Physics-Informed Data-Driven Power Capacity Prediction of Lithium-Ion Battery Against Various Temperatures

This is the authors' abstract. We don't add key points for this paper.

Lithium-ion batteries are extensively utilized in applications ranging from portable electronics to electric vehicles and renewable energy systems. Accurate prediction of the state of power capacity (SOP) in lithium-ion batteries is fundamental for guaranteeing the safe, reliable, and efficient operation of these systems. However, most existing SOP prediction algorithms only account for the external measurable state constraints of the battery, ignoring the influence of the internal electrochemical states. Using electrochemical models to model batteries can introduce the electrochemical perspective, but many related methods ignore the impact of temperature variations on model parameters. Therefore, this paper proposes an SOP estimation framework based on a physics-informed data-driven approach, which fully integrates the electrochemical model and battery operation data to provide accurate power capacity estimation against temperature effects. First, the battery is modeled using an electrochemical model, and the battery operation data is used to identify the electrochemical temperature-sensitive parameters to enhance the accuracy of the model. Secondly, safety constraints for battery operations are introduced from the perspective of the battery mechanisms and the bisection method is employed to search for the maximum current. Compared with the SOP calibration results and the state-of-the-art method, the results highlight the accuracy of the proposed method. Finally, by referring to the characteristic maps-based method and employing Gaussian process regression, the search interval of SOP is significantly reduced based on historical data, reducing the search time by 80%.