The lithium-ion batteries in the electric vehicles are nonlinear systems with complex electrochemical dynamics, and estimation of battery state-of-charge (SOC) is affected by factors such as environmental temperature and battery current. Considering the above problems, the accurate estimation of battery SOC has always been a difficult and the critical issue of battery management system (BMS). In this paper, the constant phase element (CPE) is introduced to the traditional time domain circuit model by analyzing the electrochemical impedance spectra of lithium-ion batteries. Accordingly, an equivalent circuit model based on electrochemical impedance is constructed by using fractional order theory, which has specific physical significant, leading to the improved estimation accuracy to represent battery voltage. Moreover, the polarization resistance in the model is replaced by Butler-Volmer (BV) equation, which can solve the problem caused by large current and temperature variation during the actual operation of electric vehicles. Next, based on the model, an H∞ observer is designed for battery SOC estimation, and the proposed SOC observer is tested by real-time experimental data of battery. The efficiency of the proposed model and observer are validated by some simulations and experiment tests.

In the roller kiln of cathode materials for lithium batteries, the timely measurement of temperature is very important for effective process control. However, it is sometimes difficult or costly to measure the temperature timely. To handle this problem, a kind of soft sensor modeling framework with double locally weighted kernel principal component regression based on approximate linearity dependence (ALD) is proposed, which simultaneously carries out sample and variable weighted learning in the high-dimensional and nonlinear space to solve the process time-varying and strong nonlinearity problems. Moreover, a kind of just-in-time learning framework based on ALD is adopted for selectively updating the online local models. By setting a reasonable threshold of ALD, the prediction time can be effectively reduced, and the prediction accuracy can be maintained. The effectiveness of the proposed method is demonstrated on an industrial roller kiln. The results show that the proposed method can meet the requirements of prediction accuracy and time efficiency of the roller kiln of cathode materials for lithium batteries.