A soft-constrained unscented Kalman filter estimator for Li-ion cells electrochemical model

Li-ion batteries require advanced Battery Management Systems (BMSs). The estimation of the cells internal quantities (residual energy, temperature, ions concentrations) is paramount for the correct and safe operation of Li-ion batteries. Accurate estimation of these quantities is however a challenging task. This work addresses the internal state estimation of a Li-ion cell applying the Unscented Kalman Filter (UKF) approach to the complete P2D model. The use of the complete P2D model allows for the estimation of the spatial distribution of Li-ions concentrations, along with the estimate of the bulk State of Charge (SoC). The paper illustrates how the UKF can address the two main issues involved in using the P2D model in estimation: weak observability and computational load. The observability issue is addressed imposing a soft mass conservation constraint in the UFK particles computation, while a parallelized implementation softens the computational burden. Extensive simulations validate the approach with currents up to 50C.