Electric Vehicle Assessment in Real Traffic Driving Conditions

Copyright © IFAC Transportation Systems, Tianjin, PRC, 1994

ELECTRIC VEHICLE ASSESSMENT IN REAL TRAFFIC DRIVING CONDITIONS J.A. VIDEAU·, J.Y. ZHAO·, C. ADES· • Ecole d'Ingenieurs en Genie des Systemes Industriels, Centre d'Evaluation et de Recherche App/iquee aux Vehicules Electriques, BP 211, La Rochelle Cede: 01. France

Abstract. Based on a multidisciplinary and a systems approach, a general scheme is presen~d for testing and assessing Electric Vehicles or EV components in real traffic driving conditions. The proposed method aims at the client's needs satisfaction, in terms of performance evaluation, comparison of sub-systems, "test-assimd design", and so on. Its implementation relies on a modular segmentation which allows for easy recombination of different objectives, different sets of measurements, different models, different data analysis methods, different types of results. An example is given on the problem of the EV energy balance. Key Words. Electric vehicles; data acquisition; modeling; identification; energy control

1. INrRODUCTION The future progress in Electric Vehicle technology will come from research and technical advances in the different EV subsystems, such as body, motor, power electronics, control and above all battery technology. But the effective development of the EV market will need a more complex conjunction of technology progress, environmental concerns, state regulations, lower prices and actual market growth. From a different point of view, the EV performance does not only depend on technology but also on urban fitting in, on charging and paying terminals, on drivers' training, and so on. The acknowledgement of this global systemic character of the EV in its environment -vehicle and components and charger and driver and urban traffic- leads to two main consequences. First, at all stages of Research & Development on the EV system or components, test and evaluation must be performed to check the feasibility and then the performance of the new solutions. Second, according to their objectives, these test and assessment processes must rely on the whole range of possible situations, from simple experiment conducted in the laboratory, up to multi-parameter test programme performed in real traffic conditions.

energetics, measurement, data analysis, modeling, identification, have been drawn together in order to develop a comprehensive and efficient Test and Evaluation Service -TES- for the benefit of industrial or other customers. The paper includes a presentation of the general methodology with a description of the sequential phases which have been found necessary for most applications of TES. Then the modular implementation of TES is discussed, where the main objective is the easy combination of modules to satisfy specific needs within a short development time. Finally, an example on the EV energy balance permits to consider some difficulties and to set out directions for improvements.

2. TEST AND EVALUATION METHODOLOGY The development of an EV test and evaluation process goes through different phases of work which are linked together (see Fig. 1). We call this the Test and Evaluation Methodology and discuss its main features hereafter.

2.1. Need from client Most industrial companies and city authorities involved in the EV development are facing or are going to face this test and evaluation problem. That is why different expertises in electrical engineering,

As the main objective is to provide a customer with a well-adapted test and evaluation service, it is mandatory to begin with analysing the client's needs. Some customers will come with a very precise idea

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of the test they want to be performed, for example if they are from an EV components supplier : they want this element to be tested, or the performance of this new system to be evaluated, under such and such conditions, and that is all... But in most cases, for example a qualification programme for a battery supplier, or a research conducted for a state agency, or a feasibility study for a city authority, it will be necessary to make a thorough exploration of the expected results and the use they are intended for before designing the whole assessment process.

Then the critical parameters allowing the computation of the criterion have to be defined. Depending on their nature, they can be directly measured, or estimated through sets of measurements, or computed after other parameters. It is also important at this stage to anticipate on the presentation of the results, and to check its consistency with the client's need, because it will widely affect the choice of a model or of a family of models for representing the main phenomenon at work. Of course the model definition phase appears to be essential. One has to carefully choose the formulas to be used, their validity domains, the parameters and measurements involved, and when it is necessary the division into several submodels each adapted to a specific domain of activity. For example, in the case of an energy balance study in real traffic driving, one may define three domains only - positive, zero or negative acceleration - or alternatively five domains which will give a better coverage of the whole range - start, acceleration, constant speed, deceleration, stop.

