Open AccessSOC Estimation of Modular Lithium Battery Pack Based on Adaptive Kalman Filter Algorithm
Author(s) -
Zhi Duan Cai,
Jing Yun Xu,
Xiao Yue Sun,
Li Quan
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1345/4/042069
Subject(s) - algorithm , state of charge , kalman filter , noise (video) , battery pack , computer science , weighting , battery (electricity) , control theory (sociology) , extended kalman filter , artificial intelligence , power (physics) , medicine , physics , control (management) , quantum mechanics , image (mathematics) , radiology
Lithium battery state of charge (SOC) is a core parameter in battery management systems. In order to suppress the influence of noise characteristics on the accuracy of SOC estimation, this paper proposes an adaptive kalman filter (AKF)algorithm with variable noise variance based on the battery equivalent circuit model. In order to improve the accuracy of SOC estimation, the algorithm estimates the noise variance online and applies the noise variance estimation to kalman filter iteration process. Aiming at the modular structure of the battery pack, a segmentation variable weighting coefficient algorithm is proposed to estimate the SOC value of the entire battery pack. Based on the simulation platform of matlab, this paper carries out experimental verification and analysis. The results demonstrate the effectiveness and superiority of the algorithm.