Energy Consumption Of Electric Vehicles Research Articles

Unified network tRaffic management frAmework for fully conNected and electric vehicles energy cOnsumption optimization (URANO)

Cooperative control in the presence of connected and automated vehicles has attracted substantial attention due to its pronounced benefits on the network compared with human-driven vehicles. They make possible a significant reduction of travel time/waiting time, energy consumption and emissions. In this context of new emerging technologies, traffic lights are still recognized as one of the most effective strategies in terms of energy and environmental benefits, which can be further improved by considering the integration with greener powertrains. The paper proposes a cooperative network traffic management framework for Electric Vehicles (EVs) based on a multi-objective optimization aimed at minimizing the total time spent (TTS) and energy consumption (EC) of EVs. Such framework is composed of i) a traffic control model that incorporates traffic lights design, ii) a traffic flow model to estimate TTS as a network performance indicator, and iii) an EVs model to estimate EC at the intersections. The EC function has been derived from a VT-CPEM model to simulate consumptions and thoroughly calibrated based on real-world individual trajectories. The optimization framework was implemented on a nine-node network and the results of the multi-criteria optimization (aiming at minimizing the TTS and EC of EV) are compared with results of the benchmark mono - criterion optimization (aiming at minimizing the TTS) and the mono-criterion optimization combined with the speed advisory (GLOSA; Green Light Optimized Speed Advisory). All the proposed analyses were carried out for different powertrain vehicle categories; ICEVs, and EVs.

Density-based clustering multiple linear regression model of energy consumption for electric vehicles

With the increasing popularity of electric vehicles, energy consumption has become a key performance indicator for electric vehicle drivers, automakers and policy makers. Accurate and real-time prediction of energy consumption under real-world driving conditions is critical to reducing “range anxiety” and can support optimization of battery size, energy-saving route planning and charging facility operation. In this paper, data collected from 988 electric vehicles of the same model for one year in Zhengzhou, China, are obtained to study the energy consumption of electric vehicles in actual driving conditions. An improved Density-Based Spatial Clustering of Applications with Noise (DBSCAN) model were established to classify the driving behaviors of the drivers. Then the key factors of energy consumption including velocity, accelerated velocity and temperature are studied and modeled. With that, an improved density-based clustering multiple linear regression model for energy prediction were established with driving behavior classification. The density-based clustering multiple linear regression model (DBC-MLR) has better prediction accuracy and can grasp the training features in energy consumption prediction in real driving. The proposed method shows a root mean error (RMSE) of 3.008 kwh/100 km, which is reduced by 11.3 % and 18.4 % compared to conventional machine learning method and multiple linear regression method respectively.

Demand Peak Reduction of Smart Buildings Using Feedback-Based Real-Time Scheduling of EVs

Rapid urbanization leads to increasing electricity demand. The intraday high and low electricity consumption patterns further increase challenges for grid operation. This article proposes a real-time strategy to minimize peak demand of a building by optimally scheduling the energy consumption of electric vehicles (EVs) with bidirectional power transfer. The strategy also incorporates rooftop photovoltaics (PVs) in reducing the power consumption of the building. In this article, the randomness of PV output is handled using scenario modeling under a rolling time-horizon framework, where feedback regarding EV mobility is also utilized in real time. In addition, the degradation of EV batteries is limited by controlling the charging/discharging cycles required for peak reduction. To provide incentives for contributing to peak reduction, a cooperative game is presented, wherein the EVs are conceptualized to work in a coalition with the building’s energy management system. The payoffs in the game are decided based on the power extraction of EVs from the building combined with degradation costs incurred by individual EVs. The results demonstrate up to 40% reduction in demand peaks and 25% reduction in energy costs using the proposed energy management methods.

Electric Vehicle Velocity and Energy Consumption Predictions Using Transformer and Markov-Chain Monte Carlo

Although electric vehicles (EVs) are becoming a promising alternative for ground transportation, the issue of limited battery energy hinders its market penetration. Accurate prediction of electrical energy consumption along a given route would significantly relieve drivers’ anxieties and build their confidence in EVs. A high-fidelity energy estimation relies on an accurate velocity forecast. To this end, this article proposes a novel hybrid deterministic-stochastic methodology that utilizes inputs encompassing the route information, the driver’s characteristics, and the traffic flow’s uncertainties to predict the EV’s future velocity profile and energy consumption. The method comprises two components: a deterministic machine-learning-based transformer network and a stochastic Markov-chain Monte Carlo (MCMC) algorithm. Real-world EV data collected in four routes of different lengths and features are used to evaluate the method. The results demonstrate the enhanced performance of the approach in both velocity prediction and energy consumption estimation, compared to two popular baseline algorithms.

