Continued increases in the emission of greenhouse gases by passenger ve<span style="font-family:Verdana;">hicles ha</span><span style="font-family:Verdana;">ve</span><spa...Continued increases in the emission of greenhouse gases by passenger ve<span style="font-family:Verdana;">hicles ha</span><span style="font-family:Verdana;">ve</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require </span><i><span style="font-family:Verdana;">a</span></i> <i><span style="font-family:Verdana;">priori</span></i><span style="font-family:Verdana;"> knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strate</span><span style="font-family:Verdana;">gy used is the Equivalent Consumption Minimization Strategy (ECMS),</span><span style="font-family:Verdana;"> which uses an equivalence factor to define the control strategy and the power train </span><span style="font-family:Verdana;">component torque split. An equivalence factor that is optimal for a single</span><span style="font-family:Verdana;"> drive cycle can be found offline</span></span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">with </span><i><span style="font-family:Verdana;">a</span></i> <i><span style="font-family:Verdana;">priori</span></i><span style="font-family:Verdana;"> knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require </span><i><span style="font-family:Verdana;">a</span></i> <i><span style="font-family:Verdana;">priori</span></i><span style="font-family:Verdana;"> drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.</span></span>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
Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles.Since fuel cells cannot recover energy and have a slow dynamic response,they need to b...Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles.Since fuel cells cannot recover energy and have a slow dynamic response,they need to be used with different power sources.Developing efficient energy management strategies to achieve excellent fuel economy is the goal of research.This paper proposes an adaptive equivalent fuel minimum consumption strategy(AECMS)to solve the problem of the poor economy of the whole vehicle caused by the wrong selection of equivalent factors(EF)in traditional ECMS.In this method,the kinematics interval is used to update the equivalent factor by considering the penalty term of energy recovery on SOC changes.Finally,the optimized equivalent factor is substituted into the optimization objective function to achieve efficient energy regulation.Simulation results under the New European Driving Cycle show that compared with the traditional ECMS based on fixed SOC benchmarks,the proposed method improves fuel economy by 1.7%while ensuring vehicle power and increases SOC by 30%.展开更多
The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks c...The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).展开更多
The concept of hybrid ships has gained significant attention in recent years,as they offer an effective means of enhancing energy utilization and reducing environmental pollution.However,the navigational environments ...The concept of hybrid ships has gained significant attention in recent years,as they offer an effective means of enhancing energy utilization and reducing environmental pollution.However,the navigational environments of ships are often subject to changes,which in turn affect their energy efficiency in a complex manner.It is therefore evident that enhancing the energy efficiency of hybrid ships is a worthwhile goal.In this study,we take a diesel-electric hybrid ship navigating in inland waterways as the research object,and propose a hierarchical optimization method for ship energy efficiency.The upper-layer control establishes a predictive model for propulsion motor speed and fuel consumption through multivariate time series predictions,and employs the model predictive control(MPC)method to optimize the propulsion motor speed.The lower-layer control utilizes an equivalent fuel consumption minimization method,which is based on improving the equivalence factor.This involves combining the variation of the supercapacitor’s state of charge(SOC)with the propulsion motor speed obtained from the MPC optimization in the upper-layer control.Furthermore,a proportional integral(PI)controller is used to adjust the equivalence factor,in order to adapt the equivalent fuel consumption minimization method to the working conditions.Our results demonstrate that the proposed hierarchical optimization method can reduce the energy efficiency operating indicator(EEOI)by approximately 11.54%and the fuel consumption by approximately 9.47%in comparison to the pre-optimization scenario.展开更多
In order to improve the poor discharge problem that may be caused by the unreasonable power distribution relationship of the battery pack in hybrid vehicles due to the improvement of fuel economy,this paper carries ou...In order to improve the poor discharge problem that may be caused by the unreasonable power distribution relationship of the battery pack in hybrid vehicles due to the improvement of fuel economy,this paper carries out the research of energy management strategy based on multi-objective optimization for a parallel plug-in hybrid vehicle.The optimization objectives are the optimal fuel economy and the minimum temperature rise of the battery.A vehicle power system model is established to provide a simulation platform for the subsequent verification of the control strategy.A short-term operating condition prediction model is constructed based on the Markov process,ensuring the energy management strategy meets the power balance demand in the local time domain in the future.A multi-objective optimization algorithm is used to enhance the improvement of the traditional equivalent fuel consumption minimization strategy by means of the prediction of the operating conditions,and a real-time search for the optimization is employed by selecting the equivalent factor as the control variable.By selecting the equivalent factor as the control variable for real-time optimization,the optimal time-varying equivalent factor sequence based on multi-objective optimization is obtained,which improves the power distribution between the engine and the motor drive.The results show that the improved control strategy can well trade-off the engine fuel economy and battery temperature rise index,and has excellent battery SOC maintenance capability.While confirming the effectiveness of the strategy,it is verified that it has strong robustness and multi-case generalization capability.展开更多
文摘Continued increases in the emission of greenhouse gases by passenger ve<span style="font-family:Verdana;">hicles ha</span><span style="font-family:Verdana;">ve</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require </span><i><span style="font-family:Verdana;">a</span></i> <i><span style="font-family:Verdana;">priori</span></i><span style="font-family:Verdana;"> knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. The goal of this work is to use a previously defined strategy that has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strate</span><span style="font-family:Verdana;">gy used is the Equivalent Consumption Minimization Strategy (ECMS),</span><span style="font-family:Verdana;"> which uses an equivalence factor to define the control strategy and the power train </span><span style="font-family:Verdana;">component torque split. An equivalence factor that is optimal for a single</span><span style="font-family:Verdana;"> drive cycle can be found offline</span></span><span style="font-family:;" "=""> </span><span style="font-family:;" "=""><span style="font-family:Verdana;">with </span><i><span style="font-family:Verdana;">a</span></i> <i><span style="font-family:Verdana;">priori</span></i><span style="font-family:Verdana;"> knowledge of the drive cycle. The RBF-ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data (drive cycles) are used to train the RBF-ANN. For the majority of drive cycles examined, the RBF-ANN implementation is shown to produce fuel economy values that are within ±2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF-ANN is that it does not require </span><i><span style="font-family:Verdana;">a</span></i> <i><span style="font-family:Verdana;">priori</span></i><span style="font-family:Verdana;"> drive cycle knowledge and is able to be implemented in real-time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF-ANN could be improved to produce better results across a greater array of driving conditions.</span></span>
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.
