Comparative Study on State of Power Estimation of Lithium Ion Battery Based on Equivalent Circuit Model

doi:10.3901/JME.2021.14.064

Abstract

Abstract: State of power (SOP) estimation for lithium-ion batteries plays a vital role in the battery management system (BMS) of electric vehicles. The accurate parameterized model provides important parameter and structure information for SOP estimation. From the internal mechanism of the battery, the equivalent circuit model composed of equivalent elements and equations for characterizing the electrochemical reaction process is summarized, and its voltage simulation expression is derived. Aiming at the problem of falling into local optimum and computational efficiency due to uncertain parameter boundaries, a method for determining parameter boundaries based on electrochemical mechanism is proposed. Eight kinds of parameterized models are obtained by the parameter identification data, DST driving cycle, and from the model accuracy and calculation efficiency, the robustness and timeliness of the parameterized models are verified in FUDS driving cycle. To address the issues that the nonlinear model cannot obtain the peak power analytical solution, a multi-constrained SOP optimization estimation method is proposed. Finally, according to the true value of SOP obtained under DST + constant power tests, the effectiveness of the proposed algorithm is verified. The results show that temperature has a significant influence on the dominance of electrochemical reaction and diffusion limitation. The SOP estimation error of the proposed model's optimal estimation method in the high and low SOC range is within 8%.

Key words: lithium ion battery, equivalent circuit models, parameter boundary, parameter identification, peak power

CLC Number:

TG156

CAI Xue, ZHANG Caiping, ZHANG Linjing, ZHANG Weige, GAO Le. Comparative Study on State of Power Estimation of Lithium Ion Battery Based on Equivalent Circuit Model[J]. Journal of Mechanical Engineering, 2021, 57(14): 64-76.

References

[1] Forrest K,Mac Kinnon M,Tarroja B,et al. Estimating the technical feasibility of fuel cell and battery electric vehicles for the medium and heavy duty sectors in California[J]. Applied Energy,2020,276:115439.
[2] Li X,Wang Z,Zhang L. Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles[J]. Energy,2019,174:33-44.
[3] Chen Z,Zhao Y,Cai X,et al. Study on regenerative braking control strategy for the dual-axle distributed drive electric vehicles[J]. DEStech Transactions on Environment,Energy and Earth Sciences,2019(10):14-17.
[4] 熊瑞. 基于数据模型融合的电动车辆动力电池组状态估计研究[D]. 北京:北京理工大学,2012.Xiong Rui. Estimation of battery pack state for electric vehicles using model-data fusion approach[D]. Beijing:Beijing Institute of Technology,2012.
[5] Zou C,Klintberg A,Wei Z,et al. Power capability prediction for lithium-ion batteries using economic nonlinear model predictive control[J]. Journal of Power Sources,2018,396:580-589.
[6] 孙丙香,高科,姜久春,等. 基于ANFIS和减法聚类的动力电池放电峰值功率预测[J]. 电工技术学报,2015,30(4):272-280.Sun Bingxiang,Gao Ke,Jiang Jiuchun,et al. Research on discharge peak power prediction of battery based on ANFIS and subtraction clustering[J]. Transactions of China Electrotechnical Society,2015,30(4):272-280.
[7] PNGV battery test manual:Revision 3[M]. Washington,DC:US Department of Energy,2001.
[8] Plett G. High-performance battery-pack power estimation using a dynamic cell model[J]. IEEE Transactions on Vehicular Technology,2004,53(5):1586-1593.
[9] Xiong Rui,He Hongwen,Sun Fengchun,et al. Online estimation of peak power capability of li-ion batteries in electric vehicles by a hardware-in-loop approach[J]. Energies,2012,5(5):1455-1469.
[10] Xiong Rui,He Hongwen,Sun Fengchun,et al. Model-based state of charge and peak power capability joint estimation of lithium-ion battery in plug-in hybrid electric vehicles[J]. Journal of Power Sources,2013,229:159-169.
[11] Tian J,Xiong R,Shen W,et al. A comparative study of fractional order models on state of charge estimation for lithium ion batteries[J]. Chinese Journal of Mechanical Engineering,2020,33(1):51-64.
[12] Wang B,Li S E,Peng H,et al. Fractional-order modeling and parameter identification for lithium-ion batteries[J]. Journal of Power Sources,2015,293:151-161.
[13] Xiong R,Tian J,Shen W,et al. A novel fractional order model for state of charge estimation in lithium ion batteries[J]. IEEE Transactions on Vehicular Technology,2019,68(5):4130-4139.
[14] Ouyang M,Liu G,Lu L,et al. Enhancing the estimation accuracy in low state-of-charge area:A novel onboard battery model through surface state of charge determination[J]. Journal of Power Sources,2014,270:221-237.
[15] 刘思佳. 轨道交通用钛酸锂电池建模与状态估计研究[D]. 北京:北京交通大学,2018.Liu Sijia. Study on modeling and state estimation for lithium titanate batteries used in rail transit vehicles[D]. Beijing:Beijing Jiaotong University,2018.
[16] Wang L,Zhang L,Luo W,et al. An approximate solution for electrolyte concentration distribution in physics-based lithium-ion cell models[J]. Microelectronics and Reliability,2013,53(6):797-804.
[17] Barsoukov E,Macdonald J R. Applications of impedance spectroscopy[M]. John Wiley & Sons,Inc. 2005.
[18] Doyle M,Fuller T,Newman J. Modeling of galvanostatic charge and discharge of the lithium/polymer[J]. Journal of the Electrochemical Society,1993,140(6):1526-1533.
[19] Subramanian V R,Diwakar V D,Tapriyal D. Efficient macro-micro scale coupled modeling of batteries[J]. Journal of the Electrochemical Society,2005,152(10):A2002-A2008.
[20] Novac M,Vladu E,Novac O,et al. Aspects regarding the optimization of the induction heating process using fmincon,minimax functions and simple genetic algorithm[J]. Journal of Electrical & Electronics Engineering,2009,2(2):64-69.
[21] 韩雪冰. 车用锂离子电池机理模型与状态估计研究[D]. 北京:清华大学,2014.Han Xuebing. Study on li-ion battery mechanism model and state estimation for electric vehicles[D]. Beijing:Tsinghua University,2014.
[22] Jia X,Zhang C,Wang L,et al. The degradation characteristics and mechanism of Li[Ni_0.5Co_0.2Mn_0.3] O₂ batteries at different temperatures and discharge current rates[J]. Journal of the Electrochemical Society,2020,167(2):020503.