A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile
A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile
复制标题
在新的组合动态负载曲线下估算锂离子电池充电状态的三种基于模型的算法的比较研究
DOI：
10.1016/j.apenergy.2015.11.072
复制
发表时间：
2016-02
期刊：
影响因子：
11.2
通讯作者：
Tsui Kwok-Leung
中科院分区：
文献类型：
--
作者：
Yang Fangfang;Xing Yinjiao;Wang Dong;Tsui Kwok-Leung
准确的荷电状态（SOC）估计对于电动汽车电池管理系统的安全性和可靠性至关重要。由于SOC无法直接测量，且SOC估计受环境温度、电池老化和电流变化率等因素的影响，因此需要开发一种鲁棒的SOC估计方法，以处理时变和非线性的电池系统。在本文中，三个流行的基于模型的滤波算法，包括扩展卡尔曼滤波，无迹卡尔曼滤波，粒子滤波，分别用于估计SOC和性能的跟踪精度，计算时间，对SOC的初始值的不确定性的鲁棒性，和电池退化，进行了比较。为了评估这些算法的性能，提出了一种新的组合动态加载剖面组成的动态应力测试，联邦城市驾驶时间表和US06。比较结果表明，无迹卡尔曼滤波对SOC的不同初始值具有最强的鲁棒性，而粒子滤波在SOC的初始猜测与真实SOC的初始值相差甚远时具有最快的收敛能力。
Accurate state-of-charge (SOC) estimation is critical for the safety and reliability of battery management systems in electric vehicles. Because SOC cannot be directly measured and SOC estimation is affected by many factors, such as ambient temperature, battery aging, and current rate, a robust SOC estimation approach is necessary to be developed so as to deal with time-varying and nonlinear battery systems. In this paper, three popular model-based filtering algorithms, including extended Kalman filter, unscented Kalman filter, and particle filter, are respectively used to estimate SOC and their performances regarding to tracking accuracy, computation time, robustness against uncertainty of initial values of SOC, and battery degradation, are compared. To evaluate the performances of these algorithms, a new combined dynamic loading profile composed of the dynamic stress test, the federal urban driving schedule and the US06 is proposed. The comparison results showed that the unscented Kalman filter is the most robust to different initial values of SOC, while the particle filter owns the fastest convergence ability when an initial guess of SOC is far from a true initial SOC.
登录
查看更多内容
影响因子：
3.2
作者：
Bizhong Xia;Haiqing Wang;Yong-Liang Tian;Mingwang Wang;Wei Sun;Zhihui Xu
通讯作者：
Bizhong Xia;Haiqing Wang;Yong-Liang Tian;Mingwang Wang;Wei Sun;Zhihui Xu
影响因子：
11.2
作者：
Yao He;Xingtao Liu;Chenbin Zhang;Zonghai Chen
通讯作者：
Yao He;Xingtao Liu;Chenbin Zhang;Zonghai Chen
DOI：
10.1046/j.1467-9884.2003.t01-6-00383_8.x
发表时间：
2003-12
期刊：
--
影响因子：
--
作者：
Freda Kemp
通讯作者：
Freda Kemp
DOI：
10.1109/cisp.2011.6100603
发表时间：
2011-12
期刊：
2011 4th International Congress on Image and Signal Processing
影响因子：
--
作者：
Mingyu Gao;Yuanyuan Liu;Zhiwei He
通讯作者：
Mingyu Gao;Yuanyuan Liu;Zhiwei He
具有双粒子滤波器充电状态估计的新型动力锂离子电池温度补偿模型
影响因子：
11.2
作者：
Liu Xingtao;Chen Zonghai;Zhang Chenbin;Wu Ji
通讯作者：
Wu Ji