Electric Vehicle Motor Research Articles

Optimization Design of Cogging Torque for Electric Power Steering Motors

Excessive cogging torque can cause torque fluctuations, noise, and vibration in electric power steering (EPS) motors, which is a key factor in the high-precision and high-performance optimization design of EPS motors for electric vehicles. This article takes a 12-slot 10-pole electric power steering motor for a certain car as an example. By establishing the corresponding electromagnetic field model and theoretical analysis of the motor, the influence of the pole arc coefficient and eccentricity parameters of the permanent magnet on the cogging torque of the electric power steering motor is explored. A comprehensive optimization scheme for reducing the cogging torque of the motor structure is proposed. The effectiveness of the designed scheme was verified through finite-element simulation and experimental testing of motor electromagnetism. Compared with the original design, the optimized structure of the EPS motor resulted in an 86.62% reduction in cogging torque during experimental testing.

Ways to overcome the barrier during the transition of asynchronous motors to the energy efficiency class IE5

Purpose. To provide a comprehensive analytical review on ways to increase the energy efficiency of induction motors and identify those suitable for transitioning fixed speed induction motors to IE5 level. Methodology. A thorough study of research and development in the field of increasing the energy efficiency of induction motors for various purposes was conducted. The experience of the development of traction induction motors of electric vehicles as dynamically developing is extrapolated. Results. It was established that the main limiting factor in increasing the energy efficiency of induction motors to the IE5 level is its thermal state. Analysis of ways to improve the efficiency of induction motors indicates the need for their complex application. It is most expedient to optimize and improve the elements of the air-cooling system of induction motors. To carry out the research, it is necessary to comprehensively apply electromagnetic and temperature field modeling systems of induction motors. Given certain shortcomings of thermal models of induction motors, it is necessary to combine modeling with experimental studies using both classical temperature sensors and the method of thermography. Originality. The combination of modern methods of modeling the thermal state of induction motors and experimental studies makes it possible to determine reserves for increasing the efficiency of existing motors. Using these results, it is possible to improve induction motors to the IE5 efficiency level. Practical value. Manufacturers and researchers see difficulties with the transition of induction motors of industrial purpose to the IE5 efficiency level, so they focused their attention on synchronous electric motors. The results of our research prove that, despite certain barriers, this transition can be made, albeit with certain difficulties.

Electric Motor Noise Variation Study Due to Air Gap Effect in Electric Vehicles

Motor whine is a crucial factor in the design of electric motors for electric vehicles (EVs). Predicting noise in a virtual environment is challenging due to the many possible variations during sample testing. The air gap between the stator and rotor in each sample is a significant contributor to radiated noise variation in the electric drive unit (DU). A baseline is first established for radiated noise prediction, assuming a nominal uniform airgap distribution, using the front electric DU in General Motors' Hummer EV. This study explains how the sensitivity of critical motor orders changes with static offset direction when the stiffness of DU is non-uniform and varies with radial direction. It also predicts the range of motor airborne noise variation due to offset orientation. Next, the effect of tilt direction on motor noise variation is studied at different torque conditions and motor speeds. Motor noise is found to be sensitive to tilt direction and angle as the DU stiffness changes axially. Additionally, rotor dynamic offset is investigated, which generates sideband orders in addition to motor pole pass orders. A comparison study is conducted to examine various sideband orders and their central frequency.

Design of sliding mode controller with improved reaching law through self-learning strategy to mitigate the torque ripple in BLDC motor for electric vehicles

