No abstract

To accurately estimate the state of charge (SOC) of lithium-ion power batteries in the event of errors in the battery model or unknown external noise, an SOC estimation method based on the H-infinity filter (HIF) algorithm is proposed in this paper. Firstly, a fractional-order battery model based on a dual polarization equivalent circuit model is established. Then, the parameters of the fractional-order battery model are identified by the hybrid particle swarm optimization (HPSO) algorithm, based on a genetic crossover factor. Finally, the accuracy of the SOC estimation results of the lithium-ion batteries, using the HIF algorithm and extended Kalman filter (EKF) algorithm, are verified and compared under three conditions: uncertain measurement accuracy, uncertain SOC initial value, and uncertain application conditions. The simulation results show that the SOC estimation method based on HIF can ensure that the SOC estimation error value fluctuates within ±0.02 in any case, and is slightly affected by environmental and other factors. It provides a way to improve the accuracy of SOC estimation in a battery management system.

Hydropneumatic springs are the elastic components of a vehicle’s suspension. As the nonlinear characteristic of the spring is difficult to express accurately, the statistical linearization method is introduced to analyze the dynamic response of the hydropneumatic spring. The nonlinear stiffness of a hydropneumatic spring is approximated by a quadratic polynomial at the static equilibrium position. Parameters of the hydropneumatic spring, road roughness and vehicle velocity are provided and analytical functions for equivalent stiffness and the dynamic equilibrium position are worked out in this paper. The analytical functions are validated through numerical simulation and are shown to be more accurate than those validated by existing methods. The method proposed here could be used in the design and analysis of hydropneumatic suspensions in future.