A novel steady-state optimization （SSO） of internal combustion engine （ICE） strategy is proposed to maximize the efficiency of the overall powertrain for hybrid electric vehicles, in which the ICE efficiency, the ...A novel steady-state optimization （SSO） of internal combustion engine （ICE） strategy is proposed to maximize the efficiency of the overall powertrain for hybrid electric vehicles, in which the ICE efficiency, the efficiencies of the electric motor （EM） and the energy storage device are all explicitly taken into account. In addition, a novel idle optimization of ICE strategy is implemented to obtain the optimal idle operating point of the ICE and corresponding optimal parking generation power of the EM using the view of the novel SSO of ICE strategy. Simulations results show that potential fuel economy improvement is achieved relative to the conventional one which only optimized the ICE efficiency by the novel SSO of ICE strategy, and fuel consumption per voltage increment decreases a lot during the parking charge by the novel idle optimization of ICE strategy.展开更多
In both numerical simulation and experimental research for the piston of internal combustion engine, the verification foundations are always insufficient. The reason is the measurements for its transient temperature a...In both numerical simulation and experimental research for the piston of internal combustion engine, the verification foundations are always insufficient. The reason is the measurements for its transient temperature and stress under actual operation conditions are very difficult. A multi-channel measurement-storage technology is used in the engine bench experiment to measure the piston temperature and stress in real time. The temperature and stress changes in the engine operation process are obtained. They provide reliable instructive criteria for numerical analysis and experiment of the piston working state.展开更多
Generally,road transport is a major energy-consuming sector.Fuel con-sumption of each vehicle is an important factor that affects the overall energy con-sumption,driving behavior and vehicle characteristic are the mai...Generally,road transport is a major energy-consuming sector.Fuel con-sumption of each vehicle is an important factor that affects the overall energy con-sumption,driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption.It is difficult to analyze the influence of fuel consumption with multiple and complex factors.The Adaptive Neuro-Fuzzy Inference System(ANFIS)approach was employed to develop a vehicle fuel consumption model based on multivariate input.The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting.The performance of the ANFIS network was validated using Root Mean Square Error(RMSE)and Mean Average Error(MAE)which related to the setting of ANFIS parameters.The experimental results indicated that the training data sam-ple,number,and type of membership functions are the most important factor affecting the performance of the ANFIS network.However,the number of epochs does not necessarily significantly improve the system performance,too many the number of epochs setting may not provide the best results and lead to excessive responding time.The results also demonstrate that three factors,consisted of the engine size,driving speed,and the number of passengers,are important factors that influence the change of vehicle fuel consumption.The selected ANFIS mod-els with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.展开更多
Increasing energy consumption in the transportation sector results in challenging greenhouse gas(GHG)emissions and environmental problems.This paper involved integrated assessments on GHG emissions and emergy of the l...Increasing energy consumption in the transportation sector results in challenging greenhouse gas(GHG)emissions and environmental problems.This paper involved integrated assessments on GHG emissions and emergy of the life cycle for the internal combustion engine(ICE)and electric automobiles in the USA over the entire assumed fifteen-year lifetime.The hotspots of GHG emissions as well as emergy indices for the major processes of automobile life cycle within the defined system boundaries have been investigated.The potential strategies for reducing GHG emissions and emergy in the life cycle of both ICE and electric automobiles were further proposed.Based on the current results,the total GHG emissions from the life cycle of ICE automobiles are 4.48 E+07 kg CO2-e which is320 times higher than that of the electric automobiles.The hotspot area of the GHG emissions from ICE and electric automobiles are operation phase and manufacturing process,respectively.Interesting results were observed that comparable total emergy of the ICE automobiles and electric automobiles have been calculated which were 1.54 E+17 and 2.20 E+17 sej,respectively.Analysis on emergy index evidenced a better environmental sustainability of electric automobiles than ICE automobiles over the life cycle due to its higher ESI.To the authors’knowledge,it is the first time to integrate the analysis of GHG emissions together with emergy in industrial area of automobile engineering.It is expected that the integration of emergy and GHG emissions analysis may provide a comprehensive perspective on eco-industrial sustainability of automobile engineering.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
基金National Hi-tech Research end Development Program of China (863 Program,No.2002AA501700,No.2003AA501012)
文摘A novel steady-state optimization （SSO） of internal combustion engine （ICE） strategy is proposed to maximize the efficiency of the overall powertrain for hybrid electric vehicles, in which the ICE efficiency, the efficiencies of the electric motor （EM） and the energy storage device are all explicitly taken into account. In addition, a novel idle optimization of ICE strategy is implemented to obtain the optimal idle operating point of the ICE and corresponding optimal parking generation power of the EM using the view of the novel SSO of ICE strategy. Simulations results show that potential fuel economy improvement is achieved relative to the conventional one which only optimized the ICE efficiency by the novel SSO of ICE strategy, and fuel consumption per voltage increment decreases a lot during the parking charge by the novel idle optimization of ICE strategy.
