Electric Vehicle System Research Articles

Environmental and economic evaluation of urban building-integrated photovoltaic and electric vehicle system

Facade photovoltaic (FaPV) panels can significantly enhance the capability of building-integrated photovoltaic (BIPV) systems. This study presents a comprehensive analytical framework for optimizing the operation of BIPV and electric vehicle (EV) integrated systems and evaluating their environmental and economic benefits. The Energy-Environmental-Economic (3E) parameters are measured for alternative scenarios, and sensitivity analyses are applied to validate the impacts of changes in major parameters on BIPV-EV system's performance in Xiong'an New Area (XANA). The simulation results demonstrate that BIPV-EV systems can effectively reduce CO2 emissions and electricity costs. Specifically, the installation of FaPV can increase power generation by up to 67.60 % compared to the BIPV system utilizing stand-alone rooftop photovoltaic (RPV). Regarding urban decarbonization, the adoption of BIPV system can reduce CO2 emissions by 41.91 % and 34.99 % in the short- and long-term, respectively. The evaluation of the levelized cost of energy (LCOE) across different scenarios reveal that the configurations of RPV + FaPV (SE)-EV system and RPV + FaPV (SW)-EV system are the most cost-effective options in both the short- and long-term, where BIPV contributes to 48.10 % and 31.90 % of the total electricity generation, respectively. In terms of EV power consumption, the BIPV-EV system can decrease the grid sell ratio by 15.38 % compared to the stand-alone BIPV scenario. Compared to the BIPV system with typical RPV, the appropriate utilization of FaPV can significantly increase the potential of a BIPV system and mitigate the additional electricity demand for EVs. For cities with nascent BIPV and EV markets, the proposed framework can be a strategic tool for urban planners to design BIPV-EV systems, with the objective of enhancing sustainability and energy efficiency.

Enhanced SOC estimation in Lithium-ion Batteries using GRU Neural Networks with GSA

State of charge (SOC) is one of the most important metrics to evaluate the performance of the battery management system (BMS) in electric vehicles (EVs). Lithium-ion batteries have been utilized often for SOC estimates in EV applications because of its attractive characteristics such as extended lifespans, high voltages, energy, and capacities. However, dynamic charging and discharging profiles, material degradation, battery aging, chemical reaction, and temperature fluctuations would diverge the accuracy of SOC estimation. Deep learning has proven to become an efficient method for SOC estimation in EVs due to its strong computational capabilities, enhanced generalization performance, and excellent accuracy under dynamic loading profiles. Therefore, this study proposes the use of the gated recurrent unit neural network (GRUNN) for estimating SOC in lithium-ion batteries. The best hyperparameters of GRUNN that enable improving SOC accuracy are found using gravitational search optimization (GSA). To evaluate the functional sustainability of the suggested method, a full battery test bench model is created and accordingly, robustness of the proposed approach is verified under diverse EV drive cycles and aging impacts. The effectiveness of the proposed approach is evaluated by comparing with several existing approaches. The results demonstrate that the suggested method archives SOC error and RMSE in EV driving cycles and ageing effects below 5% and 2%, respectively. The excellent outcomes obtained by the proposed method would certainly enhance the battery charging and discharging profile and EV performance. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 1-12

Analysis of green energy regeneration system for Electric Vehicles and Re estimation of carbon emissions in international trade based on evolutionary algorithms

Abstract In order to achieve maximum carbon reduction during the operation of pure electric buses, the author proposes a re estimation of carbon emissions in international trade based on evolutionary algorithm analysis of electric vehicle green energy regeneration systems. On the basis of analyzing single line scheduling, the author focuses on studying the regional scheduling mode of pure electric buses, and establishes a multi-objective optimization model for pure electric bus regional scheduling considering carbon benefits with the goals of minimizing the number of operating vehicles, minimizing the empty distance, and maximizing carbon benefits. Based on the actual operation data of pure electric buses 146 and 149 in a certain city, the author used an improved particle swarm optimization algorithm to solve the regional scheduling problem of pure electric buses according to the characteristics of the model. The results indicate that assuming other conditions remain unchanged, when the price of diesel rises to around 7.9 yuan, its operating costs will exceed those of pure electric buses, and the cost advantage of diesel vehicles will gradually decrease. Other conditions remain unchanged, and when the battery price per vehicle drops to around 300,000 yuan, the operating cost of pure electric buses will be lower than that of diesel vehicles. Conclusion: Under the premise of considering carbon benefits, adopting regional dispatch mode for pure electric buses has better economic efficiency and is more conducive to the promotion of pure electric buses.

