Electric Vehicle Power Consumption Research Articles

Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression

Electric Vehicle Power Consumption Modelling Method Based on Improved Ant Colony Optimization-Support Vector Regression

Environmental and economic evaluation of urban building-integrated photovoltaic and electric vehicle system

Facade photovoltaic (FaPV) panels can significantly enhance the capability of building-integrated photovoltaic (BIPV) systems. This study presents a comprehensive analytical framework for optimizing the operation of BIPV and electric vehicle (EV) integrated systems and evaluating their environmental and economic benefits. The Energy-Environmental-Economic (3E) parameters are measured for alternative scenarios, and sensitivity analyses are applied to validate the impacts of changes in major parameters on BIPV-EV system's performance in Xiong'an New Area (XANA). The simulation results demonstrate that BIPV-EV systems can effectively reduce CO2 emissions and electricity costs. Specifically, the installation of FaPV can increase power generation by up to 67.60 % compared to the BIPV system utilizing stand-alone rooftop photovoltaic (RPV). Regarding urban decarbonization, the adoption of BIPV system can reduce CO2 emissions by 41.91 % and 34.99 % in the short- and long-term, respectively. The evaluation of the levelized cost of energy (LCOE) across different scenarios reveal that the configurations of RPV + FaPV (SE)-EV system and RPV + FaPV (SW)-EV system are the most cost-effective options in both the short- and long-term, where BIPV contributes to 48.10 % and 31.90 % of the total electricity generation, respectively. In terms of EV power consumption, the BIPV-EV system can decrease the grid sell ratio by 15.38 % compared to the stand-alone BIPV scenario. Compared to the BIPV system with typical RPV, the appropriate utilization of FaPV can significantly increase the potential of a BIPV system and mitigate the additional electricity demand for EVs. For cities with nascent BIPV and EV markets, the proposed framework can be a strategic tool for urban planners to design BIPV-EV systems, with the objective of enhancing sustainability and energy efficiency.

Demand response of an Electric Vehicle charging station using a robust-explicit model predictive control considering uncertainties to minimize carbon intensity

This paper presents a novel approach to address uncertainties and enable demand response in Electric Vehicle (EV) charging station optimization. A two-stage optimization strategy is proposed, integrating Robust Optimization and explicit Model Predictive Control (eMPC). The first stage involves day-ahead planning using Robust Optimization technique to limit the hourly power consumption of EVs, considering worst-case scenarios caused by uncertainties in EV consumption and CO2 emissions. The objective is to minimize environmental impact by reducing CO2 emissions. An Explicit Model Predictive Control strategy is developed in the second stage for real-time operation. The explicit solution, calculated offline, models uncertainties such as the initial state of charge of the battery energy storage, photovoltaic power production, and EV power consumption. During real-time operation, the explicit solution is accessed using measured data from the charging station, refining the schedule derived from the first stage. The proposed solution is implemented and evaluated at an EV charging station in Trieste, Italy. The results demonstrate a significant 69% reduction in CO2 emissions compared to a deterministic approach while maintaining a real-time computation time of less than 0.1 s.

Multi-objective particle swarm optimization for optimal scheduling of household microgrids

Addressing the challenge of household loads and the concentrated power consumption of electric vehicles during periods of low electricity prices is critical to mitigate impacts on the utility grid. In this study, we propose a multi-objective particle swarm algorithm-based optimal scheduling method for household microgrids. A household microgrid optimization model is formulated, taking into account time-sharing tariffs and users’ travel patterns with electric vehicles. The model focuses on optimizing daily household electricity costs and minimizing grid-side energy supply variances. Specifically, the mathematical model incorporates the actual input and output power of each distributed energy source within the microgrid as optimization variables. Furthermore, it integrates an analysis of capacity variations for energy storage batteries and electric vehicle batteries. Through arithmetic simulation within the Pareto optimal solution set, the model identifies the optimal solution that effectively mitigates fluctuations in energy input and output on the utility side. Simulation results confirm the effectiveness of this strategy in reducing daily household electricity costs. The proposed optimization approach not only improves the overall quality of electricity consumption but also demonstrates its economic and practical feasibility, highlighting its potential for broader application and impact.

Investigation of WBG based Power Converters used in E-Transportation

Nowadays, Uses for transportation are evolving in the direction of getting more electrified due to rising fuel costs and environmentally hazardous pollutants due to which The grid’s need for power is increasing dramatically. As the only solution for this is to diminish the power consumption of Electric Vehicles (EV). Enhanced component efficiency can lead to improved electric vehicle performance. Wide band gap (WBG) semiconductors, in particular silicon carbide (SiC) and Gallium Nitride (GaN), are ideal candidates of choice to address the recent growing demand in both high efficiency and great power density converters about electric vehicles. Due to their the capacity to switch at a high frequency, high temperature, high voltage, reduced size and reduced conduction losses makes them superior than Si semi-conductors. WBG devices have some properties which include rapid electron mobility, high breakdown field, and big band gap low on-state resistance and capacitance, lower co-efficient of thermal expansion (CTE) and high stability. This paper includes a study on WBG devices, their properties, and the increased efficiency of power converters i.e DC-DC boost converter and single-phase full bridge inverter using LTSpice simulation tool.

Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature

The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle speed on electricity consumption. Then, we combine the vehicle’s main parameters to create a single electric vehicle charging model, use the Monte Carlo method to simulate electric vehicle travel behavior and charging, and obtain the spatial and temporal distribution of total charging load. Finally, the actual traffic road network and typical distribution network in northern China are used to analyze charging load forecast estimates for each typical functional area under real vehicle–road circumstances. The results show that the charging load demand in different areas has obvious spatial and temporal distribution characteristics and differences, and traffic conditions and temperature factors have a significant impact on electric vehicle charging load.

An Intelligent Load Balancing Strategy for Energy Cost Minimization in EV Applications

Energy management systems are the most significant field in the potential electric vehicles (EVs). Also, EVs are widely used because of their pollution-free nature worldwide and their specific feature of flexibility. However, the EV charging system’s significant challenges are power loss and high power consumption. Therefore, a robust load-balancing strategy is required to address the EV charging system’s issues. Therefore, a novel intelligent hybrid Buffalo-based Recurrent Energy Management System (BREMS) has been proposed to reduce the power consumption of EVs and mitigate power losses. Here, two primary functions have been done. Initially, hydro and solar sources are modeled with the help of energy storage components and a power grid to enhance the system’s efficiency. Then, the optimization fitness module is initiated to the hidden layer of the neural framework to monitor the loaded grids and distributes the loads to imbalanced grids. Implementation and designing processes have been done on the MATLAB platform. The simulation outcomes of the proposed design have been compared with conventional models in terms of power loss, cost of energy, utilization of power, and execution time. The comparative assessment shows that the presented system obtained better outcomes than other models.

EV-Station-Grid Coordination Optimization Strategy Considering Psychological Preferences

This paper proposes the electric vehicle (EV)-station-grid coordination optimization strategy considering user preferences, which regulates the charging behaviors of EV users from the user side to ensure the stable and safe operation of the power grid. Firstly, the spatio-temporal prediction model of charging load based on speed-temperature is developed. The model of EV power consumption per unit mileage affected by temperature and EV speed is constructed, and the shortest path algorithm is applied to determine the driving paths of EVs so as to judge the charging demand in combination with the state of charge (SOC) of the battery and to determine the charging periods and locations of the EVs, thus obtaining the spatio-temporal information of the charging load. Secondly, a multi-attribute charging decision model considering user preferences is constructed. Fuzzy clustering and rough set theory are applied to mine user behavior preferences, combined with behavioral economics to describe users’ irrational charging decision-making psychology. Lastly, a real-time charging price model considering voltage fluctuation index and user charging cost is constructed to analyze the impact of price on guiding charging behaviors. The simulation results verify the effectiveness and performance of the collaborative optimization strategy.

Multiparadigm Modeling Framework to Evaluate the Impacts of Travel Patterns on Electric Vehicle Battery Lifespan

The widespread adoption of electric vehicles (EVs) can help attain economic and environmental sustainability by reducing oil dependency and greenhouse gas emissions. However, several issues need to be addressed before EVs can become a popular vehicle choice among the general public. A key issue is the perpetual reduction in EV battery capacity caused by battery degradation over time with usage. This can lead to a reduced driving range and cause “range anxiety” for EV drivers. This becomes even more critical in developing countries where consumers are highly sensitive to battery replacement costs. Thus, to promote EVs in developing economies, policymakers and vehicle manufacturers need to develop attractive incentive schemes and warranty strategies preceded by a thorough assessment of the useable EV battery lifespan for a wide range of users. This paper develops a multiparadigm modeling framework to compute battery degradation for a large population of EVs by capturing the effects of travel patterns, traffic conditions, and ambient temperature. The proposed framework consists of four different building blocks: (i) a microscopic traffic simulation model to generate speed profiles, (ii) an EV power consumption model, (iii) a battery equivalent circuit model, and (iv) a semiempirical battery degradation model. The proposed framework can also be used to assess the battery life-cycle of electric-powered automated vehicles by adjusting their travel patterns accordingly. A case study is presented using travel diary data of around 700 households from the U.S. National Household Travel Survey of 2009 to simulate household travel patterns and corresponding battery lifespan distribution.

