A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges

Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems (BMSs) that efficiently manage the batteries. This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate. Throughout their whole life cycle, lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials. This degradation is reflected in the state of health (SOH) assessment. Therefore, this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years, highlighting common research focuses rooted in data-driven methods. It delves into various dimensions such as dataset integration and preprocessing, health feature parameter extraction, and the construction of SOH estimation models. These approaches unearth hidden insights within data, addressing the inherent tension between computational complexity and estimation accuracy. To enhance support for in-vehicle implementation, cloud computing, and the echelon technologies of battery recycling, remanufacturing, and reuse, as well as to offer insights into these technologies, a segmented management approach will be introduced in the future. This will encompass source domain data processing, multi-feature factor reconfiguration, hybrid drive modeling, parameter correction mechanisms, and full-time health management. Based on the best SOH estimation outcomes, health strategies tailored to different stages can be devised in the future, leading to the establishment of a comprehensive SOH assessment framework. This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols. This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead. Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods, offering valuable insights for the development of advanced battery management systems and embedded application research.