Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter

The use of reduced-order electrochemical models creates opportunities for battery management systems to control the battery behavior by monitoring the internal states in electrochemical processes, which are critical for safety enhancement and degradation mitigation. This paper explores a state observer for lithium-ion batteries based on an extended single-particle model, which results in a trade-off between high accuracy and low computational burden, thus enables the real-time application. An adaptive unscented Kalman filter based on this model is developed to estimate not only the state of charge but also lithium-ion concentrations and potentials, which precisely describe battery internal behaviors to avoid lithium plating. Experimental tests are carried out with a lithium-ion battery cell for both model and state estimation validations. Furthermore, the estimation accuracies of the unmeasurable states are also verified by numerical validation tests with a high-fidelity electrochemical model. All estimated states present fast convergence, robustness, and high accuracy despite a 20% initial state-of-charge error. Additionally, the enhancement in the state estimation accuracy and robustness by the new noise adaption step is demonstrated by an application-relevant evaluation framework, considering sensor noise, state uncertainty, parameter uncertainty, and computation time.