基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究

doi:10.3969/j.issn.1004-132X.2020.15.009

中国机械工程 ›› 2020, Vol. 31 ›› Issue (15): 1823-1830.DOI: 10.3969/j.issn.1004-132X.2020.15.009

基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究

丁镇涛¹, 邓涛^1,2, 李志飞¹, 尹燕莉¹

1. 重庆交通大学机电与车辆工程学院, 重庆, 400074;
2. 重庆交通大学航空学院, 重庆, 400074

收稿日期:2019-11-13 出版日期:2020-08-10 发布日期:2020-08-26
通讯作者: 邓涛(通信作者),男,1982年生,教授。研究方向为智能电动汽车理论及应用。E-mail:d82t722@cqjtu.edu.cn。
作者简介:丁镇涛,男,1994年生,硕士研究生。主要研究方向为新能源车辆理论与应用。
基金资助:
国家自然科学基金资助项目(51305473)；第五批重庆市高校优秀人才支持计划资助项目(2017)

SOC Estimation of Lithium-ion Battery Based on Ampere Hour Integral and Unscented Kalman Filter

DING Zhentao¹, DENG Tao^1,2, LI Zhifei¹, YIN Yanli¹

1. School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing, 400074;
2. School of Aeronautics, Chongqing Jiaotong University, Chongqing, 400074

Received:2019-11-13 Online:2020-08-10 Published:2020-08-26

摘要/Abstract

摘要： 基于Thevenin等效模型建立二阶RC等效电路模型，通过混合脉冲动力功率特性测试实验获取电池脉冲放电数据，进行电池等效电路参数辨识。为弥补锂离子电池荷电状态在90%~100%区间时电池模型参数辨识拟合误差而引起其估算误差的缺陷，综合采用安时积分与无迹卡尔曼滤波估算电池荷电状态。使用硬件在环仿真测试平台及环境模拟测试平台进行电池管理系统设计，在不同工况下对电池进行荷电状态估算，结果表明荷电状态估算误差范围为-1.5%~1.0%，该方法估算精度较高，效果理想。

关键词: 锂离子电池, 电池荷电状态估算, 二阶RC, 无迹卡尔曼滤波, 硬件在环

Abstract: Based on Thevenin equivalent model, the second-order RC equivalent circuit model was established. The pulse discharge data of battery were obtained by the tests of hybrid pulse power characteristics, and the parameters of battery equivalent circuit were identified. In order to make up the fitting errors of battery model parameter identification when the SOC of lithium-ion batteries was in the range of 90% to 100%, ampere hour integral and UKF were used comprehensively to estimate battery's SOC. Battery management system was designed by using HIL test platform and environmental simulation test platform. The results of SOC estimation of batteries under different operating conditions show that the estimation errors of SOC are from -1.5% to 1.0%. This method has relatively high accuracy and good effectiveness.

Key words: lithium-ion battery, state-of-charge (SOC) estimation, second-order RC, unscented Kalman filter (UKF), hardware-in-the-loop (HIL)

中图分类号:

U469.72

丁镇涛, 邓涛, 李志飞, 尹燕莉. 基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究[J]. 中国机械工程, 2020, 31(15): 1823-1830.

DING Zhentao, DENG Tao, LI Zhifei, YIN Yanli. SOC Estimation of Lithium-ion Battery Based on Ampere Hour Integral and Unscented Kalman Filter[J]. China Mechanical Engineering, 2020, 31(15): 1823-1830.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2020.15.009

