Mileage Of Electric Vehicles Research Articles

Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models

The focus on developing an effective battery thermal management system (BTMS) to maintain optimal temperatures for lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications, has grown significantly. The effective BTMS not only enhances the cooling performance of LIBs but also contributes to increased passenger safety and mileage of EVs. This study investigates BTMS configurations with fins, metal foam, and phase change material (PCM) to minimize temperature of battery during 3C discharging in varying conditions. Additionally, the study explores the impact of different BTMS material combinations and various fins lengths on system performance as a parametric investigation. Moreover, to streamline the analysis process and introduce novelty, artificial intelligence is explored as an alternative to computational fluid dynamics for predicting liquid fraction of PCM and temperature of battery, enhancing the innovative aspect of this study. Numerical simulations, using a non-equilibrium thermal model for metal foam modeling, reveal that the fourth case, integrating all three passive approaches, maintains the lowest temperature and enhances LIB cooling. The optimum BTMS shows a reduction of 3 K compared to BTMS utilizing pure PCM. During discharge process, the temperature difference in the battery decreases by approximately 75 % and 66 % in the fourth case compared to the first case (with pure PCM) under normal and harsh environmental conditions, respectively. Applying copper metal foam and copper fins yields the best results in reducing battery temperature. Increasing the length of fins and adding more fins effectively lower the battery temperature. Finally, an artificial neural network model is developed using the backpropagation learning technique coupled with the gradient descent optimization algorithm. The model exhibits excellent predictive capabilities, achieving high R-squared values of 0.98 for PCM liquid fraction and 0.99 for battery temperature.

Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties

A community-integrated energy system under a multiple-uncertainty low-carbon economic dispatch model based on the deep reinforcement learning method is developed to promote electricity low carbonization and complementary utilization of community-integrated energy. A demand response model based on users’ willingness is proposed for the uncertainty of users’ demand response behavior; a training scenario set of a reinforcement learning agent is generated with a Latin hypercube sampling method for the uncertainties of power, load, temperature, and electric vehicle trips. Based on the proposed demand response model, low-carbon economic dispatch of the community-integrated energy system under multiple uncertainties is achieved by training the agent to interact with the environment in the training scenario set and reach convergence after 250 training rounds. The simulation results show that the reinforcement learning agent achieves low-carbon economic dispatch under 5%, 10%, and 15% renewable energy/load fluctuation scenarios, temperature fluctuation scenarios, and uncertain scenarios of the number of trips, time periods, and mileage of electric vehicles, with good generalization performance under uncertain scenarios.

Investigation on electric vehicle motor challenges, solutions and control strategies

This research paper tells about the different types of defects faced by the motors used in electric vehicle such as cost of the motor and it also discusses about the solutions to reduce the problems. In recent years the sharp rise in greenhouse gas emissions have forced the population to move towards electric vehicle. This target can only be achieved by removing the drawbacks such as cost and mileage of electric vehicle. Electric motors play a huge role in electric vehicle and motors that are used in EVs are quite costly and efficiency of the motor affects the mileage of electric vehicle. In this context, this paper tells about the defects or challenges of the motors in the vehicle. Mainly three motors are used in electric vehicle such as induction motor, switched reluctance motor and permanent magnet synchronous motor. This research paper discusses different solutions, and control strategies of EV motors in order to achieve the low cost and better responses as motor plays a huge part in electric vehicle and also the future scope of these motor is forecasted. Above investigation of control strategies is carried out by MATLAB/Simulink Software.

Driving–Charging Integrated Controller for Electric Vehicles

Motor driving technology and battery charging technology are the two core technologies of electric vehicles (EVs). They have a profound impact on the performance and mileage of EVs. Based on the topology of a traditional EV motor controller, this study employs the time-sharing multiplexing insulated gate bipolar transistor (IGBT) power module form to expand the functions of the motor controller. This paper proposes an integrated driving–charging controller structure that enables the controller to realize the motor driving and on-board charging simultaneously. A decoupling analysis was performed at the topology level, mathematical models of two bidirectional converters in the integrated topology were established, and a reasonable control strategy for the double-closed-loop control system was designed. The simulation system was built in Simulink, and the simulation results verified the effectiveness of the system topology and control strategy. We made an integrated controller experimental platform based on TMS320F28335. A resistive load simulated the power battery to verify the charging mode, and a 5kW permanent magnet synchronous motor (PMSM) was used to verify the driving mode. The experimental results demonstrated the driving–charging integrated controller’s feasibility for electric vehicles.