2.3. Definition of experiment Now the phenomenon to be studied, the equipment to be tested, the variables to be measured are known. A well-adapted measurement system has to be installed, including sensors, signal conditioning interface and data acquisition system. Along this hardware implementation, one has also to determine the exact nature of the experiment. The definition of the protocol to be followed is mandatory for a laboratory experiment, and it is even more important when the experiment is to take place in real traffic conditions, because of the influence of the environment, which one might seek or on the contrary try to avoid. With regard to the quality of the obtained data, the mesurement system or the protocol definition might be adapted or reconsidered.

2.4. From test to knowledge

Fig. 1. Phases of the test and evaluation process

2.2. Translation into specifications Once the client's expectations are well-understood, several specifications must be defined, starting with the criterion on which the evaluation will finally be based. It can be for instance the global energy consumption, or the transmission system efficiency.

We find here the classical operations starting with the test itself, and leading to the estimation of the sought parameters in the model. The technical aspects of these phases will be addressed in the next chapter. But we can emphasize some pecu-liarities due to the nature of the EV, such as electromagnetic compatibility problems, impossibility of connecting to earth, differences in the dynamics between thermal processes -battery temperature for example - and electric processes motor speed or current.

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Of course the set of operations may be performed several times, with respect to the quality of the identified parameters and to the number of pieces in the model.

Data Processing Unit In order to evaluate the performances of a vehicle, numerous experiments have to be performed under real traffic conditions. A vast amount of data turns out. Data analysis and data processing become indispensable for getting maximum information and for drawing reliable conclusions with these unsystematic and noisy data. Sometimes, the result of data processing allows us to discover the defaults during the test process and to remove the suspect data. The parameters which are most often of interest are the mean, the variance, the median in the case of one sample, and the correlation coefficient in the case of multi-samples. Moreover, it is important to know the distribution model and its parameters which determine the character of the observed data. The data processing unit allows these different data treatments to be done.

2.5. Back to client Expressing the obtained results with an understandable language and in a suitable form is the objective of this last phase, where the scientist must not forget that he has been working for somebody who has to exploit these results. Checking the suitability of the final report is mandatory, at least for one who expects to keep a client. It may prove the necessity to come back to some previous important stage for adaptations.

3. IMPLEtvlENTATION The EV Test and Evaluation System developed at EIGSI allows the performances of different vehicles to be compared or improvements to components to be assessed. These performances concern the characteristics of the total vehicle system or those of the subsystems and components : top speed of vehicle, acceleration, speed range under city traffic conditions, energy consumption, energy efficiencies of the components and the subsystems of vehicle, energy flows, etc. Although easier to perform, laboratory tests do not produce all the needed information. So road tests should also be used whenever appropriate. The EV TES is composed of a series of modules devoted to data acquisition, data processing, modelling and computing. A suitable combination of these modules should be selected with regard to the purpose of the test.

3.l. Description of modules Data Acquisition Unit It consists of : a power supply of 96 volts, a 16-bit single-chip micro-controller, a 12-bit analogic acquisition-card and a digital acquisition-card both with 16 input-pins, a signal conditioning interface (0 - 10 volts). Qualified sensors are connected : three voltmeters, six current meters, six temperature sensors, two velometers and one accelerometer. It is necessary to note that EV is an environment where measurement is a matter of great delicacy. In some cases the differential input signal should be employed to eliminate the electric parasite noise and the effect of the non-linearity. The armature current and the battery current are measured with a Hall-effect probe which was made for this purpose and proved to be better than the shunt against electrical noise.