Assessment of dynamic wireless charging based electric road system: A case study of Auckland motorway

• Calibration, and validation of a part of Auckland traffic network using AIMSUN. • Estimation of dynamic wireless charging length requirement in a traffic corridor. • Impact of traffic and energy parameters on the wireless charging length requirement. • Financial feasibility of DWC system with the plug-in charging system. Urbanization and the need to improve green mobility have paved the way for electric vehicle (EV) adoption that reduces greenhouse gas emissions. Investors have a critical role in incentivizing charging infrastructure to meet EV's charging needs immediately. Dynamic wireless charging (DWC) offers a viable solution to mitigate issues related to the limited driving range, higher battery capacity (resulting in higher cost), and longer charging time of EVs. This paper examines the impact of a DWC-based electric road system (ERS) on EV flow and energy consumption. A traffic simulation-based methodological framework is developed to evaluate the effect of DWC facilities on road networks. A case study is conducted for the Auckland motorway network using various scenarios to evaluate the performance of the ERS, including the financial feasibility of the DWC system compared with the existing plug-in charging system. It provides insights into the minimum length of DWC facilities and the spatial and temporal variations in energy demand, which can be helpful to public and private stakeholders in deciding the optimal strategy to implement DWC in transportation networks on a large scale.

Evaluating the Energy Consumption of an Electric Vehicle Under Real-World Driving Conditions

<div class="section abstract"><div class="htmlview paragraph">This investigation evaluates the energy consumption of an electric vehicle (EV) and identifies key factors that affect its energy efficiency, based on real-world operation for a range of driving characteristics and climate conditions over nearly four years in the streets of the second-largest UK city. The driving modes investigated were acceleration, deceleration, idling and cruise, determined by dividing each individual trip into kinematic segments based on vehicle speed and acceleration calculated second by second. From the results obtained, the EV energy consumption is directly influenced by changes in ambient temperature outside, largely due to the corresponding loads required from the use of auxiliary systems, mainly heating and air conditioning. An increase in trip idling events directly translates to a rise in EV energy consumption, while opposite outcomes were produced during cruising state with decreasing energy consumption. During the periods of high traffic on weekdays, the energy consumption is increased by nearly 15% as a direct impact of the increase in the number of stops, as the auxiliaries still require energy while the vehicle is at idle. The difference in energy consumption between weekday and weekend driving occurs mainly during heavy traffic periods, increasing by 20% on weekdays, primarily due to the rise in the number of stops for weekday driving. The results also show that the EV specific energy consumption varies each month, reaching a 55% increase from summer of least energy consumption to winter with the most requires energy, mainly due to large average ambient temperature changes.</div></div>

Comparative Analysis of Emission and Energy Consumption of Plug-in Hybrid Electric Vehicle on Actual Road Driving

Comparative Analysis of Emission and Energy Consumption of Plug-in Hybrid Electric Vehicle on Actual Road Driving

Blockchain and Double Auction-Based Trustful EVs Energy Trading Scheme for Optimum Pricing

Electric vehicles (EVs) have gained prominence in smart transportation due to their unparalleled benefits of reduced carbon footprints, improved performance, and intelligent energy trading mechanisms. These potential benefits have increased EV adoption at massive scales, but energy management in EVs is a critical study problem. The problem is further intensified due to the scarcity of charging stations (CSs) in near EV proximity. Moreover, as energy transactions occur over open channels, it presents critical security, privacy, and trust issues among decentralized channels. To address the open limitations of trusted energy management and optimize the pricing control among EV entities (i.e., prosumers and consumers), the paper proposes a scheme that integrates blockchain and a truthful double auction strategy for trustful EV trading. To address the transaction scalability, we integrate an Interplanetary File System (IPFS) with a double auction mechanism handled through the Remix Smart Contract environment. The double auction leverages an optimal payoff condition between peer EVs. To address the communication latency, we present the scheme at the backdrop of Fifth Generation (5G) networks that minimizes the optimal payoff response time. The scheme is simulated against parameters such as convergence, profit for consumers, computation time, and blockchain analysis regarding node commit latency, collusion attacks, and EV energy consumption. The results indicate the scheme’s viability against traditional (non-blockchain) approaches with high reliability, scalability, and improved cost-efficiency.

