Motor Efficiency Research Articles

Astragali radix vesicle-like nanoparticles improve energy metabolism disorders by repairing the intestinal mucosal barrier and regulating amino acid metabolism in sleep-deprived mice

BackgroundSleep disorder is widespread and involves a variety of intricate factors in its development. Sleep deprivation is a manifestation of sleep disorder, can lead to energy metabolism disturbances, weakened immune system, and compromised body functions. In extreme situations, sleep deprivation can cause organ failure, presenting significant risks to human health.PurposeThis study aimed to investigate the efficacy and mechanisms of Astragalus Radix vesicles-like nanoparticles (AR-VLNs) in counteracting the deleterious effects of sleep deprivation.MethodsThe ICR mice were divided into control, model, AR-VLNs high dose (equivalent to 20 g/kg crude drug), AR-VLNs low dose (equivalent to 10 g/kg crude drug), AR high dose (equivalent to 20 g/kg crude drug), and AR low dose (equivalent to 10 g/kg crude drug). The REM (rapid eye movement) sleep-deprivation model was established, and evaluations were conducted for motor function, antioxidant capacity, and energy metabolism indices. Moreover, CACO-2 cells damage was induced with lipopolysaccharide to evaluate the repairing ability of AR-VLNs on the intestinal cell mucosa by measuring permeability. Furthermore, metabolomics was employed to elucidate the mechanisms of AR-VLNs action.ResultsAR-VLNs were demonstrated to enhance the motor efficiency and antioxidant capacity in REM sleep-deprived mice, while also minimized pathological damage and restored the integrity of the intestinal mucosal barrier. In vitro experiments indicated the anti-inflammatory effect of AR-VLNs against LPS-induced cell damage. Additionally, metabolomic analysis linked these effects with regulation of the amino acid metabolic pathways. Further confirmation from molecular biology experiments revealed that the protective effects of AR-VLNs against the deleterious effects of REM sleep deprivation were associated with the restoration of the intestinal mucosal barrier and the enhancement of amino acid metabolism.ConclusionAR-VLNs administration effectively improved energy metabolism disorders in REM sleep deprived mice, by facilitating the repair of the intestinal mucosal barrier and regulating the amino acid metabolism.Graphical

A light-weight factorized convolutions based dual-input fuzzy-CNN for efficient motor bearing fault diagnosis

ABSTRACT Efficient and timely identification of bearing faults is imperative to ensure operational normalcy, reduced down-times and health hazards in motor fault tolerant control systems. This paper proposes a fault diagnosis method that combines the vibration information with time-varying rotational speed for effective fault diagnosis under non-stationary conditions. The complex wavelet transform is used to encode both the vibrational and rotational signals in 2d representations for spatial feature extraction. An efficient algorithm is proposed to select the mother wavelet with the least average entropy. Moreover, a spatial decomposition-based approach using factorised convolutions is used to create a light-weight fuzzy convolutional neural network named Split-Operation Fuzzy Convolutional Neural Network (SOF-CNN) for semi rule-based feature extraction and classification. The performance was evaluated on the University of Ottawa (UOO) dataset for multiple speed conditions and cross-condition validation with the highest accuracy yield being 99.92% from the fourth condition and 3rd trial acquired via averaged 10-Fold Cross Validation. The average accuracy yield across all the scenario was 99.89% with 64.73% being the highest accuracy for cross-condition validation. The performance was evaluated across a diverse range of evaluation criterion including both quantitative and statistical tests.