基金This work was supported by National Key R&D Program of China(Grant No.2020YFB0106603)the Key Research and Development Program of Shandong Province(Grant No.2020CXGC010406)the Key Research and Development Program of Shandong Province(Grant No.2019JZZY010912).
文摘Proton exchange membrane fuel cells are widely regarded as having the potential to replace internal combustion engines in vehicles.Since fuel cells cannot recover energy and have a slow dynamic response,they need to be used with different power sources.Developing efficient energy management strategies to achieve excellent fuel economy is the goal of research.This paper proposes an adaptive equivalent fuel minimum consumption strategy(AECMS)to solve the problem of the poor economy of the whole vehicle caused by the wrong selection of equivalent factors(EF)in traditional ECMS.In this method,the kinematics interval is used to update the equivalent factor by considering the penalty term of energy recovery on SOC changes.Finally,the optimized equivalent factor is substituted into the optimization objective function to achieve efficient energy regulation.Simulation results under the New European Driving Cycle show that compared with the traditional ECMS based on fixed SOC benchmarks,the proposed method improves fuel economy by 1.7%while ensuring vehicle power and increases SOC by 30%.
文摘The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).
基金supported by the National Natural Science Foundation of China(No.52571367)and the Commissions Project of China(No.CBG4N21).
文摘The concept of hybrid ships has gained significant attention in recent years,as they offer an effective means of enhancing energy utilization and reducing environmental pollution.However,the navigational environments of ships are often subject to changes,which in turn affect their energy efficiency in a complex manner.It is therefore evident that enhancing the energy efficiency of hybrid ships is a worthwhile goal.In this study,we take a diesel-electric hybrid ship navigating in inland waterways as the research object,and propose a hierarchical optimization method for ship energy efficiency.The upper-layer control establishes a predictive model for propulsion motor speed and fuel consumption through multivariate time series predictions,and employs the model predictive control(MPC)method to optimize the propulsion motor speed.The lower-layer control utilizes an equivalent fuel consumption minimization method,which is based on improving the equivalence factor.This involves combining the variation of the supercapacitor’s state of charge(SOC)with the propulsion motor speed obtained from the MPC optimization in the upper-layer control.Furthermore,a proportional integral(PI)controller is used to adjust the equivalence factor,in order to adapt the equivalent fuel consumption minimization method to the working conditions.Our results demonstrate that the proposed hierarchical optimization method can reduce the energy efficiency operating indicator(EEOI)by approximately 11.54%and the fuel consumption by approximately 9.47%in comparison to the pre-optimization scenario.
基金supported by National Natural Science Foundation of China(52275051)the Natural Science Ranking Projects of Chongqing Jiaotong University(Grant No.XJ2023000701)+2 种基金Team Building Project for Graduate Tutors in Chongqing(Grant No.JDDSTD2022007)Joint Training Base Construction Project for Graduate Students in Chongqing(Grant No.JDLHPYJD2022001)the Natural Science Foundation of Chongqing,China(Grant No.CSTB2022NSCQ-LZX0068).
文摘In order to improve the poor discharge problem that may be caused by the unreasonable power distribution relationship of the battery pack in hybrid vehicles due to the improvement of fuel economy,this paper carries out the research of energy management strategy based on multi-objective optimization for a parallel plug-in hybrid vehicle.The optimization objectives are the optimal fuel economy and the minimum temperature rise of the battery.A vehicle power system model is established to provide a simulation platform for the subsequent verification of the control strategy.A short-term operating condition prediction model is constructed based on the Markov process,ensuring the energy management strategy meets the power balance demand in the local time domain in the future.A multi-objective optimization algorithm is used to enhance the improvement of the traditional equivalent fuel consumption minimization strategy by means of the prediction of the operating conditions,and a real-time search for the optimization is employed by selecting the equivalent factor as the control variable.By selecting the equivalent factor as the control variable for real-time optimization,the optimal time-varying equivalent factor sequence based on multi-objective optimization is obtained,which improves the power distribution between the engine and the motor drive.The results show that the improved control strategy can well trade-off the engine fuel economy and battery temperature rise index,and has excellent battery SOC maintenance capability.While confirming the effectiveness of the strategy,it is verified that it has strong robustness and multi-case generalization capability.
