Abstract: State of health (SOH) serves as a key indicator for assessing the overall condition and remaining capacity of plug-in hybrid electric vehicle (PHEV) batteries. Accurate SOH prediction is of significant value for ensuring safe and reliable vehicle operation, optimizing performance, and extending battery lifespan. This paper systematically reviews the current research status of SOH prediction techniques for PHEVs, categorizing the technical approaches into experimental methods, model-based methods, data-driven methods, and model fusion methods, while summarizing the advantages and limitations of each approach based on current research. Furthermore, the paper synthesizes industry application trends in SOH prediction and management adopted by automotive manufacturers, and identifies challenges in data acquisition and processing, model accuracy and generalization, real-time performance and computational efficiency, as well as personalized modeling and user behavior integration. By elucidating the theoretical framework and practical pathways of SOH prediction techniques for PHEV batteries, this study provides critical theoretical guidance for the industry, facilitating the optimization of battery management systems, enhancement of vehicle safety and reliability, and promotion of high-quality development in the new energy vehicle market.

Key words: plug-in hybrid electric vehicles; lithium-ion batteries; state of health; machine learning; deep learning

摘要： 电池健康状态（SOH）作为衡量插电混动汽车（PHEV）电池整体状态和剩余容量的关 键指标，其准确预测对保障插电混动汽车安全可靠运行、优化性能和延长电池寿命具有重要 价值。文章系统梳理了面向插电混动汽车的电池 SOH 预测技术研究现状，归纳出实验法、模 型法、数据驱动法和模型融合法等关键技术路径，并结合当前研究现状总结各类方法的优缺 点。此外，文章总结归纳了车企在电池 SOH 预测及管理的行业应用现状，并指出数据获取与 处理、模型精度与泛化、实时性与计算效率、个性化建模与用户行为等方面的挑战。文章通 过梳理插电混动汽车电池 SOH 预测技术的理论框架与实践路径，为行业提供关键理论指导， 助力优化电池管理系统并提升车辆安全可靠性，推动新能源汽车市场高质量发展。

关键词: 插电混动汽车；锂离子电池；电池健康状态；机器学习；深度学习