Modelling Unit When considering an evaluation process, two questions arise: a) Is a single test sufficient for estimating the performances of a vehicle 1 and if not : b) How to make a synthesis on the results of a series of tests 1 The answer concerns the modelling methodology. Actually, in order to simulate a process and control a system, we have to find the model of the parameter and the system. The model attempts to describe the structure in some objects on which measurements have been taken. Estimation, hypothesis testing, and inference are based on the data at hand and a conjectured model which define implicitly or explicitly the relationships between observed parameters. In the modelling unit, methods such as linear regression, generalised linear modelling, generalised additive modelling, ARMA modelling, and even non linear regression are employed to get various kinds of models. Calculation Unit This part of the EV TES contains a whole series of methods, formulas and diagrams with which the different kinds of performance criteria could be achieved. In general, the calculation program depends on the objectives of the test and the parameters measured. That is why a calculation chart should be carefully designed to insure that the predefined performance criterion will be generated.

3.2. Example of assessment implementation The test system has been implemented on the EV VOLTA in La Rochelle, France. During the test, 17 parameters were measured every one second and stored on discs. The following sequence of analysis and studies was then performed.

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Data Processing It provided a large quantity of information, bringing out the first image of the parameters, such as : - Correlation Coefficient Matrix, a useful tool for estimating the dependence of the different parameters.

The histogram of a parameter gives us all the values measured during the test procedure with their frequency. From the velocity histogram, we detected that three values had not arisen. Making use of another function we obtained the precise missing values which were : 12km1b. 32km1h and S4km1h. This kind of indications permits users to improve the test techniques.

Table I. Correlation matrix of VOLT A parameters

Motor input current

~ output current

Motor vohage

Vehicle Velocity

Motor velocity

Parameter Modelling Within various possible models provided by the modelling module, two kinds of models are frequently used. In the first one a parameter is described as a function of others. i.e.;

Motor input current ~ output current Motor volUge Vehicle velocity Motor velocity

yet) = f(Xi) For example, based on the observed data, we obtained the following model:

0.93 0.77

0.79

0.47

0.46

0.55

0.51

0.47

0.62

E(t) = 0. 56 yet) + O. 054

0.86

1

- Useful values and r~resentations, as shown for the vehicle velocity on Table 2. and Fig.2. Table 2. Statistical values of vehicle veloci!y

Min.

o

1st Qu. Median Mean 3rd Qu. Max. 17 37 34.74 55 72

where E(t) is the energy furnished by the battery and V(t) is the vehicle velocity. This model represents our attempt to understand better the physical process between the energy output of battery and the vehicle velocity. The second one is a local regression model. An ARMA model of vehicle velocity has been used to simulate the motion of vehicle:

Vt = 1.69Vt_,- 0.69Vt_2 + Ct - 0.22 CI-I + 0.15Ct_2 The accuracy of the model is measured in terms of its ability to imitate the data, but the relevant accuracy is actually that of inferences made about the real process.

Histogram 8....

Calculation of performance criterion The main formulas are described as follows : 8

1_." d,dilhlluhldU dd'IIII.!..nIU~ ~1~1Ud... .

o

o

1000

2000 3000 Vdesse

4000

- Motion equations

F-F.-Fr-Fp = (rn+rnr)a dO Tm- Tr=Jdt

5000

Smoothed Density Estimate

"'.-------------, oo

1

8o o

2

F. ="2PSvCx Y

8o _________ 50 100 o ·50

o~

Fr =rngcosa(f 0+ k y2) Fp = rngsin a

~

Vdesse

rnr

Fig. 2. Histogram and smoothed density estimate of vehicle velocity.

262

N

Ji

i=1

n

= L-2

Relation between the motor torque Tm and the driving force F :

7]tTm = Fro

- Motor equations :

. L di. V.= E +Rala+ a dt

E=Ctn Tm = Ctia - Powers :

Calculation programme As an example, Fig. 3 illustrates a flow chart in order to establish an EV energy balance. The energy consumption and the energy regeneration are detennined by the motion states of vehicle which can be divided into three phases: the phase of acceleration, the constant velocity phase and the breaking phase. First of all, the data such as the parameters, the coefficients of the vehicle, the motor, the battery and the environment have to be set up as the initial values. Other inputs are the measured data or their simulated values, before starting the programme.