Optimal Rotor Design for Reducing Electromagnetic Vibration in Traction Motors Based on Numerical Analysis

Interior permanent magnet synchronous motor (IPMSM) for traction applications have attracted significant attention due to their advantages of high torque and power density as well as a wide operating range. However, these motors suffer from high electromagnetic vibration noise due to their complex structure and structural rigidity. The main sources of this electromagnetic vibration noise are cogging torque, torque ripple, and radial force. To predict electromagnetic vibration noise, finite element analysis (FEA) with flux density analysis of the air gap is essential. This approach allows for the calculation of radial force that is the source of the vibration and enables the prediction of vibration in advance. The data obtained from these analyses provide important guidance for reducing vibration and noise in the design of electric motors. In this paper, the cogging torque and vibration at rated and maximum operating speed are analyzed, and an optimal cogging torque and vibration reduction model, with rotor taper and two-step skew structure, is proposed using the response surface method (RSM) to minimize them. The validity of the proposed model is demonstrated through formulations and FEA based entirely on numerical analysis and results. This study is expected to contribute to the design of more efficient and quieter electric motors by providing a solution to the electromagnetic vibration noise problem generated by IPMSM for traction applications with complex structures.

Optimization and design of electromechanical control automation based on dual motor control algorithm

IntroductionIn response to the high demand for dynamic characteristics and control in current electromechanical automatic control systems.MethodsThis study first analyzes the dual motor system. A novel electromechanical control automation model based on a dual motor control algorithm is proposed through the control strategy of dual motor backlash elimination and digital proportional integral derivative control algorithm.Results and DiscussionThe results indicated that the optimization of the model had a promoting effect on the control performance of the electromechanical automatic control system. Compared with other popular electromechanical control automation models of the same type, the performance of the research method was the best. During the no-load start-up phase, the maximum tracking error and synchronization error speed of the proposed new electromechanical control automation model showed a significant decreasing trend, with the maximum synchronization error between the two motors being only 0.02%. Under steady-state sudden load, the research model could reach a stable state within 3 s, with errors within ±5%.ConclusionIn summary, combining the dual motor control algorithm with the electromechanical control automation method can provide a theoretical basis and practical guidance for designing and implementing efficient dual motor electromechanical control systems.

Mixed reality alters motor planning and control

Compared to physical unmediated reality (UR), mixed reality technologies, such as Virtual (VR) and Augmented (AR) Reality, entail perturbations across multiple sensory modalities (visual, haptic, etc.) that could alter how actors move within the different environments. Because of the mediated nature, goal-directed movements in VR and AR may rely on planning and control processes that are different from movements in UR, resulting in less efficient motor control. The current study involved participants performing manual pointing movements on Müller-Lyer illusion stimuli to examine the relative contributions of movement planning and online control in UR, VR, and AR. Compared to UR, movements in VR were slower but were equally variable with a comparable level of online control, whereas movements in AR showed comparable speed but exhibited higher variability and less online control. Further, movements in VR and AR demonstrated a greater illusory effect in endpoint accuracy relative to UR. These findings suggested that participants in VR adopted an active compensation strategy to overcome the impact of less efficient online control, whereas participants in AR did not. The findings that movement planning and execution in VR and AR are fundamentally different from those in UR provide valuable insights into the potential neural systems engaged during movements in different realities.

Critical Minerals

Critical minerals are essential for sustaining the supply chain necessary for the transition to a carbon-free energy source for society. Copper, nickel, cobalt, lithium, and rare earth elements are particularly in demand for batteries and high-performance magnets used in low-carbon technologies. Copper, predominantly sourced from porphyry deposits, is critical for electricity generation, storage, and distribution. Nickel, which comes from laterite and magmatic sulfide deposits, and cobalt, often a by-product of nickel or copper mining, are core components of batteries that power electric vehicles. Lithium, sourced from pegmatite deposits and continental brines, is another key battery component. Rare earth elements, primarily obtained from carbonatite- and regolith-hosted ion-adsorption deposits, have unique magnetic properties that are key for motor efficiency. Future demand for these elements is expected to increase significantly over the next decades, potentially outpacing expected mine production. Therefore, to ensure a successful energy transition, efforts must prioritize addressing substantial challenges in the supply of critical minerals, particularly the delays in exploring and mining new resources to meet growing demands. ▪ The energy transition relies on green technologies needing a secure, sustainable supply of critical minerals sourced from ore deposits worldwide. ▪ Copper, nickel, cobalt, lithium, and rare earth elements are geologically restricted in occurrence, posing challenges for extraction and availability. ▪ Future demand is expected to surge in the next decades, requiring unprecedented production rates to make the green energy transition viable.

