|
|
|
| State of Charge Estimation Method of Electric Vehicle Power Battery Life Cycle Based on Auto Regression Extended Kalman Filter |
| Liu Fang1,2, Ma Jie1, Su Weixing1, Dou Ruzhen3, Lin Hui4 |
1. School of Computer Science & Technology TianGong University Tianjin 300387 China;
2. Tianjin Qingyuan Electric Vehicle Limited Liability Company Tianjin 300462 China;
3. China Automotive Technology & Research Center Tianjin 300300 China;
4. Neusoft Reach Automotive Technology Co. Ltd Automotive Research Institute Shenyang 110179 China |
|
|
|
|
Abstract The traditional extended Kalman filter(EKF) algorithm relies heavily on battery mathematic model and the dynamic battery model is difficult to obtain accurately. This paper proposes a completely data driven state of charge(SOC) estimation method based on improved EKF algorithm for battery life cycle. This method is a good combination of data-based SOC estimation method and model-based SOC estimation method. On the one hand, the SOC calculation method proposed in this paper can avoid the problem that data-driven SOC estimation method cannot eliminate the accumulated error and retain the data-driven algorithm good dynamic characteristics. On the other hand, it can improve the shortcoming of the model-based SOC estimation method that relies heavily on battery model while preserving the robustness. The hard-to-know part of the equivalent circuit is regarded as a black-box system, where the battery current is taken as input, the internal voltage as output and the internal impedance of the battery as time-varying parameter. The dynamic online identification is carried out to obtain the real state of the power battery, thereby ensuring the accuracy and dynamics of the battery model and realizing the battery life cycle SOC estimation. Simulation results show that the proposed method has good robustness and practicability.
|
|
Received: 31 August 2018
Published: 28 February 2020
|
|
|
|
|
|
[1] 吴奇, 珂程帆, 陈昕儒. “电能替代”战略中电动汽车的推广潜力及经济性分析[J]. 电气技术, 2016, 17(9): 88-97.
Wu Qi, Ke Chengfan, Chen Xinru.Potential and economic analysis of electric vehicles in ‘electric energy substitution’ strategy[J]. Electrical Engin- eering, 2016, 17(9): 88-97.
[2] Baba Atsushi, Adachi Shuichi.SOC estimation of HEV/EV battery using series Kalman filter[J]. Elec- trical Engineering in Japan, 2014, 187(2): 53-62.
[3] Hu Zhikun, Liu Bin, Lin Yong.Adaptive square root unscented Kalman filter for SOC estimation of battery[J]. Electric Machines and Control, 2014, 18(4): 111-16.
[4] Rivera-Barrera J, Munoz-GaleanoN, Sarmiento- Maldonado H. SOC estimation for lithium-ion batteries: review and future challenges[J]. Electronics, 2017, 6(4): 1-33.
[5] 刘毅, 谭国俊, 何晓群. 优化电池模型的自适应Sigma卡尔曼荷电状态估算[J]. 电工技术学报, 2017, 32(2): 108-117.
Liu Yi, Tan Guojun, He Xiaoqun.Optimized battery model based adaptive sigma Kalman filter for state of charge estimation[J]. Transactions of China Electro- technical Society, 2017, 32(2): 108-117.
[6] Xu Jun, Liu Binghe, Wang Xinyi.Computational model of 18650 lithium-ion battery with coupled strain rate and SOC dependencies[J]. Applied Energy, 2016, 172: 180-189.
[7] Cheng K W E, Divakar B P, Wu Hongjie, et al. Battery-management system(BMS) and SOC deve- lopment for electrical vehicles[J]. IEEE transactions on Vehicular Technology, 2011, 60(1): 76-88.
[8] Paschero Maurizio, Storti Gian Luca, Rizzi Antonello.A novel mechanical analogy-based battery model for SOC estimation using a multicell EKF[J]. IEEE Transactions on Sustainable Energy, 2016, 7(4): 1695-1702.
[9] 刘伟龙, 王丽芳, 廖承林, 等. 充电模态下电动汽车动力电池模型辨识[J]. 电工技术学报, 2017, 32(11): 198-207.
Liu Weilong, Wang Lifang, Liao Chenglin, et al.Parameters identification method of battery model for electric vehicles under the charging model[J]. Transactions of China Electrotechnical Society, 2017, 32(11): 198-207.
