Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries

Abstract

The production of electric vehicle (EV) batteries is playing an increasingly significant role in the decarbonization of the mobility sector. In order for EV batteries to be competitive against internal combustion engines, it is crucial to maximize the primary and secondary life cycles of batteries. This necessitates a battery management system that can ensure performance, safety, and longevity. State of Charge (SoC) estimation is important for such a system, as it ensures efficiency of the battery’s performance, and it is necessary for the prediction of the battery’s health and lifespan. Existing SoC estimation methods heavily depend on laboratory tests, which are both costly and time consuming. Additionally, the simulated nature of laboratory settings cannot guarantee robustness when the same method is applied to field data collected from real-world scenarios. A suitable alternative to this problem is the use of data-driven approaches. The goal of this work is the estimation of SoC with a real-world dataset using neural networks. Furthermore, we demonstrate how explainable AI (xAI) and importance estimate can be applied to inform what signals and which parts of a signal are important for SoC estimation. This helps to reduce redundancy, and it provides more information regarding the relationships within battery cells that are otherwise obscured by the complexity of the battery. The methods that we used resulted in a mean squared error (MSE) of as low as 3 × (Formula presented.), and the information provided by xAI suggested that it is possible to discard up to 25% of the input profile whilst retaining similar performance.