This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimi...This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.展开更多
In this paper,we propose a hybrid power model that includes the power consumption of not only the registers but also part of the combinational logic.By doing knownkey analysis with this hybrid model,power side-channel...In this paper,we propose a hybrid power model that includes the power consumption of not only the registers but also part of the combinational logic.By doing knownkey analysis with this hybrid model,power side-channel leakage caused by correct keys can be detected.In experiment,PRINTcipher and DES algorithms were chosen as analysis targets and combinational logic s-box unit was selected to build power template.The analysis results showed the signal-to-noise ratio(SNR) power consumption increase of more than 20%after considering s-box's power consumption so that the information of keys can be obtained with just half number of power traces.In addition,the side channel-leakage detection capability of our method also shows better effectiveness that can identify the correct keys.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Project(KF2029)supported by the State Key Laboratory of Automotive Safety and Energy(Tsinghua University),ChinaProject(102253)supported partially by the Innovate UK。
文摘This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.
基金supported by Major State Basic Research Development Program(No. 2013CB338004)National Natural Science Foundation of China(No.61402286, 61472250,61472249,61202372)+1 种基金National Science and Technology Major Project of the Ministry of Science and Technology of China (No.2014ZX01032401-001)Plan of Action for the Innovation of Science and Technology of Shanghai Municipal Science and Technology Commission(No.14511100300)
文摘In this paper,we propose a hybrid power model that includes the power consumption of not only the registers but also part of the combinational logic.By doing knownkey analysis with this hybrid model,power side-channel leakage caused by correct keys can be detected.In experiment,PRINTcipher and DES algorithms were chosen as analysis targets and combinational logic s-box unit was selected to build power template.The analysis results showed the signal-to-noise ratio(SNR) power consumption increase of more than 20%after considering s-box's power consumption so that the information of keys can be obtained with just half number of power traces.In addition,the side channel-leakage detection capability of our method also shows better effectiveness that can identify the correct keys.
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.