|
|
Simplified Mechanism Modeling and Discharge Characteristic Analysis of High C-Rate LiFePO4 Battery |
Yan Kangwei, Long Xinlin, Lu Junyong, Liu Yingquan, Wu Yiting |
National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval University of Engineering Wuhan 430033 China |
|
|
Abstract Mechanism modeling of high C-rate LiFePO4 battery is very important to guide the design and application of battery. However, the full order mechanism model has high complexity, and the simplified form fits bad in high C-rate currently. To solve these problems, a simplified electrochemical thermal coupling mechanism model was established by combining the simplified pseudo-two-dimensions (P2D) electrochemical model with the two-state thermal model. Through parameter identification, the model can well fit the actual battery discharge curves in a wide range of 10~40C high rate. Based on the identified model, the internal evolution mechanism corresponding to the trend of external curves under high C-rate discharge was analyzed. The results show that: the seconds level sharp decrease of battery terminal voltage in the high C-rate discharge initial period is related to the heterogeneity of Li+ concentration distribution in the electrolyte phase, and the recovery of the terminal voltage in the intermediate period is due to the increasing activity of internal materials caused by the temperature rise.
|
Received: 12 September 2020
|
|
|
|
|
[1] 龙鑫林, 鲁军勇, 魏静波, 等. 锂电池储能在电磁发射中的应用[J]. 国防科技大学学报, 2019, 41(4): 66-72. Long Xinlin, Lu Junyong, Wei Jingbo, et al.Application on lithium batteries for electromagnetic launch[J]. Journal of National University of Defense Technology, 2019, 41(4): 66-72. [2] 王榘, 熊瑞, 穆浩. 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[J]. 电工技术学报, 2020, 35(23): 4980-4987. 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. [3] 焦东升, 王海云, 朱洁, 等. 基于离散Fréchet距离的电动汽车电池健康状态诊断方法[J]. 电力系统保护与控制, 2016, 44(12): 68-74. Jiao Dongsheng, Wang Haiyun, Zhu Jie, et al.EV battery SOH diagnosis method based on discrete Fréchet distance[J]. Power System Protection and Control, 2016, 44(12): 68-74. [4] 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. Lian 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. [5] Long Xinlin, Lu Junyong, Wu Yiting, et al.Research on high rate lithium-ion batteries for electromagnetic launcher[C]//2019 22nd International Conference on Electrical Machines and Systems (ICEMS), Harbin, China, IEEE, 2019: 1671-1675. [6] 胡晓松, 唐小林. 电动车辆锂离子动力电池建模方法综述[J]. 机械工程学报, 2017, 53(16): 20-31. Hu Xiaosong, Tang Xiaolin.Review of modeling techniques for lithium-ion traction batteries in electric vehicles[J]. Journal of Mechanical Engineering, 2017, 53(16): 20-31. [7] 宫明辉, 乌江, 焦朝勇. 基于模糊自适应扩展卡尔曼滤波器的锂电池SOC估算方法[J]. 电工技术学报, 2020, 35(18): 3972-3978. Gong Minghui, Wu Jiang, Jiao Chaoyong.SOC estimation method of lithium battery based on fuzzy adaptive extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(18): 3972-3978. [8] 刘晓程, 王建明, 王武. 基于数据驱动的锂电池随机动态系统建模[J]. 电气技术, 2015, 16(5): 17-21. Liu Xiaocheng, Wang Jianming, Wang Wu. Modeling of lithium-ion battery stochastic dynamic system based on data-driven[J]. Electrical Engineering, 2015, 16(5): 17-21. [9] 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): 1769-1779. 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): 1769-1779. [10] 李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119. Li Chaoran, Xiao Fei, Fan Yaxiang, et al.An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119. [11] 李超然, 肖飞, 樊亚翔, 等. 