Electric Vehicle Applications Research Articles

A 31 L multilevel inverter topology with less switching devices for hybrid electric vehicle applications

In this article, a 12 switch 31 L multi-level inverter (MLI) is proposed with the benefits of least switching devices for electric vehicle applications. In most electric vehicles (EV), conventional inverters are utilized so the lifetime of electric vehicle induction motors is reduced due to the high THD level and high voltage stress. To rectify this, a new inverter topology is proposed with minimum switching devices by increasing the level to 31, and also the THD should be maintained within IEEE standards. This inverter topology is constructed with variable DC sources (PV system) along with required capacitors and a polarity changer. The specific feature of this topology is that it can generate any level of voltage with minimum switching devices, less voltage stress, minimum THD, and less cost are tabulated in the comparison. This type of inverter can also applicable for high voltage (HV) applications and grid-connected systems. Furthermore, the proposed topology is simulated with MATLAB software and the downscale prototype model is developed using the DSPIC30f2010 controller.

Innovations in Energy Through Nanomaterials Enhancing Solid-State Battery Safety and Efficacy

The development of solid-state batteries (SSBs) represents a significant advancement in energy storage technology, addressing the limitations of traditional liquid electrolyte batteries, such as safety concerns and efficiency issues. However, SSBs face challenges including rigid solid-to-solid contacts, poor interfacial stability, and suboptimal performance across temperature ranges. This paper explores the role of nanotechnology in enhancing the performance and safety of SSBs. It highlights the applications of nanomaterials, such as nano-composites, nano-coatings, and embedded nanoparticles, which improve ionic conductivity, mechanical strength, and thermal management. The paper also discusses the challenges of commercializing nanotechnology in SSBs, including high production costs and regulatory hurdles. Despite these challenges, nanotechnology offers promising solutions for increasing the energy density and cycle life of SSBs, making them viable for applications in electric vehicles and consumer electronics. The paper concludes with recommendations for future research directions, emphasizing the need for continued innovation to fully realize the potential of SSBs in various industries.

Manipulation in the In Situ Growth Design Parameters of Aqueous Zinc‐Based Electrodes for Batteries: The Fundamentals and Perspectives

ABSTRACTPrecise exploitation of the growth of Zn metal anode in a power converter system has re‐emerged as one of the technological interests that have surged globally in the past 5 years, specifically to improve the practical use of deep cycling metal batteries. In this review, the in situ architectures of aqueous Zn metal‐based batteries focusing on the intrinsic geometrical building block and their respective mode of assembly classifying the deposition morphologies are scrutinised and discussed. The fundamental electrochemical kinetic principles and the associated critical issues, especially associated with the metal plague deposition that influences the morphology of deposited Zn, are considered. Also, the growing interest in the interphase system, which has an intense influence in characterising the types of Zn deposition morphology, is included. Consideration of the fundamental crystal features of Zn, endowing the predominant key for its growth assembly, is provided. Last, the review offers perspectives on the current progress of Zn–Air batteries in the application of electric vehicles.

High gain Bi-directional KY converter for low power EV applications

In electric vehicles (EVs), the type of electric motor and converter technology have a significant impact on regulating the operational characteristics of the vehicle. Therefore, in this work, the modified bi-directional KY converter (BKYC) is proposed for EV applications. The main contributions of the proposed converter are high step-up/step-down conversion gain, bi-directional power flow, simplified control structure, continuous current, common ground, low volume, and high efficiency. An inductor on either side of the converter ensures continuous current flow and passive components are arranged to operate in series to offer high step-up/step-down conversion. The charging and discharging operations, steady-state analysis, and design process of the proposed converter are discussed in detail and compared with similar bi-directional converter topologies. Further, the efficiency analysis of the proposed converter is presented and found that the efficacy of 95.51 % in charging operation and 96.52 % in discharging operation of operation. The simulations are carried out using MATLAB/Simulink environment. Further, a prototype of a modified bi-directional KY converter is implemented with a TMS320F28335 processor and validated with theoretical and simulation counterparts.