Pseudo-optimal discharge pressure analysis of transcritical CO2 electric vehicle heat pumps due to temperature glide

• A pseudo-optimal discharge pressure for electric vehicle heat pumps was proposed. • The principle of pseudo-optimal discharge pressure was explained. • Pseudo-optimal discharge pressure exists in subcritical and transcritical states. • The effects of conditions on pseudo-optimal discharge pressures were revealed. Performance improvement of electric vehicle heat pumps can enhance thermal comfort and reduce the power consumption of electric vehicles, hence stimulating their development. In this paper, the cyclic characteristics of an electric vehicle heat pump were studied under different operating modes and working conditions. Different from the optimal discharge pressure of conventional CO 2 systems which was determined by the variation gradient characteristics of the transcritical isotherm, the discharge pressure of electric vehicle heat pumps at the peak COP value was defined as a pseudo-optimal discharge pressure and was determined by the obvious temperature glide at the outlet of the gas cooler. This pseudo-optimal discharge pressure existed at both transcritical and subcritical states. The difference in the pseudo-optimal discharge pressure between the subcritical or transcritical states was first evaluated. Then an in-depth analysis of the influence of key parameters on the pseudo-optimal discharge pressure was studied to develop an accurate prediction method since the traditional prediction methods were not applicable. The results showed that pseudo-optimal discharge pressure increased with the ambient temperature, inlet air temperature, and supply air temperature. This study provides valuable information for the comprehensive performance optimization of an electric vehicle thermal management system.

Real time Energy Management System of a photovoltaic based e-vehicle charging station using Explicit Model Predictive Control accounting for uncertainties

This paper proposes an Explicit Model Predictive Control (eMPC) for the energy management of an e-vehicle charging station fueled by a photovoltaic plant (PV), a battery energy storage system (BESS), and the electrical grid. The method computes offline an explicit solution of the MPC, which is stored and then used for real time control. Multiparametric programming is used to calculate the explicit solution by mapping the MPC laws as a function of uncertain parameters. In this paper, the uncertainties introduced into the multiparametric programming are the photovoltaic power production, the electricity price, the battery’s state of charge, and the electric vehicle power consumption. Moreover, the environmental impact of the charging station operation is considered through the C O 2 emissions level. The explicit solution is computed for a specific range of uncertain parameters. Then, during the real-time control, their current values are measured to evaluate the control laws saved beforehand. The proposed approach, consisting of an offline MPC-based determination of the control laws followed by their online use, reduces the computational burden without affecting the MPC performance. Numerical simulations and experimental results confirm the eMPC’s performance for the proposed application. • EV charging station based on PV/BESS uses eMPC control as its EMS. • PV and EV power, CO 2 , price, and BESS’s SoC are included as uncertainties. • eMPC creates offline the control laws as function of the uncertain parameters. • The online evaluation of the control laws takes less than one second.

A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid

Uncontrolled electric vehicle (EV) charging is known to have an adverse impact on power networks. In particular, the inherent problem of phase load unbalance (PLU), due to uneven loading of phases can be substantially increased due to uncoordinated EV charging demands. Increased unbalance would cause sub-optimal operation of the distribution grid with higher thermal losses and increased network congestion. In this paper, a day-ahead EV scheduling strategy to mitigate unbalance is proposed, by controlling single-phase charging demand of EVs with vehicle to grid (V2G) power transfer. In addition, the EVs are also scheduled as per a price-based demand response program. The aim of this work is to use multi-objective lexicographic ordering to achieve economic benefit by controlling EV charging/discharging rates as per dynamic electricity prices, while simultaneously shift EV power consumption among phases to reduce unbalance. Further, uncertainty handling optimization techniques are presented to hedge against any randomness in prices and phase-wise loads. The proposed methods are simulated for a community charging station connected to the unbalanced grid. It is found that the active power consumption among phases can be balanced almost entirely using continuous charging/discharging rates from EVs. Moreover, up to 30% reduction in charging costs can be achieved while maintaining a balanced three-phase system.

Hybrid electric vehicle power consumption analysis in tropical area

Internal Combustion Engine (ICE) is one of the biggest fossil fuel consumers. Many attempts have been made by researchers to replace ICE with electric motors and batteries as a source of energy. On the other hand, the dependence on ICE cannot be eliminated. One of the better ideas is to combine an ICE with an electric motor or called a hybrid. This study aims to determine the amount of energy consumption used by Hybrid Electric Vehicles (HEV) in urban areas in a tropical megapolitan city. The method used is to monitor the use of energy in hybrid cars directly. Sensors and data acquisition are installed on the vehicle to find out the route, speed, fuel consumption, electricity consumption, etc. Data acquisition is also placed on pure ICE vehicles with comparable engine capacity. Previous research, namely the analysis of energy consumption in the Plug-In Hybrid Electric Vehicle (PHEV), will also be used as a comparison. The results obtained indicate that HEV could travel 22 kilometers with one liter of gasoline. ICE car could cover 13 kilometers by 1 liter of petrol. And PHEV could travel 34 kilometers with 1 liter of gasoline. Thus it can be concluded that HEV has the advantage of saving gasoline usage, but PHEV is the best of them.

