Electric Vehicle System Research Articles

Innovative optimization of hybrid energy storage systems for electric vehicles: Integrating FBPINN-SAO to enhance performance and efficiency

Innovative optimization of hybrid energy storage systems for electric vehicles: Integrating FBPINN-SAO to enhance performance and efficiency

Design and Optimization of Battery Thermal Management Systems in Electric Vehicles Using Advanced Simulation Techniques

Design and Optimization of Battery Thermal Management Systems in Electric Vehicles Using Advanced Simulation Techniques

Review on the Applications of Intelligent Algorithm in Wireless Charging System for Electric Vehicles

This paper presents a comprehensive review of the intelligent algorithms (IAs) based optimization of electric vehicle (EV) wireless charging. First, a general overview of EV wireless charging system is introduced in terms of the compensation circuit, coupling coils, and control strategies, followed by the potential application of IAs. Then, the optimization target, parameter optimization, and structure design of the compensation topologies based on IAs are elaborately expounded by citing current research findings. Subsequently, the IAs are deeply explored for optimizing the systematic parameters, structural geometries, and coupling coils. Then, the IA-based research on the control strategies in the wireless charging system is discussed in detail from three aspects, including power controlling, efficiency optimizing, and parameter tracking. Finally, the prospects for the application of IAs to the EV wireless charging system are summarized and delineated. This paper will be highly beneficial to research entities as a ready reference for the wireless charging system of EVs, with information on IA-based system design.

Experimental-Based Simulation of EV Drive Mechanism

A robust experimental-based vehicle drive system model and parameters tuned to match the real-world performance of the electro-mechanical systems is presented. The model is based on battery electric vehicle systems, focusing on the individual components of the drive system. The model includes motors, a battery pack, a driveline, and a DC-DC converter. Based on the collected empirical data using an on-board data acquisition system, and fine-tuning the critical vehicle parameters to align the simulation with the empirical data, stationary and drive cycle tests were designed and performed to replicate standard day-to-day driving conditions. Performance metrics such as the power consumption, the state of charge and the range were employed for the model validation. The validated model was then used for the performance study at different operating temperatures. The results of the study demonstrate that the model successfully mirrored the performance of the powertrain under various conditions and lined up with existing research work, providing a valuable tool for further research and development in battery electric vehicle technology.

Hierarchical braking accurate control of electrohydraulic composite braking system for electric vehicles.

For the electrohydraulic composite braking system, the general total braking force calculation strategy frequently ignores the resist forces, thereby cannot track the braking intention of driver perfectly. Moreover, the torque allocation process reduces the control reliability and energy recovery effect. In this research, a novel hierarchical braking accurate control (HBAC) algorithm is designed to achieve both the control accuracy and the ideal energy recovery efficiency. It includes target calculation, parameter adjustment, and organization coordination levels. In the target calculation level, the resist forces such as air, tire roll resistances are considered to calculate the demanded-braking force accurately. In the parameter adjustment level, the ideal demand-braking force is constrained by the estimated road adhesion coefficient and the vertical load transfer. At the organization coordination level, the torque allocation process is omitted by applying a compensation control of the hydraulic braking torque. The simulation results indicated outstanding braking distances by the proposed HBAC are 111.5 m, 40.8 m, and 63.2 m under the varying adhesion, dry asphalt, and wet asphalt roads, respectively. Moreover, compared with the comparative control strategy, the energy recovery efficiency of HBAC is increased by 11.74 %, 6.67 %, and 8.4 % under these road conditions. Experimental implementation corroborates the effectiveness of proposed strategy.

