【摘要】 综述了锂离子电池智能管理算法的最新研究进展，聚焦于荷电状态估计、健康状态与剩余寿命预测、快速充电及故障诊断四个核心问题。通过整合模型驱动与数据驱动两类方法的代表性成果，对比分析其在精度、鲁棒性、计算效率及工程落地中的优势与局限性。模型驱动方法具备物理机理清晰、可解释性强的特点，但面临参数时变性与复杂工况适应性的挑战；数据驱动方法凭借非线性建模能力在复杂场景中表现优异，但受限于数据质量与可解释性不足。进一步探讨了两类方法融合的技术趋势，如物理信息嵌入的混合模型、多模态数据融合及深度强化学习框架的应用前景。此外，快速充电策略正向热-电耦合优化与自适应控制发展，故障诊断技术则通过多维传感与AI融合实现早期预警。为构建高可靠性、长循环寿命的电池管理系统提供理论参考与技术方向，同时指出未来研究需重点关注小样本学习、跨电池泛化能力及算法实时性优化等关键问题。更多还原

【Abstract】 The latest research progress in intelligent management algorithms for lithium-ion batteries has been summarized, with a focus on four core issues: state of charge(SOC) estimation, state of health(SOH) and remaining useful life(RUL) prediction, fast charging, and fault diagnosis. Representative achievements of both model-based and data-driven approaches have been integrated and compared in terms of accuracy, robustness, computational efficiency, and engineering implementation. Model-based methods, characterized by clear physical mechanisms and strong interpretability, face challenges related to parameter time-variability and adaptability to complex operating conditions. Data-driven methods, leveraging their nonlinear modeling capabilities, perform well in complex scenarios but are limited by data quality and insufficient interpretability. The integration of these two approaches has been further discussed, including the application prospects of hybrid models incorporating physical information, multimodal data fusion, and deep reinforcement learning frameworks. Fast charging strategies are evolving towards thermo-electric coupled optimization and adaptive control, while fault diagnosis techniques are achieving early warning through multidimensional sensing and AI integration. Theoretical references and technical directions are provided for the development of high-reliability and long-cycle-life battery management systems. Future research is highlighted to focus on key issues such as small-sample learning, cross-battery generalization capability, and real-time optimization of algorithms.更多还原