Enhancing Wireless Charging for Electric Vehicles: Active Load Impedance Matching and Its Impact on Efficiency, Cost and Size

This paper presents an advanced Wireless Power Transfer (WPT) system for electric vehicles (EVs) featuring Active Load Impedance Matching (ALIM) at the rectification stage. Unlike traditional synchronous rectification, ALIM dynamically adjusts load impedance, optimizing energy transfer efficiency and reducing thermal stresses, system costs, and mass. The system incorporates two circuits optimized for distinct frequency bands: one operates below 10 kHz using standard copper wiring for cost-effectiveness, and the other at 85 kHz, which significantly reduces the mass of the onboard coil and magnetic circuit while ensuring interoperability according to SAE J2954 standard. Our approach enhances charging efficiency across various operating conditions, improves thermal management, and minimizes maintenance costs. Additionally, it enables partial compensation for vehicle misalignment and ground assembly impedance, further boosting efficiency and interoperability. Experimental results demonstrate a notable increase in efficiency and reduction in system mass, confirming the superiority of the ALIM-equipped WPT system over conventional solutions. This paper underscores the potential of ALIM to advance the scalability, efficiency, and economic viability of wireless EV charging technology, promoting broader adoption and sustainability in EV infrastructures. By providing a comprehensive solution that addresses key challenges in wireless charging, our work paves the way for more efficient and cost-effective EV charging systems.

Dual Battery Charging System for Electric Vehicle

Dual Battery Charging System for Electric Vehicle

Powering the Future: Advanced Battery Management Systems (BMS) for Electric Vehicles

This paper introduces a novel approach for rapidly balancing lithium-ion batteries using a single DC–DC converter, enabling direct energy transfer between high- and low-voltage cells. Utilizing relays for cell pair selection ensures cost-effectiveness in the switch network. The control system integrates a battery-monitoring IC and an MCU to oversee cell voltage and ensure battery protection. A prototype circuit with twelve lithium-ion batteries demonstrates the method’s efficacy, achieving a remarkable balancing time of 48 min during charging with a maximum efficiency of 89.85%. Comparative analysis with other methods underscores the superior performance of the proposed balancing circuit in terms of balancing time and implementation cost. Furthermore, this paper delves into hardware aspects of battery management systems (BMSs) for electric vehicles and stationary applications. It offers an overview of prevailing concepts in state-of-the-art systems, aiding readers in assessing considerations essential for BMS design in various applications. The discussion includes examples of battery packs sourced from commercially available electric vehicles. Subsequently, the manuscript addresses implementation aspects concerning the measurement of critical physical variables such as voltage, current, and temperature, alongside balancing strategies.

Development of an Improved Communication Control System for ATV Electric Vehicles Using MRS Developers Studio

Transmission, energy management, and distribution systems are critical components of modern electric vehicles, encompassing all sectors of the power system through communication control technology. One widely used communication system in electric vehicles is the Controller Area Network (CAN). This research aims to investigate the development of CAN BUS technology, adapted from large trucks, to control the communication system within an ATV electric vehicle using a communication format similar to bus Communication. The communication control system includes several components: the engine switch, headlight, turn signal, emergency light, horn, forward/reverse gear, and accelerator. The system’s communication protocols were developed using MRS Developers Studio version 1.40 software to create the data transmission and reception formats for the vehicle’s components. The communication system employs three PLC 1.033.30B.00 type E control boxes, each with limited analog and digital input/output ports. The sequence of communication control begins with the engine start/stop operation, as the system will not function unless the engine is started first. The headlight operation is processed within the CAN BUS1 control box. Simultaneously, the turn signal and emergency light functions are controlled by CAN BUS1 and displayed on both the CAN BUS2 (front of the vehicle) and CAN BUS3 (rear of the vehicle) control boxes. Additionally, the accelerator function is managed within the CAN BUS2 control box and displayed on the CAN BUS3 control box. However, this operation is contingent upon the forward/reverse gear selection, managed by CAN BUS1 and processed by CAN BUS3. All system operations are designed within the software’s programming paths. The communication system operates using CAN-High and CAN-Low lines, and communication data fields can be monitored using the PCAN-View software version 4.2.1.533. This study demonstrates the feasibility and effectiveness of adapting CAN BUS technology for ATV electric vehicles, providing insights into the integration and control of various vehicular components within a unified communication framework.

