A novel mechanism-based HF for offline and online capacity estimation of lithium-ion batteries under unknown dynamic operating conditions

When estimating the capacity of lithium-ion batteries offline or online, it is essential to extract a health feature (HF) that can effectively characterize capacity degradation under both conventional ideal and complex dynamic operating conditions. However, the extraction of most HFs relies on complete charge-discharge cycle data, making them less adaptable to complex dynamic operating conditions. Existing mechanism HFs, while capable of characterizing capacity degradation from a mechanism perspective, suffer from limitations such as insufficient physical model expressiveness, high dimension, and redundancy of the mechanism HF. These issues increase the complexity of subsequent modeling of the relationship between HFs and capacity, thereby restricting their promotion in engineering practice. To meet this gap, this paper proposes a novel mechanism-based HF. Firstly, a multi-physical fields coupling model is developed to describe the interactions between electrochemical, thermal, and aging behaviors of the battery. Secondly, based on the aging mechanism, the accumulated charge of lithium lost during the formation of the solid electrolyte interphase (SEI) film is extracted as HF to provide a more intuitive representation of capacity degradation. Then, to reduce estimation errors caused by considering only a single aging mechanism, multiple representative regression models are employed to establish the mapping relationship between the mechanism HF and capacity, further enhancing the accuracy of final results. Finally, the proposed method is implemented and validated using real battery data under three different types of operating conditions. Experimental results demonstrate that, compared to other commonly used HFs, the proposed HF exhibits significant competitive advantages in handling incomplete cycle data, unknown operating conditions, and capacity estimation models. The minimum estimation error under ideal conditions is 0.0074, and the minimum estimation error under complex dynamic conditions is 0.0268.