| 引用本文: | 寇发荣,王甜甜,王思俊,张 宏,门 浩.基于ABC-RFEKF算法的锂电池SOC估计[J].电力系统保护与控制,2022,50(4):163-171.[点击复制] |
| KOU Farong,WANG Tiantian,WANG Sijun,ZHANG Hong,MEN Hao.Lithium battery SOC estimation based on an ABC-RFEKF algorithm[J].Power System Protection and Control,2022,50(4):163-171[点击复制] |
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| 摘要: |
| 准确、可靠的荷电状态(SOC)估计可以为电池管理系统的安全高效使用提供保障。针对锂电池SOC估计精度不足的问题，提出人工蜂群算法(ABC)和随机森林优化EKF算法(RFEKF)分别实现电池模型的参数辨识和SOC估计。在建立双极化模型的基础上，为解决在线辨识初始误差累积的问题，采用ABC算法搜索最小模型电压误差下的全局最优阻抗参数值，实现模型参数的精确辨识。在获得精确的模型参数基础上，使用随机森林(RF)对SOC后验估计误差进行在线补偿，达到弥补传统EKF算法高阶项误差的目的，进而实现SOC高精度估计。联合半实物仿真系统和电池测试平台，在EPA城市动力工况下对SOC估计算法实现快速控制原型验证。结果表明：基于ABC-RFEKF的锂电池SOC估计算法各项误差指标均低于传统SOC估计算法，平均误差在1%左右，满足实际工程需求。 |
| 关键词: 荷电状态 人工蜂群算法 随机森林 扩展卡尔曼滤波 快速控制原型 |
| DOI：DOI: 10.19783/j.cnki.pspc.210607 |
| 投稿时间：2021-05-20修订日期：2021-07-05 |
| 基金项目:国家自然科学基金项目资助(51775426)；陕西省重点研发计划项目资助(2020GY-128) |
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| Lithium battery SOC estimation based on an ABC-RFEKF algorithm |
| KOU Farong,WANG Tiantian,WANG Sijun,ZHANG Hong,MEN Hao |
| (School of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China) |
| Abstract: |
| Accurate and reliable state of charge (SOC) estimation can provide a guarantee for the safe and efficient use of battery management systems. Given that there is insufficient accuracy of SOC estimation of lithium batteries, this paper proposes the artificial bee colony algorithm (ABC) and the random forest optimized EKF algorithm (RFEKF) respectively to realize parameter identification and SOC estimation of the battery model. Based on the establishment of dual polarization model, to solve the problem of the accumulation of initial errors in online identification, the ABC algorithm is used to search for the global optimal impedance parameter value under the minimum model voltage error, and realize accurate identification of model parameters. Based on obtaining accurate model parameters, this paper uses random forest (RF) to online compensate for the SOC posterior estimation error, and achieves the purpose of making up for the error of the high-order term of the traditional EKF algorithm. Then it achieves high-precision SOC estimation. Combining a hardware-in-the-loop simulation system and battery test platform, it realizes rapid control prototype verification for the SOC estimation algorithm under EPA urban power conditions. The results show that the error indicators of the lithium battery SOC estimation algorithm based on ABC-RFEKF are lower than the traditional SOC estimation algorithm. The average error is around 1%, thereby meeting actual engineering needs.
This work is supported by the National Natural Science Foundation of China (No. 51775426). |
| Key words: state of charge artificial bee colony algorithm random forest extended Kalman filter rapid control prototype |