Energy management strategies in distribution system integrating electric vehicle and battery energy storage system: A review

AbstractThe electricity sector is witnessing a rise in renewable energy sources and the widespread adoption of electric vehicles, posing new challenges for distribution system. Additionally, the surge in carbon emissions resulting from industrialization and population growth continues to worsen global warming and climate change. In response, integrating electric vehicles (EVs) and battery energy storage systems (BESS) has emerged as a critical strategy, presenting both challenges and opportunities in effective energy management. BESSs offer potential solutions to mitigate these impacts. Furthermore, this review thoroughly explores issues related to lithium‐ion batteries, particularly in the context of EVs and energy management systems (EMS), identifies challenges, and provides recommendations for future research directions. The article concludes by outlining the current extent of investigation in the field of BESS and EV systems to provide researchers with a clear understanding. The escalation of carbon emissions stemming from industrialization and population expansion has worsened the effects of global warming and climate change. To address this challenge, the integration of Electric EVs and energy storage systems (ESS) has emerged as a pivotal strategy. This study examines optimization techniques, methodologies, and the evolving market landscape in distributed systems, with a focus on EVs and BESS. It also explores issues related to lithium‐ion batteries, particularly in the context of EVs and energy management systems. The article highlights the challenges and opportunities in the field of BESS and EV systems, emphasizing the need for ongoing research. BESSs offer potential solutions to mitigate these impacts. Moreover, it offers an extensive analysis of the existing BESS installations, outlining main areas of interest, pointing out difficulties, clarifying areas of unfinished study, and providing future directions.

Diagnostic Concepts for Condition Monitoring and Fallback Mechanisms in Vehicle Electrical Systems

Diagnostic Concepts for Condition Monitoring and Fallback Mechanisms in Vehicle Electrical Systems

Experimental Investigation of Modified Level Shift PWM with Capacitor Voltage Balancing on Single Phase Modular Multilevel Converter

Modular Multilevel Converters (MMCs) are a prominent voltage source converter topology that is rapidly gaining popularity in medium/high power/voltage applications, including high-voltage DC transmission systems and electric vehicle systems. However, MMC has the critical issue of unbalanced submodule capacitor voltages and circulating current. The MMC distributes DC-link energy evenly among its submodule capacitors, rather than storing it in a large capacitor like conventional voltage source converters. In MMC, the submodule floating capacitors interface the DC input voltage and AC output voltage. Therefore, capacitor voltages must be balanced. The existing capacitor voltage control struggles to handle many gate pulses at medium and high voltages. An effective control scheme is needed to solve the issues mentioned earlier. This paper presents a performance analysis of a 1-Φ MMC using a modified level shift PWM technique based on Universal Control Modulation Scheme (UCMS) and a sorting-based capacitor voltage balancing algorithm. MATLAB/Simulink software implements the proposed control method for a 1-Φ , seven-level MMC. The outcomes of the simulation demonstration reveal that phase disposition PWM offers less harmonic distortion of output phase voltage than the other level shift PWM techniques, such as phase disposition PWM and alternate phase disposition PWM. The simulation results also demonstrate the effective use of the sorting-based capacitor balancing algorithm to regulate the voltages of the submodule capacitors. Finally, the real-time GUI tool validates the simulation results using an experimental prototype of 1-Φ MMC with the dSPACE MicroLabBox 1202.

