Methods and systems of optimizing battery charging are disclosed. Battery state sensors are used to determine anode overpotential of a battery multiple times during multiple charge cycles. In a first phase, a reinforcement learning model (e.g., actor-critic model) is trained with rewards given throughout each charge cycle of the battery to optimize training. The reinforcement learning model can determine state-of-health characteristics of the battery over the charge cycles, and in a second phase, the reinforcement learning model is augmented accordingly. During this augmentation, the reinforcement learning model is trained with rewards given on a charge cycle-by-cycle basis, wherein rewards are given after looking at the charging optimization after the conclusion of each charge cycle. Commands are given to charge an on-field battery based on the augmented reinforcement learning model, associated state-of-health characteristics of the on-field battery are determined, and the reinforcement learning model is further augmented accordingly.
REINFORCEMENT LEARNING FOR CONTINUED LEARNING OF OPTIMAL BATTERY CHARGING
02.05.2024
Patent
Elektronische Ressource
Englisch
Reinforcement Learning Based Fast Charging of Electric Vehicle Battery Packs
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