Electric Vehicle Powertrain Research Articles

Single- and Three-Phase Dual-Active-Bridge DC–DC Converter Comparison for Battery Electric Vehicle Powertrain Application

Dual-active-bridge (DAB) DC–DC converters are of great interest for DC–DC conversion in battery electric vehicle (BEV) powertrain applications. There are two versions of DAB DC–DC converters: single-phase (1p) and three-phase (3p) architectures. Many studies have compared these architectures, selecting the 3p topology as the most efficient. However, there is a gap in the literature when comparing both architectures when single-phase-shift (SPS) modulation is not used to drive the converter. The aim of this study was to compare 1p and 3p DAB DC–DC converters driven by optimal modulation techniques appropriate for BEV powertrain applications. Mathematical loss models were derived for both architectures, and their performances were compared. A case study of a 100 kW converter was considered as an example to visualize the overall efficiency of the converter for each layout. The 1p DAB DC–DC converter architecture outperformed the 3p layout in both its Y–Y and D–D transformer configurations. The higher performance efficiency, lower number of components, and reduced design complexity make the 1p DAB DC–DC converter topology a favorable choice for BEV powertrain applications.

Boron nitride: The key material in polymer composites for electromobility

AbstractDespite the continuous development and improvement of many technologies and multifunctional materials for the electric powertrain (ePowertrain) for electric vehicles, there are still technical issues and challenges to address such as thermal management in batteries, electric motors, and power electronic devices, as most of their failures are due to poor thermal management. Consequently, conventional engineering polymer materials already used must be replaced since most of them have low thermal conductivity and are therefore limited in performance for thermal management applications. A key solution is to develop highly thermally conductive polymer composites that combine other features, such as flame‐retardant, electrical insulation, and mechanical and barrier properties, by incorporating fillers into the polymer matrix. This approach has attracted intensive research efforts. In this review, we first examine the key drivers, trends, and solutions of the ePowertrain segment, emphasizing thermal management. Second, special attention is given to the state‐of‐the‐art boron nitride (BN) polymer composites with current or potential applications in the automotive industry, especially, in batteries, electric motors, and power electronics. Third, analysis and prediction of thermal properties of BN polymer composites by finite element simulation are presented. Finally, outlooks for future research in this field are highlighted.Highlights Thermal management of batteries, electric motors and power electronics, using BN polymer composites, optimizes the functionality of electric vehicles. Cross‐linked polymers with BNNSs provide resins for high power motors, film capacitors, and Li‐metal battery electrolytes for electric vehicles. Mathematical modeling and life cycle analysis can predict trends and research gaps in ePowertrain applications.

ELECTRIC VEHICLE POWERTRAIN DESIGN: INNOVATIONS IN ELECTRICAL ENGINEERING

The electrification of vehicles is reshaping transportation, with electric vehicle (EV) powertrain design playing a key role in this shift. This review focuses on recent innovations in electrical engineering that have enhanced EV powertrains, particularly in areas like electric motors, power electronics, energy storage, and thermal management. Advances in motor technology, such as permanent magnet synchronous motors (PMSM), and the integration of silicon carbide (SiC) and gallium nitride (GaN) semiconductors have improved efficiency and reduced heat generation. Battery innovations, including solid-state technologies and advanced battery management systems, along with regenerative braking, have extended EV range and efficiency. Power electronics, including inverters and onboard chargers, now utilize wide-bandgap semiconductors to minimize energy losses and improve thermal performance. Additionally, novel cooling solutions are addressing thermal challenges in EV systems. Looking forward, modular powertrain architectures and AI-driven control strategies offer promising advancements for various vehicle types. This review provides an overview of how electrical engineering innovations are driving the future of EV powertrain design and sustainable transportation.

A Hybrid Energy Sources Based System for Powertrain of Electric Vehicles

A Hybrid Energy Sources Based System for Powertrain of Electric Vehicles

Estimation of SOC for Li-ion battery-powered three-wheeled electric vehicle using machine learning methods

