Extend the charging framework to varying temperatures

Extend the filter-augmented reinforcement-learning framework for lifelong health-aware lithium-ion battery fast charging to varying-temperature operating conditions by incorporating a temperature-aware model, retraining the controller, and conducting independent validation.

Background

The proposed controller is evaluated under an approximately isothermal operating assumption, with battery thermal management maintaining the cell near a fixed reference temperature. Consequently, the reported results do not establish performance under changing thermal conditions. Extending the framework to varying temperatures would require temperature to be represented in the controller’s state, incorporated into the safety and reward design, and reflected in the battery ageing environment, followed by retraining and independent validation.