2026-05-12 チャルマース工科大学
Fast charging shortens the life of vehicle batteries, but is necessary on longer journeys with electric vehicles. Researchers at Chalmers University of Technology, Sweden, have now developed a new AI method that adapts fast charging to the health of the battery. Their study shows that battery life can be increased by almost 23 per cent without extending the charging time. All that is required is an update of the vehicle’s software.
＜関連情報＞
- https://news.cision.com/chalmers/r/smart-ai-gives-electric-vehicle-batteries-23-per-cent-longer-life—without-increasing-the-charging-,c4346458
- https://news.cision.com/chalmers/r/smart-ai-gives-electric-vehicle-batteries-23-per-cent-longer-life—without-increasing-the-charging-,c4346458
リチウムイオン電池の健康状態を考慮した急速充電のための生涯強化学習 Lifelong Reinforcement Learning for Health-Aware Fast Charging of Lithium-Ion Batteries
Meng Yuan; Changfu Zou
IEEE Transactions on Transportation Electrification Published:24 October 2025
DOI:https://doi.org/10.1109/TTE.2025.3625421
Abstract
Fast charging of lithium-ion batteries (LIBs) remains a critical bottleneck for widespread adoption of electric vehicles (EVs) and stationary energy storage systems, as improperly designed fast charging can accelerate battery degradation and shorten lifespan. In this work, we address this challenge by proposing a health-aware fast charging strategy that explicitly balances charging speed and battery longevity across the entire service life. The key innovation lies in establishing a mapping between side-reaction overpotential and the state of health (SoH) of the battery, which is then used to constrain the terminal charging voltage in a twin delayed deep deterministic policy gradient (TD3) framework. By incorporating this SoH-dependent voltage constraint, our designed deep learning method mitigates side reactions and effectively extends battery life. To validate the proposed approach, a high-fidelity single particle model with electrolyte (SPMe) is implemented in the widely adopted PyBaMM simulation platform, capturing degradation phenomena at realistic scales. Comparative life-cycle simulations against conventional constant current–constant voltage (CC-CV), its variants, and CC–constant overpotential (COP) methods show that the TD3-based controller reduces overall degradation while maintaining competitively fast charge times. These results demonstrate the practical viability of deep reinforcement learning (RL) for advanced battery management systems (BMSs), paving the way for future explorations of health-aware, performance-optimized charging strategies.
