AI-Augmented Solid-State Batteries: Revolutionizing Power Management in Electric Mobility

Abstract

Solid-state batteries (SSBs) are emerging as a transformative energy storage technology for electric mobility due to their superior energy density, enhanced safety, and extended cycle life compared to conventional lithium-ion batteries. However, challenges related to interfacial resistance, dendrite formation, thermal instability, and real-time power management hinder their large-scale deployment. This paper presents an AI-augmented framework for intelligent power management in solid-state batteries, integrating machine learning and deep learning techniques to optimize charge–discharge behavior, predict state-of-health (SoH) and state-of-charge (SoC), and mitigate degradation mechanisms. The proposed approach leverages data-driven models for adaptive thermal regulation, fault diagnosis, and lifespan prediction under dynamic driving conditions. By combining AI-enabled battery management systems with solid-state electrochemistry, the framework enhances energy efficiency, operational safety, and reliability of electric vehicles. Experimental and simulation-based evaluations demonstrate improved power utilization, reduced aging effects, and increased driving range, highlighting the potential of AI-driven solid-state battery systems as a cornerstone for next-generation electric mobility and sustainable transportation ecosystems.