A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation

To achieve more precise and reliable lithium-ion battery state-of-charge (SOC) estimation, this paper performs a comparative study of different adaptive extended Kalman filters (AEKFs)/adaptive unscented Kalman filters (AUKFs). Firstly, three scenarios are artificially established to evaluate different AEKFs/ AUKFs' estimation accuracy, sensitivity to uncertainty existing in open-circuit-voltage (OCV)-SOC relationship and robustness ability against different forms of disturbances, respectively. Meanwhile, various AEKFs/AUKFs' difficulty of parameters tuning is also evaluated according to our experience. Subsequently, eight indexes that can reflect algorithms' comprehensive estimation performance are further extracted. On this basis, a novel multi-objective analysis decision method by fusion of analytic hierarchy process and entropy weight is adopted to allocate weights for extracted indexes and further compare various AEKFs/AUKFs' comprehensive estimation performance, whose results are shown as scores. The algorithm with highest score demonstrates that it has the optimal comprehensive estimation performance and is also recommended to be used in real application. The most remarkable contribution of this work lies in the suggestions and guidance for researchers when choosing AEKFs/AUKFs for online SOC estimation.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

In this paper, a comparative study of different adaptive extended Kalman filters (AEKFs)/adaptive unscented Kalman filters (AUKFs) is conducted to achieve more precise and reliable lithium-ion battery state-of-charge (SOC) estimation. A novel multi-objective analysis decision method is proposed to allocate weights for various extracted indexes and further compare the comprehensive estimation performance of different AEKFs/AUKFs. The results provide suggestions and guidance for researchers when choosing AEKFs/AUKFs for online SOC estimation.