Deep reinforcement learning based fast charging and thermal management optimization of an electric vehicle battery pack

Abstract

Existing optimal strategies for fast-charging electric vehicle batteries are predominantly at the cell level. The proposed high-fidelity methods for extending available cell models to pack level are associated with a large computational burden, making real-world implementation impossible. Further, fast charging optimization and thermal management problems are dependent. That is, the cooling system reduces battery temperature, allowing for higher charging current, while optimal current minimizes the need for cooling and, in turn, reduces thermal system power consumption. There is a lack of studies where fast charging optimization and battery thermal management problems are jointly solved. Therefore, this paper proposes a simulation study using a deep reinforcement learning (RL) approach that concurrently solves fast charging and thermal management problems for a battery pack with low computational complexity. In this regard, we formulate each cell using an electro-thermal-aging model, which accounts for the heat exchange between adjacent cells. The electro-thermal-aging model plays the role of the environment for RL and is not the focus of novelty in this work. The RL agent is then trained to output the optimal charging current and coolant mass flow rate. Moreover, the proposed methodology is examined through a numerical study where the outperformance of our model is showcased by comparing it with baseline algorithms of model predictive control (MPC) and CC–CV. Consequently, three battery packs comprising 20, 444, and 7104 cells are used, respectively. We demonstrate that RL requires less than a second to finish the simulation for 20 cells, while MPC requires more than 80 min. In addition, RL keeps the cells’ core temperature below 33 °C, but MPC results reach 40 °C. The RL performance in charging packs with 444 and 7104 cells is then compared with that of CC–CV. In terms of computation time, RL and CC–CV are nearly the same, while regarding the average core and surface temperature of the cells as well as the cell aging, RL attains better outcomes, extending the battery pack’s life by up to two years after 1000 fast charging cycles.