Data analysis and model verification method for batteries

doi:10.3969/j.issn.2097-0706.2023.03.011

Abstract

Abstract:

Under the construction of new power system，massive batteries are connecting the power system， which makes the development of IoT and big data technologies overwhelming. A data analysis and model verification method to analyze the operation characteristics of batteries is proposed. Firstly，the data acquisition and transmission modes of a battery are studied.Based on the parser and data extraction method of Controller Area Network（CAN）message， the specific algorithm is given.Considering the second-order equivalent circuit model of the battery，the simulation scheme for the battery is carried out，including the table look-up method for the open circuit voltage and iterative algorithm for the dynamic voltage. Finally，a PC tool of the functions above is developed.The specific application example includes the interface design and function layout.The experimental results show that the tool can be used for data analysis and model validation of batteries.

Key words: battery, CAN, second-order equivalent circuit model, open circuit voltage, dynamic voltage, new power system, big data

CLC Number:

TK02：TM912

LI Shuai, GONG Shimin, CAI Liangliang, TANG Bin, HONG Ying. Data analysis and model verification method for batteries[J]. Integrated Intelligent Energy, 2023, 45(3): 81-86.

Figures/Tables 13

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References 15

[1]	李欣然, 崔曦文, 黄际元, 等. 电池储能电源参与电网一次调频的自适应控制策略[J]. 电工技术学报, 2019, 34(18):3897-3908.
	LI Xinran, CUI Xiwen, HUANG Jiyuan, et al. The self adaption control strategy of energy storage batteries participating in the primary frequency regulation[J]. Transactions of China Electrotechnical Society, 2019, 34(18):3897-3908.
[2]	孙伟卿, 裴亮, 向威, 等. 电力系统中储能的系统价值评估方法[J]. 电力系统自动化, 2019, 43(8):47-55.
	SUN Weiqing, PEI Liang, XIANG Wei, et al. Evaluation method of system value for energy storage in power system[J]. Automation of Electric Power Systems, 2019, 43(8):47-55.
[3]	汤杰, 李欣然, 黄际元, 等. 以净效益最大为目标的储能电池参与二次调频的容量配置方法[J]. 电工技术学报, 2019, 34(5):963-972.
	TANG Jie, LI Xinran, HUANG Jiyuan, et al. Capacity allocation of BESS in secondary frequency regulation with the goal of maximum net benefit[J]. Transactions of China Electrotechnical Society, 2019, 34(5):963-972.
[4]	冯力勇, 张云. 考虑电池能效的电网侧电化学储能电站最优功率控制策略研究[J]. 华电技术, 2020, 42(4):37-41.
	FENG Liyong, ZHANG Yun. Optimal power control strategy of grid-side electrochemical energy storage stations considering battery energy efficiency[J]. Huadian Technology, 2020, 42(4):37-41.
[5]	童家麟, 洪庆, 吕洪坤, 等. 电源侧储能技术发展现状及应用前景综述[J]. 华电技术, 2021, 43(7):17-23.
	TONG Jialin, HONG Qing, LYU Hongkun, et al. Development status and application prospect of power side energy storage technology[J]. Huadian Technology, 2021, 43(7):17-23.
[6]	王晗雯, 鲁胜, 周照宇. 光伏-混合储能微电网协调控制及经济性分析[J]. 华电技术, 2020, 42(4):31-36.
	WANG Hanwen, LU Sheng, ZHOU Zhaoyu. Coordinated control and economic analysis on a PV-hybrid energy storage micro-grid system[J]. Huadian Technology, 2020, 42(4):31-36.
[7]	陈和洋, 周金平, 何春庆, 等. 基于物联网技术的变电站蓄电池设备数据采集系统研究[J]. 山东电力技术, 2022, 49(1):30-35,40.
	CHEN Heyang, ZHOU Jinping, HE Chunqing, et al. Research on data collection system of storage battery equipment in substation based on Internet of Things technology[J]. Shandong Electric Power, 2022, 49(1):30-35,40.
[8]	陈娟, 惠东, 范茂松, 等. 基于粗糙集的电池储能电站海量数据处理方法[J]. 中国电力, 2022, 55(2):44-50,208.
	CHEN Juan, HUI Dong, FAN Maosong, et al. Massive data processing method for battery energy storage power stations based on rough sets[J]. Electric Power, 2022, 55(2):44-50,208.
[9]	韩有军, 胡跃明, 王亚青, 等. 基于物联网的动力电池生产数据采集研究[J]. 电源技术, 2020, 44(10):1510-1513.
	HAN Youjun, HU Yueming, WANG Yaqing, et al. Data collection research for power battery manufacture based on IoT[J]. Chinese Journal of Power Sources, 2020, 44(10): 1510-1513.
[10]	陈息坤, 孙冬. 锂离子电池建模及其参数辨识方法研究[J]. 中国电机工程学报, 2016, 36(22):6254-6261.
	CHEN Xikun, SUN Dong. Research on lithium-ion battery modeling and model parameter identification methods[J]. Proceedings of the CSEE, 2016, 36(22):6254-6261.
[11]	李勇琦, 雷旗开, 王浩, 等. 基于BP神经网络的梯次利用电池健康状态诊断[J]. 华电技术, 2021, 43(7):42-46.
	LI Yongqi, LEI Qikai, WANG Hao, et al. State of health estimation for echelon-used batteries based on BP neural network[J]. Huadian Technology, 2021, 43(7):42-46.
[12]	袁世斐. 基于等效电路-简化电化学模型的锂离子电池模拟和仿真[D]. 上海: 上海交通大学, 2016.
	YUAN Shifei. Li-ion battery modeling and simulation with equivalent circuit-simplified electrochemical model[D]. Shanghai: Shanghai Jiao Tong University, 2016.
[13]	刘毅, 谭国俊, 何晓群. 优化电池模型的自适应Sigma卡尔曼荷电状态估算[J]. 电工技术学报, 2017, 32(2): 108-118.
	LIU Yi, TAN Guojun, HE Xiaoqun. Optimized battery model based adaptive Sigma Kalman filter for state of charge estimation[J]. Transactions of China Electrotechnical Society, 2017, 32(2):108-118.
[14]	吴小慧, 张兴敢. 锂电池二阶RC等效电路模型参数辨识[J]. 南京大学学报(自然科学版), 2020, 56(5): 754-761.
	WU Xiaohui, ZHANG Xinggan. Parameters identification of second order RC equivalent circuit model for lithium batteries[J]. Journal of Nanjing University(Natural Sciences), 2020, 56(5):754-761.
[15]	嵇艳鞠, 邱仕林, 李刚. 基于可变电阻和电容的锂电池二阶RC等效电路模型的仿真[J]. 中南大学学报(英文版), 2020, 27(9):2606-2613.
	JI Yanju, QIU Shilin, LI Gang. Simulation of second-order RC equivalent circuit model of lithium battery based on variable resistance and capacitance[J]. Journal of Central South University, 2020, 27(9):2606-2613. doi: 10.1007/s11771-020-4485-9