Alleviating Dynamic Model Uncertainty Effects for Improved Battery SOC Estimation of EVs in Highly Dynamic Environments

被引:15
作者
Wadi, Ali [1 ]
Abdel-Hafez, Mamoun [1 ]
Hussein, Ala [2 ]
Alkhawaja, Fares [1 ]
机构
[1] Amer Univ Sharjah, Dept Mech Engn, Sharjah 34754, U Arab Emirates
[2] Prince Mohammad Bin Fahd Univ, Dept Elect Engn, Khobar 26666, Saudi Arabia
关键词
State of charge; Estimation; Mathematical model; Heuristic algorithms; Vehicle dynamics; Uncertainty; Lithium-ion batteries; extended Kalman filter; lithium-ion battery; smooth variable structure filter; state-of-charge; STATE-OF-CHARGE; LITHIUM-ION BATTERIES; KALMAN FILTER; PARAMETERS; VEHICLES;
D O I
10.1109/TVT.2021.3085006
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Dynamic battery modeling uncertainties, even if low, may lead to significant performance degradation or even divergence of the state-of-charge estimation algorithm. This paper investigates the integration of the extended-Kalman-filter with the smooth-variable-structure-filter algorithms for state-of-charge estimation of lithium-ion batteries. The robustness of the presented approach to modeling uncertainty is assessed for batteries operating in highly dynamic environments. The presented approach combines the benefit of the smooth- variable-structure-filter in its robustness to model uncertainty with the benefit of the extended-Kalman-filter in its near-optimality for a given dynamics and measurement noise sequences. The algorithm is rigorously tested using various datasets including standardrized and artificial drive cycles with added dynamics. The drive cycle power profile is calculated for an electric Ford F150 truck and scaled for the 18650PF cell used in the tests. Experimental validation is performed by investigating four different scenarios in which knowledge of the initial conditions as well as accuracy of the battery model were varied. The results demonstrate a substantially enhanced estimation accuracy achieved by the adopted approach through its optimality to measurement and model noise as well as its robustness to model uncertainty. The adopted approach results in a reduction in the complexity of the state-of-charge control algorithm and therefore enhances the battery management system.
引用
收藏
页码:6554 / 6566
页数:13
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