基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法[J]. 电工技术学报, 2020, 35(9): 2051-2062. Li Chaoran, Xiao Fei, Fan Yaxiang, et al.A hybrid approach to lithium-ion battery SOC estimation based on recurrent neural network with gated recurrent unit and Huber-M robust Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062. [12] 李超然, 肖飞, 樊亚翔, 等. 一种基于LSTM-RNN的脉冲大倍率工况下锂离子电池仿真建模方法[J]. 中国电机工程学报, 2020, 40(9): 3031-3042. Li Chaoran, Xiao Fei, Fan Yaxiang, et al.An approach to lithium-ion battery simulation modeling under pulsed high rate condition based on LSTM-RNN[J]. Proceedings of the CSEE, 2020, 40(9): 3031-3042. [13] Doyle Marc, Fuller Thomas F, Newman John.Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell[J]. Journal of the Electrochemical Society, 1993, 140(6): 1526-1533. [14] Doyle Marc, Newman John.Comparison of modeling predictions with experimental data from plastic lithium ion cells[J]. Journal of the Electrochemical Society, 1996, 143(6): 1890-1903. [15] Santhanagopalan Shriram, White Ralph E.Online estimation of the state of charge of a lithium ion cell[J]. Journal of Power Sources, 2006, 161(2): 1346-1355. [16] Prada E, Domenico D Di, Creff Y, et al.Simplified electrochemical and thermal model of LiFePO4 -graphite Li-ion batteries for fast charge applications[J]. Journal of the Electrochemical Society, 2012, 159(9): A1508-A1519. [17] 庞辉. 基于电化学模型的锂离子电池多尺度建模及其简化方法[J]. 物理学报, 2017, 66(23): 312-322. Pang Hui.Multi-scale modeling and its simplification method of Li-ion battery based on electrochemical model[J]. Acta Physica Sinica, 2017, 66(23): 312-322. [18] Lin Xinfan, Perez Hector E, Mohan Shankar, et al.A lumped-parameter electro-thermal model for cylindrical batteries[J]. Journal of Power Sources, 2014, 257(1): 1-11. [19] Mao Jing, Tiedemann William, Newman John.Simulation of temperature rise in Li-ion cells at very high currents[J]. Journal of Power Sources, 2014, 271(20): 444-454. [20] Safari M, Delacourt C.Modeling of a commercial graphite/LiFePO4 cell[J]. Journal of the Electrochemical Society, 2011, 158(5): A562-A571. [21] Valøen, Lars Ole, Reimers Jan N. Transport properties of LiPF6-based Li-ion battery electrolytes[J]. Journal of the Electrochemical Society, 2005, 152(5): A882-A891. [22] Diwakar Vinten D.Towards efficient models for lithium ion batteries[D]. Cookeville, USA: Tennessee Technological University, 2009. [23] Forman Joel C, Moura Scott J, Stein Jeffrey L, et al.Genetic identification and fisher identifiability analysis of the Doyle-Fuller-Newman model from experimental cycling of a LiFePO4 cell[J]. Journal of Power Sources, 2012, 210(15): 263-275. [24] Rahman Md Ashiqur, Anwar Sohel, Izadian Afshin.Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method[J]. Journal of Power Sources, 2016, 307(1): 86-97. [25] Wu Jiangling, Sun Xiaodong, Zhu Jianguo.Accurate torque modeling with PSO-based recursive robust LSSVR for a segmented-rotor switched reluctance motor[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(2): 96-104. [26] 徐正清, 肖艳炜, 李群山, 等. 基于灵敏度及粒子群算法的输电断面功率越限控制方法对比研究[J]. 电力系统保护与控制, 2020, 48(15): 177-186. Xu Zhengqing, Xiao Yanwei, Li Qunshan, et al.Comparative study based on sensitivity and particle swarm optimization algorithm for power flow over-limit control method of transmission section[J]. Power System Protection and Control, 2020, 48(15): 177-186. [27] Zhang Liqiang, Lü Chao, Hinds Gareth, et al.Parameter sensitivity analysis of cylindrical LiFePO4 battery performance using multi-physics modeling[J]. Journal of the Electrochemical Society, 2014, 161(5): A762-A776. |
|
|
|