文摘In both numerical simulation and experimental research for the piston of internal combustion engine, the verification foundations are always insufficient. The reason is the measurements for its transient temperature and stress under actual operation conditions are very difficult. A multi-channel measurement-storage technology is used in the engine bench experiment to measure the piston temperature and stress in real time. The temperature and stress changes in the engine operation process are obtained. They provide reliable instructive criteria for numerical analysis and experiment of the piston working state.
文摘Generally,road transport is a major energy-consuming sector.Fuel con-sumption of each vehicle is an important factor that affects the overall energy con-sumption,driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption.It is difficult to analyze the influence of fuel consumption with multiple and complex factors.The Adaptive Neuro-Fuzzy Inference System(ANFIS)approach was employed to develop a vehicle fuel consumption model based on multivariate input.The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting.The performance of the ANFIS network was validated using Root Mean Square Error(RMSE)and Mean Average Error(MAE)which related to the setting of ANFIS parameters.The experimental results indicated that the training data sam-ple,number,and type of membership functions are the most important factor affecting the performance of the ANFIS network.However,the number of epochs does not necessarily significantly improve the system performance,too many the number of epochs setting may not provide the best results and lead to excessive responding time.The results also demonstrate that three factors,consisted of the engine size,driving speed,and the number of passengers,are important factors that influence the change of vehicle fuel consumption.The selected ANFIS mod-els with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.
基金financially supported by National Natural Science Foundation for Young Scientists of China(Grant No.51608531)
文摘Increasing energy consumption in the transportation sector results in challenging greenhouse gas(GHG)emissions and environmental problems.This paper involved integrated assessments on GHG emissions and emergy of the life cycle for the internal combustion engine(ICE)and electric automobiles in the USA over the entire assumed fifteen-year lifetime.The hotspots of GHG emissions as well as emergy indices for the major processes of automobile life cycle within the defined system boundaries have been investigated.The potential strategies for reducing GHG emissions and emergy in the life cycle of both ICE and electric automobiles were further proposed.Based on the current results,the total GHG emissions from the life cycle of ICE automobiles are 4.48 E+07 kg CO2-e which is320 times higher than that of the electric automobiles.The hotspot area of the GHG emissions from ICE and electric automobiles are operation phase and manufacturing process,respectively.Interesting results were observed that comparable total emergy of the ICE automobiles and electric automobiles have been calculated which were 1.54 E+17 and 2.20 E+17 sej,respectively.Analysis on emergy index evidenced a better environmental sustainability of electric automobiles than ICE automobiles over the life cycle due to its higher ESI.To the authors’knowledge,it is the first time to integrate the analysis of GHG emissions together with emergy in industrial area of automobile engineering.It is expected that the integration of emergy and GHG emissions analysis may provide a comprehensive perspective on eco-industrial sustainability of automobile engineering.
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.