Abstract The Battery Management System (BMS) serves as the heart of the electric vehicle system, in which estimating the state of charge (SOC) is the crucial part of the BMS to ensure the durability, reliability, and sustainability of the battery pack. Due to its nonlinear characteristics, accurately estimating the SOC for a slow degradation of the charge is highly cumbersome. The literature provides a series of machine learning algorithms (MLA) to estimate and predict the SOC of lithium-ion (Li-ion) battery systems for electric vehicle (EV) applications. The literature has proposed various MLA, coulomb counting, and different Kalman filter methods to address this challenge and estimate the SOC of Li-ion battery systems for EV applications. This research looks at the differences and similarities between the coulomb counting method, the unscented Kalman filter method, and a number of machine learning algorithms. These include linear regression, polynomial linear regression, support vector regression, decision trees, random forests, artificial neural networks (ANN), and long short-term memory (LSTM). The goal is to assess the MLAs' accuracy in estimating battery SOC. Analyzing model errors optimizes the battery's performance parameter. We identify ANN and LSTM as the two most efficient methods for accurate SOC estimation in an EV-operated BMS system by evaluating the performance indices of the aforementioned machine learning methodologies. Once again, the LSTM model for SOC estimation has proven to be highly accurate in analyzing the discrepancy between the actual and predicted traveling ranges of the designed prototype. We design a MATLAB/SIMULINK EV powertrain by collecting realtime data from the Li-ion battery pack, analyzing the SOC variation data, and using the previously mentioned MLA in the Python platform to estimate the SOC and its accuracy. It highlights the effectiveness of advanced MLAs in improving SOC estimation, thereby enhancing the performance and reliability of EV battery systems.

Analysis of Aluminum–Cerium Based Conductor Die-Casting Alloys for High Performance Electric Vehicle Powertrain Applications

Analysis of Aluminum–Cerium Based Conductor Die-Casting Alloys for High Performance Electric Vehicle Powertrain Applications

Simulating Noise, Vibration, and Harshness Advances in Electric Vehicle Powertrains: Strategies and Challenges

This study examines the management of noise, vibration, and harshness (NVH) in electric vehicle (EV) powertrains, considering the challenges of the automotive industry’s transition to electric drivetrains. The growing popularity of electric vehicles brings new NVH challenges as the lack of internal combustion engine noise makes drivetrain noise more prominent. The key to managing NVH in electric vehicle powertrains is understanding the noise from electric motors, inverters, and gear systems. Noise from electric motors, mainly resulting from electromagnetic forces and high-frequency noise generated by inverters, significantly impacts overall NVH performance. This article details sources of mechanical noise and vibration, including gear defects in gear systems and shaft imbalances. The methods presented in the publication include simulation and modeling techniques that help identify and solve NVH difficulties. Tools like multi-body dynamics, the finite element method, and multi-domain simulation are crucial for understanding the dynamic behavior of complex systems. With the support of simulations, engineers can predict noise and vibration challenges and develop effective solutions during the design phase. This study emphasizes the importance of a system-level approach in NVH management, where the entire drivetrain is modeled and analyzed together, not just individual components.

Exploring tribo-electro-chemistry mechanisms of h-BNO@PDA as lubricant additives under electrified conditions for electrical vehicles powertrain

The study explores the use of polydopamine-functionalized hexagonal boron nitride (h-BNO@PDA) as a lubricant/insulator additive in polyalphaolefin (PAO6) oil. This article's novelty lies in the formulation of an innovative lubricant that is free from sulfated ash, phosphorus, and sulfur (SAPS), exhibiting super-lubrication and desirable dielectric properties under electrified conditions. The h-BNO@PDA nanolubricant is specifically designed to mitigate the negative effects of shaft currents and enhance powertrain lubrication for electric vehicles (EVs). Herein, the physicochemical, dielectric strength, and tribological properties of h-BNO@PDA nanolubricant are compared to commercial fully synthetic oil (ATF-6S) used in EVs. The SRV-IV tribometer was used to assess tribological performance under various currents (0–10 A). Subsequently, characterization techniques were utilized to illustrate wear mechanisms and explore tribochemical reactions. The h-BNO@PDA nanolubricant presented superior physicochemical and dielectric properties. The h-BNO@PDA nanolubricant reduced the friction coefficient by 18–20 % and the wear rate by 4–72 % compared to ATF-6S oil under electrified conditions at temperatures of 27 °C and 100 °C. These findings can be attributed to the positive effects of oxidational wear (Fe3O4) and h-BNO@PDA on tribolayer formation under electrification conditions. Eventually, our findings will offer promising insights to enhance the durability of EVs powertrain.

Optimized flow rate and efficient shaft water cooling method for EV's PMSM temperature-dependent energy system output power increase

One possible way to enhance the power density of electric vehicle (EV) powertrains is by utilizing temperature-sensitive permanent magnet synchronous motors (PMSMs) managed through liquid cooling. The management of benefits and costs from EV PMSM liquid cooling versus power are carried out for energy system output power (ESOP) increase. This study examines the effects of optimizing flow rate into housing water jackets (HWJ) and implementing compound shaft water (SWJ) cooling as management strategies using numerical multiphysics simulations and experiments. The PMSM with higher temperature at higher speed shows larger profit space brought by enhanced cooling. The optimized HWJ flow rate is a highly-efficient strategy to increase ESOP when overall pumping efficiency e1 for HWJ is less than 5 %, increasing more than 5.6 %, 2.1 % and 2.4 % ESOP at 1500, 3500, and 7000 rpm continuous working point. ESOP is insensitive to the condition of HWJ flow rate below 6 L/min or e1 over 30 %. The SWJ cooling is proved to be a low-cost and high-yield energy management strategy roughly independent of SWJ flow rate and EV's cooling system arrangement. ESOP is further increased by up to 3.9 %, 6.2 % and 4.4 % with SWJ cooling at 1500, 3500, and 7000 rpm.

