A Comparison of the Use of Pontryagin’s Maximum Principle and Reinforcement Learning Techniques for the Optimal Charging of Lithium-Ion Batteries

Battery systems are one of the most important components for the development of flexible energy storage for future applications. These comprise energy storage in both the mobility sector and stationary applications. To ensure the safe operation of multiple battery cells connected in series and parallel in a battery pack, it is essential to implement state of charge (SOC) equalization strategies. Generally, two fundamentally different approaches can be distinguished. On the one hand, these are passive approaches for SOC equalization that are based on including additional Ohmic resistors in a battery back over which equalization currents flow as long as the correspondingly connected cells have different voltages. Despite the simple implementation of such equalization circuits, they have a major drawback, namely wasting stored energy to perform the SOC equalization. This waste of energy goes along with Ohmic heat production, which leads to the necessity of additional cooling for batteries with large power densities. On the other hand, active SOC equalization approaches have been investigated, which allow for an independent charging of the individual cells. Especially, this latter approach has big potential to be more energy efficient. In addition, the potential for a reduction of Ohmic heat production may contribute to extending the lifetime of battery cells. To perform the individual charging of battery cells in an energetically optimal manner, this paper provides a comparison of closed-form optimization approaches on the basis of Pontryagin’s maximum principle and approaches for reinforcement learning. Especially, their accuracy and applicability for the implementation of optimal online cell charging strategies are investigated.