Online Estimation Method for State of Health of Electric Vehicle Power Battery

doi:10.12068/j.issn.1005-3026.2020.04.007

Abstract

Abstract: An effective estimation method for online operation of electric vehicle power battery SOH was proposed. The advantage is that it only relies on the voltage and current data measured by the battery management system in real time， without the need of offline battery life decay curve and the initial state of the battery. Therefore， it is more in line with the actual needs of electric vehicles for SOH estimation. Based on the Thevenin and OCV-SOC models， a battery model with time and SOH as hidden variables was constructed in the battery constant-current charging mode. Based on this battery model， a fast solving algorithm based on NLS(nonlinear least square) initialization GA search range was proposed for online parameter identification， and the real-time SOH estimation value of electric vehicle was obtained. The verification results showed that the SOH estimation algorithm has good practicability and high estimation accuracy.

Key words: electric vehicle, SOH online estimation, battery model, genetic algorithm, nonlinear least squares

CLC Number:

TK02

LIU Fang， LIU Xin-yi， SU Wei-xing， LIN Hui. Online Estimation Method for State of Health of Electric Vehicle Power Battery[J]. Journal of Northeastern University Natural Science, 2020, 41(4): 492-498.

References

[1]Cano Z P，Banham D，Ye S，et al.Batteries and fuel cells for emerging electric vehicle markets［J］.Nature Energy，2018，3(4):279-289.
[2]Yang J F，Xia B，Huang W X，et al.Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis［J］.Applied Energy，2018，212:1589-1600.
[3]Shen P，Ouyang M，Lu L，et al.The co-estimation of state of charge，state of health and state of function for lithium-ion batteries in electric vehicles［J］.IEEE Transactions on Vehicular Technology，2017，67(1):92-103.
[4]Ungurean L，Gabriel C，Micea M V，et al.Battery state of health estimation:a structured review of models，methods and commercial devices［J］.International Journal of Energy Research，2016，41(2):151-181.
[5]Eddahech A，Briat O，Bertrand N，et al.Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks［J］.International Journal of Electrical Power & Energy Systems，2012，42(1):487-494.
[6]Hannan M A，Lipu M S H，Hussain A，et al.Neural network approach for estimating state of charge of lithium-ion battery using backtracking search algorithm［J］.IEEE Access，2018，6:10069-10079.
[7]Chen Z，Sun M，Shu X，et al.Online state of health estimation for lithium-ion batteries based on support vector machine［J］.Applied Sciences，2018，8(6):925-930.
[8]Hossain L M S，Hannan M A，Aini H，et al.A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles:challenges and recommendations［J］.Journal of Cleaner Production，2018，205:115-133.
[9]Zheng L，Zhu J，Lu D D C，et al.Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries［J］.Energy，2018，150:759-769.
[10]Feng X，Li J J，Ouyang M，et al.Using probability density function to evaluate the state of health of lithium-ion batteries［J］.Journal of Power Sources，2013，232(18):209-218.
[11]Wassiliadis N，Adermann J，Frericks A，et al.Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation:a use-case life cycle analysis［J］.Journal of Energy Storage，2018，19:73-87.
[12]Liu S L，Cui N X，Zhang C H，et al.State of health estimation of lithium-ion battery based on AUKF［J］.Power Electron，2017，11:122-124.
[13]Xiong R，Zhang Y，He H，et al.A double-scale，particle-filtering，energy state prediction algorithm for lithium-ion batteries［J］.IEEE Transactions on Industrial Electronics，2017，65(2):1526-1538.
[14]Guo Z，Qiu X，Hou G，et al.State of health estimation for lithium ion batteries based on charging curves［J］.Journal of Power Sources，2014，249:457-462.
[15]Liu F，Ma J，Su W X.Unscented particle filter for SOC estimation algorithm based on a dynamic parameter identification［J］.Mathematical Problems in Engineering，2019，1:1-14.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)(上接第491页)图3两个算法在Bupa liver数据集的分类正确率Fig.3Classification accuracy rates of two algorithms on the Bupa liver dataset4结语1) 针对蝙蝠算法具有易陷入局部最优，收敛速度慢和求解精度不高等不足，本文提出了一种全局混沌蝙蝠优化算法(GCBA).2) GCBA算法使用混沌映射初始化种群，增强了初始化种群的丰富性.GCBA算法引入P^l和P_g优化位置更新公式，提升了算法的全局搜索能力.3) GCBA，BBA，PSO和GA的实验结果表明，GCBA算法不仅能够得到很好的分类精度，还具有良好的稳定性和全局搜索能力.验证了GCBA算法的有效性.