考虑环境温度影响的锂离子电池改进双极化模型及其荷电状态估算

doi:10.19595/j.cnki.1000-6753.tces.200174

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Abstract It is very crucial to develop an efficient and practical battery management system by establishing an accurate and reasonable Li-ion battery (LIB) mathematical model to estimate battery terminal voltage and state-of-charge (SOC) with higher precision. This paper firstly builds an improved Li-ion battery cell dual polarization (DP) model that is dependent on ambient temperature. Next, based on two types of Li-ion battery dynamic test data, the key parameters of this proposed DP model are identified by forgetting factor least square (FFLS) approach and fitted as the ambient temperature- dependent functions. Meanwhile, by the extended Kalman filter (EKF) algorithm, a battery SOC estimation procedure suitable for different ambient temperatures is presented. Finally, the DST and US06 cycle test data at -10℃, 20℃ and 50℃ are used to simulate and verify the proposed battery DP model and its SOC estimation. The results show that the proposed battery DP model can accurately reflect the effects of ambient temperature on the battery model parameters, and has a higher precision and a wider ambient temperature application range in estimating the battery terminal output voltage and SOC.

Key words： Li-ion battery ambient temperature equivalent circuit model model parameter state of charge (SOC)

Received: 21 February 2020

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TM912

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	Pang Hui
	Guo Long
	Wu Longxing
	Jin Jiamin
	Liu Kai

Cite this article:

Pang Hui,Guo Long,Wu Longxing等. An Improved Dual Polarization Model of Li-Ion Battery and Its State of Charge Estimation Considering Ambient Temperature[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2178-2189.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.200174 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I10/2178