Impact of Climate Change on the Energy Consumption of Passenger Car Vehicles

The energy consumption of passenger vehicles is affected by the physical properties of the environment. The ambient temperature in particular has a significant impact on the operating energy consumption. To quantify the impact of a changed climate on vehicles with different drivetrain systems, we set up a model that calculates the change in energy demand with respect to multiple global warming levels. In particular, the effect of rising temperatures on the energy consumption of battery electric vehicles and vehicles with internal combustion engines was investigated. Our results indicate that climate change will likely lead to a rise in energy consumption of vehicles with an internal combustion engine. This is mostly due to the increase in cabin climatization needs caused by the higher ambient temperatures. At a global warming level (GWL) of 4.0 °C, the calculated annual energy consumption on average is 2.1% higher than without taking the climate-change-related changes in temperature into account. Battery electric vehicles, on the other hand, are expected to have a lower overall energy consumption (up to −2.4% at 4 °C GWL) in cold and moderate climate zones. They benefit from the lower heating needs during winter caused by global warming.

Performance analysis of an integrated battery electric vehicle thermal management

Fast introducing battery electric vehicles to the market leads to the accumulation of errors and recalls of the vehicle. It can be avoided by simulating battery electric vehicles (BEV) in an integrated manner. A drivetrain model is developed integrated with cabin, battery, and power electronics models. Key system-level outputs like vehicle energy consumption, system COP, and total auxiliary power are obtained, and component-level outputs include internal battery temperature, power electronics temperature, and cabin temperatures. The integrated model is validated both at the component level and system level. Simple energy-conscious actions are identified, and corresponding energy savings are quantified over the lifetime of a battery-electric vehicle. Simple energy-conscious actions can save 1336 to 4297 kWh of energy throughout the vehicle's lifetime. When the ambient temperature is doubled from 25 to 50 °C, total auxiliary power increases around six times, energy consumption increases by 35 % per kilometer, and mean COP drops by 39 % for the US06 driving cycle. This model is applied to various Indian cities to compare energy consumption. Drivetrain model parameters like vehicle mass, motor and transmission efficiency, and road gradability are varied to estimate their effect on battery electric vehicle energy consumption. Considerable effects of solar load, cabin's initial temperature, and cabin's target temperature are observed. Two different cooling loops for the battery thermal management are simulated to observe terminal voltage and internal cell temperature response. A temperature drop of 5 °C in internal cell temperature is observed when cabin exit air is blown on the battery radiator instead of ambient air. The effect of road gradability and coolant flow rate on power electronics transistor and diode temperature are also quantified.

Automation and connectivity of electric vehicles: Energy boon or bane?

With the arrival of autonomous and connected vehicles, it remains unclear how automation and connectivity will impact the energy consumption of electric vehicles. Here, we report the energy impacts induced by these emerging technologies on electric vehicles in a realistic urban environment based on high-resolution empirical data and high-fidelity simulations. We find that, contrary to optimistic predictions, automated vehicles (AVs) alone in fact increase energy consumption compared with human-driven vehicles (HDVs) in a complicated urban environment (e.g., with distributed signalized intersections), while connected and automated vehicles outperform in energy, safety, and mobility. Also, based on our field experiments, scenario AV has a lower average speed compared with scenario HDV. These findings suggest that systematic efforts integrating AV technology innovations from the private sector and connected vehicle infrastructure investment from the public sector are essential to accomplish the ultimate vision of green urban transportation.

Minimal Energy Transient Motion Control of Electrical Connected Vehicles

Vehicle transient motion control plays an important role in reducing energy consumption for both hybrid and electric vehicles since vehicle acceleration and deceleration can be optimized based on driving environment. In this article, nonlinear quadratic tracking (NQT) control is used for optimal acceleration and minimal principle is applied to deceleration to optimize energy recovery, where acceleration control generates the propulsion torque based on the current powertrain status and the error between vehicle speed and given reference provided by the connected system based on the surrounding traffic; and the deceleration (braking) control optimizes regenerative brake to maximize the recovered energy while obeying speed and braking distance constraints. Both control strategies are designed for real-time applications and updated online to respond to the rapid changing traffic environment. Cosimulation models, consisting of SUMO traffic and Simulink control models, are developed for validating the proposed control strategies through computer-in-the-loop (CIL) and hardware-in-the-loop (HIL) simulations. CIL simulation results show that in the eco-acceleration mode, the proposed strategy can save 6.8% energy over the rapid acceleration and brake control recovers 50.8% more energy than the vehicle driven by SUMO simulated human driver. HIL simulations further confirm reduction of energy consumption and adaptability to a changing traffic environment in real-time.