P=FV

P=Tmn - Table of symbols : F: motivation force Fa : aerodynamic resistance F r : rolling friction force Fp : longitudinal component of gravitation T m: motive torque

....._ _ _.... Efficiency of controller

T r : resistance torque J:

moment of inertia

p.

air density

output torque

Sv : vehicle frontal surface Calc. motor output power

C x : aerodynamic resistance coefficient

V: vehicle velocity

n:

motor velocity

g:

acceleration of gravitation

power rc:quir.

fO : minimum rolling friction coefficient

k:

Efficiency of

rolling friction coefficient

rc:gener. I}'IItm

a: slope of road n: effective weel radius

'7t : transmission efficiency m:

mass of vehicle

mr : equivalent mass of rotational part

E:

electromotive force

Ct :

torque constant

P:

dynamic power Fig. 3. Computing an EV energy balance

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Joubert, T. - Zhao, J.Y. - Mieze, A. - Ades, C. (1993). "Detailed energy evaluation of an electric vehicle on an actual urban run" . Electric Vehicle Symposium

As illustrated in Table 3., the results calculated after the VOLTA data are essentially relevant to the evaluation of the power furnished by the battery, the power absorbed by the motor and the efficiency of the controller.

no 12. November. La Rochelle. France. Perugia, A. - Spazzoli, R (1993). "Stepwise refinement in electric vehicle system design by using a simulation code". Electric Vehicle Symposium no

Table 3. Elements of EV energy balance

12. November. La Rochelle. France. Vehic. veloc.

Vehic. accel.

(kmih)

10 12 25 25 35 40 40 40 43 50 50 63

Rccup power

Motor power

(m2/s)

Battery power output (kw)

(kw)

Control. loss power (kw)

Road loss power (kw)

(kw)

1.8 -1.2 -2.5 0.9 1.3 -0.3 1.1 0 .8 -4.3 0 .3 -3 .7 -0.2

7.2 9.6 7 .8 10.2 13.8 15.3 10.5 11.2 2.5 15.2 8.8 14.5

0 0.6 1.05 0 0 0 0 0 1.5 0 1.97 0

2.2 3.3 4.1 5 9.7 8.9 6.2 10.7 0.93 13 .2 5.1 10.2

5 6.3 3.7 5.2 4.1 6.4 4.3 0.5 1.57 2 3.7 4.3

0.8 1.02 2.1 2.1 2.8 3.2 3.2 3.2 3.3 3.4 3.7 4

Reyneveld, K.E Lesster, L.E. (1992) . "Comprehensive system simulation for EV parameter evaluation". Electric Vehicle Symposium no ll.

September. Florence. italy. Tadakuma, S. - Tanaka, S. - lnagaki, J. (1976). "Evaluation of motor and controller for electric vehicles". Electrical Engineering in Japan. Vo/.96. nO],

4. CONCLUSION In this paper, we have presented the methodology, the modular structure and the principal functions of the Electric Vehicle Test and Evaluation System under development at EIGSI. As an example, the application about the energy balance of an electric vehicle showed that the main difficulties were first in the measurement process, of the foremost importance particularly in real traffic conditions, and second in the liaison to establish between the assessment criterion and the parameters.

We are confident in the possibility to get significant improvements of our Test and Evaluation System, through the following directions : enlarging the number of measured variables - torque for example -, adding laboratory experiments to initialize the modelling process performed after real traffic measurements, and optimizing the experimental protocol according to the information sought for.

5. REFERENCES Badin, F. - Maillard, P. - Jammal, A. - Grellet, G. (1992). "Simulation software for EV drive train".

Electric Vehicle Symposium no 11. September. Florence. italy.

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