovery brakingààtttMinimise DC voltage variationtttàRegulate UC and battery SOCttàààSupply high dynamic poweràttt

1

1

1

1

1

1

Fig.3.IT2fuzzy MFs for D P:PH(a),PM(b),PL(c),Z(d),NL(e),NM(f),and NH(g).

J.Solano Mart?′nez et al./Engineering Applications of Arti?cial Intelligence26(2013)1772–1779 1774

4.Experimental validation

The energy management system (software and hardware)is

implemented using a dSPACE AutoBox programmable controller.This system generates current references for the UCS and FCS as required by their control systems.The experimental validation was performed in two steps:?rstly,static validation (i.e.without moving the vehicle)and secondly,driving validation.4.1.Static validation

Static evaluation presents very interesting characteristics to evaluate the EMS:the experimentation is easy to control (and to stop if any problem occurs)and the load power consumption can be easily imposed and controlled.A variable resistive load (adjustable power resistor)is directly connected to the DC bus .The nominal value of the load is 100kW @500V (in steps of 5kW).The EMS is implemented using the parameters given in Table 3.As the dynamic control of the UC SOC based on the speed is not possible,the reference value for the UC SOC control is imposed by the software.Batteries SOC control is not considered.

The ?rst evaluation performed consists into imposing different variations of the charge and analyzing the global power distribu-tion between the different sources.The principal interest of performing this test is the fact than the load can change from zero to the nominal value much faster than in driving conditions.Fig.6presents the results of the ?rst evaluation:a load reference increasing or decreasing in variable steps (from 5[kW]to 75[kW]).The ?gure presents the power distribution between the different sources,the current in FCS and UCS and the DC bus voltage.

The FCS power regulates the UC state-of-charge and has

relatively low frequencies.

Maximum and minimum limitation values of FCS current are

validated as well as the maximum value of the UCS current. The output currents of the sources follow their references. The UCS supplies the transient power to the load (high dynamics)and enables energy recovery (in this case from the FCS).

The power contribution as well as the voltage variation of the batteries is highly minimized.The batteries reinforces the UCS when required (e.g.from time 345to 355[s]or from 425to 438[s]when a high load is applied and the UCS current reaches its maximum value.

The second evaluation performed consists into verifying

the operation of the overcharge and discharge limits in the UC and verifying the operation of the UC SOC control by the FCS.

01

01

01

01

01

1

01

Fig.4.IT2fuzzy MFs for D SOC:PH (a),PM (b),PL (c),Z (d),NL (e),NM (f),and NH (g).

Table 2

Fuzzy rules MFs:interval bounds.

D P FCS D SOC k

D P -NH

NM NL

Z

PL

PM

PH NH [à0.781,à0.697][à0.751,à0.668][à0.569,à0.492][à0.409,à0.335][à0.266,à0.207][à0.063,à0.010][0.093,0.143]NM [à0.781,à0.697][à0.569,à0.492][à0.458,à0.387][à0.277,à0.221][à0.108,à0.060][0.060,0.109][0.237,0.297]NL [à0.690,à0.609][à0.483,à0.410][à0.252,à0.198][à0.143,à0.099][à0.005,0.037][0.185,0.241][0.324,0.393]Z [à0.539,à0.462][à0.408,à0.342][à0.185,à0.137][à0.016,0.016][0.137,0.185][0.312,0.378][0.462,0.539]PL [à0.357,à0.293][à

0.166,à0.116][à0.018,0.018][0.099,0.143][0.221,0.277][0.381,0.453][0.557,0.635]PM [à0.246,à0.191][à0.085,à0.043][0.078,0.124][0.244,0.302][0.358,0.428][0.492,0.569][0.697,0.781]PH

[à0.122,à0.074]

[à0.024,

0.029]

[0.207,0.266]

[0.358,0.428]

[0.492,0.569]

[0.668,0.751]

[0.697,

0.781]

Fig.5.FCS type-2fuzzy logic normalised control surface.

J.Solano Mart?′nez et al./Engineering Applications of Arti?cial Intelligence 26(2013)1772–17791775

Fig.7(a)illustrates the evaluation of the power reference dynamic limitation for the UCS.To evaluate this limitation two different tests are performed.