Magnetic Gear Wireless Power Transfer System: Prototype and Electric Vehicle Charging

This paper investigates the potential of a magnetic gear wireless power transfer (WPT) system for electric vehicle (EV) charging, with the advantages of low-frequency operation, low foreign object interference, low electromagnetic emissions, and high misalignment tolerance. The study explores the novel impact of Halbach arrays that enhance the flux density in desirable locations while decreasing the flux in undesirable locations, which provides the benefit of decreased foreign object attraction. The initial prototype results demonstrate that the Halbach system can transmit approximately 34.65 W with a transfer efficiency of 64% across a gap of 104 mm. The Halbach system is experimentally compared to a conventional magnet arrangement, which achieved a maximum power transfer of 88 W over 104 mm. The Halbach system is applied to a personal mobility EV to enable wireless charging at low frequency. The axial design of this WPT system has the unique benefit of a 360° radial coupling angle that maintains constant, near-maximum levels of power transfer and efficiency. This full circle coupling angle allows the personal EV to park in any direct vicinity of the charger and achieve the same level of charging given a certain distance. This study delivers important contributions to advancing a low-frequency wireless EV charging technology based on magnetic gears, that sets the stage for future innovations focused on optimizing efficiency, increasing safety, and simplifying the charging process.

Integrated Design and Dynamic Response Analysis of the EV Reducer Considering Mass Eccentricity

To meet the high-performance demands of electric vehicle reducers, the authors present a novel approach for parameter matching in electric drive systems for electric vehicles. Based on fuzzy evaluation, the method identifies the optimal performance parameters for the entire vehicle and determines the best transmission ratio scheme for the reducer. The gear geometrical parameters of the reducer are derived using the transmission ratio scheme as the constraint condition. An 18-degree-of-freedom dynamic system model, incorporating factors such as time-varying mesh stiffness, the backlash, time-varying transmission error and mass eccentricity, has been built using Matlab. The dynamic response of the gear system under the designed gear parameter scheme have been analyzed and compared with the gear parameter results obtained using KISSsoft, demonstrating the accuracy and rigour of the design. This research has practical implications for the design and performance optimization of electric vehicle reducers.

Development of Hybrid Vehicle

Model-Based Design can be applied in the development of a hybrid electric vehicle system. The paper explains how Model-Based Design begins with defining the design requirements that can be traced throughout the development process. This leads to the development of component models of the physical system, such as the power distribution system and mechanical drive line. We also show the development of an energy management strategy for several modes of operation including the full electric, hybrid, and combustion engine modes

Computational Fluid Dynamic Modeling of Pack-Level Battery Thermal Management Systems in Electric Vehicles

In electric vehicles (EVs), the batteries are arranged in the battery pack (BP), which has a small layout space and difficulty in dissipating heat. Therefore, in EVs, the battery thermal management systems (BTMSs) are critical to managing heat to ensure safety and performance, particularly under higher operating temperatures and longer discharge conditions. To solve this problem, in this article, the thermal analysis models of a 3-battery-cell BP were created, including scenarios (1) natural air cooling without a BTMS; (2) natural air cooling with water cooling hybrid BTMS; and (3) forced air cooling plus water cooling composite BTMS. The thermal performances of the pack-level BPs were simulated and analyzed based on computational fluid dynamics (CFD). A variety of boundary conditions and working parameters, such as ambient temperature, inlet coolant flow rate and initial temperature, discharge rate, air flow rate, and initial temperature, were considered. The results show that without a BTMS (Scenario 1), the maximum temperature in the BP rises rapidly and continuously to reach 63.8 °C, much higher than the upper bound of the recommended operating temperature range (ROTR between +20 °C to +35 °C) under the extreme discharge rate of 3 C and even if the discharge rate is 2 C. With a hybrid BTMS (Scenario 2), the maximum temperature in BP rises to about 38.7 °C, slightly above the upper bound of the ROTR. Lowering the coolant (water) initial temperature can effectively lower the temperature up to 5.7 °C in BP, but the water flow rate cannot since the turbulence model. While with a composite BTMS (Scenario 3), the temperature can be further lowered up to 1.5 °C under the extreme discharge rate of 3C, just reaching the upper bound of the ROTR. In addition, lowering the initial coolant temperature or air temperature can effectively decrease the temperatures up to 5.1 and 1.0 °C, respectively, in BP, but the coolant flow rate (due to the turbulence model) and the air flow rate cannot. Finally, the thermal performances of the different battery cells in the BP with different cooling systems and at the different positions of the BP were compared and analyzed. The present work may contribute to the design of BTMSs in the EV industry.