A Deep Learning Dependent Controller for Advanced Ultracapacitor SoC Concept to Increase Battery Life Span of Electric Vehicles

ABSTRACTOn a global scale, significant progress is being made in the field of battery technology for Electric Vehicle (EV) applications, driven by the need to combat carbon emissions and mitigate the effects of global warming. Accurately determining critical parameters, making sure battery storage system diagnosis, and functioning are correct are critical to the feasibility of EVs. However, insufficient supervision and safety measures for these storage systems may lead to serious problems like a thermal runaway, overcharging, overheating, cell imbalances, and fire hazards. To tackle these challenges, the presence of an efficient battery management system becomes paramount. By facilitating accurate monitoring, managing heat dissipation, regulating charging‐discharging procedures, guaranteeing battery safety, and offering protection measures, this system is essential to maximizing battery performance. The key intention of this innovative approach is to improve the longevity of EV batteries during extended periods of operation. By assessing vehicle velocity, remaining battery energy, and State of Charge (SoC), the proposed method effectively manages SoC in both the battery and ultracapacitor. This control is accomplished through a two‐stage convolutional neural network‐based system known as the Charge Sustain‐CNN Controller and the Charge Deplete‐CNN Controller. These controllers are fine‐tuned using the Fractional Latrans‐Hunt optimization (FLHO) algorithm to optimize the performance. The evaluation criteria encompass the battery and ultracapacitor's energy efficiency, as well as vehicle velocity. This novel approach significantly improves the energy storage system in EVs, leading to enhanced energy efficiency and prolonged battery life. Ultimately, experimental results validate the practicality and effectiveness of this developed method. Specifically, the proposed approach attained the Battery's SoC of 72.47%, 91.99%, and 82.88% for the different drive cycles including the FTP75, J1015, and UDDS, respectively.

Artificial Neural Network-Based Hybrid Controller for Electric Vehicle Applications

Power management among different energy sources of electric vehicles (EV) is one of the complex issues during the transition from one to another. A specific control is modeled based on the current and speed range of the electric motor named as Measurement of Parameter-Based Controller (MPBC), which will play a key role during transition of energy sources as per the load requirement. Two bidirectional converters are utilized to control the pulse signals generated by the traditional controllers which are connected at the battery and Supercapacitors (SCap) ends, which are treated as passive sources of the system. The Controller's artificial neural network (ANN), fuzzy logic (FLC), and proportional-integral (PI) are utilized to generate the pulse signal to the switches present in the converters by load. Further, specific controller MPBC is combined with three controllers ANN/FLC/PI individually and obtained three separate hybrid controllers as per the proposed control technique. The MPBC+PI/FLC/ANN controller-based MATLAB/Simulink model was designed, and applied to the electric motor individually at different load conditions. This model considered three different power delivery states from the PV array and assessed the motor's performance under different load scenarios. Compare the three hybrid controllers' final results to find out which one is more effective than the others.

Advancing sustainable development: Introducing a novel fast charging technique for Li-ion batteries with supercapacitor integration

Li-ion batteries play a vital role in today's world for their wide range of applications in electrical vehicles, mobile phones, electronic gadgets etc. Applying fast charging to Li-ion batteries is crucial for increasing consumer convenience, productivity, energy efficiency, and stimulating technical innovation across various industries. With the wide-spread usage of Li-ion batteries, it is critical to consider the environmental degradation caused by their decomposition. Therefore, solutions for fast charging Li-ion batteries must not only be reliable but also sustainable to meet future demands. This paper presents a novel Li-ion battery fast charging technology with an integrated supercapacitor and battery, for quick charging without shortening the battery's life cycle. The advantage of the proposed charger is that the supercapacitor assists the Li-ion battery while charging, thereby fast charging and improving the efficiency and longevity of the battery. The utilization of supercapacitor-assisted charging diminishes the burden on lithium-ion batteries, enhancing their lifespan and consequently reducing environmental harm. To validate the performance of the proposed charging technology, an experimental investigation has been carried out. The obtained results through the investigation show the increased performance of 82 % efficiency and 40 % longevity more than the conventional method. The proposed supercapacitor-based fast charging technique is not only efficient but also sustainable and reliable.