Brushless Direct Current (BLDC) motors and their evolution in electric vehicles (EVs) garner attention for their streamlined operation and control methods, enhancing driving range while reducing energy consumption. The primary objective is integrating sliding mode control to enhance BLDC motor capabilities, efficiently regulating speed and torque. The proposed sliding mode (SM) controller mitigates torque ripple and improves dynamic performance amidst fluctuating loads. Speed stabilization and torque control involve comparing reference and predicted speeds, with error feedback integrated into the SM controller. Integrating real-time rules and numerical data with an ANN (Artificial Neural Network)-Fuzzy self-learning algorithm empowers the SM controller to anticipate and mitigate error rates in the BLDC motor, efficaciously modulating motor torque with minimal ripple. Extensive MATLAB/Simulink model-in-loop (MIL) and hardware-in-loop (HIL) simulations demonstrate SM controller superiority over conventional PID, Fuzzy, and PID-Fuzzy controllers, with a rise time of 0.01 s, steady-state error of 0.001 %, settling time of 0.02 s, and peak overshoot of 0.066 %. Moreover, the experiment results show an impressive 29.41 % reduction in torque ripple and the development of efficiency maps to attain the maximum efficiency of 96.12 %. This initiative substantiates that the proposed SM controller to enhance BLDC motor efficiency and EV driving range across real-time conditions.

Improving energy consumption with ADAS in autonomous driving communication system

The article analyses the trends in the development of energy saving and recovery technologies in electric motor vehicles. Promising directions for the development of energy saving technologies, in particular with the use of driver assistance systems, are considered and identified. Increasing the occupancy of cars with electronic driver assistance systems increases the overall energy consumption. Recommendations for choosing the optimal technologies and methods of energy saving for motor vehicles are given. For some methods, saving energy is a complement to the main area of work. The combination of intelligent vehicles and appropriate traffic management tools can help to further realise the transport benefits of intelligent vehicles.

Method of determination of parameters of heat exchangers of axial flux electric motors for electric vehicles

Method of determination of parameters of heat exchangers of axial flux electric motors for electric vehicles

Feasibility Analysis of Power Generation from Internal Combustion Engine (ICE) Using EDAS Method

The popularity and development of electric motorcycles are exploding. This is merely the beginning of what many predict will be the motorbike industry's fastest-growing sector. Research significance: The next development in motorcycle technology is the electric motorcycle. High-point electric bikes that are lighter, economical, and enjoyable to ride may be easily offered to clients and partners globally by combining the superlative capabilities of a traditional motorbike with the most cutting-edge technologies now accessible. A future era of electric engines and regulators with "ultra-high efficiency, high power-to-weight, and lightweight" has been specifically created to advance a wide range of models. Methodology: Due to the abundance of possibilities offered on the global market, conflicting situations can develop while choosing a certain motorcycle. There may not necessarily be a certain number of options available or there may be multiple alternatives to the original pick. The possibility of not having an acceptable option for the criterion exists as well. “Multiple Criteria Decision Making” is a technique designed for the optimization of problems with an “infinite or finite number of choices” and the MCDM technique “EDAS method” is used to optimize the process in this paper. The rank of “Revolt RV400 is fifth, Joy e-Bike Monster is ninth, Tork Kratos R is fourth, Komaki Ranger is third, Cyborg Bob is sixth, Odysse Evoqis is seventh, Oben Rorr is first, PURE EV eTryst 350 is eight, Pure ecoDryft is second”. The ranking order is “Oben Rorr> Pure ecoDryft> Komaki Ranger> Tork Kratos R> Revolt RV400> Cyborg Bob> Odysse Evoqis> PURE EV eTryst 350”. Depending on EDAS research in this paper, it was discovered that among all sample electric motor vehicles, “Oben Rorr had the best overall performance while PURE EV eTryst 350 had the poorest”.

