Real-time artificial intelligence for solid-state lithium metal batteries

Key Contributions

• Applies machine learning to detect incipient failure modes in solid-state lithium metal cells from voltage and current profiles
• Uses reinforcement learning to dynamically adjust charging/discharging protocols, extending cell lifetime
• Real-time inference — operates during cycling, not just post-hoc analysis
• Bridges the gap between solid-state cell development and practical battery management systems (BMS)
• Demonstrates that AI-augmented BMS can compensate for solid-state variability in early-stage manufacturing

Results / Data

• Failure detection: ML models identify degradation signatures from voltage/current data
• Lifetime extension: RL-optimized cycling protocols extend usable cell life
• Real-time operation: inference runs during charge/discharge cycles
• Applicable to solid-state lithium metal cells specifically

Source: Real-time AI for solid-state lithium metal batteries — Nature Communications, 2025