Suggested Citation:
Zhao, Jingyuan and Andrew Burke (2025)

Chapter 10: Artificial Intelligence-Driven Electric Vehicle Battery Lifetime Diagnostics

Ensuring the reliability, safety, and efficiency of electric vehicles (EVs) necessitates precise diagnostics of battery life, as the degradation of batteries directly influences both performance and sustainability. The transformative role of artificial intelligence (AI) in advancing EV battery diagnostics is explored herein, with an emphasis placed on the complexities of predicting and managing battery health. Initially, we provide an overview of the challenges associated with battery lifetime diagnostics, such as issues with accuracy, generalization, and model training. The following sections delve into advanced AI methodologies that enhance diagnostic capabilities. These methods include extensive time-series AI, which improves predictive accuracy; end-to-end AI, which simplifies system complexity; multi-model AI, which ensures generalization across varied operating conditions; and adaptable AI strategies for dynamic environments. In addition, we explore the use of federated learning for decentralized, privacy-preserving diagnostics and discuss the role of automated machine learning in streamlining the development of AI-based models. By integrating these sophisticated AI techniques, we present a comprehensive roadmap for the future of AI-driven battery prognostics and health management. This roadmap underscores the critical importance of accuracy, scalability, and sustainability in fostering advancement. Our interdisciplinary framework offers valuable insights that can accelerate the electrification of transportation and advance the evolution of energy storage systems, tackling key challenges at the intersection of battery technology and AI.