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A predictive energy management system for hybrid energy storage systems in electric vehicles

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Abstract

Energy management system plays a vital role in exploiting advantages of battery and supercapacitor hybrid energy storage systems in electric vehicles. Various energy management systems have been reported in the literature, of which the model predictive control is attracting more attentions due to its advantage in deal with system constraints. In this paper, a predictive energy management system is proposed based on a combination of Haar wavelet transform and model predictive control. Different from prior publications, the main contribution of this study is that the wavelet transform algorithm is introduced for power demand decomposition. At the same time, the power errors of the model predictive controllers are also fed to the wavelet transform algorithm for coefficient regulation. In this way, the power components distributed to the battery and supercapacitor can better match to their individual characteristics. The proposed method can reduce the maximum voltage drop of the battery up to 10.53%, 9.09% and 23.53%, the battery life cost up to 9.09%, 6.52% and 2.82%, respectively, as compared with the sole model predictive controller without wavelet transform based on NYCC, UDDS and NurembergR36 three driving cycles.

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Abbreviations

EMS:

Energy management system

HESS:

Hybrid energy storage system

MPC:

Model predictive control

WT:

Wavelet transform

EV:

Electric vehicle

SC:

Supercapacitor

DP:

Dynamic programming

PSO:

Particle swarm optimization

GA:

Genetic algorithm

SA:

Simulated annealing

DC:

Direct current

NYCC:

New York City Cycle

UDDS:

Urban Dynamometer Driving Schedule

ECMS:

Equivalent consumption minimization strategy

PMP:

Pontryagin’s minimum principle

S SC :

Control command of DC/DC connected to SC

R b :

Resistance of DC/DC connected to battery

L b :

Inductance of DC/DC connected to battery

S bat :

Control command of DC/DC connected to battery

L c :

Inductance of DC/DC converter connected to SC

R c :

Resistance of DC/DC connected to SC

R f C f :

Main cells of SC

R m C m :

Middle cells of SC

R s C s :

Slow cells of SC

R leak :

Loss resistance of SC

U f :

Main cells voltage

U m :

Middle cells voltage

U s :

Slow cells voltage

U SC :

SC output voltage

I SC :

SC output current

z :

Proportion coefficient

x(t):

Original signal

h k :

Low-pass filter coefficient

g k :

High-pass filter coefficient

P :

Prediction horizon of MPC controller

M :

Control horizon of MPC controller

y :

Output value of MPC controller

C b :

Large capacitor

C s :

Characteristic capacitor

R t :

Terminal resistance

U c :

Characteristic capacitor voltage

I bat :

Battery output current

R s :

Surface resistance

U b :

Large capacitor voltage

U bus :

Bus voltage

R e :

End resistance

U bat :

Battery output voltage

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Acknowledgements

This work is supported by Project of Liaoning Province Major Technology Platform Grant JP2017002, Guidance Plan of Natural Science Foundation of Liaoning Province Grant 20180551280, National Science Foundation of China Grant 51675257, Project of Liaoning Province Innovative Talents Grant LR2016054 and Overseas Training Program for Colleges and Universities of Liaoning Province Grant 2018LNGXGJWPY-YB014.

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  1. School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou, 121001, China

    Qiao Zhang & Gang Li

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  1. Qiao Zhang
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  2. Gang Li
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Correspondence to Qiao Zhang.

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Zhang, Q., Li, G. A predictive energy management system for hybrid energy storage systems in electric vehicles. Electr Eng 101, 759–770 (2019). https://doi.org/10.1007/s00202-019-00822-9

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