温度和老化意识融合驱动的电动车辆锂离子动力电池电量和容量协同估计

doi:10.19595/j.cnki.1000-6753.tces.191598

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Abstract As the technical bottleneck of electric vehicles, batteries have strong time-varying nonlinear characteristics and limited measurability. They are easily affected by temperature and aging during use. Accurate state estimation under the full life cycle and the wide temperature has always been a technical problem in the industry. Therefore, this paper first uses the data of different temperatures and different aging stages to establish a multi-stage model with temperature and aging awareness; then uses the probability density function to calculate the weight of the single models and proposes a multi-stage model fusion-driven battery state of charge (SOC) and capacity estimation method. Finally, the verification results considering uncertainty of aging and temperature factors show that the proposed method has high SOC and capacity estimation accuracy and is not sensitive to the initial error. The SOC estimation error is less than 2% with the -10% to 50% of the initial SOC errors, and the convergence is fast.

Key words： Power lithium-ion battery full life joint estimation state of charge capacity estimation

Received: 22 November 2019

PACS:

TM912.9

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	Wang Ju
	Xiong Rui
	Mu Hao

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Wang Ju,Xiong Rui,Mu Hao. Co-Estimation of Lithium-Ion Battery State-of-Charge and Capacity Through the Temperature and Aging Awareness Model for Electric Vehicles[J]. Transactions of China Electrotechnical Society, 2020, 35(23): 4980-4987.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.191598 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I23/4980

[1] 熊瑞. 动力电池管理系统核心算法[M]. 北京: 机械工业出版社, 2018.
[2] Galeotti M, Cinà L, Giammanco C, et al.Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy[J]. Energy, 2015, 89: 678-686.
[3] 马泽宇, 姜久春, 王占国, 等. 基于容量增量分析的石墨负极磷酸铁锂电池 SOC 估算方法研究[J]. 汽车工程, 2014, 36(12): 1439-1444.
Ma Zeyu, Jiang Jiuchun, Wang Zhanguo, et al.A research on SOC estimation for LiFePO4 battery with graphite negative electrode based on incremental capacity analysis[J]. Automotive Engineering, 2014, 36(12): 1439-1444.
[4] Xiong Rui, Sun Fengchun, Chen Zheng, et al.A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles[J]. Applied Energy, 2014, 113: 463-476.
[5] Chen Lin, Lü Zhiqiang, Lin W, et al.A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity[J]. Measurement, 2018, 116: 586-595.
[6] 范兴明, 王超, 张鑫, 等. 基于增量学习相关向量机的锂离子电池SOC预测方法[J]. 电工技术学报, 2019, 34(13): 2700-2708.
Fan Xingming, Wang Chao, Zhang Xin, et al.A prediction method of li-ion batteries SOC based on incremental learning relevance vector machine[J]. Transactions of China Electrotechnical Society, 2019, 34(13): 2700-2708.
[7] Eddahech A, Briat O, Vinassa J M.Determination of lithium-ion battery state-of-health based on constant-voltage charge phase[J]. Journal of Power Sources, 2014, 258: 218-227.
[8] You G W, Park S, Oh D.Real-time state-of-health estimation for electric vehicle batteries: a data-driven approach[J]. Applied Energy, 2016, 176: 92-103
[9] Wang Ju, Xiong Rui, Li Linlin, et al.A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach[J]. Applied Energy, 2018, 229: 648-659.
[10] Xiong Rui, Zhang Yongzhi, He Hongwen, 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.
[11] 连湛伟, 石欣, 克潇, 等. 电动汽车充换电站动力电池全寿命周期在线检测管理系统[J]. 电力系统保护与控制, 2014, 42(12): 137-142.
Lan Zhanwei, Shi Xin, Ke Xiao, et al.The whole life cycle on-line detection and management system of power battery in the electric vehicle charging and exchanging station[J]. Power System Protection and Control, 2014, 42(12): 137-142.
[12] 黄凯, 郭永芳, 李志刚. 基于信息反馈粒子群的高精度锂离子电池模型参数辨识[J]. 电工技术学报, 2019, 34(增刊1): 378-387.
Huang Kai, Guo Yongfang, Li Zhigang.High precision parameter identification of lithium-ion battery model based on feedback particle swarm optimization algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(S1): 378-387.
[13] 余波, 梁锐, 蒲亦非, 等. 超级电容器恒流充电的时域分数阶电路模型[J]. 电工技术学报, 2019, 34(17): 3533-3541.
Yu Bo, Liang Rui, Pu Yifei, et al.Time-domain fractional circuit model for constant current charging of supercapacitor[J]. Transactions of China Electrotechnical Society, 2019, 34(17): 3533-3541.
[14] Chaoui H, El Mejdoubi A, Gualous H.Online parameter identification of lithium-ion batteries with surface temperature variations[J]. IEEE Transactions on Vehicular Technology, 2017, 66(3): 2000-2009.
[15] 谢长君, 费亚龙, 曾春年, 等. 基于无迹粒子滤波的车载锂离子电池状态估计[J]. 电工技术学报, 2018, 33(17): 3958-3964.
Xie Changjun, Fei Yalong, Zeng Chunnian, et al.State-of-charge estimation of lithium-ion battery using unscented particle filter in vehicle[J]. Transactions of China Electrotechnical Society, 2018, 33(17): 3958-3964.
[16] Plett G L.Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - part 2. Modeling and identification[J]. Journal of Power Sources, 2004, 134(2): 262-276.
[17] 谷苗, 夏超英, 田聪颖. 基于综合型卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2019, 34(2): 419-426.
Gu Miao, Xia Chaoying, Tian Congying.Li-ion battery state of charge estimation based on comprehensive Kalman filter[J]. Transactions of China Electro technical Society, 2019, 34(2): 419-426.
[18] Lin Cheng, Mu Hao, Xiong Rui, et al.Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: state-of-energy[J]. Applied Energy, 2017, 194: 560-568.