Energy Consumption Of Electric Vehicles Research Articles

Electric vehicle energy consumption modelling and analysis: a Malaysia case study

Previously, the Electric vehicle (EV) range anxiety greatly hindered EV adoption. The fear of being stranded due to running out of battery becomes the main concern among EV drivers. This critical issue can be solved by deploying a bigger battery in EVs. However, deploying bigger size battery contributes to the higher price of the EV itself. Hence, the optimal design of the EV powertrain is a critical factor in ensuring EV meets the users’ needs and, simultaneously, can be owned by the potential owners at an affordable price. Accurate assessment of EV energy consumption is crucial in designing an optimal EV powertrain, particularly for developing energy-efficient driving techniques in both manual and autonomous driving. One method is to perform a real-life experiment to determine the EV energy consumption, which is expensive and time-consuming. An alternative to real-world testing is creating an EV energy consumption model with the existing driving cycles. This paper studies the parameters contributing to EV energy consumption based on EV driving cycles available in MATLAB Simulink. The findings of this study will provide insight into the factors that influence EV energy consumption and serve as a guideline in EV powertrain design.

Effect of Wind on Electric Vehicle Energy Consumption: Sensitivity Analyses and Implications for Range Estimation and Optimal Routing

The energy consumption of electric vehicles (EVs) depends on multiple factors. As it affects vehicle range, energy consumption must be accurately predicted. After a summary of the relevant literature, this paper focuses on two sensitivity studies: one on the impact of wind on energy consumption, and the other on the identifiability of wind in the absence of vehicles’ speed and acceleration profiles. The studies show that wind has a significant impact on the energy consumption for a trip, and without high-resolution knowledge of the acceleration and instantaneous velocity, minor variations in the wind condition do not drastically alter the energy consumption distribution. After that, data sources for the information on the wind velocity and direction are discussed. A data-driven approach based on fuzzy set theory is proposed to incorporate wind into the energy prediction; the best model from this approach shows a notable improvement (3.62%) over the currently implemented production-level predictive model for energy consumption on a data set of 35,139 real-world trips; the improvement is even more pronounced (∼ 7%) for trips with more substantial headwind or tailwind level. Recognizing the interplay between range prediction and route selection, we consider a Markov Decision Process (MDP) framework for battery-charge- and travel-time-aware optimal route planning that accounts for the impact of the wind and includes stops at the charging stations. Finally, we propose a framework that includes wind in the operation of EVs, which consists of learning the impact of wind, incorporating wind forecasting into range and energy prediction, and using that prediction to perform optimal routing.

Multi-Mode Switching Control Strategy for IWM-EV Active Energy-Regenerative Suspension Based on Pavement Level Recognition

Aiming at the problems of poor overall vibration reduction and high energy consumption of in-wheel motor-driven electric vehicle (IWM-EV) active suspension on mixed pavement, a multi-mode switching control strategy based on pavement identification and particle swarm optimization is proposed. First, the whole vehicle dynamic model containing active energy-regenerative suspension and the reference model was established, and the sliding mode controller and PID controller designed, respectively, to suppress the vertical vibration of the vehicle and the in-wheel motor. Second, a road grade recognition model based on the dynamic travel signal of the suspension and the road grade coefficient was established to identify the road grade, and then the dynamic performance and energy-feedback characteristics of suspension were optimized by particle swarm optimization. According to the results of pavement identification, the optimal solution of the suspension controller parameters under each working mode was divided and selected to realize the switch of the suspension working mode. The simulation results show that the control strategy can accurately identify the grade of road surface under the condition of mixed road surface, and the ride index of the optimized active energy-regenerative suspension is obviously improved, while some energy is recovered.

