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Electrical Engineering

, Volume 101, Issue 3, pp 759–770 | Cite as

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

  • Qiao ZhangEmail author
  • Gang Li
Original Paper
  • 85 Downloads

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.

Keywords

Hybrid energy storage system Battery Supercapacitor Wavelet transform and model predictive control 

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

List of symbols

SSC

Control command of DC/DC connected to SC

Rb

Resistance of DC/DC connected to battery

Lb

Inductance of DC/DC connected to battery

Sbat

Control command of DC/DC connected to battery

Lc

Inductance of DC/DC converter connected to SC

Rc

Resistance of DC/DC connected to SC

RfCf

Main cells of SC

RmCm

Middle cells of SC

RsCs

Slow cells of SC

Rleak

Loss resistance of SC

Uf

Main cells voltage

Um

Middle cells voltage

Us

Slow cells voltage

USC

SC output voltage

ISC

SC output current

z

Proportion coefficient

x(t)

Original signal

hk

Low-pass filter coefficient

gk

High-pass filter coefficient

P

Prediction horizon of MPC controller

M

Control horizon of MPC controller

y

Output value of MPC controller

Cb

Large capacitor

Cs

Characteristic capacitor

Rt

Terminal resistance

Uc

Characteristic capacitor voltage

Ibat

Battery output current

Rs

Surface resistance

Ub

Large capacitor voltage

Ubus

Bus voltage

Re

End resistance

Ubat

Battery output voltage

Notes

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.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Automobile and Traffic EngineeringLiaoning University of TechnologyJinzhouChina

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