Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression

The knowledge of the dynamic available charging and discharging power of the battery is a piece of essential information for the safety and longevity of the battery energy storage systems. An accurate real-time prediction of these quantities is very challenging due to the high nonlinearities of battery dynamics. In this paper, an electrochemical model-based online state-of-power prediction algorithm under different time horizons is developed for a safer and more reliable operation of lithium-ion batteries. The safety constraints, which define the safety operation area for the power prediction, are designed based on not only the terminal voltage but also battery internal electrochemical states, i.e., the electrode surface concentration, the electrolyte concentration, and the side reaction overpotential. The algorithm is validated by simulations and experiments under a dynamic load profile, and the dominating constraints in charging and discharging as well as the influence of predictive time horizons on the available battery power are analyzed, providing important information for further researches. Furthermore, the computational speed of the proposed iterative algorithm is improved with the integration of Gaussian process regression by up to 50%. A comparative study with a state-of-the-art equivalent circuit model-based approach highlights the significant benefits of the proposed electrochemical model-based algorithm in operation safety enhancement and battery performance improvement. • Online power prediction considering internal electrochemical safety constraints. • Computational efficiency improvement by 50% with Gaussian process regression. • Numerical and experimental verification under real-world driving cycles. • Dominating safety constraint analysis for maximum charging and discharging power. • Outperforming state-of-the-art power prediction methods in all time horizons.