Startup characteristics of a water loop heat pipe with dual heat sources for battery thermal management system in electric vehicle

Abstract The usage of electric vehicles has significantly reduced emissions of greenhouse gases and other pollutants. However, the high heat release generated by the electric vehicle batteries poses a challenge. To solve this problem, scientists have created a passive cooling thermal management system specifically for electric vehicle based on heat pipes, particularly loop heat pipes. A battery pack often consists of several battery modules, which results in multiple heat sources being dispersed according to their power capacity. Startup behavior of loop heat pipe has been investigated extensively in the literature. However, most of the studies use only one heat source. This paper aims to fill the research gap, particularly when the system is implemented in dual heat sources managed by only one evaporator. To achieve the research objectives, a custom loop heat pipe was constructed. This cooling system’s design is briefly described. The evaporator is made of copper, deionized water was selected as the working fluid because of its high merit number, which indicates strong performance as a heat pipe working fluid and the stainless-steel wire mesh serves as the porous wick. Battery simulator was built using aluminum material and a cartridge heater to mimic the heat produced by the battery. Two case studies were done. First, only one battery simulator was used. Second, two battery simulators were placed on both sides of the evaporator. A type-K thermocouple attached to the NI DAQ 9214 module was used to measure the temperature while the electric heat load varied between 10 W and 50 W. The study investigated the interaction between the heat load distribution and the startup behavior of the loop heat pipe. Startup behavior is crucial for the performance of the loop heat pipe. Based on the experimental results, the loop heat pipe demonstrates outstanding startup performance. It can effectively initiate operation even at a minimal heat load as low as 30 W for the first and second case study. The findings of the study indicate that the dual heat source arrangement effectively mitigates overshoot temperatures and enhances heat transfer performance by increasing the contact area.

Research on load-resistant enhancement control optimization of composite energy power supply system based on deep reinforcement learning

Abstract To solve the problem of insufficient inertia in the power electronics components under the development trend of power electronics in the transmission system of heavy-duty electric vehicles, combined with the composite energy power supply mode and control method, voltage-based feedback channels and virtual capacitor branches are designed for both engine-generator sets and DCDC control loops. These innovations compensate for system inertia and introduce a TD3 deep reinforcement learning-based adaptive regulation component to manage electrical systems in response to DC voltage fluctuations. This approach enhances the stability of the entire system in the face of load disturbances. Strategy validations demonstrate that this method ensures stable system operation, significantly mitigates DC voltage fluctuations during sudden load changes and is highly adaptable without relying on system models.

New Integrated DC–DC Conversion System for Electric Vehicles

New Integrated DC–DC Conversion System for Electric Vehicles

Modeling of High-Efficiency Nonsalient Pole Transmitter in High-Power Dynamic Wireless Charging System for Electric Vehicles

Modeling of High-Efficiency Nonsalient Pole Transmitter in High-Power Dynamic Wireless Charging System for Electric Vehicles

Thermal management of lithium-ion batteries with direct and counter flow channels: A comparative study of different cooling fluids

The study addresses the crucial issue of thermal management in lithium-ion batteries (LIBs) and the maintenance of safe operational temperatures. It emphasizes the importance of efficient cooling mechanisms in preventing damage. A comparative analysis was conducted to assess the impact of various cooling fluids on the thermal performance of LIBs using direct and counter fluid flow channels. The study involved the use of different cooling fluids including water, water-glycol, Novec7000, mineral oil, and silicon oil. The researchers examined temperature variation, surface Stanton number (SN), surface Nusselt number (Nu), surface heat transfer coefficient (HTC), battery surface temperature, and pressure drop to evaluate the battery cooling process with different cooling configurations. The battery cooling part proved highly effective in managing heat, achieving a significant maximum temperature reduction of 30.70 % and 37.43 % for direct and counter flow channels, respectively. The Nu values of various fluids showed notable differences, with water-glycol showing a 3.76 % increase, Novec 7000 with an 11.39 % increase, mineral oil with a 15.46 % improvement, and silicon oil with a 20.09 % increase. The HTC values in the direct flow channel proved a consistent decrease from 16.09 to 57.73 to 11.07–25.69 W/m2K, respectively. Similarly, the HTC values for the counter flow channel ranged from 15.88 to 55.38 to 11.07–25.69 W/m2K. Additionally, it was seen that silicon oil outperformed other cooling fluids with a 6.0 % improvement in HTC in both direct flow and counter flow channels. The SN showed increases of 17.34 %, 20.11 %, 26.76 %, 29.57 %, and 29.60 %, respectively, at all five liquid flow velocities through the direct channel, and 19.39 %, 22.18 %, 28.34 %, 36.58 %, and 37.64 %, respectively, through the counter flow channel. These findings clearly show the superior heat transfer capabilities of silicon oil compared to other coolants. However, it is important to note that the use of silicon oil also resulted in a lower pressure drop, a critical consideration for ensuring efficient cooling. These findings have significant implications for the advancement of battery thermal management systems for electric vehicles, as designers can optimize the cooling process by considering different cooling configurations to enhance the thermal performance of battery packs while ensuring safe and stable operation.

