To improve the performance of the battery management system in hybrid electric vehicle (HEV), the core is to estimate the state of charge (SOC) and the state of power capability (SOP) of power battery quickly and accurately on-line. Firstly, in order to improve the SOC estimation accuracy and reduce the estimation error of battery, an improved particle filter algorithm based on particle swarm optimization (PSO) is proposed. Aiming at the uncertainty of system noise in traditional particle filter (PF) algorithm, the PSO algorithm is used to optimize the system noise of PF and to improve the estimation accuracy. Secondly, a method that regards the battery voltage, current and the optimized estimation of SOC as constraints to predict the actual maximum charge-discharge power of the battery is proposed. The simulation results show that the optimized SOC estimation and SOP prediction algorithm has higher accuracy and is applicable to the dynamic estimation of the actual driving cycles of hybrid electric vehicles.
DaiH.F. et al., ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries, Energy80 (2015), 350–360.
2.
ShangL.P. et al., Airbome battery SOC estimate method study based on discharge test method, Journal of Power Supply1(51) (2014), 61–65.
3.
YuW.Z. et al., An exact SOC-estimate mathematic model of open-circuit voltage and parameters estimation, in: International Conference on Mechatronics and Automation, Chengdu, China, 2012, pp. 1302–1307.
4.
LianC.Y.LiB. and CaiJ.H., New on-line estimation method of battery SOC based on discharge through fixed resistor, Chinese Journal of Power Sources39(11) (2015), 2395–2399.
5.
ZhangC.W.LiL.Y. and ZhaoD.G., Estimation and simulation of power battery SOC based on BP neural network, Chinese Journal of Power Sources41(9) (2017), 1356–1357+1368.
6.
KangL.W.ZhaoX. and MaJ., A new neural network model for the state-of-charge estimation in the battery degradation process, Applied Energy121 (2014), 20–27.
7.
MiaoJ.S.ChengL.M. and LyuH.S., Estimation of state of charge of energy storage lithium battery based on PSO-ELM, Electric Power Engineering Technology39(1) (2020), 165–169+199.
8.
XiongR. et al., A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles, Applied Energy113 (2014), 463–476.
9.
ChengZ.YangL. and SunX.M., State of charge and state of health estimation of li-ion batteries based on adaptive square-root unscented Kalman filters, Proceedings of the CSEE38(8) (2018), 2384–2393+2548.
10.
PengP. et al., Estimation of state of charge of li-ion battery based on adaptive Kalman filtering, Chinese Journal of Power Sources41(11) (2017), 1541–1544.
11.
AnZ.G. et al., SOC estimation of lithium battery based on equivalent model of extended Kalman filter, Journal of Chongqing Jiaotong University (Natural Science)38(2) (2019), 133–138.
12.
GuM.XiaC.Y. and TianC.Y., Li-ion battery state of charge estimation based on comprehensive Kalman filter, Transactions of China Electrotechnical Society34(2) (2019), 419–426.
13.
ZhongL. et al., A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis, Applied Energy113 (2014), 558–564.
14.
HeW. et al., State of charge estimation for electric vehicle batteries using unscented Kalman filtering, Microelectron Reliab53(6) (2013), 840–847.
15.
XieC.J. et al., State-of-charge estimation of lithium-ion battery using unscented particle filter in vehicle, Electric Engineering33(17) (2018), 3958–3964.
16.
LiL.F. et al., Discharge capacity estimation for li-ion batteries based on particle filter under multi-operating conditions, Energy86 (2015), 638–648.
17.
LiS. et al., A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test, Applied Energy212 (2018), 1522–1536.
18.
CaoY.L. et al., Lead-acid battery SOC estimation based on EKF-fuzzy dynamic compensation, Battery Bimonthly49(6) (2019), 511–514.
19.
ChenZ.W. et al., Online estimation of SOC for li-ion battery based on an improved unscented Kalman filters approach, Acta Metrological Sinica40(1) (2019), 41–48.
20.
SunB.X. et al., The lithium-ion battery low temperature stress based on different degradation paths, Electric Engineering31(10) (2016), 159–167.
21.
FengT.H. et al., Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction, Journal of Power Sources281 (2015), 192–203.
22.
ZhangW.G.ShiW. and MaZ.Y., Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery, Journal of Power Sources289 (2015), 50–62.
23.
ChengZ.SunX.M. and ChenS.L., Method for estimation of state of charge and power prediction of lithium-ion battery, Electric Engineering32(15) (2017), 180–189.
24.
HuangX.P.WangY. and MiaoP.C., Principle and application of particle filter-matlabsimulation (In Chinese), Publishing House of Electronics Industry, Beijing, 2017, 1–3.
25.
QianF., Industrial application of particle swarm optimization (In Chinese), Science Press, Beijing, 2013, 28–29.
26.
YuS.W., Case analysis and application of MATLAB optimization algorithm (In Chinese), Tsinghua University Press, Beijing, 2014, 156–157.
27.
WangH.L. et al., Comparative analysis for SOC estimation algorithm for electrochemical energy storage system, Electrical & Energy Management Technology (5) (2017), 76–83.
