|
|
|
| Lithium-Ion Battery Peak Power Estimation Based on Multi-Time Scale Double Extended Kalman Filter |
| Li Qiang1, Zhang Kaixuan2, Yuan Wenwen1, Xu Yahan1, Yang Ruixin2, Fang Yu2 |
1. Weichai Power Co. Ltd Weifang 261061 China;
2. School of Mechanical Engineering Beijing Institute of Technology Beijing 100081 China |
|
|
|
Abstract State of Power (SOP) estimation is one of the core functions of battery management system (BMS). Current SOP estimation methods are mainly divided into three categories: characteristic map method, data-driven method and multi-constraint model-based estimation method. However, in practical applications, traditional SOP estimation methods are generally difficult to obtain accurate and realistic peak power. Therefore, a large number of battery cell, module and battery pack experiments are carried out under different temperatures, different working conditions and different aging states in this paper. A battery cell model and battery pack model with dual-characteristic cells uniformly distributed are established to implement the expansion of the cell model to the system algorithm, which is high prediction accuracy and low calculation amount. In addition, a double Kalman filter (DEKF) algorithm based on multi-time scale sliding windows is proposed. The model parameter library is updated based on the peak power test results, which realizes the slow time-varying estimation of the parameters and improves the accuracy and robustness of the algorithm to estimate the peak power.
Firstly, an equivalent circuit model (ECM) is established as the battery model in this paper. Then, by comparing 12 common ECMs in terms of modeling accuracy and computational complexity, the Thevenin model is finally selected to simulate battery characteristics, due to the relatively high voltage prediction accuracy and relatively small amount of calculation. Based on the battery cell model, a battery pack model with dual characteristic cells uniformly distributed is established. The two characteristic cells are the real-time highest cell voltage and the lowest cell voltage in the battery pack. The cell numbers of characteristic cells are hence changeable. At the same time, the parallel link is regarded as a large cell, and the model parameters change with the battery characteristics. Based on the assumption of uniform distribution of battery cells, the highest and lowest cell voltage and battery pack voltage are used as input. The calculation formula of the battery system is simplified, and the transformation from the single model to the system model is realized. For the Thevenin model, the open-circuit voltage at time k+1 is taken as the first-order Taylor expansion at time k, and the power battery voltage calculation model is obtained. Considering that the peak power has a positive correlation with the terminal voltage, the dichotomy method is used to try to select a certain peak power. By comparing the corresponding terminal voltage with the target terminal voltage, the dichotomy method is used to correct the peak value Power level, to achieve the purpose of constantly approaching the real peak power. Finally, a hardware-in-the-loop (HIL) simulation platform was built. The experimental objects mainly included 50 A•h three-cell battery cells and 12-string battery modules. On the basis of the initial characteristic test of power battery cells and modules, representative cells and modules that need to be tested are classified and screened, and then a half-year full-life and multi-temperature power prediction is completed in accordance with the experimental requirements and procedures verification and aging cycle test experiments are conducted, and a large amount of experimental data is obtained. The results show that the improved DEKF algorithm is effective, has high accuracy and robustness, and the SOC estimation error is less than 3%. Effective voltage error is less than 40mV.
|
|
Received: 13 January 2023
|
|
|
|
|
|
[1] 高德欣, 王义, 郑晓雨, 等. 基于深度学习的电动汽车充电状态监测与安全预警方法[J]. 电机与控制学报, 2023, 27(7): 122-132.
Gao Dexin, Wang Yi, Zheng Xiaoyu, et al.Electric vehicle charging status monitoring and safety warning method based on deep learning[J]. Electric Machines and Control, 2023, 27(7): 122-132.
[2] 武龙星, 庞辉, 晋佳敏, 等. 基于电化学模型的锂离子电池荷电状态估计方法综述[J]. 电工技术学报, 2022, 37(7): 1703-1725.
Wu Longxing, Pang Hui, Jin Jiamin, et al.A review of SOC estimation methods for lithium-ion batteries based on electrochemical model[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1703-1725.
[3] 肖迁, 穆云飞, 焦志鹏, 等. 基于改进LightGBM的电动汽车电池剩余使用寿命在线预测[J]. 电工技术学报, 2022, 37(17): 4517-4527.
Xiao Qian, Mu Yunfei, Jiao Zhipeng, et al.Improved LightGBM based remaining useful life prediction of lithium-ion battery under driving conditions[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4517-4527.
[4] 孙金磊, 唐传雨, 李磊, 等. 基于状态与模型参数联合估计的老化电池可充入电量估计方法[J]. 电工技术学报, 2022, 37(22): 5886-5898.
Sun Jinlei, Tang Chuanyu, Li Lei, et al.An estimation method of rechargeable electric quantity for aging battery based on joint estimation of state and model parameters[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5886-5898.
[5] 杨镜司, 秦文萍, 史文龙, 等. 基于电动汽车参与调峰定价策略的区域电网两阶段优化调度[J]. 电工技术学报, 2022, 37(1): 58-71.
Yang Jingsi, Qin Wenping, Shi Wenlong, et al.Two-stage optimal dispatching of regional power grid based on electric vehicles' participation in peak-shaving pricing strategy[J]. Transactions of China Electrotechnical Society, 2022, 37(1): 58-71.
