Motor Efficiency Research Articles

Prediction Of Brushless DC Motor And Propeller Efficiency Using An Artificial Neural Network Model

This study focuses on an artificial neural network model that allows users of brushless motor and propeller test rigs to compare the accuracy of data received in the interface software during testing. Brushless motors are widely used in modern aviation and industrial applications. Therefore, it is essential to analyze the factors affecting motor efficiency and accurately predict this data. This study involves the creation of an artificial neural network model from data to predict the percentage of motor efficiency for the brushless motors and propellers used in the test, whose length is measured in inches. Within the scope of this research, a useful tool is provided for users to flexibly test motor and propeller configurations and accurately analyze test results. The developed artificial neural network model has the ability to make reliable and accurate predictions for various motor-propeller configurations. Furthermore, the model is easy to use and offers expandable features. This study aims to create a valuable reference source for users of brushless motor and propeller test rigs to effectively analyze test data.

How can phase change materials improve vehicle cooling systems for better motor efficiency and thermal management?

Phase change materials, or PCMs, have become an attractive option for improving thermal management in the vehicle industry. With the increasing demand for efficient cooling systems and improved driving ranges in electric vehicles, PCMs offer an innovative approach to managing heat dissipation and improving performance. While PCMs are already applied in industries such as construction, their potential to revolutionize automotive thermal management could lead to advancements in all aspects of the field. They are a perfect option for increasing energy efficiency because of their capacity to collect and release thermal energy. This study explores the application of PCMs in vehicles and assesses their effectiveness in improving cooling systems.

Optimized design of a fault-tolerant 12-slot/10-pole six-phase surface permanent magnet motor with asymmetrical winding configuration for electric vehicles

This paper presents a comprehensive methodology for optimizing the design of a 12-slot/10-pole permanent magnet (PM) motor with a six-phase winding configuration tailored for electric vehicles (EVs). The design aims to enhance motor performance under both healthy and fault conditions. While the single neutral configuration offers superior torque during faults, it also introduces zero sequence currents and additional space harmonics, which can lead to increased torque ripple that is difficult to control. This study addresses these challenges through innovative machine design optimization. The optimization process begins with sizing equations to establish an initial design. K-means clustering techniques are then employed to identify distinct loading points that accurately represent the full EV driving cycle, effectively minimizing computational power requirements. Following this, the Full Range Minimum Loss (FRML) strategy is applied to determine optimal current profiles across these loading points, significantly reducing copper losses. Finally, a multi-objective optimization approach is utilized to minimize torque ripple, enhance average torque, and optimize machine losses. The results demonstrate substantial improvements in torque and reduced ripple, validated through experiments conducted with a 2 kW lab-scale motor. This integrated approach not only ensures a robust and efficient motor design but also enhances fault tolerance, making it well-suited for advanced EV applications.

Predictive Maintenance of Induction Motor in the Cutting Section of Paper Industry

Induction Motors are vital to the paper industry because they power a variety of equipment needed for pulp processing, drying and cutting among other tasks. Ensuring the consistent manufacture of these motors and satisfying market demands depends heavily on their dependability. However, the extreme humidity, dust and fluctuating loads that occur in the paper sector present serious obstacles to the efficiency and durability of induction motors. To maintain continuous production and satisfy consumer demand, the paper industry significantly depends on the effective operation of a variety of machinery including induction motors in the cutting area. Unexpected malfunctions in these vital parts however might result in expensive downtime and lost output. By using data-driven strategies that anticipate and avoid breakdowns before they happen, predictive maintenance techniques provide a proactive way to reduce such risks. This report provides an extensive analysis of the application of predictive maintenance techniques designed especially for induction motors in the paper industry’s cutting division. The predictive maintenance techniques includes Random Forest Tree, Linear Regression and Support Vector Machines algorithm with these algorithm the Induction Motor’s variations in temperature, vibration, sound, and speed parameters were collected and trained for predicting the failure. Among these algorithms Support Vector Machines shows greater advantage in predicting the failure with the accuracy of 84% whereas Random Forest Tree with 75% and Linear regression with 72%. As well as the real time data’s were collected and stored in the database. The performance of the Induction Motor were efficiently improved and monitored under normal and fault condition by Machine Learning techniques.