STRESS ANALYSIS SIMULATIONS OF WELDED AND BOLTED JOINTS METHOD FOR FULL STEEL AND COMPOSITE-STEEL CHASSIS STRUCTURE OF ELECTRIC LOW FLOOR MEDIUM BUS


 Stress analysis of welded steel-to-steel, bolted steel-to-steel, and bolted composite-steel chassis in an electric low floor medium bus structure is presented in this paper. The analysis was carried out on the condition that is when the bus is full of load in idle/static. This condition reflects the situation of the vehicle in full load with passengers and components, which is important to be analyzed to anticipate the unwanted structural failure of the chassis. Finite Element Method (Harmonic response simulation) is used to investigate the structural behavior of both welded and bolted methods. Several parameters such as 2 Hertz for the maximum frequency, 5000 kg for the total vehicle weight, and the uniform distribution of load are used for this study to simulate the simplified, real application in the real world. The first comparison is between the welded and bolted steel-to-steel chassis which results in the bolted method has a lower stress value by the difference of 4.3 MPa in the joint section than the welded joint. This means that the bolted joint is more recommended than welded for the use as an electric low floor medium bus and has the potential to be optimized further. In terms of reducing the weight of the chassis structure, then lightweight material (carbon fiber composite) is used to replace the full steel chassis to be a composite-steel chassis. The use of this hybrid material depicts the stress value of 61.5 MPa in the joint area, this value is still far below the limit of carbon fiber that is 3200 MPa makes this bolted composite-steel is considerably safe in full load condition as an electric low floor medium bus structure. Using this hybrid bolted composite-steel chassis structure also reduces the total chassis weight by about 22.7 % compared to the full steel chassis structure, thus one could expect to extend the mileage of electric vehicles by more than 20 %

An Improved Test Method of LiFePO4/Graphene Hybrid Cathode Lithium-Ion Battery and the State of Charge Estimation

Abstract The state of charge (SoC) of the battery is a typical characterization of the operating state of the battery and criterion for the battery management system (BMS) control strategy, which must be evaluated precisely. The establishment of an accurate algorithm of SoC estimation is of great significance for BMS, which can help the driver judge the endurance mileage of electric vehicle (EV) correctly. In this paper, a second-order resistor-capacity (RC) equivalent circuit model is selected to characterize the electrical characteristics based on the electrochemical model of the LiFePO4/graphene (LFP/G) hybrid cathode lithium-ion battery. Moreover, seven open circuit voltage (OCV) models are compared and the best one of them is used to simulate the dynamic characteristics of the battery. It is worth mentioning that an improved test method is proposed, which is combined with least square for parameters identification. In addition, the extended Kalman filter (EKF) algorithm is selected to estimate the SoC during the charging and discharging processes. The simulation results show that the EKF algorithm has the higher accuracy and rapidity than the KF algorithm.

Prediction of the heavy charging current effect on nickel-rich/silicon-graphite power batteries based on adiabatic rate calorimetry measurement

The LiNi0.8Mn0.1Co0.1O2/Silicon-carbon (NCM811/Si@C) lithium ion battery is used in the plug-in electric vehicle due to its high specific energy. The mileage of electric vehicles can be improved by increasing the energy density of batteries, but the charging process becomes a more challenge issue since the excessive charging current results in high temperature while the thermal stability of NCM811 material is poor. Also, the increasing of temperature may cause the thermal runaway of lithium ion battery. In this work, in order to study the thermal runaway prevention during charging process, the NCM811/Si@C battery model is set up, and the simulation results are verified by the experimental results. The detailed temperature distribution of the battery is observed, which can advise on the thermal management system of the batteries. Based on the thermal runaway data, the maximum safe charge current under different ambient temperature is predicted, and the relationship between maximum safe charging current and ambient temperature is found.

배터리 모듈을 이용한 전기자동차 일충전 주행거리 평가

배터리 모듈을 이용한 전기자동차 일충전 주행거리 평가

Research on the energy management of composite energy storage system in electric vehicles

To solve the problem of power allocation of battery and super capacitor in the composite energy storage system of electric vehicles, we propose the logic threshold and fuzzy control strategy to arrange battery and super capacitor to supply the average and peak power to the load, respectively. Based on the simulation software of electric vehicles, we perform simulation modelling to analyse the current of the battery, the SOC of battery and the current of the super capacitor. For verifying the feasibility of the control strategy, we set up an experimental platform to study pure electric vehicles during driving and braking. Simulation and experimental results indicate that the composite energy storage system can reduce the charging and discharging times and the current amplitude of the battery, and improve the utilisation ratio of regenerative braking energy and the mileage of electric vehicles.

Research on the energy management of composite energy storage system in electric vehicles

To solve the problem of power allocation of battery and super capacitor in the composite energy storage system of electric vehicles, we propose the logic threshold and fuzzy control strategy to arrange battery and super capacitor to supply the average and peak power to the load, respectively. Based on the simulation software of electric vehicles, we perform simulation modelling to analyse the current of the battery, the SOC of battery and the current of the super capacitor. For verifying the feasibility of the control strategy, we set up an experimental platform to study pure electric vehicles during driving and braking. Simulation and experimental results indicate that the composite energy storage system can reduce the charging and discharging times and the current amplitude of the battery, and improve the utilisation ratio of regenerative braking energy and the mileage of electric vehicles.