Analysis of the Current Status and Prospects of Extended Range Electric Vehicle Powertrain under the Background of Carbon Peaking and Carbon Neutrality Goals

Analysis of the Current Status and Prospects of Extended Range Electric Vehicle Powertrain under the Background of Carbon Peaking and Carbon Neutrality Goals

Study on the sound quality of the electric vehicle powertrain under acceleration conditions

The evaluation and prediction of the sound quality (SQ) of electric vehicle (EV) powertrains are critical to the overall SQ of EVs. Firstly, a grouping method for noise samples is proposed to achieve rational grouping when SQ evaluations are performed using the grouped paired comparison method and its improvements. Secondly, aiming at the limitations of psychoacoustic parameters in predicting SQ under nonstationary conditions, a SQ prediction model based on the energy features of intrinsic mode functions (IMF) of signals is proposed. Finally, SQ evaluations are conducted, comparing the prediction performance of two SQ models based on energy features and psychoacoustic parameters. The prediction results show that the mean absolute percentage error (MAPE) of the model with energy features is 4.18%, while the MAPE of the model with psychoacoustic parameters is 8.88%, which demonstrates that energy features are superior in predicting the SQ of EV powertrains under acceleration conditions.

Review of Hybrid Energy Storage Systems for Hybrid Electric Vehicles

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles is discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we compare key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For energy storage systems employing ultra capacitors, we present characteristics such as cell voltage, cycle life, power density, and energy density. Furthermore, we discuss and evaluate the interconnection topologies for existing energy storage systems. We also discuss the hybrid battery–flywheel energy storage system as well as the mathematical modeling of the battery–ultracapacitor energy storage system. Toward the end, we discuss energy efficient powertrain for hybrid electric vehicles.

Optimizing electric vehicle powertrains peak performance with robust predictive direct torque control of induction motors: a practical approach and experimental validation

Enhancing the efficiency of the electric vehicle’s powertrain becomes a crucial focus, wherein the control system for the traction motor plays a significant role. This paper presents a novel electric vehicle traction motor control system based on a robust predictive direct torque control approach, an improved version of the conventional DTC, where the traditional switching table and the hysteresis regulators are substituted with a predictive block based on an optimization algorithm. Additionally, a robust predictive speed loop regulator is employed instead of the proportional-integral regulator, which integrates a new cost function with a finite horizon, incorporating integral action into the control law based on a Taylor series expansion. This technique’s primary benefit is its independence from the necessity to measure and observe external disturbances, as well as uncertainties related to parameters. The effectiveness of the suggested system was confirmed through simulation and experimental results under the OPAL-RT platform. The findings indicate that the proposed approach outperforms the conventional method in terms of rejecting disturbances, exhibiting robustness to variations in parameters, and minimizing torque ripple.

Comparative Simulation Analysis of Electric Vehicle Powertrains with Different Configurations Using AVL Cruise and MATLAB Simulink

Electric vehicles are now recognized as a crucial answer in the worldwide effort to achieve sustainable and environment-friendly transportation. As the automotive industry moves towards using electric power, it is crucial to assess and improve the powertrain configurations in electric vehicles. This study aims to meet this need by conducting an in-depth comparative analysis of single, double, and quad electric vehicle powertrain systems. The performance of these configurations is rigorously simulated by using two widely used platforms: AVL Cruise and MATLAB Simulink. This study mainly covers the analysis of energy efficiency, which is a critical factor in determining the environmental impacts and feasibility of electric vehicles. In order to conduct a thorough comparison, the energy consumption per kilometer is evaluated as a crucial performance measure. Our research primarily focuses on model validation, namely by comparing it with manufacturer data to determine the accuracy and reliability of the simulation results. The findings reveal a compelling narrative in the pursuit of sustainable transportation. The use of a dual motor setup stands out as a prominent example of energy efficiency, demonstrating remarkable outcomes in both simulation platforms. Significantly, these findings closely correspond with the manufacturer's data for the Volvo XC40, confirming the appropriateness and dependability of our simulation models. Furthermore, the quad motor configuration shows significant energy efficiency, providing helpful insight regarding its applicability and performance. The broader implications of this research go beyond powertrain configurations, including the trustworthiness of simulation models in the automotive industry. These findings improve the continuous advancement of electric vehicle design and development, indicating a more environment-friendly and energy-efficient future for the automotive industry. This study offers invaluable insights and benchmarks for the transition to environment-friendly transportation solutions, as the world advances towards sustainable mobility.

