This paper presents a cost-effective driving system for automotive applications based on a double rotor electrically excited flux switching machine(FSM).Benefiting from a double rotor topology,this FSM can realize a d...This paper presents a cost-effective driving system for automotive applications based on a double rotor electrically excited flux switching machine(FSM).Benefiting from a double rotor topology,this FSM can realize a drum winding design and thus winding ends are effectively shorten and the copper loss is mitigated.The machine structure,operation principle and design consideration are studied and further verified by time-stepping finite element method.Moreover,three topologies of drive circuit for the proposed FSM are introduced.By using electromagnetic-circuit coupling simulation,a comparison between three different three drive systems are performed,with focus on the system cost and overall electromagnetic performance,especially the effect of current control and torque ripple.A prototype is established and tested.Relevant experimental results verify the effectiveness of the proposed new FSM drive system.展开更多
Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduli...Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduling plan of regional charging load,which can be derived to realize the optimal vehicle to grid benefit.In this paper,a regional-level EV ultra STLF method is proposed and discussed.The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles,and then constructed by our collected EV charging transactiondata in thefield.Secondly,these usagedegrees are combinedwithhistorical charging loadvalues toform the inputmatrix for the deep learning based load predictionmodel.Finally,long short-termmemory(LSTM)neural network is used to construct EV charging load forecastingmodel,which is trained by the formed inputmatrix.The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditionalmethods.In addition,load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us,and these fluctuation factors are used to assess the prediction accuracy of the EV charging load,together with the mean absolute percentage error(MAPE).展开更多
声学黑洞(Acoustic Black Holes,ABH)效应是利用结构厚度以一定幂函数形式减小,致使弯曲波的相速度逐渐减小而实现能量逐渐聚集,理想情况下弯曲波波速减小为0从而无法传递到结构边缘,也就不会发生反射。声学黑洞效应使得结构产生高能量...声学黑洞(Acoustic Black Holes,ABH)效应是利用结构厚度以一定幂函数形式减小,致使弯曲波的相速度逐渐减小而实现能量逐渐聚集,理想情况下弯曲波波速减小为0从而无法传递到结构边缘,也就不会发生反射。声学黑洞效应使得结构产生高能量密度区域,因此能高效应用于能量回收和振动噪声控制。为了研究二维声学黑洞结构具有弯曲波能量聚集效应,运用有限元分析软件ABAQUS建立了二维声学黑洞模型,从时域上研究弯曲波在声学黑洞区域的传播过程,结合有限元数值结果与振动功率流的结果分析弯曲波能量聚集过程。最后通过激光超声实验系统对二维声学黑洞中弯曲波传播过程进行成像与分析,实验结果验证了二维声学黑洞结构对弯曲波能量的聚集效应。展开更多
基金This work was supported by the Research Grant Council of the Hong Kong Government under Project PolyU 152509/16E,1ZE5P,and in part by the National Natural Science Foundation of China under Grant 51707171.
文摘This paper presents a cost-effective driving system for automotive applications based on a double rotor electrically excited flux switching machine(FSM).Benefiting from a double rotor topology,this FSM can realize a drum winding design and thus winding ends are effectively shorten and the copper loss is mitigated.The machine structure,operation principle and design consideration are studied and further verified by time-stepping finite element method.Moreover,three topologies of drive circuit for the proposed FSM are introduced.By using electromagnetic-circuit coupling simulation,a comparison between three different three drive systems are performed,with focus on the system cost and overall electromagnetic performance,especially the effect of current control and torque ripple.A prototype is established and tested.Relevant experimental results verify the effectiveness of the proposed new FSM drive system.
基金supported by National Key R&D Program of China(No.2021YFB2601602).
文摘Electric vehicle(EV)charging load is greatly affected by many traffic factors,such as road congestion.Accurate ultra short-term load forecasting(STLF)results for regional EV charging load are important to the scheduling plan of regional charging load,which can be derived to realize the optimal vehicle to grid benefit.In this paper,a regional-level EV ultra STLF method is proposed and discussed.The usage degree of all charging piles is firstly defined by us based on the usage frequency of charging piles,and then constructed by our collected EV charging transactiondata in thefield.Secondly,these usagedegrees are combinedwithhistorical charging loadvalues toform the inputmatrix for the deep learning based load predictionmodel.Finally,long short-termmemory(LSTM)neural network is used to construct EV charging load forecastingmodel,which is trained by the formed inputmatrix.The comparison experiment proves that the proposed method in this paper has higher prediction accuracy compared with traditionalmethods.In addition,load characteristic index for the fluctuation of adjacent day load and adjacent week load are proposed by us,and these fluctuation factors are used to assess the prediction accuracy of the EV charging load,together with the mean absolute percentage error(MAPE).
文摘声学黑洞(Acoustic Black Holes,ABH)效应是利用结构厚度以一定幂函数形式减小,致使弯曲波的相速度逐渐减小而实现能量逐渐聚集,理想情况下弯曲波波速减小为0从而无法传递到结构边缘,也就不会发生反射。声学黑洞效应使得结构产生高能量密度区域,因此能高效应用于能量回收和振动噪声控制。为了研究二维声学黑洞结构具有弯曲波能量聚集效应,运用有限元分析软件ABAQUS建立了二维声学黑洞模型,从时域上研究弯曲波在声学黑洞区域的传播过程,结合有限元数值结果与振动功率流的结果分析弯曲波能量聚集过程。最后通过激光超声实验系统对二维声学黑洞中弯曲波传播过程进行成像与分析,实验结果验证了二维声学黑洞结构对弯曲波能量的聚集效应。