Data-driven probabilistic energy consumption estimation for battery electric vehicles with model uncertainty

ABSTRACT This paper presents a novel probabilistic data-driven approach to trip-level energy consumption estimation of battery electric vehicles (BEVs). As there are very few electric vehicle (EV) charging stations, EV trip energy consumption estimation can make EV routing and charging planning easier for drivers. In this research article, we propose a new driver behavior-centric EV energy consumption estimation model using probabilistic neural networks with model uncertainty. By incorporating model uncertainty into neural networks, we have created an ensemble of neural networks using Monte Carlo approximation. Our method comprehensively considers various vehicle dynamics, driver behavior, and environmental factors to estimate EV energy consumption for a given trip. We propose relative positive acceleration (RPA), average acceleration, and average deceleration as driver behavior factors in EV energy consumption estimation, and this paper shows that the use of these driver behavior features improves the accuracy of the EV energy consumption model significantly. Instead of predicting a single-point estimate for EV trip energy consumption, this proposed method predicts a probability distribution for the EV trip energy consumption. The experimental results of our approach show that our proposed probabilistic neural network with weight uncertainty achieves a mean absolute percentage error of 9.3% and outperforms other existing EV energy consumption models in terms of accuracy.

Simulation model for rendering and analyzing the prediction of electric vehicle energy consumption in Matlab/Simulink

Abstract: This work presents the development of a simulation model for predicting the electricity consumption of electric vehicles (EV), with a focus on creating a model that enables precise monitoring of the electric vehicle battery's state. A simple method is provided for predicting the state of charge (SoC) of electric vehicle batteries without computational complexity and other processes. Relevance of the study is clearly demonstrated through the ease of adaptability to all types of electric vehicles, using reliable methodologies for predicting the energy consumption of electric vehicle. This approach aims to contribute to the advancement of the technological infrastructure of electric vehicles, their chargers, and routing application software.

Layered energy-saving speed planning and control method for electric vehicle on continuous signal lights road

Emergency start-stop in front of signal lights is one of the main reasons for additional energy consumption and ride discomfort of Electric Vehicle (EV). Existing research on this issue rarely takes into account both energy consumption and ride comfort. Therefore, the layered energy-saving speed planning and control method is proposed. The upper is the layer of energy-saving speed planning. This layer reduces energy consumption of EV by reducing the number of stops on continuous signal lights road and minimizing the range of speed change. On this basis, the sinusoidal variable speed curve is used to smooth the acceleration process to improve ride comfort. Finally, the energy-saving speed considering ride comfort is obtained. This layer makes up for the issue that existing research rarely takes into account both energy consumption and ride comfort of EV, and is an extension and innovation of existing research. The lower is the layer of Model Predictive Controller (MPC)-based speed control. Based on the longitudinal dynamics model of EV, the MPC-based speed controller is established to control EV to track the energy-saving speed. The controller is easy to understand and implement, and it is also suitable for other research on EV, which has certain application value. The simulation results show that under various working conditions, the maximum energy consumption of EV passing through continuous signal lights road without stopping is 604.29 kJ/km, and the minimum is 244.76 kJ/km. The energy consumption is lower than that of actual road test, and it can be saved by 23.18 % compared with the method in the same field. The maximum Root Mean Square of accelerations (RMSa) is 0.25 m/s2, and the minimum is 0.10 m/s2. The values of RMSa above are lower than 0.315 m/s2, which indicates that the ride comfort is good. The utilized method can reduce energy consumption of EV, improve its range and ride comfort, which has important reference significance for promoting the development of EV.

Prediction of Electric Vehicle Energy Consumption in an Intelligent and Connected Environment

Accurate energy consumption prediction is essential for improving the driving experience. In the urban road scenario, we discussed the influencing factors of energy consumption and divided the modes from various perspectives. The differences in energy consumption characteristics and distribution laws for electric vehicles using the IDM and CACC car-following models under different traffic flows are compared. An energy consumption prediction framework based on the LightGBM model is proposed. According to the study, driving range, acceleration, accelerating time, decelerating time and cruising time all significantly impact the overall energy consumption of electric vehicles. There are apparent differences in energy consumption characteristics and distribution laws under different traffic flows: average energy consumption is lower under low flow and increased under high flow. The CACC-electric vehicles consume more energy in low flow than IDM-electric vehicles. Under high flow, the opposite is true. The results show that the proposed framework has a high accuracy: the MAPE based on IDM datasets is 3.45% and the RMSE is 0.039 kWh; the MAPE based on CACC datasets is 5.57% and the RMSE is 0.042 kWh. The MAPE and RMSE are reduced by 33.7% and 50.6% (maximum extent) compared to the best comparison algorithm.

