Review of the Fault Mechanism and Diagnostic Techniques for the Range Extender Hybrid Electric Vehicle

Xu, Xiaowei; Wang, Hongxia; Zhang, Nan; Liu, Zhenxing; Wang, Xiaoqing

doi:10.1109/access.2017.2725298

Cited by 18 publications

(9 citation statements)

References 40 publications

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“…In [22][23][24][25], to recover the braking energy as much as possible and appropriately reduce the braking stability, the front and rear axle actual brake force distribution lines are paralleled to the I curve outside. Literature [26][27][28] uses a new type of energy recovery system on electric buses. Simulation analysis and experimental studies have shown that braking energy can be more effective.…”

Section: Related Workmentioning

confidence: 99%

Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

2018

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Aiming at the braking energy feedback control in the optimal energy recovery of the two-motor dual-axis drive electric vehicle (EV), the efficiency numerical simulation model based on the permanent magnet synchronous motor loss was established. At the same time, under different speed and braking conditions, based on maximum recovery efficiency and data calculation of motor system, the optimization motor braking torque distribution model was established. Thus, the distribution rule of the power optimization for the front and rear electric mechanism was obtained. This paper takes the Economic Commission of Europe (ECE) braking safety regulation as the constraint condition, and finally, a new regenerative braking torque distribution strategy numerical simulation was developed. The simulation model of Simulink and CarSim was established based on the simulation object. The numerical simulation results show that under the proposed strategy, the average utilization efficiency of the motor system is increased by 3.24% compared with the I based braking force distribution strategy. Moreover, it is 9.95% higher than the maximum braking energy recovery strategy of the front axle. Finally, through the driving behavior of the driver obtained from the big data platform, we analyze how the automobile braking force matches with the driver’s driving behavior. It also analyzes how the automobile braking force matches the energy recovery efficiency. The research results in this paper provide a reference for the future calculation of braking force feedback control system based on big data of new energy vehicles. It also provides a reference for the modeling of brake feedback control system.

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Section: Related Workmentioning

confidence: 99%

Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

2018

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“…The method of the research results improves the diagnosis and optimization efficiency of HPMSM abnormal noise. Xu et al [ 8 ] proposed the literature analysis of the fault mechanism and diagnosis technology of range-extended hybrid electric vehicles and discussed the development trend of the fault diagnosis technology, providing the theory for the practical application of the state monitoring and fault diagnosis of the range extender of electric vehicles. Zhou et al [ 9 ] proposed a motor torque fault diagnosis method for four-wheeled independent electric vehicles using an unscented Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults

Lin

2021

Sensors

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Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.

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“…Hence, the fuel cell becomes an alternative solution to power electric train vehicles. Both the Fuel Cell Vehicles (FCVs) and HEVs technologies are gaining more attention in research, and they are going to play a vital role for the next decades [7]- [11]. The average temperature of the earth is increasing slowly due to human activities, by transportation, deforestation, etc., responsible for the emission of CHG [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Survey of DC-DC Non-Isolated Topologies for Unidirectional Power Flow in Fuel Cell Vehicles

et al. 2020

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The automobile companies are focusing on recent technologies such as growing Hydrogen (H2) and Fuel Cell (FC) Vehicular Power Train (VPT) to improve the Tank-To-Wheel (TTW) efficiency. Benefits, the lower cost, 'Eco' friendly, zero-emission and high-power capacity, etc. In the power train of fuel cell vehicles, the DC-DC power converters play a vital role to boost the fuel cell stack voltage. Hence, satisfy the demand of the motor and transmission in the vehicles. Several DC-DC converter topologies have proposed for various vehicular applications like fuel cell, battery, and renewable energy fed hybrid vehicles etc. Most cases, the DC-DC power converters are viable and cost-effective solutions for FC-VPT with reduced size and increased efficiency. This article describes the state-of-the-art in unidirectional nonisolated DC-DC Multistage Power Converter (MPC) topologies for FC-VPT application. The paper presented the comprehensive review, comparison of different topologies and stated the suitability for different vehicular applications. This article also discusses the DC-DC MPC applications more specific to the power train of a small vehicle to large vehicles (bus, trucks etc.). Further, the advantages and disadvantages pointed out with the prominent features for converters. Finally, the classification of the DC-DC converters, its challenges, and applications for FC technology is presented in the review article as stateof-the-art in research.

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Review of the Fault Mechanism and Diagnostic Techniques for the Range Extender Hybrid Electric Vehicle

Cited by 18 publications

References 40 publications

Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults

Survey of DC-DC Non-Isolated Topologies for Unidirectional Power Flow in Fuel Cell Vehicles

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