Enhancing the Extended Kalman Filter with Bio-inspired Algorithms for Battery SOC Estimation

The battery management system (BMS) serves as the brain of electric vehicles, playing a crucial role in alleviating range anxiety for drivers and providing real-time monitoring of battery safety. Accurate estimation of the state of charge (SOC) is a core function of BMS. This study aims to develop a...

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Bibliographic Details
Main Authors: Chen, Bing-Jie, Liao, Chung-Jie, Li, Chun-Ju, Ku, Hung-Chih, Yu, Wei-Lun, Liu, En-Jui
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Batteries
Battery Electric Vehicle
Biological system modeling
Computational modeling
Discharges (electric)
Estimation error
Extended Kalman Filter
Heuristic algorithms
Honey Badger Algorithm
Kalman filters
Li-ion Battery
State of charge
Vehicle dynamics
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Summary:The battery management system (BMS) serves as the brain of electric vehicles, playing a crucial role in alleviating range anxiety for drivers and providing real-time monitoring of battery safety. Accurate estimation of the state of charge (SOC) is a core function of BMS. This study aims to develop a method for rapidly estimating battery SOC to address the dynamic nature of battery models. By enhancing the Kalman filter with bio-inspired computation, we can swiftly adjust the filter's noise variables, achieving high-precision SOC estimation in a short amount of time.In the 0.5C discharge model, the estimation error of HBA-EKF is 0.016601; in the 1C discharge model, the estimation error is 0.007991; and in the 2C discharge model, the estimation error is 0.014172. The research results indicate that HBA-EKF can quickly adapt to different discharge models and irregular behaviours such as battery polarization phenomena, making it suitable for SOC estimation in vehicle dynamic models.
ISSN:2693-0854
DOI:10.1109/GCCE62371.2024.10760629