Battery state-of-health estimation based on random charge curve fitting and broad learning system with attention mechanism

Abstract

Due to the usage habits, it is challenging to conduct the complete process of lithium-ion batteries (LIBs) from fully discharged to the maximum charge in real situations. To achieve battery accuracy state-of-health (SOH) estimation in random charging situations, this study proposes a novel health feature extraction strategy based on random charging curve fitting and an enhanced broad learning system (BLS). First, a multi-objective particle swarm optimization (MOPSO) algorithm is utilized to determine the optimal voltage interval for data extraction. Second, the random charging curve segments are fitted by a quadratic function to characterize health features (HFs). Finally, this study proposes a battery SOH estimation model, i.e., the attention mechanism-based BLS (A-BLS). The attention mechanism reduces the uncertainty caused by the random weights of the BLS for the inputs. A dropout layer is incorporated into the BLS model to mitigate the risk of overfitting. Experiments are conducted on the NASA, Oxford, and Michigan datasets, with most estimation errors below 1 %. Experimental results demonstrate that the proposed method has the potential for implementation in practical situations involving LIBs. Furthermore, the estimation efficacy of the battery SOH is both reliable and accurate.