The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term ...The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).展开更多
This paper proposes a two-stage hierarchy control system with model predictive control(MPC)for connected parallel HEVs with available traffic information.In the first stage,a coordination of on-ramp merging problem us...This paper proposes a two-stage hierarchy control system with model predictive control(MPC)for connected parallel HEVs with available traffic information.In the first stage,a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging.After formulating the merging problem into a nonlinear optimization problem,a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road,respectively.The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage.In the second stage,an energy management strategy(EMS)for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency.The formulated nonlinear optimization problem is solved by sequential quadratic programming(SQP)method under the same receding horizon.The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.展开更多
This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus.Large scale vehicles in a traffic flow is targeted instead of individual vehicles,and it is...This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus.Large scale vehicles in a traffic flow is targeted instead of individual vehicles,and it is assumed that the propulsion power of vehicles is hybrid electric powertrain.The control scheme is designed in the following two stages.In the first stage,in order to achieve speed consensus,the acceleration control law is designed by applying the MFG(mean-field game)theory.In the second stage,optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint.The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.展开更多
This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD a...This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4 WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control(MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge(SOC) sustainability is formulated to optimize the equivalent factors(EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol(UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method.展开更多
This paper proposes a powertrain controller for a solar photovoltaic battery powered hybrid electric vehicle (HEV). The main objective of the proposed controller is to ensure better battery management, load regulation...This paper proposes a powertrain controller for a solar photovoltaic battery powered hybrid electric vehicle (HEV). The main objective of the proposed controller is to ensure better battery management, load regulation, and maximum power extraction whenever possible from the photovoltaic panels. The powertrain controller consists of two levels of controllers named lower level controllers and a high-level control algorithm. The lower level controllers are designed to perform individual tasks such as maximum power point tracking, battery charging, and load regulation. The perturb and observe based maximum power point tracking algorithm is used for extracting maximum power from solar photovoltaic panels while the battery charging controller is designed using a PI controller. A high-level control algorithm is then designed to switch between the lower level controllers based on different operating conditions such as high state of charge, low state of charge, maximum battery current, and heavy load by respecting the constraints formulated. The developed algorithm is evaluated using theoretical simula-tion and experimental studies. The simulation and experimental results are presented to validate the proposed technique.展开更多
文摘The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).
文摘This paper proposes a two-stage hierarchy control system with model predictive control(MPC)for connected parallel HEVs with available traffic information.In the first stage,a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging.After formulating the merging problem into a nonlinear optimization problem,a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road,respectively.The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage.In the second stage,an energy management strategy(EMS)for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency.The formulated nonlinear optimization problem is solved by sequential quadratic programming(SQP)method under the same receding horizon.The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.
文摘This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus.Large scale vehicles in a traffic flow is targeted instead of individual vehicles,and it is assumed that the propulsion power of vehicles is hybrid electric powertrain.The control scheme is designed in the following two stages.In the first stage,in order to achieve speed consensus,the acceleration control law is designed by applying the MFG(mean-field game)theory.In the second stage,optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint.The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.
基金supported by the National Hi-Tech Research and Development Program of China(Grant No.2015BAG17B04)China Scholarship Council(Grant No.201506690009)U.S.GATE Program
文摘This paper presents an optimized equivalent consumption minimization strategy(ECMS) for four-wheel-drive(4 WD) hybrid electric vehicles(HEVs) incorporating vehicle connectivity. In order to be applicable to the 4 WD architecture, the ECMS is designed based on a rule-based strategy and used under the condition that a certain propulsion mode is activated. Assuming that a group of 4 WD HEVs are connected and position information can be shared with each other, we formulate a decentralized model predictive control(MPC) framework that compromises fuel efficiency, mobility, and inter-vehicle distance to optimize the velocity profile of each individual vehicle. Based on the optimized velocity profile, an optimization problem considering both fuel economy and battery state of charge(SOC) sustainability is formulated to optimize the equivalent factors(EFs) of the ECMS for HEVs over an appropriate time window. MATLAB User Datagram Protocol(UDP) is used in the codes run on multiple computers to simulate the wireless communication among vehicles, which share position information via UDP-based communication, and dSPACE is used as a software-in-the-loop platform for the simulation of the optimized ECMS. Simulation results validate the control effectiveness of the proposed method.
文摘This paper proposes a powertrain controller for a solar photovoltaic battery powered hybrid electric vehicle (HEV). The main objective of the proposed controller is to ensure better battery management, load regulation, and maximum power extraction whenever possible from the photovoltaic panels. The powertrain controller consists of two levels of controllers named lower level controllers and a high-level control algorithm. The lower level controllers are designed to perform individual tasks such as maximum power point tracking, battery charging, and load regulation. The perturb and observe based maximum power point tracking algorithm is used for extracting maximum power from solar photovoltaic panels while the battery charging controller is designed using a PI controller. A high-level control algorithm is then designed to switch between the lower level controllers based on different operating conditions such as high state of charge, low state of charge, maximum battery current, and heavy load by respecting the constraints formulated. The developed algorithm is evaluated using theoretical simula-tion and experimental studies. The simulation and experimental results are presented to validate the proposed technique.
