Energy Consumption Of Electric Vehicles Research Articles

Experimental platform for studying energy regeneration in electric vehicle powertrains

AbstractInvestigation into the energy consumption in electric vehicles (EVs) plays a pivotal role in determining their autonomy and assessing the electric system performance across diverse operational scenarios. This study focuses on the concept of energy regeneration, encompassing the recovery and storage of kinetic mechanical energy during braking or descent in EVs. Employing control systems in power electronics becomes necessary to establish a seamless workflow across operational quadrants to ensure efficient energy regeneration in an electric machine functioning as both a motor and a generator. To seamlessly integrate new technologies into practical applications, it is essential to conduct thorough evaluations in laboratories prior to deployment. This paper introduces an experimental platform specifically designed to analyze energy consumption and storage in EVs by emulating their powertrains in a controlled laboratory environment. The platform comprises key components for emulating the powertrain of a single-motor electric vehicle with single-axle traction, including a power converter configured in two quadrants, an energy storage system, a primary rotating electric machine, and a mechanically coupled point load torque (another motor). This paper provides a detailed guide on implementing such a laboratory and for facilitating the testing of diverse motor technologies and controllers under varied operational conditions. This comprehensive approach allows for the assessment of electromechanical system efficiency, focusing on both energy recovery and comprehensive control of electric power converters. Validation tests conducted under urban conditions and on steep terrains demonstrate the effectiveness of the platform in analyzing the energy efficiency of both the induction machine and the power controller.

Electric vehicle energy consumption taking into account the route topology

Purpose. Determining the impact of the route topology factor on the costs of mechanical work of an electric vehicle is the main task of this work. The impact is determined by calculating the costs of mechanical work during the movement of an electric vehicle, taking into account energy recovery. The task also includes assessment of the forces acting on an electric vehicle using the example of the 2014 Nissan Leaf AZEO. Methodology. The paper uses a mathematical model that estimates the amount of mechanical work required to overcome one of the chosen routes, taking into account energy recovery. Evaluation is performed using the most common standardized cycle WLTC class 3b. Findings. The result of the research is a developed mathematical model that will allow one to effectively estimate the amount of mechanical work to overcome the given route and the possible recovery energy. The proposed method makes it possible to determine the most economical route from the starting point to the destination, taking into account the cost of mechanical energy. Originality. A description of the main components affecting the consumption of electricity is given, taking into account the full picture of the forces acting on the electric vehicle during movement. Practical value. The obtained results are of practical importance for choosing the most optimal route of the electric vehicle, which contributes to the efficient use of energy. The proposed technique can be used in practice to plan routes from the point of view of maximum energy recovery.

Efficiency Analysis of Electric Vehicles with AMT and Dual-Motor Systems

With the rapid development of automobiles, energy shortages and environmental pollution have become a growing concern. In order to decrease the energy consumption of electric vehicles (EVs), this study aims to improve EV efficiency with AMT and dual-motor systems. Firstly, the paper establishes an Automated Manual Transmission (AMT) model for EVs, which is then simulated using MATLAB R2022a software. In order to eliminate the impact of gear ratio selection, the genetic algorithm is used to optimize the AMT gear ratios. Meanwhile, a dual-motor EV model is constructed, and three different torque distribution schemes are simulated and analyzed. The results indicate that due to the elongation of the energy transmission chain in AMT-equipped EVs, energy losses increase, leading to some improvement in optimized power consumption. However, these EVs remain inferior to those with only a single-stage main reducer. The study also found that the torque distribution based on optimal efficiency further improves results.

Energy Consumption of Electric Vehicle with Roof-Mounted Solar Panel in Various Driving Situations

Energy Consumption of Electric Vehicle with Roof-Mounted Solar Panel in Various Driving Situations

Spatial-temporal Distribution Prediction of Electric Vehicle Charging Load Considering Vehicle-road-station-network Integration

Aiming at the problem of inaccurate prediction of spatial-temporal distribution of electric vehicle charging load due to insufficient consideration of vehicle-road-station-network interaction, a prediction model of spatial-temporal distribution of electric vehicle charging load based on gravitation model was proposed. Firstly, considering road network traffic flow and ambient temperature, the relationship between external environment and energy consumption of electric vehicles is analyzed. Secondly, considering the influence of external environmental factors such as temperature on user travel, a travel chain model based on travel intention modification is established. Finally, the selection model of EV charging station based on gravitation model is established. The simulation results show that the proposed model can take into account the interaction of EV, road network, charging station and power grid, and accurately calculate the spatio-temporal distribution of EV charging load, and analyze the characteristics of EV charging demand load in multi-regions.