http://www.cmemo.org.cn/CN/Y2020/V31/I15/1823

参考文献

[1] PURWADI A, RIZQIAWAN A, KEVIN A, et al. State of Charge Estimation Method for Lithium battery Using Combination of Coulomb Counting and Adaptive System with Consideringthe Effect of Temperature[C]//The 2nd IEEE Conference on Power Engineering and Renewable Energy (ICPERE). Bali, Indonesia, 2014:91-95.
[2] SNIHIR I, REY W, VERBITSHIY E, et al. Battery Open-circuit Voltage Estimation by a Method of Statistical Analysis[J]. Journal of Power Source, 2006, 159(22):1484-1487.
[3] ORCHARD M, HEVIAKOCH P, ZHANG B, et al. Risk Measure for Particle-Filtering-Based State-of-charge Prognosis in Lithium-ion Batteries[J]. IEEE Transactions on Industrial Electronics, 2013, 60(11):5260-5269.
[4] CHEMALI E, KOLLMEYER P, PREINDL M, et al. State-of-charge Estimation of Li-ion Batteries Using Deep Neural Networks:a Machine Learning Approach[J]. Journal of Power Source, 2018, 400:242-255.
[5] YU Weibo, YANG Tingting, FENG Cuiyuan, et al. The Research of Power Battery SOC Estimation Based on Adaptive Kalman Filter Algorithm[J]. Applied Mechanics and Materials, 2013, 433-435:754-759.
[6] 李哲,卢兰光,欧阳明高. 提高安时积分法估算电池SOC精度的方法比较[J]. 清华大学学报(自然科学版),2010, 50(8):1293-1296. LI Zhe, LU Languang, OUYANG Minggao. Comparison of Methods for Improving SOC Estimation Accuracy through an Ampere-hour Integration Approach[J]. Journal of Tsinghua University (Science and Technology), 2010, 50(8):1293-1296.
[7] 史永胜,史禄培,魏浩,等. 一种改进型锂离子电池SOC估算方法[J]. 电子器件,2019,42(1):139-141. SHI Yongsheng, SHI Lupei, WEI Hao, et al. An Improved SOC Estimation Method for Lithium Ion Battery[J]. Chinese Journal of Electron Devices, 2019, 42(1):139-141.
[8] 谷苗,夏英超,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):420-426. GU Miao, XIA Yingchao, Tian Congying. Li-ion Battery State of Charge Estimation Based on Comprehensive Kalman Filter[J]. Transactions of China Electrotechnical Society, 2019, 34(2):420-426.
[9] XU Yidan, HU Minghui, FU Chunyun, et al. State of Charge Estimation for Lithium-ion Batteries Based on Temperature-dependent Second-order RC Model[J]. Electronics, 2019, 8(9):1012.
[10] LENG F, TAN C, YAZAMI R, et al. A Practical Framework of Electrical Based Online State-of-charge Estimation of Lithium Ion Batteries[J]. Journal of Power Source, 2014, 255:423-430.
[11] 方明杰,王群京. 基于扩展卡尔曼滤波算法的锂离子电池的SOC估算[J]. 电工电能新技术,2013,32(2):40-42. FANG Mingjie, WANG Qunjing. Strategy of Estimation State of Charge for Lithium Ion Battery Based on Extended Karlman Filter[J]. Advanced Technology of Electrical Engineering and Energy, 2013, 32(2):40-42.
[12] JOKIC I, ZECEVIC Z, KRSTAJIC B. State-of-Charge Estimation of Lithium-ion Batteries Using Extend Kalman Filter and Unscented Kalman Filter[C]//23rd International Scientific-professional Conference on Information Technology. Zabljak, Montenegro, 2018.
[13] 石刚,赵伟,刘珊珊. 基于无迹卡尔曼滤波估算电池SOC[J]. 计算机应用,2016,36(12):3492-3498. SHI Gang, ZHAO Wei, LIU Shanshan. Battery SOC Estimation Based on Unscented Kalman Filtering[J]. Journal of Computer Applications, 2016, 36(12):3492-3498.
[14] 申晓康. 基于无迹卡尔曼滤波算法的电动汽车动力电池SOC估计[D]. 西安:长安大学,2017. SHEN Xiaokang. SOC Estimation of Lithium Ion Battery Based on Unscented Kalman Filter[D]. Xi'an:Chang'an University, 2017.
[15] JOHNSON V H. Battery Performance Models in ADVISOR[J]. Journal of Power Source, 2002, 110(2):321-329.
[16] TING T O, MAN K L, LIM E G, et al. Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-charge Estimation in Battery Management System[J]. The Scientific World Journal, 2014:176052.
[17] 许可珍,金鹏. 基于优化的Thevenin模型的镍氢电池仿真[J]. 电池,2017,47(3):144-147. XU Kezhen, JIN Peng. Simulation of Ni-MH Battery Based on Optimized Thevenin Model[J]. Battery Bimonthly, 2017, 47(3):144-147.
[18] 杨阳,汤涛峰,秦大同,等. 电动汽车锂电池PNGV等效电路模型与SOC估算方法[J]. 系统仿真学报,2012, 24(4):938-942. YANG Yang, TANG Taofeng, QIN Datong,et al. PNGV Equivalent Circuit Model and SOC Estimation Algorithm of Lithium Batteries for Electric Vehicle[J]. Journal of System Simulation, 2012, 24(4):938-942.

