充电模态下电动汽车动力电池模型辨识

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Abstract The battery model and parameters identification are the base of the charge and discharge optimal control of electric vehicle traction batteries. And the parameters of the battery model are affected by the working condition of the traction battery. In order to model the traction battery and identify the model parameters, modeling algorithm of traction battery and parameters identification method were studied in this paper. A variable order equivalent circuit model was established, which is based on the electrode impedance spectrum theory. A parameter identification algorithm was proposed, which is based on the forgetting factor recursive extended least square (FFRELS). A selection algorithm for the optimal order of the battery model was built, which is based on the Bayesian information criterions (BIC). A battery open circuit voltage model that was used for calibration of the proposed parameter identification algorithm was created, which is based on the lattice gas model (LGM). In the end, the battery model parameters identification algorithm and the optimal order selection under the charging mode was achieved. Validation results show that the proposed modeling and parameters identification algorithm is efficient.

Key words： Battery model parameters identification forgetting factor Bayesian information criterions lattice gas model

Received: 05 March 2016 Published: 20 June 2017

PACS:

TM912.8

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	Articles by authors
	Liu Weilong
	Wang Lifang
	Liao Chenglin
	Wang Liye

Cite this article:

Liu Weilong,Wang Lifang,Liao Chenglin等. Parameters Identification Method of Battery Model for Electric Vehicles under the Charging Mode[J]. Transactions of China Electrotechnical Society, 2017, 32(11): 198-207.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I11/198

[1] 党杰, 汤奕, 宁佳, 等. 基于用户意愿和出行规律的电动汽车充电负荷分配策略[J]. 电力系统保护与控制, 2015, 43(16): 8-15.
Dang Jie, Tang Yi, Ning Jia, et al.A strategy for distribution of electric vehicles charging load based on user intention and trip rule[J]. Power System Protection and Control, 2015, 43(16): 8-15.
[2] Smith K A, Rahn C D, Wang C Y. Control oriented ID electrochemical model of lithium-ion battery[J]. Energy Conversion and Management, 2007, 48(9): 2565-2578.
[3] Smith K A, Rahn C D, Wang C Y. Model-based electrochemical estimation and constraint management for pulse operation of lithium-ion batteries[J]. IEEE Transactions on Control Systems Technology, 2010, 18(3): 654-663.
[4] Shen W X, Chan C C, Lo E W C, et al. A new battery available capacity indicator for electric vehicles using neural network[J]. Energy Conversion and Management, 2002, 43(6): 817-826.
[5] Chau K T, Wu K C, Chan C C. A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system[J]. Energy Conversion and Management, 2004, 45(11-12): 1681-1692.
[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] Waag W, Fleischer C, Sauer D U. On-line estimation of lithium-ion battery impedance parameters using a novel varied-parameters approach[J]. Journal of Power Sources, 2013, 237(3): 260-269.
[8] Waag W, Sauer D U. Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination[J]. Applied Energy, 2013, 111(4): 416-427.
[9] He Hongwen, Xiong Rui, Guo Hongqiang. Online estimation of model parameters and state-of-charge of LiFePO 4 batteries in electric vehicles[J]. Applied Energy, 2012, 89(1): 413-420.
[10] 李勇, 王丽芳, 廖承林, 等. 基于子空间技术的电动汽车电池模型辨识研究[J]. 电工电能新技术, 2015, 34(1): 1-6.
Li Yong, Wang Lifang, Liao Chenglin, et al. Research on subspace-based identification of battery model for electric vehicles[J]. Advanced Technology of Electrical Engineering and Energy, 2015, 34(1): 1-6.
[11] 程泽, 董梦男, 杨添剀, 等. 基于自适应混沌粒子群算法的光伏电池模型参数辨识[J]. 电工技术学报, 2014, 29(9): 245-252.
Cheng Ze, Dong Mengnan, Yang Tiankai, et al. Extraction of solar cell model parameters based on self-adaptive chaos particle swarm optimization algorithm[J]. Transactions of China Electrotechnical Society, 2014, 29(9): 245-252.
[12] 冯飞, 宋凯, 逯仁贵, 等. 磷酸铁锂电池组均衡控制策略及荷电状态估计算法[J]. 电工技术学报, 2015, 30(1): 22-29.
Feng Fei, Song Kai, Lu Rengui, et al. Equalization control strategy and SOC estimation for LiFePO 4 battery pack[J]. Transactions of China Electrotechnical Society, 2015, 30(1): 22-29.
[13] 冯飞, 逯仁贵, 朱春波. 一种锂离子电池低温SOC估计算法[J]. 电工技术学报, 2014, 29(7): 53-58.
Feng Fei, Lu Rengui, Zhu Chunbo. State of charge estimation of Li-ion battery at low temperature[J]. Transactions of China Electrotechnical Society, 2014, 29(7): 53-58.
[14] Mitsuda K, Takemura D. Polarization study of a lithium-ion battery with an extra positive electrode using eight reference electrodes[J]. Electrochemistry, 2008, 76(12): 880-885.
[15] He Hongwen, Xiong Rui, Guo Hongqiang, et al. Comparison study on the battery models used for the energy management of batteries in electric vehicles[J]. Energy Conversion and Management, 2012, 64(4): 113-121.
[16] 刘艳莉, 戴胜, 程泽, 等. 基于有限差分扩展卡尔曼滤波的锂离子电池SOC估计[J]. 电工技术学报, 2014, 29(1): 221-228.
Liu Yanli, Dai Sheng, Cheng Ze, et al. Estimation of state of charge of lithium-ion battery based on finite difference extended kalman filter[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 221-228.
[17] Huet F. A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries[J]. Journal of Power Sources, 1998, 70(1): 59-69.
[18] 庞中华, 崔红. 系统辨识与自适应控制MATLAB仿真[M]. 北京: 北京航空航天大学出版社, 2013.
[19] 商云龙, 张奇, 崔纳新, 等. 基于AIC准则的锂离子电池变阶RC等效电路模型研究[J]. 电工技术学报, 2015, 30(17): 55-62.
Shang Yunlong, Zhang Qi, Cui Naxin, et al. Research on variable-order RC equivalent circuit model for lithium-ion battery based on the AIC criterion[J]. Transactions of China Electrotechnical Society, 2015, 30(17): 55-62.
[20] Kalikmanov V I, Koudriachova M V, de Leeuw S W. Lattice-gas model for intercalation compounds[J]. Solid State Ionics, 2000, 136(11): 1373-1378.
[21] Islam M S, Fisher C A J. Lithium and sodium battery cathode materials: computational insights into voltage, diffusion and nanostructural properties[J]. Chemical Society Reviews, 2014, 43(1): 185-204.