Design and performance evaluation of a multi-load and multi-source DC-DC converter for efficient electric vehicle power systems

This paper introduces the design and comprehensive performance evaluation of a novel Multi-Load and Multi-Source DC-DC converter tailored for electric vehicle (EV) power systems. The proposed converter integrates a primary battery power source with a secondary renewable energy source—specifically, solar energy—to enhance overall energy efficiency and reliability in EV applications. Unlike conventional multi-port converters that often suffer from cross-regulation issues and limited scalability, this converter ensures stable power distribution to various EV subsystems, including the motor, air conditioning unit, audio systems, and lighting. A key feature of the design is its ability to independently manage multiple power loads while maintaining isolated outputs, thus eliminating the inductor current imbalance that is common in traditional systems. Experimental validation using a 100 W prototype demonstrated the converter’s ability to deliver stable 24 V and 48 V outputs from a 12 V input, with output voltage deviations kept within ± 1%, significantly improving upon the ± 5% deviations typically seen in existing converters. Furthermore, the system achieved an impressive 93% efficiency under variable load conditions. The modular nature of the converter makes it not only suitable for EV applications but also for a broader range of industries, including renewable energy systems and industrial power supplies. This paper concludes by discussing optimization strategies for future improvements and potential scaling of the technology for commercial use in sustainable energy applications.

Data-Driven Approaches for State-of-Charge Estimation in Battery Electric Vehicles Using Machine and Deep Learning Techniques

One of the most important functions of the battery management system (BMS) in battery electric vehicle (BEV) applications is to estimate the state of charge (SOC). In this study, several machine and deep learning techniques, such as linear regression, support vector regressors (SVRs), k-nearest neighbor, random forest, extra trees regressor, extreme gradient boosting, random forest combined with gradient boosting, artificial neural networks (ANNs), convolutional neural networks, and long short-term memory (LSTM) networks, are investigated to develop a modeling framework for SOC estimation. The purpose of this study is to improve overall battery performance by examining how BEV operation affects battery deterioration. By using dynamic response simulation of lithium battery electric vehicles (BEVs) and lithium battery packs (LIBs), the proposed research provides realistic training data, enabling more accurate prediction of SOC using data-driven methods, which will have a crucial and effective impact on the safe operation of electric vehicles. The paper evaluates the performance of machine and deep learning algorithms using various metrics, including the R2 Score, median absolute error, mean square error, mean absolute error, and max error. All the simulation tests were performed using MATLAB 2023, Anaconda platform, and COMSOL Multiphysics.

Aging mechanism of Ni-rich cathode-based lithium-ion batteries: Focusing on upper cut-off voltages

Ni-rich cathode-based lithium-ion batteries are subject to certain limitations in electric vehicle applications due to their cycle life. Therefore, this study aims to investigate the aging mechanism of Ni-rich cathode-based lithium-ion batteries with different upper cut-off voltages, providing ideas for improving their cycle life. The change in the cathode lattice during the charging and discharging process obtained by in-situ XRD shows that the H2-H3 phase transition exacerbates the battery capacity degradation. In addition, the capacity degradation caused by lattice strain induced by the rapid release of anisotropic stress accumulation (ASA) in the H2 region cannot be ignored as well. After lowering the upper cut-off voltage, the surplus lithium in the cathode plays a pivotal role in avoiding the above rapid battery degradation, driven by the advantages of its ability to stabilize lattice structure and reduce the degree of Li/Ni cation mixing. Meanwhile, the low upper cut-off voltage effectively avoids side reactions such as electrolyte decomposition and transition metal dissolution. The above results would contribute to slowing down battery degradation, which would provide strong support for the design of high energy density and long-life lithium-ion batteries.

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.

