Battery State Prediction through Hybrid Modeling: Integrating Neural Networks with a Single Particle Model

Batteries are key components for the storage of energy, which are vital for the widespread use of renewable energy resources and electric vehicles. To ensure the safety and optimal performance of these devices, analyzing their operation through physical and data-driven models is essential. While physical models can accurately identify the underlying physicochemical processes, they are often too complex for integration in onboard diagnostics. Conversely, data-driven models are usually more flexible and easier to implement, but they lack a physical description of the battery. In response, this work accurately models the battery state by using the single particle model as a baseline for subsequent predictions made with neural networks. To achieve this, two neural networks were leveraged: one to apply a correction to the voltage obtained from the physical model, and the second to evaluate the battery state of health and aging states. This predictive performance of this new hybrid model demonstrated better performances if compared to neural networks alone. Results were benchmarked using two real-world battery cycling datasets, including one collected in-house. Given the promise of this new hybrid model, it is expected that the present work will pave the way for advanced modeling of batteries.