Regenerative Braking of Electric Vehicles Based on Fuzzy Control Strategy

Regenerative braking technology is a viable solution for mitigating the energy consumption of electric vehicles. Constructing a distribution strategy for regenerative braking force will directly affect the energy saving efficiency of electric vehicles, which is a technical bottleneck of battery-powered electric vehicles. The distribution strategy of the front- and rear-axle braking forces of electric vehicles that possess integrated front-wheel-drive arrangements is established based on the Economic Commission of Europe (ECE) regulations, which enables the clarification of the total braking force of the front axle. The regenerative braking torque model of the motor is adjusted to optimize the ratio of motor braking force to the whole front-axle braking force. The regenerative braking process of electric vehicles is influenced by many factors, such as driving speed and braking intensity, so regenerative braking presents characteristics of nonlinearity, time variability, delay, and incomplete models. By considering the impact of fuzzy controllers having better robustness, adaptability, and fault tolerance, a fuzzy control strategy is employed in this paper to accomplish the regenerative braking force distribution on the front axle. A regenerative braking model is created on the Simulink platform using the braking force distribution indicated above, and experiments are run under six specific operating conditions: New European Driving Cycle (NEDC), World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), China Light-Duty Vehicle Test Cycle-Passenger (CLTC-P), and New York City Cycle (NYCC). The findings demonstrate that in six typical cycling road conditions, the energy saving efficiency of electric vehicles has greatly increased, reaching over 15%. The energy saving efficiency during the WLTC driving condition reaches 25%, and it rises to 30% under the FTP-72, FTP-75, and CLTC-P driving conditions. Furthermore, under the NYCC road conditions, the energy saving efficiency exceeded 40%. Therefore, our results verify the effectiveness of the regenerative braking control strategy proposed in this paper.

Investigating the influential factors in real-world energy consumption of battery electric vehicles

The transportation industry is undergoing a major shift towards electrification to mitigate carbon emissions and decrease reliance on fossil fuels in response to global climate change and greenhouse gas concerns. Recently, battery electric vehicles (BEVs) have made significant progress and are becoming a more viable option for achieving zero-emission transportation. The aim of this study is to investigate the energy consumption patterns of BEVs operating in real-world driving scenarios encompassing various route conditions. The vehicle sensor data employed in this study was acquired through onboard diagnostic devices that directly gathered raw data and subsequently transmitted it to mobile applications. Various significant factors, such as payload, road slope level, speed range, acceleration, and loads of heating, ventilation, and air conditioning, are considered as variables influencing energy consumption. By utilizing the large amount of data collected, machine learning (ML) techniques were applied to develop a predictive model of energy consumption and identify variables that influence energy consumption. The outcomes of this study hold the potential to offer guidance to transportation policymakers and furnish valuable insights for prospective buyers considering BEVs. Furthermore, the application of ML in the development of a predictive model demonstrated efficacy and exhibits promising potential for wider-ranging applications.

Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework

As greener mobility becomes the norm with the advent of electric vehicles (EVs), a natural question arises: how big of a change are we seeing in terms of the stochastic energy demands imposed by EVs? There have been considerable difficulties in analyzing the adoption of all types of EV infrastructure due to the lack of publicly available individual EV user data across various service providers, such as distribution network operators, EV aggregators, EV users, utilities, regulators, and academics. In this study, we introduce the JETPANN (Joint EV energy consumption and charging duration Training Prediction using Artificial Neural Networks), a novel jointly trainable artificial deep neural network framework for predicting stochastic EV user behavior by predicting their stay/charging duration and energy consumption simultaneously. We used a large-scale dataset of individual EV users’ charging transactions collected at a multi-site UCLA campus, the Los Angeles Department of Water and Power (LADWP), the Port of LA, the City of Santa Monica, and the City of Pasadena over a five-year period. This includes more than 50 EVSEs (Electric Vehicle Supply Equipment) and 216 EV charging points. Over 50,000 real-time transactions were recorded in our database with 341 distinct EV users. In this research, essential attributes of each charging session were identified, extracted from our database, and then fed into our JETPANN network. Using a jointly trained framework, our AI-enabled network predicts the stay/charging duration and the energy consumption of all the recorded EV users with an accuracy of above 99 percent. The proposed technique was tested and validated using all the collected historical charging data from individual EV users via realizing mean-absolute errors of training loss versus validation loss. Utilizing hyperparameter tuning and semi-grid search, the JETPANN with semi-optimized hyperparameters was achieved with the lowest mean-absolute errors of 0.927 and 0.068 in predicting stay/charging durations and energy consumptions, respectively, in a jointly trainable framework. This study demonstrates the potential of the JETPANN framework in the prediction of EV users’ behavior by using large-scale real-world data collected from a diverse pool of EV users, including faculty and students, occasional and frequent users, and early and late adopters.