Heat transfer and economic characteristics of direct oil cooling for electric traction motor under driving cycles

Currently, the energy density of electric traction motor is continuously increasing to match the performance and reliability of electric vehicles with IC engine vehicles. The commercially employed water jacket cooling fails to achieve the desired efficiency of high energy density electric traction motor. Direct oil cooling is one of potential cooling strategies to replace the conventional cooling method for achieving the desired thermal management of next generation electric traction motors. However, the database to commercialize the direct oil cooling based on thermal management and economic aspects is not enriched sufficiently and thus extensive research studies need to be conducted in this field. Therefore, the heat transfer and economic characteristics of direct oil cooling for electric traction motor are investigated considering different cooling oils and driving duty cycles in the present work. The integrated electromagnetic-thermal numerical model is developed using ANSYS Motor-Cad commercial software to simulate the heat transfer and economic characteristics. The reliability of coupled numerical model is assured by validating the simulated results with experimental and reference data in error range of ± 5 %. The winding temperature, heat transfer coefficient, expected insulation lifetime, levelized cost of energy (LCOE), and net cumulative savings are evaluated as heat transfer and economic characteristics. Three driving duty cycles namely, UDDS (Urban Dynamometer Driving Schedule), NEDC (New European Driving Cycle) and US06 (Highway Driving Cycle) and transmission oil, shell thermal oil and Novec dielectric oil are investigated. The performance of electric traction motor with direct oil cooling is superior compared to that with water jacket cooling. The direct cooling with Novec dielectric oil shows improved heat transfer and economic characteristics compared to transmission oil and shell thermal oil. The direct cooling with Novec dielectric oil restricts the maximum winding temperature within 80.21 °C, 67.65 °C and 116.82 °C under driving cycles of UDDS, NEDC and US06, respectively. In addition, the enhanced insulation lifetime, heat transfer coefficient, LCOE and net cumulative savings of 7.91 × 105h, 23036.73 W/m2-K, 0.170 $/kWh and 47151.13 $, respectively are reflected for direct cooling with Novec dielectric oil.

Optimal powertrain system design and V2V and V2I based hierarchical control strategy of FRIDEV under vehicle-following scenarios

In this paper, the front- and rear-independent-drive electric vehicle (FRIDEV) is selected as the target vehicle for its good dynamic and economy performance. Based on the analysis of the cost, efficiency, and loss characteristics of different motors, the interior permanent magnet synchronous motor (IPMSM) and induction motor (IM) are applied in the target powertrain system. Then, the proposed powertrain system is optimized based on the analysis of driving cycles. The optimal control strategy for the target powertrain system is developed, which consists of the driving mode switching algorithm and the driving torque distribution based on adaptive particle swarm optimization (APSO). Furthermore, in order to achieve the efficient autonomous driving of the target vehicle under the vehicle-following scenarios, a hierarchical control strategy is proposed with the combination of upper-level control of the speed planning strategy and the lower-level control of powertrain system control strategy. The upper-level control of speed planning strategy is designed, which can determine and track the target vehicle speed based on the information from vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. Finally, simulation analysis in MATLAB/Simulink and virtual driving experiments are conducted to verify the superiority of the proposed powertrain system and hierarchical control strategy.

Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future.

Orthographic character complexity modulates dynamic neural activity in skilled handwriting.