[6] Lu Languang, Han Xuebing, Li Jianqiu, et al.A review on the key issues for lithium-ion battery management in electric vehicles[J]. Journal of Power Sources, 2013, 226: 272-288.
[7] Han Xuebing, Lu Languang, Zheng Yuejiu, et al.A review on the key issues of the lithium ion battery degradation among the whole life cycle[J]. eTransportation, 2019, 1: 100005.
[8] Hannan M A, Lipu M S H, Hussain A, et al. A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations[J]. Renewable and Sustainable Energy Reviews, 2017, 78: 834-854.
[9] He Hongwen, Xiong Rui, Fan 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.
[10] Lin Cheng, Zhang Xiaohua, Xiong Rui, et al.A novel approach to state of charge estimation using extended Kalman filtering for lithium-ion batteries in electric vehicles[C]//2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), Beijing, China, 2014: 1-6.
[11] 陈铖. 车用锂离子电池容量和荷电状态的多尺度联合估计研究[D]. 北京: 北京理工大学, 2016.
Chen Cheng.Research on the multi-scale estimation of the lithium-ion battery capacity and SOC for electric vehicles[D]. Beijing: Beijing Institute of Technology, 2016.
[12] 熊瑞. 基于数据模型融合的电动车辆动力电池组状态估计研究[D]. 北京: 北京理工大学, 2014.
Xiong Rui.Estimation of battery pack state for electric vehicles using model-data fusion approach[D]. Beijing: Beijing Institute of Technology, 2014.
[13] 刘素贞, 袁路航, 张闯, 等. 基于超声时域特征及随机森林的磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2022, 37(22): 5872-5885.
Liu Suzhen, Yuan Luhang, Zhang Chuang, et al.State of charge estimation of LiFeO4 batteries based on time domain features of ultrasonic waves and random forest[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5872-5885.
[14] Pan Rui, Wang Yujie, Zhang Xu, et al.Power capability prediction for lithium-ion batteries based on multiple constraints analysis[J]. Electrochimica Acta, 2017, 238: 120-133.
[15] Sun Fengchun, Xiong Rui, He Hongwen, et al.Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries[J]. Applied Energy, 2012, 96: 378-386.
[16] Guo Ruohan, Shen Weixiang.A data-model fusion method for online state of power estimation of lithium-ion batteries at high discharge rate in electric vehicles[J]. Energy, 2022, 254: 124270.
[17] Tang Xiaopeng, Liu Kailong, Liu Qi, et al.Comprehensive study and improvement of experimental methods for obtaining referenced battery state-of- power[J]. Journal of Power Sources, 2021, 512: 230462.
[18] Li Bowen, Wang Shunli, Fernandez C, et al.A linear recursive state of power estimation method based on fusion model of voltage and state of charge limitations[J]. Journal of Energy Storage, 2021, 40: 102583.
[19] Wu Muyao, Qin Linlin, Wu Gang.State of power estimation of power lithium-ion battery based on an equivalent circuit model[J]. Journal of Energy Storage, 2022, 51: 104538.
[20] Wang Yujie, Pan Rui, Liu Chang, et al.Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estim- ation[J]. Journal of Power Sources, 2018, 374: 12-23.
[21] Wang Yujie, Zhang Xu, Liu Chang, et al.Multi-timescale power and energy assessment of lithium-ion battery and supercapacitor hybrid system using extended Kalman filter[J]. Journal of Power Sources, 2018, 389: 93-105.
[22] Tan Yaqian, Luo Maji, She Liyang, et al.Joint estimation of ternary lithium-ion battery state of charge and state of power based on dual polarization model[J]. International Journal of Electrochemical Science, 2020, 15(2): 1128-1147.
[23] Chen Wenxin, Xu Cheng, Chen Manlin, et al.A novel fusion model based online state of power estimation method for lithium-ion capacitor[J]. Journal of Energy Storage, 2021, 36: 102387.
[24] 王洪利. 电动汽车动力电池组上位机监控及管理平台设计[D]. 天津: 天津大学, 2014.
Wang Hongli.Design of monitoring and management platform for upper computer of electric vehicle power battery pack[D]. Tianjin: Tianjin University, 2013.
[25] Gong Xianzhi, Xiong Rui, Mi C C.Study of the characteristics of battery packs in electric vehicles with parallel-connected lithium-ion battery cells[C]//2014 IEEE Applied Power Electronics Conference and Exposition - APEC, Fort Worth, TX, USA, 2014: 3218-3224.
[26] Lin J C.Development of a two-staged balancing scheme for charging lithium iron cells in series[J]. IET Electrical Systems in Transportation, 2016, 6(3): 145-152.
[27] 彭飞. 锂离子电池状态估算方法研究与实现[D]. 成都: 电子科技大学, 2014.
Peng Fei.Research And Realization of state estimation for lithium-ion batteries[D]. Chengdu: University of Electronic Science and Technology of China, 2014.
[28] Hu Xiaosong, Li Shengbo, Peng Huei.A comparative study of equivalent circuit models for Li-ion batteries[J]. Journal of Power Sources, 2012, 198: 359-367. |
|
|
|
2024, Vol. 39 
