Energy Consumption Of Electric Vehicles Research Articles

A comparative study of vehicle powertrain efficiency: Data-driven analyzing energy consumption and environmental impact

This study investigates the energy consumption of electric vehicles (EVs) in real-world driving conditions, using in-vehicle sensor data and location tracking. The energy consumption of EVs is compared to that of an internal combustion engine vehicle (ICEV) under similar driving tests. Furthermore, an energy-centric life cycle assessment was conducted to assess the environmental impact of the transition to electrified transportation by reporting the carbon emissions of the EVs and ICEV. Since a large volume of driving data was acquired in the experiment, machine learning (ML) techniques are employed to interpret the obtained data and identify a key feature. ML algorithms can help develop predictive models that estimate energy consumption and explain the major factors that impact such consumption. The findings also offer valuable insights into energy consumption, emission, and the underlying factors that can inform policy discussion related to transport electrification and enhance energy efficiency. This research is significant in comprehending the potential of current EVs and their superiority over traditional vehicles. It is vital to facilitate the shift toward electric transportation, which can be driven by the results.

Electric vehicle energy consumption prediction for unknown route types using deep neural networks by combining static and dynamic data

Accurate energy consumption prediction of electric vehicles (EVs) is crucial for drivers considering long trips. All the data should be provided beforehand to determine the energy consumption at the beginning of the trip. Although dynamic vehicle data (vehicle speed, state-of-charge, acceleration, etc.) cannot be known before the trip, factors related to the specified route (route type, elevation, average speed, weather, driving time, etc.) can be used to predict the consumed energy. These factors can be categorized as static and dynamic features, and thus, the question of how to effectively use static and dynamic data arises. This paper investigates the problem of predicting the energy consumption of an EV for a predetermined trip using a deep neural network (DNN) model that effectively uses static features along with dynamic segment features. Furthermore, we address the problem where the route types are unknown in advance. To include more information in the prediction model, we clustered the speed profiles using shape-based clustering with dynamic time warping (DTW) to predict the route type and used the cluster labels as static inputs. Real driving data collected from various drivers of a specific EV were used to train the DNN. The proposed DNN model was compared with the average energy consumption (AEC) model and five machine learning models. The results show that labels obtained from shape-based clustering improved the prediction more than feature-based cluster labels. The prediction errors were minimized with the proposed DNN model, where static features are introduced to the first and second layers twice.

Electric delivery vehicle routing problem optimization model with time-varying traffic congestion

In order to plan a reasonable scheme for the fleet travel and minimize the energy consumption of electric vehicles during traffic congestion periods, while relieving the mileage anxiety of delivery personnel, the pure electric delivery vehicle routing problem considering time-varying traffic congestion was studied. Firstly , a novel mixed integer programming model (TD-EVRP) is established to step for its extended model of EVRP, which especially characterizes the time-varying traffic congestion state and measures the energy consumption of electric delivery vehicles. This model also refines its constraints including customer time window, vehicle load, and vehicle charging requirements. Secondly, a traffic congestion avoidance strategy is introduced into the model, which allows vehicles to park and charge at customer points during peak traffic periods in order to actively avoid unnecessary energy consumption caused by traffic congestion. Finally, the improved ant colony algorithm is designed to acquire the optimal solution.

“Optimizing And Predicting Energy Consumption In Electric Vehicles Using Deep Learning: A Comparative Study Of GRU And LSTM Models”

“Optimizing And Predicting Energy Consumption In Electric Vehicles Using Deep Learning: A Comparative Study Of GRU And LSTM Models”

A particle swarm optimizer-based optimization approach for locating electric vehicles charging stations in smart cities

Conventional fossil fuel-powered vehicles are gradually being replaced by electric and hydrogen vehicles in the transportation sector. Even with all the recognized benefits and recent advancements in energy efficiency, decrease of noise, and environmental impact reduction, the market of electric and hydrogen mobility is still not up to par. Allocating charging stations in metropolitan areas for electric vehicles is considered as one of the most significant obstacles preventing electric and hydrogen vehicles from becoming more widely used. In this paper, an efficient approach aimed at finding optimal locations for electric vehicles (EV) charging stations in urban areas is proposed. Particle Swarm Optimization algorithm technique is utilized with the proposed approach. Various parameters were taken into consideration in this work, such as the horizontal distance that EVs travel to reach charging stations (CSs), and positive slope that EVs face to reach charging stations. The optimization problem is formulated as a Mixed-integer problem. The objective function works on minimizing the energy consumption of EVs to reach CSs in the investigated area. Difference constraints are incorporated with the proposed approach in order to increase the accuracy and efficiency of the proposed approach. The proposed approach is applied on real world datasets and is experimentally validated using through comparison with Genetic Algorithm and the greedy approach. The results demonstrate that the proposed approach saves energy about 22% and 43% compared to the genetic algorithm and greedy technique, respectively.