Handwriting is an outstanding case of a highly complex and efficient fine motor skill. However, little is known about its neural underpinnings during continuous handwriting production. In the present study, we examined the effects of orthographic character complexity (i.e. the stroke number of a Chinese character) on both neural and behavioural activities during an EEG-based naturalistic fluent sentence-handwriting task from 102 adult Chinese native speakers. For each written character, the interval between finishing the preceding character and its onset (inter-character interval) as well as the amplitudes of the onset-synchronized event-related potential (ERP) in pre- and post-onset time windows was defined as dependent variables. The effects of character complexity and other confounding factors were analysed with linear mixed models. Character complexity increased the inter-character interval and significantly affected ERP amplitudes in both pre- and post-onset time windows. The ERP pattern in the pre-event time window exhibited a dipole-like activation in the left motor cortex, and its amplitude increased with character complexity in line with the documented relationship between the lateralized readiness potential and motor complexity. This study demonstrates the feasibility of studying neurocognitive processes in complex naturalistic motor tasks and extends our knowledge about the dynamic pattern of handwriting-related neural activities.

Analysis of the influence of the parameters of a new low-loss magnetic material of the stator core on the efficiency of a high speed electric motor

Analysis of the influence of the parameters of a new low-loss magnetic material of the stator core on the efficiency of a high speed electric motor

Augmenting microgrid control with GHNO for efficient BLDC motor integration and renewable energy utilization

Augmenting microgrid control with GHNO for efficient BLDC motor integration and renewable energy utilization

New method for determining iron power losses in switched reluctance motors with laminated and composite magnetic cores

Power losses from motors and drives can decrease their efficiency. The development of devices with high efficiency is a necessity nowadays, with a view to reducing CO2 emissions. The application of new magnetic materials is one method for increasing the efficiency of motors, although prediction and calculation of the losses in the magnetic circuits of motors are also very important. To reduce the iron losses, iron powder composites are used here instead of Fe-Si laminated steel sheets, in a low-power switched reluctance motor (SRM), and a new method of determining the iron loss in the magnetic circuit is developed. Through the use of new materials in the motor, we can achieve a 19% decrease in the iron loss in comparison to a commercial motor. A comparison of the measurement results for our new method of iron loss prediction showed that the difference is approximately 20-26%, depending on the type of magnetic circuit used for the motor. The usage of soft magnetic composites rather than electrical steel sheets can decrease the loss in magnetic circuits. The results from our new method of determination of iron loss are in good agreement with results of measurements, meaning that it can be applied in a detailed analysis of SRMs. The application of soft magnetic composites in electromagnetic and electromechanical converters is a new method for lowering iron losses in new devices. The determination of such losses in SRMs is still challenging, and the proposed method broadens the state of the art in this area. The method developed here is based on motor current measurements, FEM calculations, and measurements of the magnetisation curves and iron loss for samples according to IEC standards. The results obtained in this work can be used by designers for the development of new kinds of SRMs.

Design and Application of Parameter Self-tuning Regulator for DC Motor based on Neural Network

This study introduces a cutting-edge approach to regulating DC motors, featuring a unique combination of Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. This innovative system capitalizes on the adaptive learning capabilities of ANNs to dynamically fine-tune the control parameters of DC motors. This adaptability ensures optimal motor performance across diverse operational conditions, addressing the challenges posed by fluctuating loads and varying speed requirements. The integration of LSTM networks into this framework adds a layer of predictive functionality, allowing the system to anticipate future motor states. Such foresight enables the regulator to make proactive adjustments, significantly enhancing its responsiveness to changes in operational demands. The dual application of ANN’s adaptive control mechanisms and LSTM’s predictive capabilities is particularly effective in overcoming the non-linearity and variability that are typical challenges in DC motor control. This synergy ensures that the motor operates efficiently, stably, and with a quick response time, even under varying and unpredictable conditions. The practical application of this advanced regulator in real-world scenarios has shown marked improvements in motor performance. These enhancements are evident in the increased efficiency, stability, and responsiveness of the motors, making them more suitable for a wide range of industrial applications. This study marks a notable progression in the field of DC motor control technology. By integrating advanced machine learning techniques, it offers a solution that is not only more efficient and reliable but also adaptable to the evolving demands of industrial environments. The innovative combination of ANN and LSTM networks in this regulator design paves the way for smarter, more responsive, and efficient motor control systems, potentially transforming how motors are managed in various industrial applications.

