Joint state estimation of lithium-ion batteries combining improved equivalent circuit model with electrochemical mechanism and diffusion process

Accurate state estimation plays a key role for guaranteeing the safety and reliability of lithium-ion batteries. This paper develops a novel joint state estimation method for lithium-ion batteries based on a hybrid model combining improved equivalent circuit model (IECM) with electrochemical mechanism and diffusion process, it mainly includes state-of-charge, state-of-health, state-of-power and state-of-energy. Firstly, an IECM by combining the internal electrochemical mechanism and traditional equivalent circuit model is established to simulate the battery dynamic characteristics. The model parameters are offline identified by an electrochemical mechanism decoupling approach and online updated with the battery aging. Next, a hybrid model is presented by combining the diffusion process-based empirical aging model and the IECM, a co-estimation algorithm for state-of-charge and state-of-health by applying the dual extended Kalman filter is proposed. Then, the peak current is online calculated to evaluate the state-of-power under the model limitation, and the future working condition is predicted to evaluate the state-of-energy. Finally, several case studies are implemented to verify the effectiveness of developed method, the results indicate that the proposed state joint estimation method has higher accuracy and stronger robustness, and the root mean square error does not exceed 1 % with the battery aging.

Automated summary

This paper presents a novel joint state estimation method for lithium-ion batteries based on a hybrid model. The method accurately estimates the state of charge, state of health, state of power, and state of energy, and it has been verified to have high accuracy and strong robustness through experiments.