Enhanced operation of PVWPS based on advanced soft computing optimization techniques

This study introduces three soft computing (SC) optimization algorithms aimed at enhancing the efficiency of photovoltaic water pumping systems (PVWPS). These algorithms include the Gorilla Troop Algorithm (GTO), Honey Badger Algorithm (HBA), and Snake Algorithm (SAO). The goal of the SC optimizers is to maximize the output power of the PV array (PPV) and enhance the efficiency of the DC motor (η), thereby optimizing the water flow rate (Q) of the pumping system. The analytical modeling approach proposed in this study involves forecasting the optimal duty cycle (Dop) for a buck-boost converter, taking into account variables such as solar radiation (G) and ambient temperature (T). A comparative analysis is conducted between the suggested SC optimizers and analytical modeling. MATLAB simulation is employed to explore an adaptive neuro-fuzzy inference system (ANFIS) trained for the proposed system. The objective is to assess system performance and accuracy. Findings indicate a strong convergence between the analytical model and the simulation model utilizing SC optimizers. Moreover, the neuro-fuzzy system trained offline, coupled with the proposed SC optimizers, demonstrates superior performance compared to traditional control methods like perturb and observe (P&O) and incremental conductance (IC). This superiority is evident across various metrics including motor efficiency (η), photovoltaic (PV) output power (PPV), water flow rate (Q), and time response.

Environmental impacts of electric motor technologies: Life cycle approach based on EuP Eco-Report

The industrial sector's need for efficient electric motors is paramount for both energy conservation and climate change mitigation. This study evaluates the environmental impacts of three motor technologies—Squirrel Cage Induction Motors (SCIM) at IE3, Synchronous Reluctance Motors (SynRM), and Permanent Magnet Synchronous Motors (PMSM) at IE5—using a life cycle assessment (LCA) approach with the EuP Eco-Report tool. The focus is on 11 kW, 4-pole motors, representing typical industrial applications. Results show that IE5 SynRMs and PMSMs offer significantly higher operational efficiency, with PMSMs reaching 95 % efficiency compared to 92.7 % for SCIMs. However, the environmental trade-offs are notable: SynRMs require 156.9 kg of materials, including 80.5 kg of electrical steel, while PMSMs demand 1.8 kg of rare earth materials, contributing to higher manufacturing impacts. Total energy consumption over 15 years in the operational phase reveals a consumption of 3.88 TJ for SCIM, 3.83 TJ for SynRM, and 3.79 TJ for PMSM, indicating substantial savings from higher efficiency motors. Despite their efficiency, PMSMs exhibit higher impacts in water use (1172 l in manufacturing) and heavy metal emissions, primarily due to challenges in recycling rare earth components. This analysis highlights the environmental cost-benefit trade-offs between improving operational efficiency and managing the resource-intense production of higher-efficiency motors. As electric motor technology evolves, careful consideration of life cycle impacts, especially in manufacturing and end-of-life phases, is essential to achieve sustainable energy solutions.

Health monitoring and fault analysis of induction motors: a review

Abstract Induction motors are in high demand for industrial applications due to their reliable and efficient performance compared to other electrical motors. Therefore, the implementation of a predictive maintenance scheme is necessary to minimise financial losses in terms of revenue and maintenance costs. This scheme involves health monitoring system, aimed to enhance motor reliability, extend their useful lifespan and reduce maintenance expenses. Furthermore, traditional health monitoring systems often involve human intervention, which can slow down the diagnosis process. Therefore, a self-sufficient automated system has evolved to improve motor performance and reliability. This paper presents a comprehensive study of induction motor failures and the various methods used for their diagnosis. Additionally, it also discussed the importance of predictive maintenance and the pivotal role played by automated systems in ensuring the sustained efficiency of induction motors in industrial settings.