Data Privacy of Wireless Charging Vehicle to Grid (V2G) Networks With Federated Learning

Wireless charging vehicle to grid (V2G) system is not-so-futuristic. It can maintain the power supply balance and stabilize the grid through a wireless link between vehicles using batteries based on energy supply-demand statistics. Most of the existing privacy preservation methods are based on data tampering approaches (e.g., encryption, adding noise, aggregation), which are not practicable over V2G due to complexity in billing and inaccurate state estimation. In this paper, we develop a novel adaptive demand-side energy management framework by employing federated learning-based privacy preservation for the wireless charging V2G systems. Our framework learns the temporal evolution of energy consumption of dynamic charging electric vehicles in a distributed fashion and exploits the reinforcement learning model for cost-saving and reward maximization. The convergence and the privacy preservation properties of the framework are demonstrated with extensive evaluations.

The Impact of Road Types on the Energy Consumption of Electric Vehicles

The widespread adoption of battery electric vehicles (BEVs) is hindered by their limited ranges and long charging times. Optimizing eco-driving strategies and BEV-specific routing through a thorough understanding of the BEV discharge behavior is vital to overcome these barriers in the short term. Therefore, this study investigates the impact of road types on the BEV discharge behavior while accounting for explanatory variables (i.e., ambient temperature, the initial state of charge, and driver). Thirty participants drove a 2017 Volkswagen eGolf along two predefined routes in Rhode Island. The results illustrate that BEVs are the most efficient on-road types with medium speed and low variation (i.e., “major collectors,” “minor arterials,” and “other principal arterials”). Meanwhile, findings confirmed a significantly higher average energy consumption rate on roads with higher average speeds (“interstates” and “other freeways/expressways”). Moreover, “local roads,” associated with a low average travel speed and a high variation in speed, showed a negative effect on BEV efficiency. The study further supported previous findings that BEVs are less efficient in colder temperatures. Thus, adapting eco-driving strategies, including the alteration of route choice to avoid “local roads” and “interstates,” can offer BEV drivers the potential for energy savings and range extensions. We propose a consideration of these findings to mitigate the effects of BEV range limitations and ease BEV adoption and ownership.

Battery Electric Vehicles Energy Consumption Breakdown from On-Road Trips <xref rid="FN_1" ref-type="fn"><sup>1</sup></xref>

<div class="section abstract"><div class="htmlview paragraph">Battery Electric Vehicle (BEV) sales have been spiking up due to a series of factors: zero tailpipe emissions, wider model availability, increased customer acceptance, reduced purchase price, improved performance and range. The latter is a crucial factor the consumers consider when purchasing a BEV, and it largely depends on how the vehicle operates (e.g. average speed), traffic, ambient conditions, and battery size. When driven on the roads, the actual range of BEVs can be significantly smaller than the certified value obtained from laboratory testing at standard conditions. To understand the factors influencing vehicle range in real-world operation, the study team performed on-road tests on three production passenger vehicles currently available in the European market. The measured quantities, including vehicle signals from OBD/UDS, were used to quantify the vehicle energy consumption. Global Navigation Satellite System (GNSS) data was used to calculate vehicle positioning and resistances, including altitude. Findings show an average consumption of 201.5 Wh/km for mid-sized passenger cars, ranging between 150 to 293.4 Wh/km (minimum and maximum observed values from a B-Segment vehicle and a 9-Seater VAN, respectively). Ambient temperature is one of the factors introducing a high variability in real-world energy consumption, as electric energy is used both for cabin heating and cooling, which might lead to range reductions of 30-50 % under extreme conditions. An energy breakdown is presented for each trip, describing the typical share of propulsion, cooling/heating needs and other auxiliaries.</div></div>

Energy Consumption Estimation for Electric Buses Based on a Physical and Data-Driven Fusion Model

The energy consumption of electric vehicles is closely related to the problems of charging station planning and vehicle route optimization. However, due to various factors, such as vehicle performance, driving habits and environmental conditions, it is difficult to estimate vehicle energy consumption accurately. In this work, a physical and data-driven fusion model was designed for electric bus energy consumption estimation. The basic energy consumption of the electric bus was modeled by a simplified physical model. The effects of rolling drag, brake consumption and air-conditioning consumption are considered in the model. Taking into account the fluctuation in energy consumption caused by multiple factors, a CatBoost decision tree model was constructed. Finally, a fusion model was built. Based on the analysis of electric bus data on the big data platform, the performance of the energy consumption model was verified. The results show that the model has high accuracy with an average relative error of 6.1%. The fusion model provides a powerful tool for the optimization of the energy consumption of electric buses, vehicle scheduling and the rational layout of charging facilities.

