• Energy Research

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time.

A CCHP is modeled here, which is operated based on thermal energy provision because the target consumer demands more heat and cooling than electricity. The main components of the CCHP system are a PEMFC as the main generator, absorption chiller, electric chiller, and auxiliary boiler. The fuel consumption, cost, and carbon release improvements compared to a separated generation system (SGS) are maximized in this system using the proposed modified mayfly algorithm. The system design and operation are optimized in two different stages. A hotel building has been chosen as the case study to demonstrate the effectiveness of the proposed CCHP system and optimization in comparison to the traditional mayfly algorithm (MA) and genetic algorithm (GA). The proposed optimization algorithm has performed 8.15% and 10.06% better compared to MA and GA, respectively. It also reached its solution in far shorter time. The optimum size of PEMDC and share of auxiliary chiller (AC) in cooling provision is determined to be 548 kW and 43.1%, respectively. It is shown that improvement achieved by using the AC in the CCHP is increased by 23.21% compared to a CCHP without one. Furthermore, the AC make the improvement of CCHP more constant and robust to load variation. Moreover, the sensitivity of the optimum operation condition of the CCHP against the electricity and fuel price changes and share of the AC in supplying the cooling demand are studied to provide the system operators with the necessary tools needed to optimally handle price changes.