Energy Consumption of Electric Vehicles in Europe

As the European Union advances its regulatory framework on energy efficiency, the introduction of an energy label for electric cars appears increasingly relevant. Anticipating this policy development, we present a scoping analysis of energy consumption and efficiency trade-offs across 342 fully electric cars available in Europe. Our results suggest that certified and real-world energy consumption average 19 ± 4 kWh/100 km and 21 ± 4 kWh/100 km, translating into drive ranges of 440 ± 120 km and 380 ± 110 km, respectively. Energy consumption is correlated with mass, frontal area, and battery capacity but less so with rated power and vehicle price. Each 100 kg of vehicle mass and 0.1 m2 of frontal area increases energy consumption by 0.2 ± 0.1 kWh/100 km and 0.9 ± 0.1 kWh/100 km, respectively. Raising battery capacity by 10 kWh elevates vehicle mass by 143 ± 4 kg, energy consumption by 0.6 ± 0.1 kWh/100 km, drive range by 44 ± 2 km, and vehicle price by 12,000 ± 600 EUR. Efficient cars are available at any price, but long drive ranges have a cost. These findings point to considerable efficiency trade-offs that could be revealed to consumers through a dedicated energy label. We propose several options for classifying vehicles on an efficiency scale from A to G, with and without drive range and battery capacity as utility parameters. Our analysis provides a rationale for the energy labeling of electric cars in the European Union and could inspire similar analyses for other vehicle categories such as e-scooters, lightweight electric three- and four-wheelers, e-busses, e-trucks, and electric non-road machinery.

A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation

Global warming and carbon emissions have drawn attention to the need to decarbonize transport. Promoting electric vehicles (EVs) has become an important strategy towards this goal. Although EVs have significant advantages in emission reduction and energy saving, their wider adoption is limited by factors such as range and charging infrastructure. Accurate prediction and modeling of EV energy consumption is the key to solving these challenges. This review first summarizes traditional energy consumption estimation models and, thereafter explores the application of interpretable machine learning, emphasizing its importance in improving model transparency and practicality. The potential of neural networks to enhance prediction accuracy through feature extraction and pattern recognition is also discussed. This paper aims to provide a systematic review of the latest advances in EV energy consumption forecasting. It serves as a reference for researchers and practitioners to optimize and upgrade urban transport systems for sustainable development.

Life Cycle Assessment of Plug-In Hybrid Electric Vehicles Considering Different Vehicle Working Conditions and Battery Degradation Scenarios

This study establishes a life cycle assessment model to quantitively evaluate and predict material resource consumption, fossil energy consumption and environmental emissions of plug-in hybrid electric vehicles (PHEVs) by employing the GaBi software. This study distinguishes the environmental impact of different vehicle working conditions, power battery degradation scenarios, and mileage scenarios on the operation and use stages of PHEVs, BEVs, and HEVs. The findings indicate that under urban, highway, and aggressive driving conditions, PHEVs’ life cycle material resource and fossil fuel consumption exceed that of BEVs but are less than HEVs. Battery degradation leads to increased material resource consumption, energy use, and environmental emissions for both PHEVs and BEVs. When the power battery degrades to 85%, the material resource and fossil energy consumption during the operation and use phase increases by 51.43%, 72.68% for BEVs and 29.37%, 36.21% for PHEVs compared with no degradation, respectively, indicating that the environmental impact of BEVs are more sensitive than those of PHEVs to the impact of power battery degradation. Among different mileage scenarios, PHEVs demonstrate the lowest sensitivity to increased mileage regarding life cycle material resource consumption, with the smallest increase. Future projections for 2025 and 2035 suggest life cycle GWP of HEV, PHEV and BEV in 2035 is 1.21 × 104, 1.12 × 104 and 1.01 × 104 kg CO2-eq, respectively, which shows reductions of 48.7%, 30.9% and 36.1% compared with those in 2025. The outcomes of this study are intended to bolster data support for the manufacturing and development of PHEV, BEV and HEV under different scenarios and offer insights into the growth and technological progression of the automotive sector.