Transportation systems management considering dynamic wireless charging electric vehicles: Review and prospects

This paper reviews and prospects the research in transportation systems management with the presence of dynamic wireless charging (DWC) electric vehicles (EVs). DWC is based on inductive coupling that allows EVs to get charged wirelessly by driving over the coils installed in the ground. Thus, DWC is also known as ‘charging-while-driving’ or ‘in-motion charging’, which is regarded as one of the most promising charging solutions for EVs. The road that is equipped with wireless chargers underneath its surface is called a wireless charging lane (WCL). The deployment of WCLs will lead to a profound change in the field of transportation systems management. We give a review of recent studies on related topics including WCL allocation and related decisions, energy consumption of EVs considering WCLs, and pricing of EVs for the use of DWC facilities. Based on the analysis of the existing studies, we identify the needs for more flexible WCL design and allocation models, as well as novel ways that incorporate the complexities in energy consumption by EVs and routing behaviour into traffic network analysis. In addition, we emphasise the research opportunities of dynamic traffic modelling and real-time control considering DWC and EVs with state-of-charge (SOC) constraints. A high-level dynamic system model and a general model-based control problem are formulated in this regard. Finally, we foresee a demand for finer traffic management in the WCL environment enabled by emerging technologies such as the internet of vehicles (IoV) and autonomous driving.

Energy consumptions and CO2 emissions of different powertrains under real-world driving with various route characteristics

Electric vehicles (EVs) are broadly admired as an encouraging solution to decarbonize and relieve air pollution in metropolitan transportation. However, each powertrain gives different benefits depending on the actual usage context. This study investigates the energy consumption of various EVs compared to a conventional internal combustion engine (ICE) vehicle. The considered EVs include a hybrid electric vehicle (HEV), plug-in hybrid vehicle (PHEV), and battery electric vehicle (BEV). Different road characteristics in the biggest city of northern Thailand are tested as driving route modes, i.e., city, rural, and hill modes, to study the energy consumption of the powertrains under real-world driving scenarios. Additionally, the efficiency of the CO2 emission reduction of each powertrain, analyzed from the energy consumption, is presented through the Well-to-Wheel (WtW) basis. The results show that the BEV performs well with all route characteristics, while both hybrids are appropriate in different driving scenarios. On average, of all the route modes, the WtW CO2 emission of the HEV, PHEV, and BEV is about 65%, 50%, and 35%, respectively, compared to the ICE. The BEV only has WtT CO2 emission from its electricity source. Therefore, it can yield low emissions if renewable energy, such as solar or wind energy, is used as its electricity source instead of fossil fuels.

Flexible Simulation of an Electric Vehicle to Estimate the Impact of Thermal Comfort on the Energy Consumption

The energy consumption of electric vehicles depends not only on the traction energy but also on the thermal comfort energy. Some studies lead to the estimation of this energy consumption from real measurements on different driving and climatic conditions. However, those results rely on a large number of vehicle tests, which is time-consuming. Moreover, the impacts of the different subsystems cannot be differentiated in such global studies. A flexible simulation tool can help to analyze the impact of the different parts of a vehicle. This article proposes a multiphysical parametrized model of an electric vehicle, including the traction and comfort subsystems. A flexible model of a Renault Zoe is developed due to the energetic macroscopic representation. This model is validated by experimental tests of the real vehicle. Then, the impact of the heating, ventilation, and air conditioning (HVAC) subsystem is studied for different driving cycles and climatic conditions. In very cold conditions, the use of the HVAC subsystem represents an increase of up to 248% of the total energy consumption, compared to summer conditions.

Effect of Different Types of Electric Drive Units on the Energy Consumption of Heavy Commercial Electric Vehicles

The increasing demand for electric vehicles (EVs) in the transportation industry, especially for efficient battery–electric trucks, has led to an increase in studies on the efficiency or energy consumption of commercial vehicles. In this paper, average energy consumption was investigated in terms of the effect of different transmission types in vehicle models considering three routes, and the effect of the number of gears on energy consumption for each transmission type was analyzed. Target performance specifications and packaging were also evaluated. The optimal design could be identified in terms of transmission type, the number of gears, vehicle performance, and packaging. Vehicle models with two types of electric drive units (EDUs) were developed in a MATLAB/Simulink environment. Driving cycles were obtained from collected road load data of municipal, intercity, and regional areas operated by heavy-duty trucks using nCode software. The battery model was developed based on the electric circuit network (ECN) modeling technique. The main research purpose of this study was to investigate the effect of multispeed and multimodal EDUs and the number of gears on the energy consumption of heavy commercial electric vehicles from actual road conditions in Turkey. The three-speed EDU was the optimal design, providing 7.83, 7.26, and 7.21% less energy consumption on the three routes, compared with three-mode electric drive units. Consequently, the energy consumption difference was 7.5% for combined real road conditions.