Research on the Development and Application of Charging Piles Based on the Development of New Energy Vehicles

In this paper, based on the cloud computing platform, the reasonable design of the electric vehicle charging pile can not only effectively solve various problems in the process of electric vehicle charging, but also enable the electric vehicle users to participate in the power management. It makes the charging and power consumption of electric vehicles more scientific and reasonable, realizes the communication and interaction between users and power grid, meets the charging requirements of electric vehicles, improves the overall service quality, and contributes to the comprehensive promotion of the technology.

Data-driven smart charging for heterogeneous electric vehicle fleets

The ongoing electrification of mobility comes with the challenge of charging electric vehicles (EVs) sufficiently while charging infrastructure capacities are limited. Smart charging algorithms produce charge plans for individual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet. In practice, EV charging processes follow nonlinear charge profiles such as constant-current, constant-voltage (CCCV). Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power consumption. Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available. In this work we propose a data-driven approach for integrating a machine learning model to predict arbitrary charge profiles into a smart charging algorithm. We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models. Each charging process includes the time series of charging power. After preprocessing, the dataset contains 10.595 charging processes leading to 1.2 million data points in total. We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error (MAE) of 126W and a relative MAE of 0.06. Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21% more energy charged compared to smart charging without considering charge profiles. Furthermore, an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions. However, charging features are required including the number of phases used for charging.

A study of stability and power consumption of electric vehicles using different modern control strategies

This research paper aims at applying modern control techniques to study the stability and power consumption of electric vehicles. The main purpose is to propose an artificial neural network controller to estimate the direct yaw moment required for stabilizing the dynamics of electric vehicles with four in-wheel motors. Only one state variable, that can be measured using a cheap and standard sensor, is used to define the error signal for the controller. The algorithm of equality-constrained quadratic program is used to split the estimated yaw moment onto the four in-wheel motors. The neural network controller is trained using data obtained from fuzzy logic controller. In addition, the classical sliding mode control is applied for the sake of comparison. Simulation results prove that in comparison to classical controller, the mentioned modern controller provides better stability of the electric vehicle's lateral dynamics and lower usage of electric power for the motors.

Simultaneous optimization of steering/driving input and cornering stiffness for reduction of power consumption of electric vehicles

Simultaneous optimization of steering/driving input and cornering stiffness for reduction of power consumption of electric vehicles

A Novel Ensemble Method for Electric Vehicle Power Consumption Forecasting: Application to the Spanish System

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms' performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.

A SystemC-AMS Framework for the Design and Simulation of Energy Management in Electric Vehicles

Driving range is one of the most critical issues for electric vehicles (EVs): running out of battery charge while driving results in serious inconvenience even comparable to a vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. This may discourage EVs for current and potential customers. As an effect, the dimensioning of the energy subsystem of an EV is a crucial issue: the choice of the energy storage components and the policies for their management should be validated at design time through simulations, so to estimate the vehicle driving range under reference driving profiles. Thus, it is necessary to build a simulation framework that considers an EV power consumption model that accounts for the characteristics of the vehicle and the driving route, plus accurate models for all power components, including batteries and renewable power sources. The goal of this paper is to achieve such an early EV simulation, through the definition of a SystemC-AMS framework, which models simultaneously the physical and mechanical evolution, together with energy flows and environmental characteristics. The proposed solution extends the state-of-the-art framework for the simulation of electrical energy systems with support for mechanical descriptions and the AC domain, by finding a good balance between accuracy and simulation speed and by formalizing the new information and energy flows. The experimental results demonstrate that the performance of the proposed approach in terms of accuracy and simulation speed w.r.t. the current state-of-the-art and its effectiveness at supporting EV design with an enhanced exploration of the alternatives.

Pricing model for the charging of electric vehicles based on system dynamics in Beijing

This paper proposes a system dynamics model to develop a real-time charge pricing (RCP) mechanism of electric vehicles (EVs). The model includes six modules: power consumption of EVs, generator set dispatching, charge pricing, user response, benefit evaluation of all stakeholders and charging stations' life-cycle net income. We consider four charge pricing scenarios and design a RCP mechanism for Beijing according to the simulation results. Sensitivity analysis proves that the model is robust, and the increased charging power of EVs is beneficial for charging service operators. The empirical results indicate that RCP based on the peak-valley time-of-use tariff is propitious for the existing development scale of EVs. In addition, the government subsidies are important to drive EV development in the initial period. However, it should be phased out to reduce the financial burden accompanying the amplification of the scale of EVs.