Electric Vehicles Load Profiles Research Articles

A reliable optimal electric Vehicle charging stations allocation

This paper presents a new and effective approach for optimally allocating Electric Vehicle (EV) stations in a distribution network by integrating electrical and road constraints. The proposed methodology, a two-stage optimization technology, addresses the challenges posed by the increasing energy demand caused by the adoption of EVs. In the first stage, the stochastic demand load of EVs is simulated considering the probability and hierarchic clustering of EV load based on the travelling distance of users. This stage focuses on emulating the uncertainty of EV user travel patterns, time of arrival and departure, and their impact on the EV load profile. In the second stage, a multi-objective problem is formulated to minimize power losses, voltage deviation, and enhance system reliability. To achieve this, charging station zones are determined by clustering the transportation network into groups, and statistical simulations of EV user behaviour are conducted numerous times based on probability. To obtain the optimal solution for the investigated system, which consists of a 25-node transportation network interconnected with the IEEE 69 bus distribution network, the Galaxy Gravity Optimization technique is applied. The results obtained from this study illustrate how the placement of charging stations influences reliability indices, power loss, and voltage deviation.

Techno-economic and environmental assessment of solar-based electrical vehicles charging stations integrated with hydrogen production

With the growing interest in adopting both commercial and residential electric vehicles (EVs) utilizing green renewable energy, the techno-economic assessment of EV charging stations with solar energy is a critical aspect of the transition to sustainable transportation. However, battery storage capacity for variable solar energy production is becoming increasingly less cost-effective due to the higher Lithium price. Complementing solar energy production and battery storage, the potential of combining green hydrogen fuel energy integration with solar energy for EV charging stations can provide a sustainable alternative to reach carbon-free transportation. This research aims to assess the technical and economic viability of grid-connected Photovoltaic (PV)-based EV charging stations across Kentucky based on existing EV load profiles. The study considers different PV system sizes, locations, orientations, and the impact of net metering policies and electricity tariffs on the profitability of the PV systems. Using excess PV energy, Lithium-Ion batteries will be charged, which will be assessed considering the techno-economic and environmental impacts of green EV charging stations. To further enhance green renewable energy production, we also investigated the feasibility of on-grid PV-based EV charging stations with Lithium-Ion batteries charged during low tariff periods. In comparison with these scenarios, we studied the potential benefits of integrating green hydrogen energy production using excess solar energy with and without governmental incentives. Furthermore, we assess the environmental impact of these green EV charging stations across Kentucky. Our study indicates that EV stations across Kentucky have similar techno-economic feasibility with insignificant deviation in the levelized cost of electricity, payback period, and PV energy fraction with/without Li-Ion batteries. However, with green hydrogen production, the marginal H2 production cost indicates the first scenario (on-grid PV energy charging) is the most significant, roughly 10 USD/Kg, which is ∼1.5 times higher than the second (charging during low-tariff periods) and the third scenarios (power grid to satisfy the demand deficit – approximately 6.5 USD/Kg). From the environmental perspective, the second scenario would be a good option for either hybrid EV/H2 or EV charging stations in Kentucky, factoring in the hydrogen production cost and the carbon footprint.

Data-driven method for electric vehicle charging demand analysis: Case study in Virginia

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55 % home, 9 % work, and 36 % public) in September and 45,920,358 kWh (61 % home, 9 % work, and 30 % public) in February.

Distribution network voltage analysis with data-driven electric vehicle load profiles

The number of electric vehicles (EVs) is expected to increase significantly in the near future, which will affect the operation of future power networks. This study investigates the impact of EVs on the distribution network voltage profile by collecting measurements from residential, slow commercial, and fast public EV charging stations throughout the annual cycle. It is revealed from the data that annual utilization factors for residential, slow commercial, and fast public charging stations are 89%, 55%, and 24%, respectively. The optimal power flow solutions have been presented without the EV and with EV (home, commercial, and public) load profiles. Simulation results identify the voltage violation's extent, instant, and duration that may affect the future distribution grid. Most voltage violations happen in the evening, weekday afternoon, and midday for residential, slow commercial, and fast public EV charging stations, respectively. Furthermore, multiple enabling technologies have been demonstrated to reduce voltage violations, such as coordinated charging (reduced voltage violation by 50%), renewable charging (by 50%), and combinations of multiple types of customers (by 30%). This study presents some key insights into future power system operational scenarios with widespread EV charging stations that can be managed by multiple enabling technologies.

