Enhancing battery electrochemical-thermal model accuracy through a hybrid parameter estimation framework

Electrochemical-thermal models offer profound insight into the internal state of batteries, demonstrating significant potential in energy storage. However, model complexity makes accurate parameter acquisition challenging. This study proposes a novel hybrid parameter estimation framework for effective parameterisation of battery electrochemical-thermal models. A high-fidelity reduced-order electrochemical-thermal model was established to accelerate the simulation of the entire time-domain process. A global sensitivity analysis of the model parameters was conducted, comprehensively evaluating their impact on simulated voltage and temperature. Highly sensitive parameters were categorised into static and time-dependent dynamic estimation groups for hierarchical estimation. A deep learning method was applied to generate preliminary parameter estimates to accelerate the convergence of estimation process. Subsequently, a genetic algorithm was employed to optimise the preliminary estimates of static parameters. A full temporal domain estimation was conducted using Markov Chain Monte Carlo methods for time-dependent dynamic parameters. The experimental data covers six dynamic and nine constant current conditions to validate the proposed method's feasibility. After parameter estimation, the proposed method reduces the comprehensive normalised root mean square error of voltage and temperature by up to 80.36% compared to four conventional methods. The minimum average absolute errors of voltage and temperature under constant and dynamic current conditions are 10.63 mV and 0.10 °C, respectively. The proposed method provides new insights for the parameterisation of battery models.