EVS28KINTEX, Korea, May 3-6, 2015

An Energy Management Strategy of

Hybrid Energy Storage Systems for

Electric Vehicles Electric Vehicles

Chunhua Zheng1, Zhongming Pan1, Guoqing Xu2 , SukWon Cha31Shenzhen Institutes of Advanced Technology CAS, 1068 Xueyuan Avenue, Shenzhen University Town,

Shenzhen, P.R.China, ch.zheng@siat.ac.cn2The Chinese University of Hong Kong

3Seoul National University

Contents

Introduction

The vehicle model

The proposed energy management strategy

2

The proposed energy management strategy

Conclusion

Introduction

I. Background

1. Currently, pure electric vehicles (PEVs) usually use the single energy

storage system (ESS), i.e. the battery;

2. Batteries have a limited power density due to their chemical

characteristics;characteristics;

3. Super-capacitors present good power density, however they are

difficult to provide a good performance on the energy density;

4. A hybrid energy storage system (HESS) which consists of a battery

and a super-capacitor shows an improved performance considering

both the power and energy densities compared to the single ESS case.

3

Introduction

II. Objectives

1. This paper proposes an energy management strategy of the HESS for

PEVs, which is based on the Minimum Principle;

2. The control objective of the energy management strategy is to

optimize the entire efficiency of the HESS and meanwhile to

prolonging the battery lifetime.

4

The vehicle model

I. Configuration of an EV with the HESS

1. The battery and the super-capacitor can recover the vehicle braking

energy;

2. The battery can also provide a part of energy to the super-capacitor in

some cases.

5

Battery

Super-capacitor

Motor Vehicle

The vehicle model

II. Battery model

1. The internal resistance model is used;

2. The open circuit voltage (OCV) and the internal resistance are

dependent on the battery state of charge (SOC).

Battery

Super-capacitor

Motor Vehicle

6

0 0.2 0.4 0.6 0.8 1310

320

330

340

350

360

Battery SOC

Bat

tery

OC

V (

V)

0 0.2 0.4 0.6 0.8 10.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

Battery SOC

Inte

rnal

res

ista

nce

(O

hm

)

Charging

Discharging

Vbat(SOCbat)

Rbat(SOCbat)Ibat

Pbat

2( ) ( ) 4 ( )1

2 ( )bat bat bat bat bat bat bat

bat

bat bat bat

V SOC V SOC R SOC PSOC

Q R SOC

•

⋅− − ⋅= −

The vehicle model

III. Super-capacitor model

1. A simple model is used for the super-capacitor;

2. The super-capacitor module data are described below.

Battery

Super-capacitor

Motor Vehicle

( )

7

( )2

,max ,max

,max

41

2

sup sup sup sup sup sup

sup

sup sup sup

SOC V SOC V R PSOC

V C R

• ⋅ ⋅⋅

⋅

− − ⋅= −

Vsup

RsupIsup

Psup

Csup (F) 165

Vsup,max (V) 48.6

Rsup(mΩ) 10

The vehicle model

IV. Motor and vehicle models

1. An efficiency may is used for the electric motor;

2. The vehicle data are described below.

Battery

Super-capacitor

Motor Vehicle

8

Vehicle total mass (kg) 1500

Final drive gear efficiency (%) 95

Tire radius (m) 0.29

Aerodynamic drag coefficient 0.37

Vehicle frontal area (m2) 2.59

Air density (kg/m3) 1.21

Rolling resistance coefficient 0.014Speed (rad/s)

To

rqu

e (N

m)

0 100 200 300 400 500

-500

-400

-300

-200

-100

0

100

200

300

400

500

86

88

90

92

94

96

98

The proposed energy management strategy

I. Problem formulation

State equations: (state variables: battery SOC and super-capacitor SOC)

( )( )sup sup

,

,

bat bat bat

bat

SOC F P SOC

SOC f P SOC

•

•

=

=

Performance measure to be minimized: (control variable: battery power)

( ) ( ) 0

2

0, ,

ft

bat bat bat bat bat batt

J P P SOC k I P SOC dt= + ⋅∫

The total power is known, thus the

battery power can be used here.

9

Hamiltonian

Necessary conditions for obtaining the optimal

solution:

0t∫

( ) ( ) ( ) ( )2

0 1 2 sup, , , ,

bat bat bat bat bat bat bat bat batH P P SOC k I P SOC p F P SOC p f P SOC= + ⋅ + ⋅ + ⋅

( ) ( )

* *

sup

1 2

* *

1 2

sup

* * * * * * * * *

sup 1 2 sup 1 2

; ;

; ;

, , , , , , , ,

bat

bat

bat bat bat bat

H HSOC SOC

p p

H Hp p

SOC SOC

SOC SOC P p p SOC SOC P p pH H

• •

• •

∂ ∂= =∂ ∂∂ ∂= − = −

∂ ∂

≤

Battery energy term Battery lifetime term

The proposed energy management strategy

II. Simulation results of the proposed strategy1. Two examples of the Hamiltonian are illustrated below, which are calculated at

60s and 500s of the Japan1015 driving cycle respectively;

2. The third term of the Hamiltonian is neglected here, given that it overlaps with

the first term.

-100 -80 -60 -40 -20 0 20 40 60 80 100-2

0

2x 10

5

H1

-100 -80 -60 -40 -20 0 20 40 60 80 100-2

0

2x 10

5

H1

10

-100 -80 -60 -40 -20 0 20 40 60 80 1000

5

10x 10

5

H2

-100 -80 -60 -40 -20 0 20 40 60 80 100-2

0

2x 10

5

H4

-100 -80 -60 -40 -20 0 20 40 60 80 1000

5

10x 10

5

H

Battery power

-100 -80 -60 -40 -20 0 20 40 60 80 1000

5

10x 10

5

H2

-100 -80 -60 -40 -20 0 20 40 60 80 100-2

0

2x 10

5

H4

-100 -80 -60 -40 -20 0 20 40 60 80 1000

5

10x 10

5

H

Battery power

The proposed energy management strategy

II. Simulation results of the proposed strategy

1. Japan1015 driving cycle is used to the simulation;

2. The results are obtained by considering both the HESS efficiency and

the battery lifetime;

3. The battery power for the entire driving cycle does not change too

much, this is beneficial to the battery lifetime.

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0 100 200 300 400 500 600 700-15

-10

-5

0

5

10

15

20

time (s)

Po

wer

(k

W)

Battery power

Super-capacitor power

0 100 200 300 400 500 600 7000

20

40

60

80

Veh

icle

sp

eed

(k

m/h

)

0 100 200 300 400 500 600 700-10

0

10

20

30

To

tal

po

wer

(kW

)

time (s)

Conclusion & discussion

1. The proposed energy management strategy considers both the HESS

efficiency and battery lifetime;

2. The battery power does not change too much during entire driving,

this is beneficial to the battery lifetime;this is beneficial to the battery lifetime;

3. The proposed strategy needs to be further improved considering the

tradeoff between the battery energy saving and battery lifetime.

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Thank you!

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