Fuzzy energy management strategy for parallel HEV based on pigeon-inspired optimization algorithm

Improvements in fuel consumption and emissions of hybrid electric vehicle (HEV) heavily depend upon an efficient energy management strategy (EMS). This paper presents an optimizing fuzzy control strategy of parallel hybrid electric vehicle employing a quantum chaotic pigeon-inspired optimization (QCPIO) algorithm. In this approach, the torque of the engine and the motor is assigned by a fuzzy torque distribution controller which is based on the battery state of charge (SoC) and the required torque of the hybrid powertrain. The rules and membership functions of the fuzzy torque distribution controller are optimized simultaneously through the use of QCPIO algorithm. The simulation ground on ADVISOR demonstrates that this EMS improves fuel economy more effectually than original fuzzy and PSO_Fuzzy EMS.     

Velocity forecasts using a combined deep learning model in hybrid electric vehicles with V2V and V2I communication

Vehicle velocity forecast is an important clue in improving the performance of energy management in hybrid electric vehicles(HEV). This paper presents a new combined model for predicting vehicle's velocity time series. The main features of the model are to combine the feature extraction capability of deep restricted Boltzmann machines(DBM) and sequence pattern predicting capability of bidirectional long short-term memory(BLSTM). Hence, the model is named as DBMBLSTM. In addition, the DRMBLSTM model utilizes the vehicle driving information and roadside infrastructure information provided respectively through vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communication channels to predict vehicle velocity at various length of prediction horizon. Furthermore, the predictions results of this study are compared with the state of the art of vehicle velocity forecasts. The root mean square error(RMSE) is used as an evaluation criteria of predictions accuracy. Finally,these compared prediction model are applied in model predictive control(MPC) energy management strategy for the verifications of fuel economy improvement of a HEV. Simulation results confirm that the proposed combined deep learning model performs better than other five prediction methods. Therefore, it is a means of arriving at a reliable forecast model for HEV.     

薄壁管轴向冲击的数值模拟与吸能优化

从抗撞性角度观察车辆设计,薄壁构件具有良好的吸能性能,为了深入探讨车辆吸能元件抗撞的性能,应用LS-DYNA对不同正多边形截面薄壁管进行计算机数值模拟,旨在获得工程抗撞设计中对某些抗撞部位具有借鉴性的吸能元件。采用斐波那契(Fibonacci)搜索对正多边形截面薄壁管吸能最大对应的最佳边数的搜索求解,得在正多边形截面薄壁管冲击压缩相同长度时,正十一边形截面薄壁管的吸能最大。