A novel combined estimation method of online full‐parameter identification and adaptive unscented particle filter for Li ‐ion batteries SOC based on fractional‐order modeling

Abstract

Accurate estimation of the state of charge (SOC) of Li-ion battery can ensure the reliability of the storage system. A combined estimator of online fullparameter identification and adaptive unscented particle filter for Li-ion battery SOC based on an improved fractional-order model is proposed, which overcomes the shortcomings of the traditional SOC cumulative error and the difficulty of OCV acquisition. The proposed adaptive fractional unscented particle filter algorithm introduces fractional parameters as hidden parameters and reduces the complexity of the algorithm iteration by reducing the number of particles. At the same time, the noise adaptive algorithm based on the residual sequence can solve the divergence problem of the filter and improve the adaptability of the algorithm. To verify the feasibility of the algorithm under complex operating conditions, the urban dynamometer driving schedule dynamic working conditions of Li-ion batteries are verified. The experimental results show that the evaluation index of the algorithm is the best, the RMSE is 0.67%, and the SOC estimation is more accurate. It shows that the algorithm has strong robustness and fast convergence. K E Y W O R D Sadaptive fractional-order unscented particle filter, full-parameter identification, improve fractional-order equivalent circuit model, Li-ion battery, state of charge

Towards interactional management for power batteries of electric vehicles

“…141 The error can be less than 5% considering the extreme temperatures and dynamic conditions. 142 Considering OCV aging, 143 hysteresis, 98 noise adaptive, 144 etc. will be more effective in improving accuracy under 1%.…”

A novel combined estimation method for state of energy and predicted maximum available energy based on fractional-order modeling

“…Our previous work [35] has shown that the online identification algorithm is more suitable for model parameter identification under dynamic unknown conditions. Due to the nonlinear relationship between the order of the model and the output variables, the fixed value of the fractional order is obtained by the offline method, and the rest parameters are obtained by the online parameter identification method.…”

A Review of Recent Advances in Fractional-Order Sensing and Filtering Techniques

“…The results show that show that the SoC estimation accuracy can be significantly improved using the proposed method, with an estimation error in the range of 3% [131]. An adaptive unscented particle filter for lithium-ion battery SoC based on an improved fractional-order model is also proposed in [132]. The algorithm uses the fractional orders as hidden parameters, which reduces the number of particles and hence the complexity of the algorithm iteration.…”