Abstract: Safe and efficient lithium battery charging control strategy plays an important role in promoting the development of electric vehicles. Aiming at the problem of fast charging of lithium batteries, a multi-objective optimal charging control strategy is proposed that comprehensively considers the charging speed, energy consumption and safety constraints of lithium batteries. Based on the action-evaluation network framework, a deep reinforcement learning algorithm based on proximal policy optimization is used to train a charging policy neural network and a policy evaluation neural network that maximize the reward function corresponding to the charging target. Then, using the trained charging strategy neural network to intelligently decide the optimal charging current according to the current electricity price and battery SOC. The advantage of this charging control strategy is that it can minimize charging costs while ensuring fast charging. At the same time, the online calculation amount of the charging strategy neural network is small, and compared with the model-based online optimization algorithm, it can better meet the real-time requirements of charging control. Finally, the simulation results show that, compared with the traditional constant current-constant voltage method, the charging control strategy has the advantages of taking into account the charging speed and electricity expenditure, and can reduce the charging cost by up to 25% while meeting the requirements of fast charging tasks.

Key words: fast charging control algorithm, charging cost optimization, deep reinforcement learning, lithium-ion batteries