Application of the EDAS Technique for Selecting the Electric Motor Vehicles

The popularity and development of electric motorcycles are exploding. This is merely the beginning of what many predict will be the motorbike industry's fastest-growing sector.The next development in motorcycle technology is the electric motorcycle. High-point electric bikes that are lighter, economical, and enjoyable to ride may be easily offered to clients and partners globally by combining the superlative capabilities of a traditional motorbike with the most cutting-edge technologies now accessible. A future era of electric engines and regulators with "ultra-high efficiency, high power-to-weight, and lightweight" has been specifically created to advance a wide range of models. Due to the abundance of possibilities offered on the global market, conflicting situations can develop while choosing a certain motorcycle. There may not necessarily be a certain number of options available or there may be multiple alternatives to the original pick. The possibility of not having an acceptable option for the criterion exists as well. “Multiple Criteria Decision Making” is a technique designed for the optimization of problems with an “infinite or finite number of choices” and the MCDM technique “EDAS method” is used to optimize the process in this paper. The rank of “Revolt RV400 is fifth, Joy e-Bike Monster is ninth, Tork Kratos R is fourth, Komaki Ranger is third, Cyborg Bob is sixth, Odysse Evoqis is seventh, Oben Rorr is first, PURE EV eTryst 350 is eight, Pure ecoDryft is second”. The ranking order is “Oben Rorr> Pure ecoDryft> Komaki Ranger> Tork Kratos R> Revolt RV400> Cyborg Bob> Odysse Evoqis> PURE EV eTryst 350”. Depending on EDAS research in this paper, it was discovered that among all sample electric motor vehicles, “Oben Rorr had the best overall performance while PURE EV eTryst 350 had the poorest”.

Motor Bearing Fault Diagnosis Based on Current Signal Using Time–Frequency Channel Attention

As they are the core components of the drive motor in electric vehicles, the accurate fault diagnosis of rolling bearings is the key to ensuring the safe operation of electric vehicles. At present, intelligent diagnostic methods based on current signals (CSs) are widely used owing to the advantages of the easy collection, low cost, and non-invasiveness of CSs. However, in practical applications, the fault characteristics of the CS are weak, resulting in diagnostic performance that fails to meet the expected standards. In this paper, a diagnosis method is proposed to address this problem and enhance the diagnosis accuracy. Firstly, CSs from two phases are processed by periodic resampling to enhance data features, which are then fused through splicing operations. Subsequently, a feature enhancement module is constructed using multi-scale feature fusion for decomposing the input. Finally, a diagnosis model is constructed by using an improved channel attention module (CAM) for enhancing the diagnosis performance. The results from experiments containing two different types of bearing datasets show that the proposed method can extract high-quality fault features and improve the diagnosis accuracy, presenting great potential in intelligent fault diagnosis and the maintenance of electric vehicles.

Enhanced Thermal Modeling of Electric Vehicle Motors Using a Multihead Attention Mechanism

The rapid advancement of electric vehicles (EVs) accentuates the criticality of efficient thermal management systems for electric motors, which are pivotal for performance, reliability, and longevity. Traditional thermal modeling techniques often struggle with the dynamic and complex nature of EV operations, leading to inaccuracies in temperature prediction and management. This study introduces a novel thermal modeling approach that utilizes a multihead attention mechanism, aiming to significantly enhance the prediction accuracy of motor temperature under varying operational conditions. Through meticulous feature engineering and the deployment of advanced data handling techniques, we developed a model that adeptly navigates the intricacies of temperature fluctuations, thereby contributing to the optimization of EV performance and reliability. Our evaluation using a comprehensive dataset encompassing temperature data from 100 electric vehicles illustrates our model’s superior predictive performance, notably improving temperature prediction accuracy.

Reynolds number based optimization on liquid cooling system for permanent magnet synchronous motor of electric vehicle

The electric motor thermal management system (TMS) is a pivotal unit for electric vehicles (EVs). To study the factors affecting cooling performance and cooling efficiency of spiral housing water jacket (HWJ), simulations were carried out on a 150 kW jacked-cooled permanent magnet synchronous motor (PMSM) with ethylene glycol & water (EGW) as coolant. The coupling between channel numbers (N) and coolant flow rate (Q) on winding average temperature (T) and pressure drop (P) were investigated focusing on the Reynolds number (Re) via analytical and numerical thermal models. The results indicated that the cooling performance was nearly the same for coolant under fully turbulent. Both the cooling performance and cooling efficiency were not conducive when N > 8, and meanwhile, N < 6 also hindered the heat extraction. Resorting to the investigation, a Re-based multi-objective optimization algorithm was put forward. Compared the optimized jacket N6Q12 (denoting 6 channels with 12 L per minute flow rate) with the sub-optimized N12Q6 at the same Re, winding average temperature T was found 6.5 deg C lower, and pressure drop P was reduced by up to 71.6 %.

