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Aug 2026

Energy Consumption in Electric Motors Operating under Different Fault Conditions

Electric motors are essential components of industrial systems and are extensively used in applications such as fans, pumps, compressors, and conveyors. As energy conversion devices, they typically operate with high rated efficiency, particularly in the case of medium- and high-power motors. However, the actual operating efficiency may be significantly reduced when motors are affected by faults or degradation mechanisms. Such faults introduce additional electrical, mechanical, and thermal losses, leading to increased energy consumption that is not converted into useful mechanical output. This paper presents an experimental investigation of the influence of different fault types on the efficiency of induction motors. Several representative fault conditions are examined, including broken rotor bars, stator asymmetries, misalignments and loosened bolts, cooling system deficiencies, and insulation degradation. For each fault scenario, efficiency curves are obtained under a wide range of load levels and fault severities using a controlled laboratory test bench. The experimental results demonstrate that all investigated faults lead to measurable efficiency reductions across the entire operating range, with increasingly pronounced effects as fault severity rises and as the motor operates near rated load conditions. These findings confirm that motor faults impact not only operational reliability but also energy efficiency, often resulting in hidden energy losses during prolonged operation. Consequently, the study highlights the importance of condition monitoring and maintenance strategies as effective tools for preventing failures and ensuring energy-efficient operation of industrial electric motors.

J. Antonino-Daviu, Jonathan Herrera-Guachamin, Larisa Dunai et al. · 0 citations
Open access Aug 2026

Design of an intelligent diagnostic system for powertrains in an electric-drive vehicle

This study examines the process of detecting hidden faults and complex relationships in the operation of an internal combustion engine, which cannot always be established in a timely manner using conventional diagnostic approaches. Hidden faults in the fuel system are determined by analyzing deviations between the calculated torque of the internal combustion engine (ICE) and the generator torque under an energy recovery mode. The engine torque is determined by analyzing the generator torque when charging a traction battery in a hybrid vehicle. This approach makes it possible to obtain additional information about the technical condition of the powertrain and increase the reliability of diagnostic conclusions. The results have been used as a component of intelligent diagnostic systems for hybrid vehicles that operate under the mode of continuous monitoring of the technical condition. The architecture of an intelligent system for diagnosing the technical condition of a hybrid powertrain has been designed. The key diagnostic signs of fuel system degradation as part of the integrated vehicle power system are described, which are based on a comparison of the calculated torque of the internal combustion engine and the generator torque under the energy recovery mode. The deviation between these values is used as a diagnostic sign to detect hidden malfunctions of the fuel system. A feature of the proposed solution is a comprehensive approach to assessing the operation of a hybrid powertrain, which takes into account both the technical parameters of its functioning and the economic aspects of vehicle operation. This provides a more objective assessment of the state of the power system and powertrain compared to conventional control methods. The practical implementation of an intelligent diagnostic system involves its integration into the vehicle's on-board information and analytical systems using automated data collection, continuous monitoring, and intelligent information processing

Unknown authors · 0 citations
Review Open access 2026

Design of an Energy-Efficient Electric Vehicle Drive System

The energy efficiency has become a distinguishing performance metric used in electric vehicles (EVs) that has a direct impact on driving range, battery longevity, thermal stability, and overall cost of ownership. Electric motor, power electronics, energy storage interface, transmission, and control algorithms are the main components of an electric vehicle drive system that helps to determine the efficiency of the entire vehicle. In contrast to traditional internal combustion engine drive systems, EV drive systems work over broad torque -speed ranges and under conditions of highly dynamic loads, efficiency optimization is a multi-dimensional engineering problem. In this paper a full-fledged design-based investigation into an energy efficient system of electric vehicle drive system is performed, which incorporates innovations in the choice of motor topology, inverter design, control methods and system-wide energy management of the energy efficient electric vehicle. It is a systematic study of the mechanisms of losses on electrical, magnetic, mechanical, and thermal scales that highlight the importance of co-optimization, but not component-level optimization. An analytical methodology is offered which integrates both analytical modeling and control-oriented efficiency mapping with drive cycles based assessment to inform design compelling choices. The literature review concentrates on how the EV drive system advanced, a DC motor-based system to a current permanent magnet synchronous motor (PMSM) and induction motor (IM) systems, and the replacement of silicon power electronics by wide-bandgap semiconductor devices. The presented through these understandings, there is a hierarchical design-based approach to the context of the proposed methodology, where motor-inverter matching, field-oriented control optimization, regenerative braking integration, and thermal-conscious operating point selection are promoted. Employing simulations in the results have shown efficiency gains measurable through standard urban and highway drive cycles and have also shown decreases in inverter switching losses, increased partial-load motor efficiency and increased regenerative energy recovery. Actual design trade-offs, scalability and future EV implications have been highlighted in the discussion. The paper has ended with research directions in AI-assisted drive control, integrated motor drives, and ultra-high-efficiency power electronics.