[10] 李晓宇, 朱春波, 魏国, 等. 基于分数阶联合卡尔曼滤波的磷酸铁锂电池简化阻抗谱模型参数在线估计[J]. 电工技术学报, 2016, 31(24): 142-149.
Li Xiaoyu, Zhu Chunbo, Wei Gou, et al.Online parameter estimation of a simplified impedance spectroscopy model based on the fractional joint Kalman filter for LiFePO4 battery[J]. Transactions of China Electrotechnical Society, 2016, 31(24): 142-149.
[11] Panday Aishwarya, Bansal Hari Om, Srinivasan Pramod.Thermoelectric modeling and online SOC estimation of Li-ion battery for plug-in hybrid electric vehicles[J]. Modelling and Simulation in Engineering, 2016(4): 1-12.
[12] 苏玉刚, 陈龙, 吴学颖, 等. 基于遗传算法的SS型磁耦合WPT系统负载与互感识别方法[J]. 电工技术学报, 2018, 33(18): 4199-4206.
Su Yugang, Chen Long, Wu Xueying, et al.Load and mutual inductance identification method of S/S-type magnetically-coupled WPT system based on genetic algorithm[J]. Transactions of China Electrotechnical Society, 2018, 33(18): 4199-4206.
[13] Zhong Fuli, Li Hui, Zhong Shouming.An SOC estimation approach based on adaptive sliding mode observer and fractional order equivalent circuit model for lithium-ion batteries[J]. Communications in Nonlinear Science and Numerical, 2015, 24(1-3): 127-144.
[14] Miao Zhixin, Xu Ling, Disfani Vahid R.An SOC-based battery management system for micro- grids[J]. IEEE Transactions on Smart Grid, 2014, 5(2): 966-973.
[15] Taborelli C, Onori S, Maes S.Advanced battery management system design for SOC/SOH estimation for e-bikes applications[J]. International Journal of Powertrains, 2016, 5(4): 325-357.
[16] Liu Fang, Su Weixing, Zhao Jianjun, et al.Outlier detection for control process data based on improved ARHMM[J]. Wireless Pers Commun, 2018, 103: 11-24.
[17] Arun K S, Huang T S, Blostein S D.Least-squares fitting of 2 3-D point sets[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(5): 699-700.
[18] Gillard Jonathan.Circular and linear regression: fitting circles and lines by least squares[J]. Journal of the Royal Statistical Society Series A-Statistics in Society, 2011, 174(3): 843.
[19] Li Gechen, Gu Yanlei.SOC of lithium-ion recharge- able battery predicted by electrochemical impedance spectroscopy[J]. Chinese Journal of Power Sources, 2008, 32(9): 599-602.
[20] Zhao Jianjun, Zhou Junwu, Su Weixing, et al.Online outlier detection for time-varying time series on improved ARHMM in geological mineral grade analysis process[J]. Earth Sciences Research Journal, 2017, 21(3): 135-139.
[21] Charkhgard Mohammad, Farrokhi Mohammad.State- of-charge estimation for lithium-ion batteries using neural networks and EKF[J]. IEEE Transactions on Industrial Electronics, 2010, 57(12): 4178-4187.
[22] Gauvain Jean-Luc, Lee Chin-Hui.Maximum a posteriori estimation for multivariate gaussian mixture observations of Markov chains[J]. IEEE Transactions on Speech and Audio Processing, 1994, 2(1): 291-298.
[23] Krone Tanja, Albers Casper J, Timmerman Marieke E.A comparative simulation study of AR(1) estimators in short time series[J]. Quality & Quantity, 2017, 51(1): 1-21.
[24] Tewfik Lounis.Optimal tests in AR(m) time series model[J]. Communications in Statistics-Simulation and Computation, 2017, 46(2): 1583-1610.
[25] Su Weixing, Zhu Yunlong, Liu Fang.On-line outlier and change point detection for time series[J]. Journal of Central South University, 2013, 20(1): 114-122.
[26] He Hongwen, Qin Hongzhou, Sun Xiaokun.Com- parison study on the battery SOC estimation with EKF and UKF algorithms[J]. Energies, 2013, 6(10): 5088-5100. |
|
|
|
2020, Vol. 35 