A novel interpolator designed for laser scanning welding of hairpin windings in electric vehicle motors

A novel interpolator designed for laser scanning welding of hairpin windings in electric vehicle motors

Features of the operation of the training stand “Electric vehicle motor” in determining the operation characteristics of electric transport

In the modern world, the problem of the ecological state of our planet is acute. The environmental situation depends on many factors, these include both natural phenomena and human actions. One solution to this problem is to replace traditional internal combustion engines with more environmentally friendly electric vehicles.THE PURPOSE. The objective of this study is to analyze the internal structure of an electric vehicle, based on the training stand “Electric Vehicle Motor”, as well as to study the functions of this device, located at the Kazan State Energy University.METHODS. The authors of the article processed and analyzed the capabilities of modern laboratory equipment that simulates the operation of a particular component of an electric vehicle.RESULTS. The technology of the laboratory bench operating the electric vehicle, together with the DVT Customer software, allows for full control of the electric motor, as well as monitoring and changing a large number of parameters that affect the operation of the entire device as a whole.CONCLUSION. Since electric vehicles are gaining more and more popularity every year, the demand for people capable of diagnosing, repairing, and even improving existing ones and developing new components and assemblies in the field of electric vehicles is growing significantly. Using the “Electric Vehicle Motor” stand for teaching technical subjects and conducting research work can serve as a good basis for the formation of a general knowledge base about electric transport, its structure, the operating principle of all the main elements, monitoring and diagnostics of an electric vehicle.

Creating a computer model of a synchronous motor for the implementation of the project «Creation of a series of electric motors with a modernized rotor for electric transport of low power»

RELEVANCE. The challenges of creating modern permanent magnet synchronous motors require solving technical problems in conjunction with economic realities. Due to the use of permanent magnets in the construction of synchronous motors, their cost limits their industrial application. Therefore, there is an urgent issue of optimizing the design of synchronous motors, which can be solved by changing the topology.TARGET. The goal is to create more economical permanent magnet synchronous motors (PMSMs) in low-power electric vehicles. It is planned to implement a new method for constructing PMSM. This solution will reduce weight and size parameters and increase specific power, which are important indicators for electric transport.METHODS. To modernize modern synchronous motors, a complex topological optimization method based on a genetic algorithm is proposed.RESULTS. A computer model was obtained in the form of calculation of electromagnetic fields in the stator and rotor of a synchronous motor with permanent magnets.CONCLUSION. Modeling the parameters of synchronous motors with permanent magnets has a low degree of development in the domestic mechanical engineering due to the lack of developed infrastructure in the field of low-power electric transport; the creation of domestic developments in this area is of a strategic nature.

Strategic electromagnetic interferences suppression in boost converters: zero-switch techniques

This article delves into the growing demand for efficient power conversion technologies accompanying the rise in electric vehicle (EV) adoption. Boost converters, essential for increasing the battery pack voltage to propel EV motors, pose a challenge due to the electromagnetic interference (EMI) generated by the high switching frequency of power devices. To address this issue, practitioners employ zero-voltage switching (ZVS) and zero-current switching (ZCS) techniques. In this comparative study, we systematically evaluate the effectiveness of these soft switching techniques in reducing conducted EMI in boost converters designed for EV applications. The results illuminate the potential of both ZVS and ZCS in significantly mitigating EMI emissions when compared to conventional hard-switching methods. Notably, ZVS soft switching emerges as more efficient and effective, particularly under higher loads, while ZCS soft switching excels in reducing EMI at lighter loads. In conclusion, the study asserts that ZVS soft switching presents a more promising solution for curtailing conducted EMI in boost converters for EV applications, particularly in high-load scenarios. However, it underscores the importance of considering specific operational conditions when deciding between the two techniques.