Electric vehicle eco-driving strategy at signalized intersections based on optimal energy consumption

Electric vehicles (EVs), which are a great substitute for gasoline-powered vehicles, have the potential to achieve the goal of reducing energy consumption and emissions. However, the energy consumption of an EV is highly dependent on road contexts and driving behavior, especially at urban intersections. This paper proposes a novel ecological (eco) driving strategy (EDS) for EVs based on optimal energy consumption at an urban signalized intersection under moderate and dense traffic conditions. Firstly, we develop an energy consumption model for EVs considering several crucial factors such as road grade, curvature, rolling resistance, friction in bearing, aerodynamics resistance, motor ohmic loss, and regenerative braking. For better energy recovery at varying traffic speeds, we employ a sigmoid function to calculate the regenerative braking efficiency rather than a simple constant or linear function considered by many other studies. Secondly, we formulate an eco-driving optimal control problem subject to state constraints that minimize the energy consumption of EVs by finding a closed-form solution for acceleration/deceleration of vehicles over a time and distance horizon using Pontryagin’s minimum principle (PMP). Finally, we evaluate the efficacy of the proposed EDS using microscopic traffic simulations considering real traffic flow behavior at an urban signalized intersection and compare its performance to the (human-based) traditional driving strategy (TDS). The results demonstrate significant performance improvement in energy efficiency and waiting time for various traffic demands while ensuring driving safety and riding comfort. Our proposed strategy has a low computing cost and can be used as an advanced driver-assistance system (ADAS) in real-time.

Vehicle Acceleration and Speed as Factors Determining Energy Consumption in Electric Vehicles

Energy consumption in electric vehicles is a key element of their operation, determining energy efficiency and one of its main indicators, i.e., range. Therefore, in this article, mathematical models were developed to evaluate the impact of selected factors on energy consumption in electric vehicles. The phenomenon of energy recuperation was also examined. The study used data from mileage measurements of the electric vehicle (EV) driving on a motorway and in built-up areas. The results obtained showed a strong correlation between acceleration, vehicle speed, battery power, and energy consumption. In urban conditions, engine RPM and vehicle speed had an additional impact on energy consumption. Findings from this study can be used to optimize vehicle acceleration control modules to increase their range, develop eco-driving styles for EV drivers, and better understand the energy efficiency factors of EVs.

Analysis of the Impact of Hybrid Power System on Teachers' Psychology and Teaching Environment under Smart Energy Transformation and Energy System Planning

For this paper we study the structure and operational characteristics of plug-in hybrid Vs, Using the life-cycle evaluation method, The analysis model of energy consumption and environmental impact of plug-in hybrid electric vehicle fuel cycle suitable for China's national conditions is established, With the unit mileage of the plug-in hybrid car as the functional unit, Using the whole vehicle NEDC test cycle and combined with the relevant literature, Fuel consumption and environmental emission factors for this functional unit, The fuel cycle energy consumption, conventional gas emissions and greenhouse gas emissions are obtained, Evaluate the effect of power structure change on the above indexes, And compared the plug-in hybrid vehicles with conventional gasoline vehicles and compressed natural gas vehicles. As follows: compared with the current technical level of gasoline cars, under the background of road traffic, plug-in hybrid car fuel consumption is reduced by 40.7% less than gasoline cars, VOC, NOx, SO2 and greenhouse gas emissions than traditional gasoline vehicles reduced by 31.5%, 15.2%, 66.7% and 55.8%, this to alleviate increasingly severe urban air pollution is of great significance. Plug-in hybrid vehicles offer advantages over traditional gasoline cars in terms of energy consumption and emissions, both at the vehicle level and throughout the fuel cycle. Energy consumption is 56.3% lower, including a 56.4% reduction in crude oil usage, reducing oil dependency and promoting energy security. Environmentally, plug-in hybrids emit significantly less than gasoline cars, with VOC, NOx, SO2, PM10, and CO emissions reduced by up to 56%. Greenhouse gas emissions, including N2O, CH4, and CO2, are also lower. Additionally, emissions tend to be concentrated upstream, facilitating centralized pollution control.

Integrated analysis of on-road energy consumption and range optimization in the conversion of an IC engine vehicle to a battery-electric vehicle: a comprehensive research study

Abstract A major goal of this research project was to analyze how energy consumption and range of electric vehicles are affected by a variety of factors. The study includes an analysis of data based on factors such as state of charge, wheel traction power, power due to drag and aerodynamics, potential and kinetic energy changes, elevation, current, voltage, speed, and total loss due to traffic, and their effect on the consumption of energy and range of the electric vehicles. The data was analyzed using statistical methods such as MATLAB, Excel, and Python to find correlations between the various factors mentioned above. This research paper thoroughly explores the intricate relationships governing consumption of energy and range of the electric vehicles. Notably, this paper discovered connections between altitude and state of charge, current and state of charge, and even speed and voltage drop, highlighting the interplay of these elements. This paper also explored how factors like vehicle speed, slope, current, wheel traction power, and power due to gravity collectively shape EV propulsion dynamics. In simpler terms, this paper quantified energy losses due to traffic and emphasized how efficient motors and regenerative systems can significantly reduce these losses. Notably, regenerative efficiency stands out, cutting total energy losses by nearly half compared to scenarios where it is not used. These findings contribute to academic discussions and offer practical insights that undergraduate students can grasp, informing future EV designs and operations and promoting an energy-conscious transportation landscape.