Examination of the cooling performance of the lithium-ion battery module created using phase change material at different discharge rates and different ambient temperatures

Batteries are one of the most important systems in electric vehicles. The performance of batteries varies depending on temperature, and high battery temperatures reduce battery efficiency and life. In recent years, intensive studies have been carried out on passive systems to prevent the battery module from heating, and many studies have been conducted especially on the use of phase change material (PCM). In this study, a novel battery module design created using PCM is tested by setting up experimental setups at both different discharge rates and different ambient temperatures, and the cooling performance of PCM is examined. As a result, it was found that the temperature increase in the PCM decreases during the melting process and the increase in the battery surface temperature varies according to the current value of the total heat generated in the battery. If the total heat generated in the battery increases, both the battery surface temperature and the temperature rise rate increase; if the total heat generated in the battery is constant or decreasing, the battery surface temperature increases but the temperature rise rate decreases.

Efficient Energy Management System for AC–DC Microgrid and Electric Vehicles Utilizing Renewable Energy With HCO Approach

ABSTRACTThe reliability of various energy sources can be increased and distributed production and renewable energy can be fully integrated into the power grid on a wide scale through the growth and development of the microgrid (MG). Global energy difficulties are brought about by the finite supply of fossil fuels and the world's expanding energy consumption. Due to these challenges, the electric power system has to convert to a renewable energy‐based power generation system to produce clean, green energy. However, because of the unpredictable nature of the environment, the shift toward the use of renewable energy sources raises uncertainty in the production, control, and power system operation. This manuscript proposes a renewable energy‐based energy management system for electric vehicles and AC–DC MGs. The proposed method is Hermit Crab Optimizer (HCO). The major goal of the proposed strategy is to supply steady power regardless of generation disparity, which should stop the storage devices from degrading too quickly. The HCO approach provides a stable power balance for MG operation. The proposed technique efficiently strikes a power balance to meet load requirements and recharge electric cars. By then, the proposed strategy is implemented in the MATLAB platform and the execution is computed with the existing procedure. The proposed technique displays better outcomes in all existing systems like biogeography‐based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and artificial neural network (ANN). The existing technique shows the cost of 25$, 30$, 35$, 40$, and the proposed technique displays the cost of 20$ which is lower than the other existing techniques.

Research on the High-Efficiency Wireless Bidirectional Charging and Discharging System for Electric Vehicles Based on the LCC-S Compensation Network

In response to the existing issues of poor anti-offset performance and insufficient width of the matchable voltage range at the DC end in the current bidirectional wireless power transfer (BWPT) system for electric vehicles, an efficient wireless bidirectional charging and discharging system for electric vehicles based on the LCC-S compensation network is proposed. Compared with the topological structure of the traditional two-stage wireless bidirectional charging and discharging system, this system adds a DC-DC module in the on-board equipment, while the BWPT link adopts the LCC-S compensation topology. Moreover, this system realizes DC side voltage matching and transmission power regulation through the DC converter on the vehicle side. Under the premise of no control-level data communication between the primary and secondary sides, it achieves efficient, stable and reliable wireless bidirectional charging and discharging control. Finally, the working characteristics of the proposed system are tested through experiments. The results indicate that the system can maintain efficient, stable and reliable bidirectional operation under the conditions of a large offset range and wide voltage range at the DC side

Optimization of fractional PI controller parameters for enhanced induction motor speed control via indirect field-oriented control

