Current Issue : April-June Volume : 2022 Issue Number : 2 Articles : 5 Articles
With the increasing demand of driving range of new energy vehicle (NEV), design optimization for energy efficiency of traction motors became more important. However, traction motor design is complex since multiple objectives should be satisfied, such as the required torque-speed operating range and package and thermal constraints. This dramatically increases the computation time of the design optimization process, while the additional energy efficiency objective of the whole driving cycle. This paper proposes an equivalent driving cycle points extraction method, based on energy consumption equivalence to facilitate the design optimization of traction motors. This paper presents necessary rules of multiobjective optimization methods, and then gives an optimization process and proves the effectiveness....
Due to the complex and diverse forms of automobile emission detection faults and various interference factors, it is difficult to determine the fault types effectively and accurately use the traditional diagnosis model. In this paper, a multicondition auto fault diagnosis method based on a vehicle chassis dynamometer is proposed. 3σ method and data normalization were used to pretreat tail gas data. BPNN-RNN (Back Propagation Neural Networks-Recurrent Neural Networks) variable speed integral PID control method was used to achieve high-precision vehicle chassis dynamometer control. Accurate tail gas data were obtained. 'e simulation and test results of BPNN-RNN variable speed integral PID control were verified and analyzed. 'e PID control method can quickly adjust PID parameters (within 10 control cycles), control overshoot within 2% of the target value, eliminate the static error, and improve the control performance of the vehicle chassis dynamometer. Combined with BPNN (Back Propagation Neural Network) and SOM (Self-organizing Maps) network, a BPNN-SOM fault diagnosis model is proposed in this paper. By comparing and analyzing the fault diagnosis performance of various neural networks and SOMBPNN algorithm, it is found that the SOM-BPNN model has the best comprehensive result, the prediction accuracy is 98.75%, the time is 0.45 seconds, and it has good real-time stability. 'e proposed model can effectively diagnose the vehicle fault, provide a certain direction for maintenance personnel to judge the vehicle state, and provide certain help to alleviate traffic pollution problem....
A great many EVs in cold areas suffer from range attenuation in winter, which causes driver anxiety concerning the driving range, representing a hot topic. Many researchers have analyzed the reasons for range attenuation but the coupling mechanism of the battery as well as the vehicle and driving conditions have not been clearly estimated. To quantitatively investigate the driving range attenuation of electric vehicles (EVs) during winter, an EV model mainly integrated with a passengercabin thermal model, battery model, and vehicle dynamic model was constructed and simulated based on the mass-producedWuling HongGuang Mini EV. Real vehicle dynamic driving data was used to validate the model. Based on NEDC driving conditions, the driving range calculation formula and energy flow diagram analysis method were used. The reason for attenuation was evaluated quantitatively. Results show that battery energy loss and breaking recovery energy loss contribute nearly half of the range attenuation, which may be alleviated by battery preheating. Suggestions for extending driving range are proposed based on the research....
Due to increasing sales figures, the energy consumption of battery-electric vehicles is moving further into focus. In addition to efficient driving, it is also important that the energy losses during AC charging are as low as possible for a sustainable operation. In many situations it is not possible or necessary to charge the vehicle with the maximum charging power e.g., in apartment buildings. The influence of the charging mode (number of phases used, in-cable-control-box or used wallbox, charging current) on the charging efficiency is often unknown. In this work, the energy consumption of two electric vehicles in theWorldwide Harmonized Light-Duty Vehicles Test Cycle is presented. In-house developed measurement technology and vehicle CAN data are used. A detailed breakdown of charging losses, drivetrain efficiency, and overall energy consumption for one of the vehicles is provided. Finally, the results are discussed with reference to avoidable CO2 emissions. The charging losses of the tested vehicles range from 12.79 to 20.42%. Maximum charging power with three phases and 16 A charging current delivers the best efficiencies. Single-phase charging was considered down to 10 A, where the losses are greatest. The drivetrain efficiency while driving is 63.88% on average for the WLTC, 77.12% in the “extra high” section and 23.12% in the “low” section. The resulting energy consumption for both vehicles is higher than the OEM data given (21.6 to 44.9%). Possible origins for the surplus on energy consumption are detailed. Over 100,000 km, unfavorable charging results in additional CO2 emissions of 1.24 t. The emissions for an assumed annual mileage of 20,000 km are three times larger than for a class A+ refrigerator. A classification of charging modes and chargers thus appears to make sense. In the following work, efficiency improvements in the charger as well as DC charging will be proposed....
To conserve rare earth resources, consequent-pole permanent-magnet (CPPM) machine has been studied, which employs iron-pole to replace half PM poles. Meanwhile, to increase fluxweakening ability, hybrid excitation CPPM machine with three-dimensional (3-D) flux flow has been proposed. Considering finite element method (FEM) is time-consuming, for the analysis of the CPPM machine, this paper presents a nonlinear varying-network magnetic circuit (NVNMC), which can analytically calculate the corresponding electromagnetic performances. The key is to separate the model of CPPM machine into different elements reasonably; thus, the reluctances and magnetomotive force (MMF) sources in each element can be deduced. While taking into account magnetic saturation in the iron region, the proposed NVNMC method can accurately predict the 3-D magnetic field distribution, hence determining the corresponding back-electromotive force and electromagnetic power. Apart from providing fast calculation, this analytical method can provide physical insight on how to optimize the design parameters of this CPPM machine. Finally, the accuracy of the proposed model is verified by comparing the analytical results with the results obtained by using FEM. As a result, with so many desired attributes, this method can be employed for machine initial optimization to achieve higher power density....
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