Optimization Effect of the Improved Power System Integrating Composite Motors on the Energy Consumption of Electric Vehicles

The multi-power source coupled transmission system is a high-performance and energy-saving potential power transmission system, and most of the commonly used pure electric vehicles in the market that use multi-power source coupled drive adopt the motor dual-axis distributed independent drive scheme. The configuration design method for multi-power source fusion hybrid systems mainly focuses on the search and selection of power split hybrid systems based on planetary gear mechanisms. But it has not yet covered the configuration design of transmission systems, resulting in a lack of universal expression and generation methods for the configuration of multi-power source fusion hybrid systems in pure electric vehicles. Therefore, to solve the configuration optimization design problem of a dual-motor single-planetary-array power system, an improved general matrix topology design method is proposed to generate all feasible topology structures. And energy consumption, economy, and the dynamic performance of alternative configurations are optimized and simulated through the control strategy based on a dynamic programming algorithm. Under comprehensive testing conditions, 25 alternative options that met the screening criteria were selected, and, ultimately, five optimized configuration options were obtained. Configuration 1 has the best economy, reducing energy consumption by about 6.3%and increasing driving range by about 6.7%. Its 0–100 km/h acceleration time is about 31.4% faster than the reference configuration. In addition, the energy consumption economy during actual driving is almost the same as the theoretical optimal energy consumption economy, with a difference of only 0.3%. The success of this study not only provides an innovative method for optimizing the configuration of dual-motor single-row star train power systems, but also has a positive impact on improving energy utilization efficiency, reducing energy consumption, and improving the overall performance of electric vehicles.

Evaluation of fuel consumption and emissions benefits of connected and automated vehicles in mixed traffic flow

Introduction: The presence of connected and automated vehicles (CAV) in mixed traffic flows with different market penetration rates (MPRs) in urban road scenarios has a significant effect on fuel consumption and exhaust emissions.Methods: Therefore, in this study, real-world road networks and traffic data are simulated using SUMO based on actual data from a survey. The fuel consumption and emission benefits of CAVs in mixed traffic flows are well-evaluated, and the energy-saving performance of CAVs under low-speed vehicle interference is tested. In addition, this study explores both the energy consumption and emissions of purely electric vehicles.Results: The results show that with 100% CAV penetration, fuel vehicles have a maximum reduction in fuel consumption of 18% and a maximum increase in average speed of 31.6%, while the energy consumption of electric vehicles increases due to communication, detection, and collaboration between CAVs.Discussion: However, the results clearly demonstrate that the carbon emissions of electric vehicles are significantly lower than fuel vehicles. In addition, the increase in low-speed vehicles will result in an increase in energy consumption and emissions. Therefore, increasing the percentage of electric vehicles on the roads and transitioning from manual to autonomous driving systems is crucial to curbing carbon emissions.