Ambient Temperature Effects on Energy Consumption and CO2 Emissions of a Plug-in Hybrid Electric Vehicle

The ambient temperature affects the operation of different powertrain systems, including electric, hybrid electric, and internal combustion engines. This study investigated the effect of the ambient temperature on the energy consumption and CO2 emissions of a plug-in hybrid electric vehicle running in different powertrain modes. The vehicle was driven for 4150 km following a selected route 199 times in different powertrain modes and in different ambient temperatures ranging from −24 °C to 32 °C. Instantaneous and cumulative fuel consumptions were measured using a fuel flow meter, and the battery energy usage was determined from the vehicle telematics during each test. The total energy consumption and total CO2 emissions were affected by the ambient temperature in all powertrain modes, including electric, hybrid electric (charge-depleting and charge-sustaining), and conventional internal combustion engine modes. The highest increase was associated with the charge-depleting hybrid electric mode, with 350% and 290% increases in energy consumption and CO2 emissions when the ambient temperature dropped from 29 °C to −24 °C. The conventional internal combustion engine mode was the least affected, with only 7% and 8% increased in energy consumption and CO2 emissions, respectively.

Дослідження впливу зовнішніх факторів на експлуатаційні властивості електромобіля

Many factors affect the range of an electric vehicle: speed, driving style and ambient temperature can reduce the range of a full charge. Cold weather poses two main problems for electric vehicles: cold air limits battery performance, and the robo-heater significantly drains the battery. Studies have shown that 21.5 degrees is the optimal temperature for the operation of an electric car battery. Because an average of 21.5 degrees Celsius is the most optimal temperature when it comes to the range of an electric car. This actually prolongs its operation. At 21.5 degrees, the electric car's temperature system does not work, so energy is used for its movement. At -20°C, an electric car loses an average of 50% of its battery charge range. At -10 – 40%, at 0 – 20%. When the temperature is +10o C, the real driving distance of the electric car corresponds to 100% charging of the battery. At +21 o C, the real distance can be increased to 15%. When it is +40oC, the performance of the battery drops significantly to 80%, and the rate of charge rapidly decreases as the temperature increases. The energy consumption of electric vehicles is largely influenced by weather factors. Studies have shown the influence of ambient temperature and wind speed on energy consumption. The results show that at low speeds (30 km/h) the relative influence of ambient temperature is extremely high. The lowest energy consumption is achieved at a temperature of 20° C. At higher speeds (130 km/h), the relative influence of the temperature of the external environment is much lower. It has been proven that 21.5 degrees is the optimal ambient temperature for the operation of an electric car battery during operation, a decrease or increase in temperature leads to a decrease in the efficiency of electric car operation.

Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework

As the market share of electric vehicles (EVs) continues to grow, driving range capability has emerged as a primary concern for drivers, car manufacturers, and policymakers. Accurate real-time energy consumption prediction is essential in mitigating range anxiety and fostering the adoption of EVs. Consequently, an EV energy consumption prediction framework that comprehensively considers vehicle and environmental factors, with special attention to individual driving styles and driving conditions, constructed based on long short-term memory (LSTM) and Transformer models is proposed. This framework addresses the issue of long-term dependencies and captures characteristics of time-series data with increased efficiency. Upon rigorous evaluation, the model achieved the mean absolute percentage error (MAPE) of 4.63 % for state of charge (SOC) of EV's battery in experimental dataset, outperforming LSTM and multivariate regression (MLR). The ablation experiment shows that the MAPE of the model is reduced by 18.47 % and 15.27 % respectively after considering the individual driving style of drivers and the driving conditions of vehicles for both two types of vehicles. Based on this framework, a long-distance EV energy consumption prediction strategy based on short-distance is proposed, with a MAPE of 6.7 % when SOC value is reduced from 90 to 40 in the selected dataset.

Finding the optimal reliable energy consumption path for electric vehicles under rainfall conditions

ABSTRACT This paper presents a new path-finding problem to ensure reliable energy consumption for electric vehicles (EVs) under rainfall conditions. The objective function of the proposed model aims to find a reliable path that minimises energy consumption while ensuring a certain probability of completing the trip without exhausting a given battery energy budget. By considering the influence of adverse weather conditions at different periods, the existing model is expanded. To address the non-additivity and non-linearity characteristics of the optimisation model, an enhanced heuristic algorithm is proposed, incorporating inequality techniques, the K-shortest algorithm, and path-updating strategies. Lastly, the proposed algorithm is validated using Hong Kong’s grid-based road network as a case study, which demonstrates the correctness and effectiveness of the algorithm. The results indicate that by considering adverse weather conditions, the estimation of energy consumption can be significantly improved in terms of accuracy, achieving more efficient and reliable optimal path recommendations.

Experimental platform for studying energy regeneration in electric vehicle powertrains

Investigation 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