Electrochemical Model-Based State-Space Approach for Real-Time Parameter Estimation of Lithium-Ion Batteries

Abstract

Lithium-ion batteries are widely used in various applications, including electric vehicles and renewable energy storage systems, due to their high energy density, long cycle life, and low self-discharge rate. Accurate estimation of battery state of charge (SOC) and state of health (SOH) is crucial for their safe and efficient operation.In this study, we propose a model-based state-space solver for real-time monitoring of lithium-ion batteries under various operating conditions. The state-space model simplifies a single-particle model and enables simulations considering the current state of the battery using a Kalman filter. The model includes a one-dimensional Single Particle Model with Electrolyte (SPMe) and uses eigenfunction expansion to solve the mass conservation equation for the electrolyte phase. The final form of the state-space model for the solid and electrolyte phase includes a voltage equation using lithium-ion concentration. The proposed solver was validated by comparing it with the SPMe solver, and the results agree well.To achieve on-line updating of the battery model parameters, we propose a multi-scale parameter adaptive method based on dual Kalman filters for estimating the SPMe model parameters. The fast time-varying parameters are separated from the slowly time-varying parameters, and then each group is estimated by a different Kalman filter with a different time constant. This method enables the estimation of both the SOC and all parameters, including the SPMe model parameters. Additionally, we provide a parameter adjustment method for dual extended Kalman filters when estimating multiple parameters.The proposed method reduces the influence of the initial state error on the algorithm and improves its robustness. Experimental results demonstrate that the accuracy of the algorithm is improved by separating the fast and slow parameters and estimating them with different Kalman filters. The proposed solver can be used with Kalman filters and the like in an onboard environment due to its fast calculation speed. Figure 1