PROCESS MONITORING IN HYBRID ELECTRIC VEHICLES BASED ON DYNAMIC NONLINEAR METHOD

Highway third-level faults can significantly deteriorate the reliability and performance of hybrid electric vehicle (HEV) powertrains. This study presents a novel process monitoring method aimed at addressing this issue. We propose a multivariate statistical method based on dynamic nonlinear improvement, namely dynamic neural component analysis (DNCA). This method does not require the establishment of precise analytical models; instead, it only necessitates acquiring data from HEV powertrains. Through numerical simulation and real HEV experiments, we demonstrate the effectiveness of this approach in monitoring highway third-level faults. The testing outcomes demonstrate that DNCA outperforms traditional dynamic methods like dynamic principal component analysis (DPCA), conventional nonlinear methods such as kernel PCA (KPCA) and NCA, as well as traditional dynamic nonlinear methods like DKPCA.

Design, Analysis, and Mode Shifting of a Novel Dual-Motor Powertrain for Electric Vehicles

Design, Analysis, and Mode Shifting of a Novel Dual-Motor Powertrain for Electric Vehicles

Introducing a 12/10 Induction Switched Reluctance Machine (ISRM) for Electric Powertrains

The induction switched reluctance machine (ISRM) is a novel electric machine that integrates the switched reluctance machine (SRM) with rotor inductive conductors to enhance performance in electric vehicle (EV) powertrain applications. In this topology, the rotor conductors act as a magnetic shield, diverting magnetic flux and preventing magnetic field lines from penetrating the rotor body. By engineering this design, short magnetic flux paths are created in both the stator and rotor of the electric machine. Since its recent introduction, the ISRM represents an emerging technology in the early stages of development. Similar to conventional SRMs, the ISRM can take on various topologies with different stator and rotor pole numbers. Minimizing rotor copper loss is a critical consideration in the ISRM design process. This paper examines two distinct ISRM topologies (12/10 and 12/8), and their characteristics are analyzed using the finite element method. Simulation results, including power density, torque density, efficiency, and copper loss, are presented and compared. Finally, the optimal ISRM topology is proposed for hybrid electric powertrains.

AC Direct Charging for Electric Vehicles via a Reconfigurable Cascaded Multilevel Converter

This paper presents a charging architecture for the Reconfigurable Cascaded Multilevel converter, which was specifically designed for electric vehicle (EV) powertrain applications. The RCMC topology is capable of executing power conversion and actively managing battery systems concurrently. The active battery management is achieved using the Reconfigurable Battery Module, which regulates the serial connection of cells via a switch pattern. In this paper, the RCMC is directly interfaced with an AC three-phase power system, facilitating the dynamic control over battery cells charging. Its inherent design allows for the implementation of various charging algorithms, customizable to specific requirements, without necessitating additional intermediary power stages. Firstly, an overview of the RCMC topology is given, and an analysis to define the optimal filter inductance is carried out. Subsequently, after the AC system characteristics are explained, two charging algorithms are presented and described: one prioritizes State of Charge (SOC) balancing among battery cells, while the other focuses on minimizing power losses. Moreover, a time estimation computation for the RCMC is carried out considering a two-level AC charging station. The result is compared with the time required for a conventional battery pack. The results show a reduction of 10 s in charging time for a mere 20% increase in SOC. Finally, the experimental setup is presented and used to validate the efficacy of the proposed algorithms.

Algebraic Speed Estimation for Sensorless Induction Motor Control: Insights from an Electric Vehicle Drive Cycle

Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme.

Application of Digital Twin in Electric Vehicle Powertrain: A Review

Digital Twin (DT) is widely regarded as a highly promising technology with the potential to revolutionize various industries, making it a key trend in the Industry 4.0 era. In a cost-effective and risk-free setting, digital twins facilitate the interaction and merging of the physical and informational realms. The application of digital twins spans across different sectors, including aerospace, healthcare, smart manufacturing, and smart cities. As electric vehicles have experienced rapid growth, there is a growing demand for the development of innovative technologies. One potential area for digital twins application is within the automotive sector. The powertrain system of electric vehicles (EVs) consists of three parts, power source, power electronic system, and electric motor, which are considered as the core components of electric vehicles. The focus of this paper is to conduct a methodical review regarding the use of digital twins in the powertrain of electric vehicles (EVs). While reviewing the development of digital twin technology, its main application scenarios and its use in electric vehicle powertrains are analysed. Finally, the digital twins currently encounter several challenges that need to be addressed, and so the future development of their application to electric vehicles are summarized.