Research on Location Problem of Electric Vehicle Charging Stations

This paper studies the location problem of electric vehicle charging station in the case that electric vehicles are used as commuter tools. Firstly, according to the length of the commuter road, the number of electric vehicles which would be used as commuter tools on the road, candidate charging stations and the maximum mileage of electric vehicles, a weighted network including two types of edges is constructed. Secondly, the location problem of electric vehicle charging station is transformed into a maximum covering problem of the weighted network. Then an integer nonlinear programming model for the location problem of electric vehicle charging station is formulated, the objective function of the mathematical model is to maximize covering the electric vehicles. A heuristic algorithm is designed to solve the model. Finally, we do simulation using a numerical example. The results show that the mathematical model and algorithm are effective in solving the location problem of electric vehicle charging station.

Analysis of Energy Saving and Environmental Characteristics of Electric Vehicles in Regionally Disaggregated World Energy Model

SUMMARYThis paper investigates the impact of an extensive introduction of electric vehicles (EV) and plug‐in hybrid vehicles (PHEV) into the global energy system by 2050. The significant growth of automobile ownership in emerging countries is likely to increase world oil demand and the associated carbon dioxide emissions. In order to address these energy, security, and environmental concerns, the deployment of clean energy vehicles, such as EV and PHEV, is expected to play a crucial role due to their high fuel efficiency. Consequently, we develop both a global energy system model and a world vehicle penetration model, which can explicitly analyze the impact of EV introduction into the seasonal daily electric load curve, with consideration of the specific electricity charging profile through 2050. The simulation results confirm that EV deployment contributes to energy conservation, because oil demand reduction outstrips the growth in electricity demand and the associated fuel input into the power generation mix. Concerning carbon dioxide abatement, the magnitude of the impact relies on the carbon intensity of the power generation mix. If the intensity is low enough to guarantee a carbon mitigation effect due to EV fuel saving, emissions reduction is well assured. It should be noted, however, that in regions with high carbon intensity in the power generation mix, carbon emissions per mileage of EVs is almost equivalent to that of efficient gasoline vehicles such as hybrid vehicles, and the figure for PHEV is slightly higher than for hybrid vehicles. © 2013 Wiley Periodicals, Inc. Electr Eng Jpn, 186(4): 20–36, 2014; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/eej.22373

SOC Estimation of LiFePO4 Battery based on Improved Ah Integral Method

State of charge (SOC) is the most important status parameters of energy storage system, which is able to predict the available mileage of electric vehicle. In fact, the accuracy of SOC estimation plays a vital role in the usability and security of the battery. To fully consider the practical demands, a novel method to predict SOC of LiFePO4 battery is presented in this paper, which defines the correct coefficient separately under two working conditions of charging and discharging. Based on effective factors such as coulombic efficiency, charge and discharge current, and temperature, an Ah integral SOC estimation method with two kinds of efficiency correct coefficients is established by performing massive experimental study. Experiments prove that the estimated error of SOC is less than 5%. Compared with the original Ah method, the improved Ah method is more advantageous in the accuracy and reliability. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3016

Simulation of the Integrated ABS and DYC Control for 4WID Electric Vehicle with Regenerative Braking

Study the lateral stability control method with regenerative braking for 4WID electrical vehicle whiling braking, an integrated control strategy with primary objective to enhance vehicle lateral stability was proposed, by which the regenerative braking, hydraulic braking, ABS and direct yaw moment control system were coordinated effectively. Simulation results on split-μ road indicated that compared with traditional ABS, the integrated control method can improve the lateral stability of vehicle at urgent braking condition, and increase the mileage of electric vehicles.

1210 充電器の渋滞情報と電費をかみした電気自動車の充電計画設計法

This paper presents a method to make a charging plan of Electric Vehicles (EV) considering charging traffic at charging point and mileage. This method uses past records of charging stations to estimate when and which charging point is busy and waiting time at the point. Additionally, mileage of EV in this method was calculated by actual data and it contributes to make decision how much users have to charge at a charging point. By using this method, users can drive faster and more appropriate route to a destination. Finally, in simulation, this method made faster charging plan for typical Okinawa sightseeing route.

State of Energy Estimation Based on AUKF for Lithium Battery Used on Pure Electric Vehicle

State of Energy can be used to predict the driving mileage of electric vehicles, design the control strategy of vehicle energy distribution, and improve the safety of electric vehicle. Accurate estimaion of state of energy is one of the key technologies in the study on battery management system of electric vehicle. In this paper, the State of Energy is estimated by using Unscented Kalman Filter, while the process noise and measurement noise is adjusted by using the Sage-Husa adaptive algorithm, as a result the estimation accuracy is improved. The result shows that the State of Energy estimation by using Adaptive Unscented Kalman Filter algorithm is satisfactory to electric vehicle.