First,the load resistor power was selected to be higher than the maximal output power of the FCS,then,the UCS power

Table 3

Experimental static validation EMS parameters.Parameter (references)

Value FCS maximal current 40(A)FCS minimal current

4(A)FCS maximal current rate change 5(A/s)UC SOC reference

0.5UC maximal state-of-charge 0.85UC charge limitation threshold 0.8UC minimal state-of-charge

0.25UC discharge limitation threshold

0.3

UCS maximal current (charge and discharge)200(A)UC maximal current (charge and discharge)

400

(A)

Fig.6.Experimental results —static validation I power distribution (a),DC bus voltage (b),UC current (c),FCS current (d),and UC SOC

(e).

Fig.7.Experimental results —static validation II UC SOC (a)and UC SOC detail

(b).

Fig.8.Experimental results —static validation III power distribution (a)and FCS current reference variation (b).

Table 4

Experimental validation EMS parameters.Parameter (references)

Value FCS minimal-maximal current 4–40(A)FCS maximal current rate change 40(A/s)UC SOC reference

0.5UC maximal state-of-charge 0.85UC charge limitation threshold 0.8UC minimal state-of-charge

0.25UC discharge limitation threshold

0.3

UCS maximal current (charge and discharge)200(A)UC maximal current (charge and discharge)400(A)Batteries minimal–maximal state-of-charge 0.64–0.88Batteries maximal charge–discharge power 5000(W)Batteries minimal-maximal voltage 500–600(V)Batteries

reference voltage

540(V)

J.Solano Mart?

′nez et al./Engineering Applications of Arti?cial Intelligence 26(2013)1772–17791776

reference is always positive.It can be observed (from time 170to

210[s])that the UC SOC remains in the predetermined limits (discharge protection validation).As a consequence,the batteries discharge while supplying power to the resistor.Second,the load resistor is switched off;the FCS is manually operated in order to supply a maximal power.It can be observed (from time 600to 800[s])that the UC SOC remains in the predetermined limits (overcharge protection).The batteries charge in this operation conditions.Fig.7(b)illustrates the evaluation of the UC SOC control.The dynamic reference is temporarily modi?ed to study how the UC SOC follows the reference.It is important to highlight that the speed of the UC SOC controller can vary drastically regarding the operating conditions.

The UC can be relatively fast discharged when the requested load power is high or relatively fast charged when the requested load power is low.

If the requested load power is low or null,the UC discharge is only possible by charging the batteries.This is however limited by the EMS (limits in DC bus voltage,FCS minimal current and battery power).This is illustrated in Fig.7(b).

If the load power is high (higher than the FCS nominal power),

the charge is only possible using the batteries.This operation is also very limited by the EMS.

The third evaluation consists into verifying the operation of the limits in maximal current rate change of the FCS as de?ned in Table 3.The evaluation consists into applying a high load when the FCS power is low and the UC relatively discharged.The FCS EMS then imposes a maximal variation of the reference power (to supply the power and to recharge the UC).Fig.8shows that when the power load is applied,the FCS power increases at a maximum rate until the FCS reaches the limit in maximal power.

4.2.Driving validation

Driving validation is performed on a drive circuit located in PANHARD.The evaluation of the EMS is performed to analyze its operation in the vehicle under real-world situations.The EMS is uploaded into the dSPACE Autobox using the parameters given in Table 4.

Figs.9and 10present nominal operation conditions with UCS–FCS and batteries experimental results.They are satisfactory and in agreement with the simulation results presented in Solano

Mart?

′nez et al.(2012b).The objectives of the EMS as de?ned in Section 3.1are ful?lled.Fig.9presents the power distribution between the energy sources.Fig.10additionally presents the speed,the UC and battery SOC and the DC bus voltage.

Reference power:the amount of available braking energy was

lower than expected because the driving circuit was very constrained in terms of slope and curves.Moreover,when driving in negative slopes (where the energy is expected to be recovered)the circuit is very curved and the energy is used to supply the power steering.

Battery power:it remains almost constant,except when the UC are relatively discharged and the battery reinforces it.

Ultracapacitor system power:the UCS supplies most of the dynamic power solicitations.

Fuel cell system power:the FCS supplies the mean energy and controls the UC and batteries SOC.The output power remains into determined limits.

DC bus voltage:it varies with the battery output power,but stays in assigned limits.

Battery state-of-charge:the battery SOC control is veri?ed:a contribution of 5kW is observed almost all the time because the SOC is higher than the reference.

Ultracapacitor state-of-charge:the UC SOC control is veri?ed:at low speeds the UC is charged by the FCS,at high speeds the UC discharges.

To emulate the UCS fail,the output of this element is imposed to be zero.In this degraded operation the batteries supply the dynamic response and the output voltage is not stable as in the con?guration including the UCS.The batteries SOC is never-theless controlled by the FCS.Experimental results are presented in Fig.11.

To emulate the FCS fail,the output of this element is imposed to be zero.In this con?guration the UCS supplies the dynamic response and the output voltage variation is much lower than in the degraded con?guration without UCS (this element minimizes the DC bus voltage variation).The batteries control the UC SOC,however,the batteries SOC cannot be controlled and then a progressive decrease in the batteries voltage can obviously be noticed.Experimental results are presented in Fig.12

.

Fig.9.Experimental results —drive validation I reference (a),batteries (b),UCS (c),and FCS power (d).

J.Solano Mart?′nez et al./Engineering Applications of Arti?cial Intelligence 26(2013)1772–17791777

5.Conclusion and further work

Experimental validation of a type-2fuzzy logic based energy management system is performed using a mobile laboratory.As expected in simulation Solano Mart?

′nez et al.(2012b),it can be observed an energy management system which ful?lls the control objectives.These experimental results can be seen as evidence that T2-FLCs are well adapted for use in

energy

Fig.10.Experimental results —drive validation II speed (a),power sharing (b),DC bus voltage (c),UC SOC (d),and battery SOC

(e).

Fig.11.Experimental results —drive validation III (without the UCS).Power

distribution (a)and DC bus voltage

(b).

Fig.12.Experimental results —drive validation IV (without the FCS).Power distribution (a)and DC bus voltage (b).

J.Solano Mart?

′nez et al./Engineering Applications of Arti?cial Intelligence 26(2013)1772–17791778

management of hybrid electrical vehicles.However,as the research presented in this paper does not consider optimization of the FLC,it does not present enough evidence to af?rm that type-2-FLC are better performing than type-1FLC.Nevertheless, thanks to the proposed survey and the use of type-2FLC,the knowledge of different experts among the world can easily be integrated into one single fuzzy controller.

As perspectives of research,regarding the energy manage-ment,it could be interesting to rede?ne the dynamical references of the state-of-charge of the UC and the batteries by considering the road slope and/or forecasting of the traf?c(https://www.sodocs.net/doc/184419972.html,ing GPS information).It could be also interesting to consider energy management of hybrid sources where the load pro?le can be partially known in advance(such as in stationary applications or in railroad transport).Regarding the fuzzy logic controller,inter-val T2-FL membership functions were retained in this research. Further researches are currently considering the implementation of general type-2membership functions.It could be also interesting to study the optimization of the type-2fuzzy logic controllers.

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J.Solano Mart?′nez et al./Engineering Applications of Arti?cial Intelligence26(2013)1772–17791779

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第10课 日本的季节 日本的一年有春、夏、秋、冬四个季节。 3月、4月和5月这三个月是春季。春季是个暖和的好季节。桃花、樱花等花儿开得很美。人们在4月去赏花。 6月到8月是夏季。夏季非常闷热。人们去北海道旅游。7月和8月是暑假，年轻人去海边或山上。也有很多人去攀登富士山。富士山是日本最高的山。 9月、10月和11月这3个月是秋季。秋季很凉爽，晴朗的日子较多。苹果、桔子等许多水果在这个季节成熟。 12月到2月是冬季。日本的南部冬天不太冷。北部非常冷，下很多雪。去年冬天东京也很冷。今年大概不会那么冷吧。如果冷的话，人们就使用暖气炉。 第12课 乡下 我爷爷住哎乡下。今天，我要去爷爷家。早上天很阴，但中午天空开始变亮，天转好了。我急急忙忙吃完午饭，坐上了电车。 现在，电车正行驶在原野上。窗外，水田、旱地连成一片。汽车在公路上奔驰。 这时，电车正行驶在大桥上。下面河水在流动。河水很清澈，可以清澈地看见河底。可以看见鱼在游动。远处，一个小孩在挥手。他身旁，牛、马在吃草。 到了爷爷居住的村子。爷爷和奶奶来到门口等着我。爷爷的房子是旧房子，但是很大。登上二楼，大海就在眼前。海岸上，很多人正在全力拉缆绳。渐渐地可以看见网了。网里有很多鱼。和城市不同，乡下的大自然真是很美。 第13课 暑假 大概没有什么比暑假更令学生感到高兴的了。大学在7月初，其他学校在二十四日左右进入暑假。暑假大约1个半月。 很多人利用这个假期去海边、山上，或者去旅行。学生中，也有人去打工。学生由于路费等只要半价，所以在学期间去各地旅行。因此，临近暑假时，去北海道的列车上就挤满了这样的人。从炎热的地方逃避到凉爽的地方去，这是很自然的事。一般在1月、最迟在2月底之前就要预定旅馆。不然的话可能会没有地方住。 暑假里，山上、海边、湖里、河里会出现死人的事，这种事故都是由于不注意引起的。大概只能每个人自己多加注意了。 在东京附近，镰仓等地的海面不起浪，因此挤满了游泳的人。也有人家只在夏季把海边的房子租下来。 暑假里，学校的老师给学生布置作业，但是有的学生叫哥哥或姐姐帮忙。 第14课 各式各样的学生 我就读的大学都有各种各样的学生入学。学生有的是中国人，有的是美国人，有的是英国人。既有年轻的，也有不年轻的。有胖的学生，也有瘦的学生。学生大多边工作边学习。因此，大家看上去都很忙。经常有人边听课边打盹。 我为了学习日本先进的科学技术和日本文化来到日本。预定在这所大学学习3年。既然特意来了日本，所以每天都很努力学习。即便如此，考试之前还是很紧张。其他学生也是这

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教育界的科技革命 如果让生活在年的人来到我们这个时代，他会辨认出我们当前课堂里发生的许多事情——那盛行的讲座、对操练的强调、从基础读本到每周的拼写测试在内的教学材料和教学活动。可能除了教堂以外，很少有机构像主管下一代正规教育的学校那样缺乏变化了。 让我们把上述一贯性与校园外孩子们的经历作一番比较吧。在现代社会，孩子们有机会接触广泛的媒体，而在早些年代这些媒体简直就是奇迹。来自过去的参观者一眼就能辨认出现在的课堂，但很难适应现今一个岁孩子的校外世界。 学校——如果不是一般意义上的教育界——天生是保守的机构。我会在很大程度上为这种保守的趋势辩护。但变化在我们的世界中是如此迅速而明确，学校不可能维持现状或仅仅做一些表面的改善而生存下去。的确，如果学校不迅速、彻底地变革，就有可能被其他较灵活的机构取代。 计算机的变革力 当今时代最重要的科技事件要数计算机的崛起。计算机已渗透到我们生活的诸多方面，从交通、电讯到娱乐等等。许多学校当然不能漠视这种趋势，于是也配备了计算机和网络。在某种程度上，这些科技辅助设施已被吸纳到校园生活中，尽管他们往往只是用一种更方便、更有效的模式教授旧课程。 然而，未来将以计算机为基础组织教学。计算机将在一定程度上允许针对个人的授课，这种授课形式以往只向有钱人提供。所有的学生都会得到符合自身需要的、适合自己学习方法和进度的课程设置，以及对先前所学材料、课程的成绩记录。 毫不夸张地说，计算机科技可将世界上所有的信息置于人们的指尖。这既是幸事又是灾难。我们再也无须花费很长时间查找某个出处或某个人——现在，信息的传递是瞬时的。不久，我们甚至无须键入指令，只需大声提出问题，计算机就会打印或说出答案，这样，人们就可实现即时的"文化脱盲"。 美中不足的是，因特网没有质量控制手段；"任何人都可以拨弄"。信息和虚假信息往往混杂在一起，现在还没有将网上十分普遍的被歪曲的事实和一派胡言与真实含义区分开来的可靠手段。要识别出真的、美的、好的信息，并挑出其中那些值得知晓的, 这对人们构成巨大的挑战。 对此也许有人会说，这个世界一直充斥着错误的信息。的确如此，但以前教育当局至少能选择他们中意的课本。而今天的形势则是每个人都拥有瞬时可得的数以百万计的信息源，这种情况是史无前例的。 教育的客户化 与以往的趋势不同，从授权机构获取证书可能会变得不再重要。每个人都能在模拟的环境中自学并展示个人才能。如果一个人能像早些时候那样"读法律"，然后通过计算机模拟的实践考试展现自己的全部法律技能，为什么还要花万美元去上法学院呢？用类似的方法学开飞机或学做外科手术不同样可行吗？ 在过去，大部分教育基本是职业性的：目的是确保个人在其年富力强的整个成人阶段能可靠地从事某项工作。现在，这种设想有了缺陷。很少有人会一生只从事一种职业；许多人都会频繁地从一个职位、公司或经济部门跳到另一个。 在经济中，这些新的、迅速变换的角色的激增使教育变得大为复杂。大部分老成持重的教师和家长对帮助青年一代应对这个会经常变换工作的世界缺乏经验。由于没有先例，青少年们只有自己为快速变化的"事业之路"和生活状况作准备。