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.     

银系异质结光催化剂的制备及其性能分析

以硝酸银和氨水、磷酸氢二钠、非离子型表面活性剂PVP、溴化钠等物质为原料通过化学沉淀法合成了Ag3PO4@AgBr@Ag异质结光催化剂。采用XRD、SEM、UV-Vis等仪器分析表征,发现样品成玉米棒状,以亚甲基蓝(MB)为目标降解物研究单体Ag3PO4、复合物Ag3PO4@AgBr和异质光催化剂的光催化活性,研究表明多元复合物Ag3PO4@AgBr@Ag光催化活性最高,降解率达99.74%,揭示了银系异质光催化剂活性增强机理,并检验样品催化性能的稳定性和可回收利用率,实验数据显示反复十次降解MB降解率仍高达74.87%左右,同时回收率为89.40%。