Efficient DC motor speed control using a novel multi-stage FOPD(1 + PI) controller optimized by the Pelican optimization algorithm.

This paper introduces a novel multi-stage FOPD(1 + PI) controller for DC motor speed control, optimized using the Pelican Optimization Algorithm (POA). Traditional PID controllers often fall short in handling the complex dynamics of DC motors, leading to suboptimal performance. Our proposed controller integrates fractional-order proportional-derivative (FOPD) and proportional-integral (PI) control actions, optimized via POA to achieve superior control performance. The effectiveness of the proposed controller is validated through rigorous simulations and experimental evaluations. Comparative analysis is conducted against conventional PID and fractional-order PID (FOPID) controllers, fine-tuned using metaheuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO), and sine-cosine algorithm (SCA). Quantitative results demonstrate that the FOPD(1 + PI) controller optimized by POA significantly enhances the dynamic response and stability of the DC motor. Key performance metrics show a reduction in rise time by 28%, settling time by 35%, and overshoot by 22%, while the steady-state error is minimized to 0.3%. The comparative analysis highlights the superior performance, faster response time, high accuracy, and robustness of the proposed controller in various operating conditions, consistently outperforming the PID and FOPID controllers optimized by other metaheuristic algorithms. In conclusion, the POA-optimized multi-stage FOPD(1 + PI) controller presents a significant advancement in DC motor speed control, offering a robust and efficient solution with substantial improvements in performance metrics. This innovative approach has the potential to enhance the efficiency and reliability of DC motor applications in industrial and automotive sectors.

A Study on Enhancing Axial Flux Motor Efficiency Using Cladding Core Technology

With the rise of eco-friendly policies, advanced motor technologies are being developed to replace fossil fuel-based engines in the mobility industry. Axial flux motors, known for their ability to reduce size and increase output torque compared to radial flux motors, require different materials and manufacturing techniques. Specifically, the production of complex stator cores and segmented magnets presents significant challenges, often leading to higher costs. To address this issue, soft magnetic composite (SMC) materials, which offer greater design flexibility, are being explored for use in stator cores. However, soft magnetic composite materials exhibit lower permeability and saturation flux density compared to laminated silicon steel, resulting in reduced output torque and efficiency. This paper investigates the effects of stator geometry on axial flux motor performance and explores cladding core technology, which combines soft magnetic composite materials with silicon steel. By conducting finite element method (FEM) analysis to evaluate the output torque and efficiency based on the shape of the silicon steel within the cladding core, this study proposes an optimized cladding core design to enhance the efficiency and output torque of axial flux motors.

Low Vibration Control Scheme for Permanent Magnet Motor Based on Resonance Controllers

For an electric locomotive traction motor, it is necessary to maintain relatively low vibration and noise due to the higher design standards. By using effective motor control strategies and implementing current harmonic suppression schemes, motor efficiency and vibration and noise suppression can be effectively improved. This study investigates the current harmonic suppression strategy for permanent magnet synchronous motors by (1) constructing a mathematical model of the permanent magnet motor to explore the sources of low-order harmonics currents such as fifth and seventh harmonics, as well as high-order harmonics at switch frequencies and their multiples, and analyzing the electromagnetic force characteristics generated by the current, and (2) establishing a vector control system for the permanent magnet motor. To suppress the fifth and seventh harmonic components in the current, a resonance controller is constructed, which utilizes the parallel connection of a resonator and PI controller to achieve low-order harmonic suppression. The factors affecting the effectiveness of the resonance controller’s suppression are also analyzed. The experiments are conducted, and the current harmonic suppression scheme constructed in this study can effectively reduce the harmonics in the current, thereby reducing motor vibration and noise.

