Mitigating the impact of data dependence in machine-learning-based state-of-health estimation for lithium-ion batteries: Challenges, strategies and perspectives

Accurate state-of-health (SOH) estimation is a prerequisite for ensuring the safety and performance of lithium-ion batteries (LIBs). Among available approaches, machine learning (ML) has attracted significant attention for SOH estimation but its performance is constrained by its reliance on the quality of data. This constraint hinders its real-world deployment. To address this challenge, recent studies have introduced various strategies to mitigate the impact of data dependence from the different perspectives. This review represents the first systematic analysis dedicated to mitigating the impact of data dependence in ML-based SOH estimation, distinguishing it from prior reviews focused on models or feature selection. Its key innovations reside in discussing the challenges and strategies for mitigating the impact from three aspects: data, learning paradigms, and models. Additionally, it summarizes the challenges of current strategies and provides an outlook on future research directions. By emphasizing the deployment implications of different strategies in electric vehicles, this review provides valuable insights for engineers and researchers facing data quality challenges. Ultimately, the impact of data dependence in real-world deployments can be further addressed through advances in strategies, bridging the gap between academic research and industrial applications.