[1] 张振宇, 汪光森, 聂世雄, 等. 脉冲大倍率放电条件下磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2019, 34(8): 215-225.
Zhang Zhenyu, Wang Guangsen, Nie Shixiong, et al.State of charge estimation of LiFePO₄: battery under the condition of high rate pulsed discharge[J]. Transactions of China Electrotechnical Society, 2019, 34(8): 215-225.
[2] Wang Qianqian, Wang Jiao, Zhao Pengju, et al.Correlation between the model accuracy and model- based SOC estimation[J]. Electrochimica Acta, 2017, 228: 146-159.
[3] Ali M U, Zafar A, Nengroo S H, et al.Towards a smarter battery management system for electric vehicle applications: a critical review of lithium-ion battery state of charge estimation[J]. Energies, 2019, 12(3): 446.
[4] He Hongwen, Xiong Rui, Fa Jinxin.Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach[J]. Energies, 2011, 4(4): 582-598.
[5] Zhang Cheng, Li Kang, Deng Jing.Real-time esti- mation of battery internal temperature based on a simplified thermoelectric model[J]. Journal Power Sources, 2016, 302: 146-154.
[6] Chen Yang, Ma Yan, Chen Hong.State of charge and state of health estimation for lithium-ion battery through dual sliding mode observer based on AMESim- Simulink co-simulation[J]. Journal of Renewable and Sustainable Energy, 2018, 10(3): 034103.
[7] 李晓宇, 徐佳宁, 胡泽辉, 等. 磷酸铁锂电池梯次利用健康特征参数提取方法[J]. 电工技术学报, 2018, 33(1): 9-16.
Li Xiaoyu, Xu Jianing, Hu Zehui, et al.The health parameter estimation method for LiFePO₄ battery echelon use[J]. Transactions of China Electrotech- nical Society, 2018, 33(1): 9-16.
[8] 刘伟, 吴海桑, 何志超, 等. 一种均衡考虑锂电池内部能量损耗和充电速度的多段恒流充电方法[J]. 电工技术学报, 2017, 32(9): 112-120.
Liu Wei, Wu Haisang, He Zhichao, et al.A multistage current charging method for Li-ion battery con- sidering balance of internal consumption and charging speed[J]. Transactions of China Electro- technical Society, 2017, 32(9): 112-120.
[9] 靳立强, 孙志祥, 刘志茹, 等. 不同温度下锂电池剩余电量估算的仿真研究[J]. 汽车工程, 2019, 41(5): 114-122.
Jin Liqiang, Sun Zhixiang, Liu Zhiru, et al.Simu- lation study on stale of charge estimation of lithium- ion battery at different temperatures[J]. Automotive Engineering, 2019, 41(5): 114-122.
[10] 刘新天, 孙张驰, 何耀, 等. 基于环境变量建模的锂电池SOC估计方法[J]. 东南大学学报: 自然科学版, 2017, 47(2): 306-312.
Liu Xintian, Sun Zhangchi, He Yao, et al.SOC estimation method based on lithium-ion cell model considering environmental factors[J]. Journal of Southeast University: Natural Science Edition, 2017, 47(2): 306-312.
[11] 华寅, 许敏. 不同温度下基于模型滤波的锂离子电池SOC估计[J]. 电源技术, 2016, 40(4): 814-817.
Hua Yin, Xu Min.SOC estimation of lithium-ion batteries by model-based filtering method under different thermal conditions[J]. Chinese Journal of Power Sources, 2016, 40(4): 814-817.
[12] Chin Cheng, Gao Zuchang, Chiew J, et al.Nonlinear temperature-dependent state model of cylindrical LiFePO₄ battery for open-circuit voltage, terminal voltage and state-of-charge estimation with extended Kalman filter[J]. Energies, 2018, 11(9): 2467.
[13] Xia Bizhong, Huang Rui, Lao Zizhou, et al.Online parameter identification of lithium-ion batteries using a novel multiple forgetting factor recursive least square algorithm[J]. Energies, 2018, 11(11): 3180.
[14] Xing Yinjiao, He Wei, Pecht M, et al.State of charge estimation of lithium-ion batteries using the open- circuit voltage at various ambient temperatures[J]. Applied Energy, 2014, 113: 106-115.
[15] 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): 221-228.
Gu Miao, Xia Chaoying, Tian Congying.Li-ion battery state of charge estimation based on com- prehensive Kalman filter[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 221-228.
[16] Wang Yebin, Fang Huazhen, Zhou Lei, et al.Revisiting the state-of-charge estimation for lithium- ion batteries a methodical investigation of the extended Kalman filter approach[J]. IEEE Control Systems Magazine 2017, 37(4): 73-96.
[17] Kong S N, Chin-Sien M, Chen Yiping, et al.Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries[J]. Applied Energy, 2009, 86(9): 1506-1511.
[18] Hu Xiaosong, Li Shengbo, Peng H, et al.Robus- tness analysis of state-of-charge estimation methods for two types of Li-ion batteries[J]. Journal Power Sources, 2012, 217: 209-219.
[19] Ding Feng, Chen Tongwen.Performance bounds of forgetting factor least-squares algorithms for time- varying systems with finite measurement data[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2005, 52(3): 555-566.
[20] Petr Zagalak.Discrete-time control systems[M]. Englewood Cliffs, NJ: Prentice Hall Inc., 1987.
[21] 李江, 王义伟, 魏超, 等. 卡尔曼滤波理论在电力系统中的应用综述[J]. 电力系统保护与控制, 2014, 42(6): 135-144.
Li Jiang, Wang Yiwei, Wei Chao, et al.A survey on the application of Kalman filtering method in power system[J]. Power System Protection and Control, 2014, 42(6): 135-144.
[22] 潘海鸿, 吕治强, 李君子, 等. 基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2017, 32(21): 1-8.
Pan Haihong, Lü Zhiqiang, Li Junzi, et al.Estimation of lithium-ion battery state of charge based on grey prediction model-extended Kalman filter[J]. Transa- ctions of China Electrotechnical Society, 2017, 32(21): 1-8.
[23] Ines B, Sabeur J, Bilal M, et al.Improved OCV model of a Li-ion NMC battery for online SOC estimation using the extended Kalman filter[J]. Energies, 2017, 10(6): 764.