Improving Sustainability in Urban and Road Transportation: Dual Battery Block and Fuel Cell Hybrid Power System for Electric Vehicles

Improving Sustainability in Urban and Road Transportation: Dual Battery Block and Fuel Cell Hybrid Power System for Electric Vehicles

Impact of Additional PV Weight on the Energy Consumption of Electric Vehicles With Onboard PV

Impact of Additional PV Weight on the Energy Consumption of Electric Vehicles With Onboard PV

An adaptive neuro-fuzzy with nonlinear PID controller design for electric vehicles

In this work, an adaptive neuro-fuzzy (ANF) with a nonlinear proportional integral derivative (NLPID) controller (ANF-NLPID) has been proposed to solve the speed-tracking problem in electric vehicles (EVs) with brushless DC motor (BLDC). The ANF-NLPID controller eliminates the external disturbances caused by environmental or internal issues and uncertainties caused by parameter variations that lead to insufficient speed-tracking performance and increased energy consumption in EVs. An improved particle swarm optimization based on the chaos theory (IPSO-CT) algorithm is introduced to obtain the parameters of the fuzzy logic controller membership function and nonlinear PID controller and present the optimal performance for the EV. Employing the chaos technique with PSO helps to prevent the system from being trapped in the local minimum or optimum problem. The performance of the IPSO-CT algorithm is tested using a numerical comparison with other existing works. The outstanding performance of the ANF-NLPID controller has been evaluated by measuring the speed-tracking performance for the new European driving cycle (NEDC) and circular trajectories. Three case studies have been presented based on measuring the ANF-NLPID controller performance without disturbances, with disturbances, with disturbances, and uncertainties effects, respectively. Furthermore, the ANF-NLPID controller has been employed in different EV models to study the performance of this type of controller. Each of the three cases includes other existing works along with the ANF-NLPID controller to provide an insightful comparison using statistical functions to obtain each controller’s overall objective function value. The other existing works are fuzzy fractional order PID (Fuzzy FOPID), fuzzy integer order PID (Fuzzy IOPID), and integer order PID (IOPID) controllers. A sensitivity analysis has been conducted to test the proposed controller’s ability to present high speed-tracking performance while changing the disturbances and uncertainty rates. The results demonstrate that the ANF-NLPID controller is superior in speed-tracking control regulation for the new European cycle drive (NEDC) and circular speed trajectories and overcomes the external disturbances and uncertainties problem with low error results. In the end, the results reveal that the ANF-NLPID controller is more efficient than the fuzzy FOPID, fuzzy IOPID, and IOPID controllers in each case.

Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving

Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks.

Estimating of energy consumption of electric vehicles under different road characteristics: a case study for Nanjing, China

Estimating of energy consumption of electric vehicles under different road characteristics: a case study for Nanjing, China

A Generic Model for Accurate Energy Estimation of Electric Vehicles

A systematic simulation model is proposed in this research paper to estimate the energy consumption of electric vehicles. The main advantage of this model is that it is made in a generic and simplified way in order to be adaptable to different electric vehicles. The overall electrical power corresponding to the performed maneuver is estimated considering: a tabular form of electric motor efficiency, mechanical power losses, a generalized efficiency map of the power electronics, the auxiliary power losses, and an electro-thermal Lithium-Ion battery pack model. The battery model was developed in a previous work, which simulates the open circuit voltage curves at different temperatures and the alteration in the internal resistance of the battery cells. The proposed model is validated with experimental data from the maneuver tests. The battery model proved high accuracy in estimating the voltage values relevant to the WLTP2 driving cycle on the chassis roller test bench. Furthermore, the mechanical and electrical power were estimated with excellent matching compared to actual test field driving test measurements, giving only the measured vehicle speed and auxiliary power losses. Finally, the state of charge change is predicted accurately along the performed test field dynamic maneuver.

New control methodology of electric vehicles energy consumption optimization based on air conditioning thermal comfort

This article discusses the new control methodology of vehicle energy consumption optimization for thermal comfort. To address the issues of exceeding boundaries and redundancy in the application of PMV(Predicted Mean Vote) indicators in vehicles, three different control strategies for electric vehicles are proposed. To address the issue of inaccurate prediction results caused by considering air conditioning energy consumption as a part of vehicle driving energy consumption, a method combining air conditioning average power method is proposed to estimate the remaining range of vehicles. The impact of air conditioning energy consumption on the estimation results of vehicle remaining range under three control strategies is analyzed. Finally, the simulation and experimental results demonstrate the differences in the energy consumption values of the entire vehicle under the three modes, providing a solution for balancing energy saving and comfort control in the use of electric vehicles. In addition, the experimental results show that the error rate between the actual value of estimating the remaining driving distance of vehicles using the average power method of air conditioning and the simulated data is less than 3%. The proposed algorithm provides new ideas and methods for estimating the remaining driving range of electric vehicles.