Precise Disturbance Rejection for Dynamic Wireless Charging System of Electric Vehicle Using Internal Model-Based Regulator With Disturbance Observer

Precise Disturbance Rejection for Dynamic Wireless Charging System of Electric Vehicle Using Internal Model-Based Regulator With Disturbance Observer

Innovative approach to develop a thermal management system for a battery electric vehicle

Nowadays all the automotive OEMs were investing huge amount of dollars to develop an innovative technology to handle the thermal management system of their battery electric vehicle (BEV) and hybrid electric vehicle (HEV) . Lots of techniques were used to develop this thermal management system (TMS) by investing huge amount of man hours . In this paper an innovative is handled to understand the available available possible ways to achieve a cost optimized TMS for a HEV and BEV. This is a very basic, but efficient methodology which leads to provide an enhanced system to balance the cost and achieve the customer requirements. The pugh DFSS methodology is used to compare,analyze and evaluate using the simulation techniques. Going forward this proven solution can be implemented in the real time vehicles.

Optimal cooperative scheduling strategy of energy storage and electric vehicle based on residential building integrated photovoltaic

With the development of photovoltaic (PV) power generation technology, more and more families have begun to use solar energy. However, the uncertainty of solar energy production and user electricity demand has brought huge challenges to the development of efficient energy scheduling strategies. To deal with the uncertainty of electricity behavior, Monte Carlo generates the electricity scenario sets based on real data. According to the principle of minimum average distribution error (ADE), the scene with 200 scenes is selected for scheduling. Then, the Transformer model with random error (RE-Trans) is used to predict solar radiation and outdoor temperature. Through verification, it is found that the Mean Absolute Error (MAE) of the former is 0.389 and the Root Mean Square Error (RMSE) is 0.514. The MAE of the latter is 0.212 and RMSE is 0.413. To make effective decisions in the agent with the environment, a Deep Reinforcement Learning model with self-attention mechanism (SAN-DRL) is designed. The model aims to achieve a dynamic balance between electricity costs, carbon emissions and electricity sales income. Through continuous interaction with the environment, the agent finally obtains the optimal disordered charging and discharging behavior of energy storage systems (ESS) and electric vehicle systems (EVs), which greatly improves the utilization of solar energy resources. Finally, through simulation experiments, the proposed method is compared with both the baseline model and the ideal scheduling. The results demonstrate that the proposed method can reduce electricity costs and carbon emissions by 83.72 % and 72.08 %, respectively. Additionally, it achieves a net income of 38.59 CNY.

Decoupled dual‐channel loosely coupled transformer with hybrid nanocrystalline and ferrite core for current sharing of modular wireless power transfer system

AbstractCross‐coupling and current sharing are the main problems faced by modular wireless power transfer (WPT) systems for electric vehicle (EV) applications. In this study, a decoupled dual‐channel wireless power transfer loosely coupled transformer (LCT) with a hybrid core composed of nanocrystalline and ferrite is proposed for modular WPT systems, as well as WPT systems with specific hybrid topology or detuned compensation. Firstly, the requirements of the LCT for modular parallel dual‐channel WPT system and detuned compensation are discussed. For these requirements, the structure and corresponding optimization design method of the decoupled dual‐channel LCT with a hybrid core is then proposed, and an LCT is designed with this method for the case study. Furthermore, the LCT is analysed by the finite element method (FEM). It is revealed that the mutual inductances of the proposed LCT between transmitting and receiving coils can be symmetrical under misalignment in multiple directions, while the cross‐coupling is eliminated. Finally, the designed LCT is manufactured and verified in a modular parallel dual‐channel WPT system. The experimental results show that the current and power sharing is realized under misalignment in multiple directions.

An NMPC-Based Integrated Longitudinal and Lateral Vehicle Stability Control Based on the Double-Layer Torque Distribution.

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro-mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles.