Lithium-ion battery prognostics and health management (BPHM) systems are vital to the longevity, economy, and environmental friendliness of electric vehicles and energy storage systems. Recent advancements in deep learning (DL) techniques have shown promising results in addressing the challenges faced by the battery research and innovation community. This review article analyzes the mainstream developments in BPHM using DL techniques. The fundamental concepts of BPHM are discussed, followed by a detailed examination of the emerging DL techniques. A case study using a data-driven DLinear model for state of health estimation is introduced, achieving accurate forecasts with minimal data and high computational efficiency. Finally, the potential future pathways for research and development in BPHM are explored. This review offers a holistic understanding of emerging DL techniques in BPHM and provides valuable insights and guidance for future research endeavors.

中文翻译：

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通过深度学习推进锂离子电池健康预测：回顾和案例研究

锂离子电池预测和健康管理（BPHM）系统对于电动汽车和储能系统的寿命、经济性和环境友好性至关重要。深度学习 (DL) 技术的最新进展在解决电池研究和创新社区面临的挑战方面取得了可喜的成果。这篇综述文章分析了使用深度学习技术的 BPHM 的主流发展。讨论了 BPHM 的基本概念，然后详细检查了新兴的深度学习技术。介绍了使用数据驱动的 DLinear 模型进行健康状况估计的案例研究，以最少的数据和较高的计算效率实现了准确的预测。最后，探讨了 BPHM 未来潜在的研究和开发途径。这篇综述提供了对 BPHM 中新兴深度学习技术的全面理解，并为未来的研究工作提供了宝贵的见解和指导。