Flagellum Pumping Efficiency in Shear-Thinning Viscoelastic Fluids.

Microorganism motility often takes place within complex, viscoelastic fluid environments, e.g., sperm in cervicovaginal mucus and bacteria in biofilms. In such complex fluids, strains and stresses generated by the microorganism are stored and relax across a spectrum of length and time scales and the complex fluid can be driven out of its linear response regime. Phenomena not possible in viscous media thereby arise from feedback between the swimmer and the complex fluid, making swimming efficiency co-dependent on the propulsion mechanism and fluid properties. Here we parameterize a flagellar motor and filament properties together with elastic relaxation and nonlinear shear-thinning properties of the fluid in a computational immersed boundary model. We then explore swimming efficiency, defined as a particular flow rate divided by the torque required to spin the motor, over this parameter space. Our findings indicate that motor efficiency (measured by the volumetric flow rate) can be boosted or degraded by relatively moderate or strong shear-thinning of the viscoelastic environment.

Bearing Electrical Erosion Signal Detection Technique Based on Current Signal Analysis for Electric Motors

Abstract In modern industries, enhancing the efficiency and performance of electric motors is a critical requirement. Pulse width modulation (PWM) inverters utilized to enhance the energy efficiency of electric motors generate complex shaft voltages and bearing currents, leading to bearing electrical erosion. This study proposes a new method for detecting high-frequency (HF) circulating current and electric discharge machining (EDM) current signals in bearings for electric motors. The proposed method utilizes common mode voltage (CMV) and bearing current data, analyzing the relationship between these signals. Subsequently, it applies filtering techniques and differentiation for signal preprocessing. The Interquartile Range (IQR) method is used to detect outliers, classify HF circulating current and EDM current signals, and perform time-series data clustering to determine the occurrence frequency and timing of EDM signals. Finally, the implementation outcomes of the proposed method are validated, and its classification efficacy is evaluated and benchmarked against established methodologies through a comprehensive performance analysis. The proposed technique is anticipated to be applicable to the maintenance and prediction of modern electric motors in future developments, contributing to enhanced durability and reliability.

A multi-objective optimal control approach to motor strategy changes in older people with mild cognitive impairment during obstacle crossing.

Mild cognitive impairment (MCI) may lead to difficulty maintaining postural stability and balance during locomotion. This heightened susceptibility to falls is particularly evident during tasks such as obstacle negotiation, which demands efficient motor planning and reallocation of attentional resources. This study proposed a multi-objective optimal control (MOOC) technique to assess the changes in motor control strategies during obstacle negotiation in older people affected by amnestic MCI. Motion data from 12 older adults with MCI and 12 controls when crossing obstacles were measured using a motion capture system, and used to obtain the control strategy of obstacle-crossing as the best compromise between the conflicting objectives of the MOOC problem, i.e., minimising mechanical energy expenditure and maximising foot-obstacle clearance. Comparisons of the weighting sets between groups and obstacle heights were performed using a two-way analysis of variance with a significance level of 0.05. Compared to the controls, the MCI group showed significantly lower best-compromise weightings for mechanical energy expenditure but greater best-compromise weightings for both heel- and toe-obstacle clearances. This altered strategy involved a trade-off, prioritising maximising foot-obstacle clearance at the expense of increased mechanical energy expenditure. The MCI group could successfully navigate obstacles with a normal foot-obstacle clearance but at the cost of higher mechanical energy expenditure. MCI alters the best-compromise strategy between minimising mechanical energy expenditure and maximising foot-obstacle clearances for obstacle-crossing in older people. These findings provide valuable insights into how MCI impacts motor tasks and offer potential strategies for mitigating fall risks in individuals with MCI. Moreover, this approach could serve as an assessment tool for early diagnosis and a more precise evaluation of disease progression. It may also have applications for individuals with impairments in other cognitive domains.