Analytical Modeling of Eddy Current Losses and Thermal Analysis of Non-Uniform-Air-Gap Combined-Pole Permanent Magnet Motors for Electric Vehicles

Analytical Modeling of Eddy Current Losses and Thermal Analysis of Non-Uniform-Air-Gap Combined-Pole Permanent Magnet Motors for Electric Vehicles

Optimal shape design to improve torque characteristics of interior permanent magnet synchronous motor for small electric vehicles

Optimal shape design to improve torque characteristics of interior permanent magnet synchronous motor for small electric vehicles

Electric vehicle motor fault diagnosis using improved wavelet packet decomposition and particle swarm optimization algorithm

This study addresses the issue of diagnosing faults in electric vehicle motors and presents a method utilizing Improved Wavelet Packet Decomposition (IWPD) combined with particle swarm optimization (PSO). Initially, the analysis focuses on common demagnetization faults, inter turn short circuit faults, and eccentricity faults of permanent magnet synchronous motors. The proposed approach involves the application of IWPD for extracting signal feature vectors, incorporating the energy spectrum scale, and extracting the feature vectors of the signal using the energy spectrum scale. Subsequently, a binary particle swarm optimization algorithm is employed to formulate strategies for updating particle velocity and position. Further optimization of the binary particle swarm algorithm using chaos theory and the simulated annealing algorithm results in the development of a motor fault diagnosis model based on the enhanced particle swarm optimization algorithm. The results demonstrate that the chaotic simulated annealing algorithm achieves the highest accuracy and recall rates, at 0.96 and 0.92, respectively. The model exhibits the highest fault accuracy rates on both the test and training sets, exceeding 98.2%, with a minimal loss function of 0.0035. Following extraction of fault signal feature vectors, the optimal fitness reaches 97.4%. In summary, the model constructed in this study demonstrates effective application in detecting faults in electric vehicle motors, holding significant implications for the advancement of the electric vehicle industry.

Simulation modeling of PMBLDC motor speed control in electric propulsion of EV system using adaptive nonlinear fuzzy sliding mode controller (ANF-SMC)

In the current scenario of transportation system, vehicles of conventional are replaced with Electric Vehicles (EVs) with Permanent Magnet Brushless Direct Control Motors (PMBLDCMs) because of the many advantages of the usage of such motors in EVs. Despite the many advantages of the usage of EVs with PMBLDCMs, there were some disadvantages, among them were speed control and dealing with uncertainty problems in the usage of PMBLDCMs in EVs. In rectifying the above-mentioned problems, this work proposed and implemented an ideology of using the nonlinear controller strategy Adaptive Neuro-Fuzzy Sliding Mode Control (ANF-SMC) in the Electric Propulsion Subsystem (EPSS) of EVs with the usage of the proposed nonlinear controller under not loaded and fully loaded vehicles with constant, variable and sudden disturbance in speed conditions. Simulation was carried out using MATLAB version R2020b with Fuzzy Tool Kit (FTK) and simulation performance comparison was done with the existing four (one linear and three nonlinear) controllers. Comparative simulation results infer that the proposed nonlinear controller performs well with zero overshoot and less settling time than that of the existing four (one linear and three nonlinear controllers).

Greases for electric vehicle motors: Bearing friction torque under driving cycle conditions and the thickener effect on oil release

Performance of Lithium Complex (LiX), Polyurea (PU), and Polypropylene (PP) greases in SKF6208 bearings subjected to driving cycle conditions for 28 days (equivalent to 23,000 km of electric vehicle operation) was studied by continuously measuring bearing friction torque and temperature. The energy dissipation was correlated to the differences in oil bleeding and rheology for the three greases. Evolution of the friction torque, friction torque hysteresis, and changes in grease rheology were dominated by the oil release property. The latter was determined by the thickener system and its particular response to the conditions imposed by the driving cycle. A quantitative estimate of the carbon footprint from using these greases to lubricate bearings under driving cycle conditions is also presented.