A Methodology to Optimize PMSM Driven Solar Water Pumps Using a Hybrid MPPT Approach in Partially Shaded Conditions

Solar water pumps are crucial for farmers, significantly reducing energy costs and providing independence from conventional fuels. Their adoption is further incentivized by government subsidies, making them a practical choice that aligns with sustainable agricultural practices. However, the cost of the required solar panels for the chosen power makes it essential to optimize solar water pumping systems (SWPS) for economic viability. This study enhances the efficiency and reliability of permanent magnet synchronous motor (PMSM)-driven SWPS in rural areas using hybrid maximum power point tracking (MPPT) algorithms and voltage-to-frequency (V/f) control strategy. It investigates the sensorless scalar control method for PMSM-based water pumps and evaluates various MPPT algorithms, including grey wolf optimization (GWO), particle swarm optimization (PSO), perturb and observe (PO), and incremental conductance (INC), along with hybrid combinations. The study, conducted using MATLAB Simulink, assesses these algorithms on convergence time, MPPT accuracy, torque ripple, and system efficiency under different partial shading conditions. Findings reveal that INC-GWO excels, providing higher accuracy, faster convergence, and reduced steady-state oscillations, thus boosting system efficiency. The V/f control strategy simplifies control mechanisms and enhances performance. Considering system non-idealities and maximum duty cycle limitations, PMSM-based SWPS achieve superior efficiency and stability, making them viable for off-grid water pumping applications.

Low-Voltage Water Pump System Based on Permanent Magnet Synchronous Motor

This paper designs a safe, low-cost, and efficient permanent magnet synchronous motor (PMSM) booster pump system. The aim is to enhance the pump’s safety and reduce the incidence of electric shock accidents, while also achieving cost reduction and efficiency improvement. The pump components are made of a plastic material, and a safe voltage of 36 V is used as the operating voltage. Additionally, the PMSM is chosen to replace the induction motor (IM) as the pump’s driving device, utilizing sensorless control and field-weakening control strategies. The study results show that when the flow rate is 1.51 m3/h, the efficiency of the PMSM low-voltage pump can reach up to 20.86%. At the same flow rate of 1 m3/h, compared to other pumps, the PMSM low-voltage pump exhibits higher head, energy savings, and efficiency. The proposed PMSM low-voltage pump offers advantages such as high efficiency, energy savings, safety, and low cost. This study provides a reference for the domestic PMSM pump industry.

Analysis of the Effect of Motor Waste Heat Recovery on the Temperature and Driving Range of Electric Heavy Truck Batteries

In some scenarios, electric heavy-duty trucks with battery swapping mode (ETBSm) are more cost-effective than battery charging mode. The viability of battery swapping stations is contingent upon the operational requirements and range capabilities of the ETBSm. Low temperatures have the effect of reducing the range of the ETBSm, thereby creating difficulties for battery swapping. This article proposes the use of motor waste heat recovery (MWHR) to heat batteries, which would improve range. A number of subsystem models have been established, including the ETBSm, battery, motor, and thermal management system (TMS). The calibration of battery temperature and motor efficiency is achieved with a model error of less than 5%. Comparison of performance, such as temperature, energy consumption, and range, when using only positive temperature coefficient (PTC) heating and when using both PTC heating and motor waste heat. The results indicate a 15% increase in the rate of rise in battery temperature and a 10.64 kW·h reduction in energy consumption under Chinese heavy-duty vehicle commercial vehicle test cycle (CHTC) conditions. Then, the motor waste heat percentage, energy consumption, and range are analyzed at different ambient temperatures. At an ambient temperature of −20 °C, −10 °C, and 0 °C, the percentage of the motor waste heat is 32.1%, 35%, and 40.5%; when 75% of the state of charge (SOC) is consumed, the range is improved by 6.55%, 4.37%, and 4.49%. Additionally, the effect of the PTC heater on temperature characteristics and power consumption is investigated by changing the target temperature of the coolant at the battery inlet. In accordance with the stipulated conditions of an ambient temperature of −20 °C and a target coolant temperature of 40 °C at the battery inlet, the simulation results indicated a battery temperature rise rate of 0.85 °C/min, accompanied by a PTC power consumption of 15.6 kW·h. This study demonstrates that as the ambient temperature increases, the utilization of motor waste heat becomes more effective in reducing PTC heating power consumption. At the lowest ambient temperature tested, the greatest improvement in driving range is observed. It is important to note that while an increase in the target heating temperature of the PTC helps to raise the battery temperature more rapidly, this is accompanied by a higher energy consumption. This article provides a reference for the ETBSm with MWHR.