Research on the interaction between energy consumption and power battery life during electric vehicle acceleration

Most studies on the acceleration process of electric vehicle focus on reducing energy consumption, but do not consider the impact of the power battery discharge current and its change rate on the battery life. Therefore, this paper studied the interaction between electric vehicle energy consumption and power battery capacity attenuation during acceleration. First, a power battery life model for electric vehicle under driving conditions is established, and the percentage of battery capacity loss per kilometer is used to measure the capacity loss under different acceleration conditions. Then, the relationship between the percentage of battery capacity loss per kilometer and velocity and acceleration is explored, and the capacity attenuation mechanism of power battery under different acceleration processes is analyzed. Finally, the energy consumption and battery capacity attenuation is studied when the electric vehicle accelerated with multiple accelerations curves, and the interaction of the first acceleration and acceleration time on the electric vehicle energy consumption and the power battery capacity attenuation characteristics is discussed. The research results indicate that when the electric vehicle accelerates with different multiple accelerations curves, the change of energy consumption per kilometer and percentage of battery capacity loss per kilometer with acceleration and acceleration time is different, and the change of the two is basically opposite.

A comparative analysis of energy consumption in conventional and electric vehicles

A comparative analysis of energy consumption in conventional and electric vehicles

A comparative analysis of energy consumption in conventional and electric vehicles

A comparative analysis of energy consumption in conventional and electric vehicles

Energy Efficiency Analysis and Research of Pure Electric Vehicles under Actual Working Conditions

Pure electric vehicles are a hot topic in today’s social development. From beginning to end, the energy density of power batteries in pure electric vehicles is generally low, resulting in short range, inaccurate range estimation, and driver anxiety. This is also one of the reasons that hinder the promotion of pure electric vehicles. This article discusses the energy consumption of pure electric vehicles based on actual road tests. By comparing the battery energy efficiency of various roads (including urban roads, suburban roads, and highways) using electric vehicles from Guangzhou to Qingyuan, the energy consumption patterns of pure electric vehicles are analyzed. Experiments have shown that continuously improving the energy density of power batteries is the key technology to solve the problem of insufficient range for electric vehicles. Based on the existing power battery technology, this study investigates the relationship between energy consumption and various factors affecting electric vehicles, which can provide better reference for drivers to predict energy consumption and range.

Exploring the utilization of Electric-vehicles’ free public charging facilities in small towns. A case of the town of Cary, North Carolina

This study explores the influence of spatial–temporal factors on the utilization of publicly available electric vehicle (EV) charging stations. It utilized the EV energy consumption data collected between 2019 and 2022 from six charging locations in the town of Cary, North Carolina. Descriptive analysis, multiple linear regression, and survival models were applied to explore energy consumption and the charging duration. Study results revealed a great variation in energy consumption and charging duration. Sundays have the highest frequency of charging activities, the largest amount of energy consumed, and the longest total duration, while Mondays have the lowest frequency of charging activities. Most of the charging activities occurred between 6 a.m. and 9p.m. The relationship between charging duration and energy consumed revealed that, for each charging minute, the EV consumed between 87 and 111.3 Wh of energy. The observed variations were mainly associated with the location of a charging facility and charging model type. The regression model results revealed that, unlike nighttime, energy consumption is statistically significantly lower in the daytime. Exploration of the day of the week suggested that weekdays, especially Mondays and Thursdays, have higher energy consumption than weekends. Further, the energy consumption increased over time, as indicated by the higher energy consumed in 2022 compared to 2019. The pattern for charging duration is similar to that of energy consumption except for the days of the week. The findings are crucial to planners for better planning and deployment of charging facilities based on the expected utilization.

A comparative study of energy-oriented driving strategy for connected electric vehicles on freeways with varying slopes

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies.

A three-heat source segmented heating control strategy based on waste heat recovery technology for electric vehicles

The control strategy of thermal management systems is crucial for electric vehicles to ensure thermal comfort of the cabin and thermal safety of the battery and motor. However, this will also impact the energy consumption of the vehicle. In this study, a three-heat source segmented heating control strategy was proposed to reduce the heating energy consumption of electric vehicles, with the three sources denoted motor waste heat, air, and positive temperature coefficient (PTC) heaters. Specifically, a simulation model of an integrated thermal management system for heat pump air conditioning, motor thermal management, and battery thermal management systems was first established and validated using published experimental results. Subsequently, the performance of three cabin heating modes was investigated based on the standard driving cycle. The optimal opening and closing strategies for each mode were discussed to ensure the temperature requirements of the cabin, and the optimal method for using motor waste heat and the PTC heater to heat the battery at low temperatures was explored. The segmented heating control strategy was developed by determining the heating priority for the battery and cabin, as well as the heating capacity of heating modes using air source. Compared to the traditional heating strategy of the cabin and battery, the proposed control strategy could reduce energy consumption by 18.49 % while running at an ambient temperature of − 10 °C for 2.5 h.