Joint estimation of state of charge and state of health of lithium-ion battery based on fractional order model

This paper proposes a joint estimation scheme for the state of charge (SoC) and state of health (SoH) for lithium-ion batteries in electric vehicles. The estimation accuracy is improved from four aspects. First, to overcome the shortcomings of the electrochemical model and equivalent circuit model, the battery model is established by a fractional order (FO) model. Second, a genetic algorithm is used to identify the model parameters, realizing optimal parameter identification. Third, the FO adaptive extended Kalman filter-based SoC estimator is developed, and the innovation accuracy of the algorithm is improved by multi- innovation theory. Fourth, the joint estimation of SoC and SoH is formulated through a multi-timescale framework. The proposed model and method are verified through dynamic operating condition experiments, and the main results are as follows. (1) In the entire SoC range, the accuracy of the FO model is better than that of the integer order (IO) model. (2) The effectiveness of the optimized SoC estimation method is verified, and the estimation error can be controlled within 3%. (3) The effectiveness of the proposed joint estimation method in dynamic conditions is verified, and it shows high accuracy.