Introduction. Induction Motors (IM) possess advantages such as stability, reliability, and ease of control, making them suitable for many purposes; the literature elucidates control methodologies for IM drives, primarily focusing on scalar and vector control techniques; the conventional method utilized in manufacturing is scalar control, which unfortunately demonstrates optimal performance solely in steady-state conditions. The absence of significant instantaneous torque control restricts flux and dissociated torque, resulting in subpar dynamic responsiveness. Indirect Field Oriented Control (IFOC) for IM drives has proven beneficial for various industrial applications, particularly electric vehicle propulsion. The primary advantages of this approach include the decoupling of torque and flux characteristics and its straightforward implementation. The novelty of the work consists of a proposal for a driving cycle model for testing the control system of electric vehicles in Mosul City (Iraq), and using a Complex Fractional Order Proportional Integral (CFOPI) controller to control IMs via IFOC strategies, the Artificial Bee Colony (ABC) algorithm was applied, which is considered to be highly efficient in finding the values of controllers. Purpose. Improvement IFOC techniques for the regulation of IM speed. Methods. Using the ABC algorithm in tuning the two unique CFOPI controller, and a Real Fractional Order Proportional Integral (RFOPI) controller, to regulate the speed of a three-phase IM via IFOC techniques. Results. The CFOPI controller outperforms the RFOPI controller in obtaining the best performance in controlling the IM. Practical value. The CFOPI controller demonstrates superiority over the RFOPI controller, as evidenced by the lower integral time absolute error in motor speed tracking during the driving cycle 2.1004 for the CFOPI controller compared to 2.1538 for the RFOPI controller. References 27, tables 5, figures 4.

Bio-inspired optimizer with deep learning model for energy management system in electric vehicles

Bio-inspired optimizer with deep learning model for energy management system in electric vehicles

Performance Evaluation of a Nearest Level Control-Based TCHB Multilevel Inverter for PMSM Motors in Electric Vehicle Systems

Performance Evaluation of a Nearest Level Control-Based TCHB Multilevel Inverter for PMSM Motors in Electric Vehicle Systems

Towards integrated thermal management systems in battery electric vehicles: A review

Towards integrated thermal management systems in battery electric vehicles: A review

Priority-Based Pre-Booking System for Efficient Electric Vehicle Charging Station Utilization

Priority-Based Pre-Booking System for Efficient Electric Vehicle Charging Station Utilization

Design and Optimisation of a Double Wishbone Independent Suspension System for an L6 electric Vehicle: A Response Surface Methodology- based design application

Design and Optimisation of a Double Wishbone Independent Suspension System for an L6 electric Vehicle: A Response Surface Methodology- based design application

Applications of 18650 lithium-ion batteries pack in 3 kw electric vehicles

Abstract One of the important components in the development of electric vehicles is battery. Battery conditions must be monitored in real-time. This research begins with assembling a 18650 Lithium-ion battery pack with a maximum capacity of 83.5V. A PZEM-017 is used to calculate the value of voltage and current of the battery. In this study, Arduino Atmega 2560 is used for microcontroller and LCD display to show the measurement results. Before being applied to electric vehicle, the measurement and monitoring system needs to be calibrated first. The callibration results show that the Voltage and Current sensors have maximum error under 2% (0.31% for Voltage and 1.56% for Current). While the minimum precisions are 99.59% for voltage and 98.10% for current. This measurement and monitoring system has been successfully applied to 3KW electric vehicle. Where the measurement results show that the increase in driving distance and driver weight will be proportional to the increase in energy consumption from the battery. The electric motor, as a source of vehicle propulsion, will work longer and harder. The highest decrease in voltage value occurred when the distance was 1.8 km and the driver’s weight was 83kg at 3.8V with a power consumption of 136.29Wh.

Frosting characteristics and performance impact on R744 heat pump systems for electric vehicles at low ambient temperatures: An experimental approach

Frosting characteristics and performance impact on R744 heat pump systems for electric vehicles at low ambient temperatures: An experimental approach