Aimed at the relatively lower energy density and complicated coordinating operation between two power sources,a special energy control strategy is required to maximize the fuel saving potential.Then a new type of conf...Aimed at the relatively lower energy density and complicated coordinating operation between two power sources,a special energy control strategy is required to maximize the fuel saving potential.Then a new type of configuration for hydrostatic transmission hybrid vehicles(PHHV) and the selection criterion for important components are proposed.Based on the optimization of planet gear transmission ratio and the analysis of optimal energy distribution for the proposed PHHV on a representative urban driving cycle,a fuzzy torque control strategy and a braking energy regeneration strategy are designed and developed to realize the real-time control of energy for the proposed PHHV.Simulation results demonstrate that the energy control strategy effectively improves the fuel economy of PHHV.展开更多
In this paper, implantation of fuzzy logic controller for parallel hybrid electric vehicles （PHEV） is presented. In PHEV the required torque is generated by a combination of internal-combustion engine （ICE） and an...In this paper, implantation of fuzzy logic controller for parallel hybrid electric vehicles （PHEV） is presented. In PHEV the required torque is generated by a combination of internal-combustion engine （ICE） and an electric motor. The controller simulated using the SIMULINK/MATLAB package. The controller is designed based on the desired speed for driving and the state of speed error. In the other hand, performance of PHEV and ICE under different road cycle is given. The hardware setup is done for electric propulsion system; the system contains the induction motor, the three phase IGBT inverter with control circuit using microcontroller. The closed loop control system used a DC permanent generator whose output voltage is related to motor speed. Comparison between simulation and experimental results show accurate matching.展开更多
A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was...A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller （FLC） was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm （GA） for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy （0.2%） or CYC _ WVUSUB optimized fuzzy （0.7%）. The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions.展开更多
EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the ...EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.50375033)the National Key Laboratory of Vehicular Transmission(Grant No.51457050105HT0112)
文摘Aimed at the relatively lower energy density and complicated coordinating operation between two power sources,a special energy control strategy is required to maximize the fuel saving potential.Then a new type of configuration for hydrostatic transmission hybrid vehicles(PHHV) and the selection criterion for important components are proposed.Based on the optimization of planet gear transmission ratio and the analysis of optimal energy distribution for the proposed PHHV on a representative urban driving cycle,a fuzzy torque control strategy and a braking energy regeneration strategy are designed and developed to realize the real-time control of energy for the proposed PHHV.Simulation results demonstrate that the energy control strategy effectively improves the fuel economy of PHHV.
文摘In this paper, implantation of fuzzy logic controller for parallel hybrid electric vehicles （PHEV） is presented. In PHEV the required torque is generated by a combination of internal-combustion engine （ICE） and an electric motor. The controller simulated using the SIMULINK/MATLAB package. The controller is designed based on the desired speed for driving and the state of speed error. In the other hand, performance of PHEV and ICE under different road cycle is given. The hardware setup is done for electric propulsion system; the system contains the induction motor, the three phase IGBT inverter with control circuit using microcontroller. The closed loop control system used a DC permanent generator whose output voltage is related to motor speed. Comparison between simulation and experimental results show accurate matching.
文摘A genetic-fuzzy HEV control strategy based on driving cycle recognition （DCR） was built. Six driving cycles were selected to represent different traffic conditions e.g. freeway, urban, suburb. A neural algorithm was used for traffic condition recognition based on ten parameters of each driving cycle. The DCR was utilized for optimization of the HEV control parameters using a genetic-fuzzy approach. A fuzzy logic controller （FLC） was designed to be intelligent to manage the engine to work in the vicinity of its optimal condition. The fuzzy membership function parameters were optimized using the genetic algorithm （GA） for each driving cycle. The result is that the DCR_ fuzzy controller can reduce the fuel consumption by 1. 9%, higher than only CYC _ HWFET optimized fuzzy （0.2%） or CYC _ WVUSUB optimized fuzzy （0.7%）. The DCR_ fuzzy method can get the better result than only optimizing one cycle on the complex real traffic conditions.
文摘EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.