Numerical Energy Analysis of In-Wheel Motor Driven Autonomous Electric Vehicles

Autonomous electric vehicles are being widely studied nowadays as the future technology of ground transportation, while their conventional powertrain systems limit their energy efficiencies and may hinder their broad applications in future. Here we report a study on the energy consumption, efficiency improvement and greenhouse gas emissions of a mid-size autonomous electric vehicle driven by in-wheel motors, through the development of a numerical energy model, validated and implemented in a case study. The energy analysis was conducted under three driving conditions: flat road, upslope, and downslope driving, considering autonomous driving patterns, motor efficiency optimization and regenerative braking. The case study based on the baseline electric vehicle driving data in West Los Angeles showed that an in-wheel motor driven autonomous electric vehicle can save up to 17.5% of energy during the slope driving. Additionally, it can reduce around 5.5% greenhouse gas emissions annually in each state in the United States. Using the efficiency maps of a commercial in-wheel motor, the energy model and validated results in this study are in line with actual situations and can be used to support the future development of energy-efficient autonomous electric vehicles and sustainable energy transitions in ground transportation.

Estimating Energy Consumption of Battery Electric Vehicles Using Vehicle Sensor Data and Machine Learning Approaches

Transport electrification, which entails replacing fossil fuel-powered engines with electric drivetrains through the use of electric vehicles (EVs), has been identified as a potential strategy for reducing emissions in the transportation sector. As the adoption of EVs increases, there is a growing need to understand their performance and characteristics, particularly the factors that influence energy consumption under actual driving conditions. This study sought to investigate the actual energy consumption of commercial battery electric vehicles (BEVs) in Thailand by conducting real-world driving tests under various route conditions, including urban and rural route modes. Data collection was performed through the use of onboard diagnostics and global positioning system devices. The result shows that the average energy consumption of the BEVs in this study was 148.03 Wh/km. Moreover, several machine learning (ML) techniques were utilized to analyze the collected dataset to predict energy consumption and identify the key factors influencing energy consumption. A comprehensive investigation of factor significance was carried out by employing a specific algorithm in conjunction with the SHapley Additive exPlanations (SHAP) approach. This investigation provided insights into the influence of battery current and vehicle speed on the energy consumption of BEVs, particularly in the context of urban route conditions. The results of this study provide valuable insights into the energy consumption of BEVs and the factors affecting it, which can aid in improving energy efficiency and informing policy decisions related to transport electrification.

Personalized Velocity and Energy Prediction for Electric Vehicles With Road Features in Consideration

Electric vehicles (EVs) seem to be an eminent alternative for ground transportation. Yet, human drivers may suffer from EV’s range anxiety, which is engendered by EV’s limited battery capacity and sparse charging stations, particularly in rural areas. A reliable prediction of EV battery energy consumption for the intended route before traveling can alleviate this feeling of unease. Fundamentally, an accurate speed trajectory forecast lays the foundation for a dependable energy consumption prediction. This paper originates a data-driven paradigm to predict an EV’s speed considering various road features and individuals’ driving characteristics. Two modified Transformer neural networks, which outperform the traditional recurrent neural networks, are exploited to extract information from input features and predict vehicular acceleration or velocity profile. Additionally, a novel energy consumption model is suggested to pioneeringly take the tire slip ratio into account and utilize a neural-network-predicted powertrain efficiency map. By design, the proposed algorithm can offer EV speed and energy consumption estimations prior to the start of a trip. Experimental data collected in real-world EV test drives on four different routes are employed to validate the method. Compared to a baseline approach, the proposed scheme yields a superior accuracy on both velocity and energy consumption predictions for EVs.

Deep Learning Based Optimization Model for Energy Consumption of New Electric Vehicles

Deep Learning Based Optimization Model for Energy Consumption of New Electric Vehicles

A data-driven travel demand model to predict electric vehicle energy consumption: focusing on the rural demographic in the UK

ABSTRACT This paper presents a 7-day Travel Demand Model (TDM) for UK rural areas to aid the Electric Vehicle (EV) transition in these regions. Utilising data from both the UK Census Survey and UK National Travel Survey (NTS), private passenger vehicle travel patterns for a rural village in the Peak District National Park (UK), were modelled. This model is adaptable to any rural community within the UK, requiring only publicly available information on households and vehicles for that community. Using a novel approach through the development of lifestyle scenarios to understand the required household activities, the TDM incorporates five different trip purposes as the building blocks for a vehicle’s activity. Over a period of one week, 13,520 miles were driven by 84 vehicles across 49 households, that shows an EV fleet serving this community would consume 3562 kWh energy per week.