基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究

SOC Estimation of Lithium-ion Battery Based on Ampere Hour Integral and Unscented Kalman Filter

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	唐先智, 张晓壮, 郝少澎, 王波, 张宇. 考虑轮胎载荷转移影响的复合式胎压监测补偿修正方法[J]. 中国机械工程, 2023, 34(23): 2881-2888.
[2]	许万, 程兆, 夏瑞东, 陈汉成. 一种基于动态残差的自适应鲁棒无迹卡尔曼滤波器定位算法[J]. 中国机械工程, 2023, 34(21): 2607-2614.
[3]	孙东, 汪若尘, 丁仁凯, 陈轶杰. 基于灰狼算法的矿用自卸车油气悬架半主动控制[J]. 中国机械工程, 2023, 34(04): 490-497.
[4]	董思捷, 张新春, 汪玉林, 刘南南, 陈学晋. 不同挤压载荷下圆柱形锂离子电池的失效机理试验研究[J]. 中国机械工程, 2022, 33(08): 915-920，951.
[5]	闫丽萍, 张华良, 董学智, 陈海生, 谭春青, . 燃气轮机气路与传感器耦合故障诊断模型研究[J]. 中国机械工程, 2021, 32(24): 2960-2966，2974.
[6]	张凯1;赵鹏2;王友仁1;徐智童1;陈则王1. 基于荷电状态的锂离子电池组主动均衡控制[J]. 中国机械工程, 2020, 31(16): 1931-1939.
[7]	李伟1;刘伟嵬1;邓业林2. 基于扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 中国机械工程, 2020, 31(03): 321-327,343.
[8]	凤鹏飞1，2;金会庆1，2;王慧然3;刘法勇4. 考虑路面附着和自适应时间系数影响的车道保持控制[J]. 中国机械工程, 2019, 30(07): 804-811.
[9]	隗寒冰1;曹旭1;赖锋2. 智能汽车环境感知算法测试评价系统开发[J]. 中国机械工程, 2018, 29(19): 2298-2305,2311.
[10]	赵林峰, 徐磊, 陈无畏. 基于自抗扰控制的自动泊车路径跟踪[J]. 中国机械工程, 2017, 28(08): 966-973.
[11]	张任, 胥芳, 陈教料, 潘国兵. 基于PSO-RBF神经网络的锂离子电池健康状态预测[J]. 中国机械工程, 2016, 27(21): 2975-2981.
[12]	阚萍, 邱利宏, 钱立军, 王金波. 基于Willans Line的双轴驱动混合动力越野车实时能量管理[J]. 中国机械工程, 2016, 27(11): 1546-1553.
[13]	张荣芸, 赵林峰, 陈无畏, 黄鹤. 基于LPV/H∞控制的EPS系统及其硬件在环研究[J]. 中国机械工程, 2015, 26(4): 545-552.
[14]	钱立军, 邱利宏, 陈朋. 基于模糊PID扭矩识别的混合动力汽车优化控制[J]. 中国机械工程, 2015, 26(13): 1752-1759.
[15]	黄智, 刘剑, 胡凯, 刘李盼. 气压ABS关键技术研究与试验[J]. 中国机械工程, 2014, 25(8): 1126-1130.