Optimized Rear Drive Torque Allocation Strategy for Dual-Motor Mining Dump Trucks

This paper takes the dual-motor pure electric mining dump truck as the research object and designs a dual-motor rear-drive torque optimization allocation strategy in view of the problems such as the large load variation of the dump truck and the facts that the motor output torque cannot accurately express the driver’s dynamic intention and that the overall output efficiency of the dual motor is low. The strategy first divides the demand torque of the whole vehicle into two parts, the base torque and the compensation torque, which are determined by the accelerator pedal opening and the motor speed, and the compensation torque is fuzzy-controlled by taking the vehicle speed, the rate of change of the accelerator pedal opening, and the state of the battery charge as inputs. Subsequently, the dual-motor drive torque allocation is optimized using a particle swarm algorithm, with the objective of minimizing power loss in the dual motors. Furthermore, the energy-saving effect of the torque optimization allocation strategy proposed in this paper is compared with that of the traditional torque average allocation strategy under three working conditions: the driving conditions of Chinese dump trucks, the unloaded uphill movement of mining dump trucks, and the fully loaded downhill movement of mining dump trucks. The results show that the average efficiency of the dual-motor drive using the torque optimization allocation strategy is improved by 2.32%, 4.23%, and 2.24%, respectively, and battery energy savings are improved by 0.5%, 0.47%, and 0.24%, respectively.

Solar photovoltaic fed synchronous reluctance motor with optimized rotor flux barriers: Design and performance optimization

This paper focuses on enhancing the design of a synchronous reluctance motor (SyRM) specifically tailored for solar photovoltaic (PV) water pumping applications. With growing adoption of green technologies and renewable energy sources, particularly solar power, there is a need to boost the motor's efficiency to meet the demands of such applications. Utilizing renewable energy for water pumping offers significant advantages in terms of sustainability, scalability, and environmentally friendly energy generation. Achieving optimal efficiency and reliability is paramount, as it reduces overall system cost and enhances reliability. SyRM design methodology in this study integrates both analytical and finite element (FE) procedures. Unlike other motor types, the rotor structure of the SyRM does not include any cages, magnets, or windings, resulting in a robust, reliable, and cost-effective manufacturing process. In SyRM, the rotor is constructed solely with barriers, which significantly impact torque shaping, torque ripple content, and power factor. This paper primarily examines the influence of various air-barrier shapes on different motor performance parameters, such as average torque, torque ripple, saturation, cross-section, and saliency ratio. The selected motor configuration for this investigation is a 4-pole, 100 Hz synchronous reluctance motor with 24 stator slots. The accuracy of the FE analysis findings is validated through a prototype of designed SyRM, followed by comprehensive performance evaluations conducted in a controlled laboratory environment. Additionally, this paper addresses an improved control strategy for a SyRM-based photovoltaic (PV) water pumping system. It eliminates the need for a speed controller in the position sensor-less field-oriented control (FOC) of SyRM. The solar water pump drive involves two stages of power conversion.