Analysis of speed limit and energy consumption in electric vehicles

This paper presents an analysis of the mileage energy consumption for an electric passenger vehicle in terms of the introduction of numerous speed limits. Regulations concerning the limiting of vehicle speed to 30 km/h in cities or residential areas are particularly common. This restriction is intended to increase traffic safety, but at the same time introduces increased mileage fuel or energy consumption in electric drivetrain. Regardless of the energy carrier, any increase in energy causes negative effects for the environment. The analysis was focused on the mileage energy consumption of electric passenger cars for a constant speed under real traffic conditions. During the tests, the tested vehicles’ speed on a specially designated road section was changed gradually by 10 km/h, with simultaneous recording of the car’s traction parameters and mileage energy consumption. An analysis of the mileage energy consumption was then carried out for the assumed fleet of cars travelling one after another (in a so-called traffic jam), while maintaining a safe distance. This allowed for the calculation of the environment’s energy burden caused by a fleet of vehicles travelling on a given road section, indicating that a reduction in vehicle speed causes an increase in the vehicles’ energy consumption. Both total and mileage energy consumption of electric vehicles were analysed during the tests.

Data-driven analysis of battery electric vehicle energy consumption under real-world temperature conditions

Demand for battery electric vehicles has grown significantly in recent years as global oil prices have climbed. The current development of battery electric vehicles is still in its infancy, and its energy consumption also requires great attention. Five same model battery electric vehicles' driving condition data of a certain brand in Tianjin area are collected for a whole year, and their energy consumption is analyzed. The results show that the energy consumption of the actual driving conditions in the four seasons is generally higher than the test results under New European Driving Cycle, and only some vehicles' energy consumption in spring and autumn is consistent with the test results under Worldwide Harmonized Light Vehicles Test Cycle. Energy consumption tends to increase at both ends with the change of temperature. The impact of low temperature is greater, because the air conditioner usage rate and power in Winter are higher than those in Summer. Through principal component contribution rate analysis and K-means clustering calculation of micro-trips, the results show that the average energy consumption in urban, suburban and high-speed driving conditions decreases gradually. Driving habits such as rapid acceleration ratio and rapid deceleration ratio also have an impact on energy consumption. The decrease of energy braking recovery indirectly caused by rapid deceleration ratio increase makes the energy consumption per 100 km increase. And the increase of rapid acceleration ratio leads to an increase in energy consumption per 100 km. Finally, the Spearman rank correlation analysis shows that energy consumption has the greatest correlation with the average vehicle speed and air conditioner usage status, followed by the constant speed proportion, idle speed proportion and temperature, and finally the recovery of braking energy per 100 km, acceleration proportion, deceleration proportion and rapid acceleration proportion.

Impact of scaling laws of permanent magnet synchronous machines on the accuracy of energy consumption computation of electric vehicles

This paper compares the impact of two scaling methods of electric machines on the energy consumption of electric vehicles. The first one is the linear losses-to-power scaling method of efficiency maps, which is widely used in powertrain design studies. While the second is the geometric scaling method. Linear scaling assumes that the losses of a reference machine are linearly scaled according to the new desired power rating. This assumption is questionable and yet its impact on the energy consumption of electric vehicles remains unknown. Geometric scaling enables rapid and accurate recalculation of the parameters of the scaled machines based on scaling laws validated by finite element analysis. For this comparison, a reference machine design of 80 kW is downscaled with a power scaling factor of 0.58 and upscaled considering a power scaling of 1.96. For comparative purposes, optimal combinations of geometric scaling factors are determined. The scaled machines are derived to fit the driving requirements of two electric vehicles, namely a light-duty vehicle and a medium-duty truck. The comparison is performed for 9 standardized driving cycles. The results show that the maximal relative difference between linear and geometric scaling in terms of energy consumption is 3.5% for the case of the light-duty vehicle, compared with 1.2% for the case of the truck. The findings of this work provide evidence that linear scaling can continue to be used in system-level design studies with a relatively low impact on energy consumption. This is of high interest considering the simplicity of linear scaling and its potential for time-saving in the early development phases of electric vehicles.