High-Efficiency e-Powertrain Topology by Integrating Open-End Winding and Winding Changeover for Improving Fuel Economy of Electric Vehicles

The fuel economy of electric vehicles (EVs) is an important factor in determining the competitiveness of EVs. Since the fuel economy is affected by the efficiency of an e-powertrain composed of a motor and inverter, it is necessary to select a high-efficiency topology for the e-powertrain. In this paper, a novel topology of e-powertrains to improve the fuel economy of EVs is proposed. The proposed topology aims to improve the system efficiency by integrating open-end winding (OEW) and winding changeover (WC). The proposed OEW-PMSM with WC enables to drive a permanent magnet synchronous motor (PMSM) in four different modes. Each mode can increase inverter efficiency and motor efficiency by changing motor parameters and maximum modulation index. In this paper, the system efficiency of the proposed topology was evaluated using electromagnetic finite element analysis and a loss model of power semiconductors. In addition, the vehicle simulations were performed to evaluate the fuel economy of the proposed topology, thereby proving the superiority of the proposed topology compared with the conventional PMSM.

Optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant

This paper presents optimization of Steam Boiler Airflow Control System in a Typical Oil Refinery Power Plant. The power plant is equipped with four steam boilers using a damper-louvers airflow control system. Analysis has revealed significant energy wastage due to inefficiency and over-air supply, with the system requiring 59,904,436 kWh annually at a financial cost of N1, 093,255,884. The existing system's inefficiency results in an annual electrical energy waste of 8,502,302 kWh. This inefficiency is attributed to the Force Draft Fan (FDF) motor operating at maximum speed continuously, regardless of the steam profile or airflow requirements. This study aimed at optimizing the steam boiler airflow control system in a typical oil refinery power plant for improved power efficiency, energy conservation, and cost savings citing Port Harcourt Refinery Company (PHRC) at Alesa-Eleme, Rivers State, Nigeria. To address this, the damper-louvers system was replaced with a Variable Frequency Drive (VFD), which would adjust the motor speed to match the required airflow, thereby reducing energy wastage. The process was simulated and VFD/FDF motor analyzed in MATLAB Simulink, determining the optimal speed, airflow, and power requirements. Results obtained show that the VFD control system improves the FDF motor's power efficiency from 47.7% to 85%, with an annual energy saving of 8,502,302 kWh (8.5 GWh). This results in a total cost benefit of N478.18 million annually, representing a 43.8% cost saving. The implementation cost of the VFD infrastructure was estimated at N111, 440,000, with a payback period of approximately eleven months. The findings suggest deployment of higher FDF motor speed ratings to enhance speed control and energy efficiency in Oil refinery power plant.

Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation

Motor design involves multiple complex parameters, and traditional methods rely on experience and experimentation, which are inefficient and difficult to optimize. With the rapid development of emerging industries such as electric vehicles and intelligent manufacturing, the performance requirements for motors are constantly increasing. Optimizing design under multi-objective and multi-constraint conditions has become a key challenge. To address this, this paper proposes a motor design performance optimization algorithm based on Graph Neural Networks (GNN) representation and adaptive weighting. GNN, as a deep learning model capable of handling complex structured data, can model the multi-parameter relationships in motor design and automatically extract key features through its feature propagation mechanism, thus overcoming the difficulty of capturing parameter dependencies with traditional methods. At the same time, Mixed-Integer Linear Programming (MILP) provides a powerful global optimization tool that can find the global optimal solution when dealing with complex decision variables and constraints, overcoming the shortcomings of traditional optimization algorithms in terms of global convergence. Moreover, the adaptive weighting mechanism allows the optimization algorithm to dynamically adjust the weights according to the influence of parameters on motor performance, ensuring the accuracy and adaptability of optimization results in different scenarios. Through the organic combination of these three methods, this paper aims to solve the problems of low efficiency, poor global convergence, and inability to dynamically adjust the importance of design parameters in traditional motor design optimization. By introducing advanced machine learning models and optimization algorithms, this work constructs an efficient motor design performance optimization framework.

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.