Transition from Electric Vehicles to Energy Storage: Review on Targeted Lithium-Ion Battery Diagnostics

This paper examines the transition of lithium-ion batteries from electric vehicles (EVs) to energy storage systems (ESSs), with a focus on diagnosing their state of health (SOH) to ensure efficient and safe repurposing. It compares direct methods, model-based diagnostics, and data-driven techniques, evaluating their strengths and limitations for both EV and ESS applications. This study underscores the necessity of accurate SOH diagnostics to maximize battery reuse, promoting sustainability and circular economy objectives. By providing a comprehensive overview of the battery lifecycle—from manufacturing to recycling—this research offers strategies for effective lifecycle management and cost-effective, environmentally sustainable secondary battery applications. Key findings highlight the potential of second-life EV batteries in ESSs. The integration of the considered diagnostic methods was shown to extend battery lifespan by up to 30%, reduce waste, and optimize resource efficiency, which is crucial for achieving circular economy objectives. This paper’s insights are crucial for advancing sustainable energy systems and informing future research on improving diagnostic methods for evolving battery technologies.

A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model’s superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.

Research on Performance of Interior Permanent Magnet Synchronous Motor with Fractional Slot Concentrated Winding for Electric Vehicles Applications

The fractional-slot, concentrated-winding, interior permanent magnet synchronous motor (FSCW IPMSM) has advantages, such as reducing motor copper consumption, improving flux-weakening capability, and motor fault tolerance, and has certain development potential in application fields such as electric vehicles. However, fractional-slot concentrated-winding motors often contain rich harmonic components due to their winding characteristics, leading to increased motor losses and back electromotive force harmonics, thereby affecting the efficiency and constant power speed regulation range of the motor. Based on this, this article first uses the winding function method to explore the inductance and saliency ratio of the interior permanent magnet synchronous motor with different slot pole combinations in the fractional-slot concentrated- winding of electric vehicles. Secondly, this article will establish a 2D finite element parameterized model to analyze and compare the performance of fractional-slot concentrated-winding motors with different slot pole combinations, including air gap magnetic density, back electromotive force distortion rate, overload multiple, and torque. The structural parameters of the motor were optimized with the objective of minimizing the torque ripple under the constraint of minimizing the average torque reduction. The motor slot width, permanent magnet angle, and permanent magnet pole arc angle were analyzed and optimized. The simulation results showed that 12 slots and 8 poles were the optimal design schemes, providing a theoretical basis for the selection of slot pole coordination in the fractional-slot concentrated-winding interior permanent magnet synchronous motor for electric vehicles.

Enhanced Vehicle Logo Detection Method Based on Self-Attention Mechanism for Electric Vehicle Application

Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the self-attention mechanism. Our feature-sampling structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the multi-head attention for multi-scale feature fusion module to capture multi-scale contextual information effectively. Moreover, we incorporate the bidirectional aggregation mechanism to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Our experimental evaluation using the VLD-45 dataset achieves a state-of-the-art result of 90.3% mAP. Our method has also improved AP by 10% for difficult samples, such as HAVAL and LAND ROVER. Our method provides a new detection framework for small-size objects, with potential applications in various fields.

Prediction of the Remaining Useful Life of Lithium-Ion Battery Using Multilayer Perceptron

Cogitating the reliability of the supply and ensuring continuous delivery of power to the loads, especially in the growing demand for Lithium-Ion batteries in electric vehicle applications, prediction of the remaining useful life of Lithium-Ion batteries is crucial for the timely replacement. For prediction of non-linear and chaotic relationship, experience-based approach, physics-based approach and data driven approach are used among which data driven approach is a model free, accurate and reliable approach. Therefore, a driven approach in predicting remaining useful life can be implemented in the battery management system. This research uses a multilayer perceptron to predict the remaining useful life of the battery. The NASA Ames Prognostics Center of Excellence (PCoE) battery dataset is used to test the proposed methodology. The use of multilayer perceptron for remaining life prediction seems promising despite the significant number of jump points, gaps in data and a small quantity of experimental data in the National Aeronautics and Space Administration (NASA) dataset. The predicted result was obtained with 8.52 % mean absolute error and 9.59 % root mean square error. When compared with the predicted results of different literatures, proposed multilayer perceptron with sliding window approach outperforms most of the existing approach. Incorporation of optimization techniques and hybrid algorithm in proposed approach can further enhance the accuracy of the model.

Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks

Accurate estimation of the state of charge (SoC) in electric vehicle (EV) batteries is essential for effective battery management and optimal performance. This study investigates the application of Kolmogorov-Arnold Networks (KAN) for SoC estimation, comparing its performance against Artificial Neural Networks (ANN) and a hybrid Barnacles Mating Optimizer-deep learning model (BMO-DL). The dataset, derived from simulations involving a lithium polymer cell model (ePLB C020) in an electric car similar to Nissan Leaf EV, encompasses 68,741 instances, divided into training and testing sets. Three KAN models were developed and evaluated based on root mean square error (RMSE), mean absolute error (MAE), maximum error (MAX), and coefficient of determination (R2). Residual analysis indicates that KAN-Model 1 performs the best, with residuals closely clustered around zero and no significant patterns, suggesting reliable and unbiased predictions. KAN-Model 2 also performs well but exhibits some nonlinear trends in the residuals. ANN and BMO-DL models show larger deviations and less consistent performance. These findings highlight the potential of KAN for enhancing SoC estimation accuracy in EV applications.

Design and analysis of the speed and torque control of IM with DTC based in predictive control and load angle control for electric vehicle applications

Design and analysis of the speed and torque control of IM with DTC based in predictive control and load angle control for electric vehicle applications

Review Paper for SOC Estimation Techniques: Challenges and Future Areas for Improvement

This paper explores the advancements and challenges in State of Charge (SOC) estimation techniques for lithium-ion batteries, particularly within the context of electric vehicles (EVs) and renewable energy systems. As the global demand for sustainable energy solutions grows, accurate SOC estimation becomes essential for optimizing battery performance, enhancing safety, and extending battery life. The paper categorizes various SOC estimation methods into three primary approaches: direct measurement techniques, model-based methods, and data-driven algorithms. Direct measurement techniques, such as Coulomb counting and Open Circuit Voltage (OCV), are straightforward but often lack precision under dynamic conditions. Model-based methods, including Equivalent Circuit Models (ECM) and electrochemical models, provide detailed insights into battery behavior but can be computationally intensive. In contrast, data-driven approaches utilizing machine learning algorithms exhibit promising adaptability and accuracy, albeit relying heavily on large datasets. Despite these advancements, several limitations persist. Current SOC estimation methods are hindered by their dependence on accurate battery models and quality data, which can degrade over time. Furthermore, hybrid methods, which combine strengths from various techniques, introduce complexity and demand substantial computational resources. The integration of SOC estimation techniques in EV applications poses additional challenges due to varying operational conditions, necessitating efficient processing of real-time data from multiple sensors. Emerging trends in Battery Management Systems (BMS) are also examined, highlighting the role of AI and machine learning in improving real-time SOC computations and predictive capabilities. Cloud-based BMS systems are identified as a significant development for remote monitoring and control, enhancing the overall reliability of battery systems. This paper aims to provide a comprehensive overview of SOC estimation techniques, identify ongoing challenges, and suggest future research directions to enhance the effectiveness of battery management strategies in the evolving landscape of energy systems. DOI: https://doi.org/10.52783/pst.906

State of Charge Estimation of Lithium-Ion Batteries for Electric Vehicle Application Using Gaussian Process Regression Approach

For the purpose of ensuring a secure, dependable and affordable performancealong with clean energy in electric vehicles, the estimation of the precise state of charge of LIB is very important. In this article, Gaussian Process Regression with different kernel functions-based SOC prediction is proposed and their performance with good health and well-beingare evaluated and analyzed. A useful benefit of employing GPR is the ability to quantify and estimate uncertainties, allowing for the evaluation of the SOC estimate's dependability. The kernel function serves as a crucial hyperparameter that improves GPR performance. GPR considers the temperature and voltage of the battery, which are independent of one another, as their respective input parametersthat relates Industry, innovation and infrastructure where target-dependent variable is battery SOC. Initially, the training process involves determining the ideal hyperparameters of a kernel function to accurately represent the characteristics of the data. The accuracy of predicting SOC of the battery is evaluated using test data. According to the simulation outcomes, the squared exponential kernel function-based GPR estimates SOC with high accuracy and lower RMSE and MAE which ensures energy efficiency and quality education.