Sandra Miguel · 0 citations
Jul 2026

Research on Reliability Test Methods for Electric Drive Axle Differentials

With the rapid development of the global economy, issues such as the energy crisis and environmental pollution have become increasingly severe. Owing to their environmental friendliness, structural simplicity, and high energy efficiency, electric vehicles have attracted widespread attention. Electric drive technology serves as the most promising and versatile propulsion solution for battery electric vehicles, hybrid electric vehicles, and fuel cell vehicles. As an advanced mechatronic transmission system, the electric drive axle offers high transmission efficiency, flexible packaging, and ease of digital and active chassis control integration, and has thus been increasingly adopted in modern vehicle architectures. The differential is a key component within the electric drive axle, responsible for regulating the rotational speed difference between the left and right wheels and ensuring balanced torque distribution. It plays a decisive role in vehicle stability and traction performance. This study focuses on the reliability testing methodology for differentials in electric drive axles, primarily including the extraction of reliability test conditions and the feasibility analysis of the proposed testing scheme. Specifically, based on the parameters of a given electric vehicle, a Simulink model of the motor and differential is established, and a complete four-wheel-drive vehicle model is constructed. Through simulation under typical driving conditions, operational data of the rear-drive axle differential are obtained. The collected data are then preprocessed and subjected to dimensionality reduction using Principal Component Analysis. The selected principal components are further analyzed using K-means clustering to construct representative differential reliability test conditions. The limitations of existing testing methods are analyzed based on the simulated results and relevant literature. Finally, a reinforced fatigue testing method for the differential is designed according to the extracted test conditions, and the feasibility of the corresponding test bench is evaluated.

Hongyu Zheng, Ziyu Li, Dajiang Wang et al. · 0 citations
Review Open access Aug 2026

A Review of Current Approaches to Fault Diagnosis in Lithium-Ion Batteries

In the age of rapid digital transformation, electric vehicles have become more and more common, and have gradually displaced the status of diesel. It is essential for people to pay more attention to the safety of lithium batteries. To illustrate, a lithium-ion battery is prone to error under long-term charging or discharging and high temperature. If the fault diagnosis is not at the right time, it might lead to the declining performance of the lithium-ion battery and shorten the servicing life. This study aims to discuss the technologies for detecting the error in lithium batteries, which are categorized into three forms: Model-based method, Signal processing-based method and Machine learning method. Meanwhile, these technologies have been widely applied in the area of electric vehicle and battery management systems, detecting the state of batteries in real-time. Overall, this study considers that there are no any fault diagnosis methods that are suitable for all the application scenarios, and researchers should choose the approach according to their needs. The future development trend is the combination of various fault diagnosis techniques, improving the accuracy, reliability and performance.

Shunpang Zeng · 0 citations
Open access Aug 2026

Fault Diagnosis Method Based on Temperature Rise Detection for Switched Reluctance Motor Drive Systems in Electrical Transportation

In this paper, a fault diagnosis method based on temperature rise detection is proposed for power converters in switched reluctance motor drive systems used in electrical transportation equipment. First, the total power losses of all power devices are calculated and recorded under different operating conditions in both healthy and faulty states. A finite-element electrothermal model is then established to characterize the relationship between fault-induced power-loss redistribution and variations in the temperature rise of the converter devices. Based on the power-loss analysis, temperature rise is used as a key characteristic, and a corresponding fault diagnosis method is proposed. To account for the influence of operating conditions on the diagnostic criterion, three independent backpropagation neural network (BPNN) models are developed to predict fault-specific temperature-rise thresholds using rotor speed, load torque, and ambient temperature as inputs. During diagnosis, the real-time temperature evolution of the power diodes is compared with the selected thresholds to detect power converter faults. Finally, experimental results demonstrate the validity of the proposed fault diagnosis method.

Xiangsu Wang, Zhijie Zhang, Qing Wang et al. · 0 citations

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