摘要
荷电状态精确估计是锂离子动力电池安全应用的关键技术之一,现有的估计方法并不完全适配锂离子电池系统,在准确性、稳定性和实用性方面均有较大的提升空间。为了准确描述锂离子电池系统动态特性并且提升估计精度与稳定性,提出一种基于分数阶电池模型的自适应扩展卡尔曼粒子滤波方法。在采用免疫遗传算法对锂离子电池分数阶模型进行参数辨识的过程中,应用“记忆库”减小算法计算量,引入“亲和度”缓解算法陷入局部最优解的问题。采用基于模型开发的手段,将提出的控制算法下载到电池管理系统控制器中,经过ECE和UDDS工况测试对比验证:二阶分数阶电路模型的端电压误差最大不超过13.96 mV,平均误差为2.4~4.2 mV,表明分数阶模型对电流的变化更为敏感且更能表现电池的电压变化性能,可以有效保证电池荷电状态的计算精度。提出方法较EKF的荷电状态估计精度提升50%以上,并且收敛时间大大缩短,表明在粒子滤波中引入自适应卡尔曼滤波进行校正,可以滤除噪声、增强估计算法准确性和鲁棒性。
How to accurately estimate the state of charge is one of the key technologies for safe application in lithium-ion batteries.However,the current approaches are not fully fit for lithium-ion battery systems,and there needs improvement in accuracy,stability and practicality.In order to describe the dynamic characteristics of lithium-ion battery systems and improve the accuracy and stability,an adaptive extended Kalman particle filter is proposed based on fractional order battery model to estimate the state of charge of lithium-ion.In the process of parameter identification of fractional order battery model with immune genetic algorithm,the"memory vault"is used to reduce the calculation amount of the algorithm,and the"affinity degree"is introduced to solve the problem of local convergence of the algorithm.The proposed control algorithm was downloaded into the battery management system controller by means of model-based development and compared and verified by ECE and UDDS condition tests.The terminal voltage error of the second-order fractional battery model is not more than 13.96 mV,and the average error is 2.4-4.2 mV,which indicates that the fractional battery model is more sensitive to the change of current,and can better represent the performance of the battery voltage change,and can effectively ensure the calculation accuracy of the battery SOC.Compared with the EKF,the accuracy of SOC estimation is improved by more than 50%,and the convergence time is greatly reduced,which indicates that the introduction of adaptive Kalman filter in particle filter for correction can filter out noise and enhance the accuracy and robustness.
作者
石琴
蒋正信
刘翼闻
魏宇江
胡晓松
贺林
SHI Qin;JIANG Zhengxin;LIU Yiwen;WEI Yujiang;HU Xiaosong;HE Lin(School of Automotive and Transportation Engineering,Hefei University of Technology,Hefei 230009;Anhui Key Laboratory of Autonomous Vehicle Safety Technology,Hefei 230009;Anhui Center of Intelligent Transportation and Vehicle,Hefei University of Technology,Hefei 230009;School of Automotive Engineering,Chongqing University,Chongqing 400044)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2024年第8期224-232,244,共10页
Journal of Mechanical Engineering
基金
江苏省重点研发计划(BE2021006-2)
安徽省重点研究与开发计划(202304a05020087)
安徽省自然科学基金(2308085ME163)资助项目。
