Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.

锂离子电池已广泛用于许多重要应用中。然而，锂离子电池仍面临许多挑战，其中之一是退化。电池退化是一个复杂的问题，涉及阳极，电解质和阴极中的许多电化学副反应。操作条件会严重影响性能下降，从而影响电池寿命。准确预测各种工作条件下的剩余电池寿命至关重要。这对于确保电池管理系统可靠运行和及时维护至关重要，对于电池续航应用也至关重要。介绍退化机制后，本文对电池寿命预测技术进行了及时而全面的回顾，重点是基于模型，数据驱动和混合方法的最新进展。介绍，分析和比较了这些方法的详细信息，优点和局限性。介绍了未来的趋势，并讨论了主要的挑战和机遇。