Seasonal electric vehicle forecasting model based on machine learning and deep learning techniques

In this paper, multiple featured machine learning algorithms and deep learning algorithms are applied in forecasting the electric vehicles charging load profile from real datasets of Spain's electrical grid. The study aims to provide realistic datasets of electric vehicle load profiles to cope with the potential increase in electric vehicle penetration taking into consideration the seasonality effects. Technical issues are caused by the distribution network of the electricity grid; such as the huge charging power and stochastic charging behaviors of the drivers of electric vehicles due to the mass rollout of electric vehicles. Forecasting electric vehicles’ load profile is necessary to face challenges to solve the problem of the potential mass rollout of electric vehicles penetration. However, Electric vehicle is considered one of the most promising solutions that develops faster than other stochastic renewable solution to reduce greenhouse emissions. The seasonality effect is one of the huge challenges on electrical loads, so it is investigated by creating four separate forecasting networks to increase system accuracy and studying the effect of seasonal factors such as temperature fluctuation in the four seasons affecting the electric vehicles’ battery in charging and draining modes. These factors are affecting the accuracy of the forecasting model. Four featured algorithms are investigated. Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems are applied as machine learning algorithms, and Long Short-Term Memory and the Gated Recurrent Units are also utilized as deep learning algorithms. The Gated Recurrent Units model performs slightly better than the long short-term memory employed on the hourly average daily historical data of charging electric vehicles. While the Adaptive Neuro-Fuzzy Inference System gathers both Artificial Neural Network and Fuzzy Inference System advantages.

Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems

Charging of electric vehicles is going to be a major electrical load in the near future, as more and more population shift to electric auto-motives from conventional internal combusted engine-powered vehicles. Integration of electric vehicle charging stations (EVCS) might even burden the existing grid to a point of collapse or grid failure. Establishing charging stations interfaced with bipolar DC microgrids along the roads and highways is the most realistic and feasible solution to avoid the overburdening of the existing power system. The bipolar DC microgrid is a far better microgrid structure than the unipolar microgrid structure in many aspects like reliability, flexibility, and controllability. It can provide multiple voltage level interfaces according to the load demands, which is very apt for different charging levels of electric vehicles (EVs). Operation of multiple sources and multiple loads connected to bipolar DC microgrid will affect DC voltage regulation, capacitance-voltage balancing, and overall stable operation of the grid. In order to mitigate these power quality problems arising in multi-node bipolar DC microgrids, a decentralized model predictive control is proposed in this paper. EV charging load profiles are modeled and developed by considering standard driving cycles, state of charge, and power demand of multiple vehicles to study the effect of unpredictable varying EV loads in the bipolar DC microgrid. EVCS thus modeled are connected to solar photovoltaic-battery energy storage fed bipolar DC microgrid with three-level/bipolar converters and analyzed under dynamic conditions for capacitance–voltage unbalance mitigation, voltage regulation, and the stability of operation with model predictive control. Simulation studies are carried out in MATLAB/Simulink to verify the effectiveness of the system.

The impact of plug-in behavior on the spatial–temporal flexibility of electric vehicle charging load

While electric vehicles (EVs) are expected to support decarbonizing transport, EVs can challenge the electricity system. Investigating the EV charging load and its flexibility, e.g., by shifting load, is therefore crucial to ensure a secure and sustainable energy system. We develop an agent-based model to investigate how different plug-in behaviors can affect (future) EV charging load profiles and their spatial–temporal flexibility. We contribute to extant literature by (1) revealing the effect of diverse plug-in behaviors on EV load profiles, particularly the flexibility potential resulting from different plug-in behaviors; (2) presenting the (future) charging load in different spatial structures, i.e. urban, rural, or suburban, and home, work, or public charging locations; and (3) demonstrating the effect of detailed driving profiles in high spatial and temporal resolution. We implement three future scenarios regarding EV and charging infrastructure diffusion and technology developments. We find that the impact of potential changes in plug-in behavior on EV charging load would be highest for urban areas and increases as charging infrastructure becomes more spatially diversified. Decision-makers in policy and industry can use these insights to evaluate the impact of EV charging on distribution grids and design incentives to leverage the flexibility potential of EVs.

State of charge and state of health diagnosis of batteries with voltage-controlled models

The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.

A Dual Distributed Optimal Energy Management Method for Distribution Grids With Electric Vehicles

Future distribution grids are expected to face an increasing penetration of electric vehicles (EVs) and heterogeneous distributed energy resources (DERs). This demands a distributed energy management (EM) to manage power generation and delivery of energy sources to maintain power quality under the impact of EV charging, to save operating costs, and to enhance resiliency. However, the global optimality of the distributed EM’s optimization problem is still an issue in existing works because of the non-convex nature of the optimization problem. In this paper, a distributed EM strategy for grid-connected distribution networks is proposed. In particular, the EM strategy is composed of two steps. In the first step, some conditions of the EM optimization task are relaxed to apply an algorithm converging to the global optimality. The results of the first step are used to reconfigure constraints of the full optimization problem in Step 2. The proposed scheme is validated by implementing the real-time controller-hardware-in-the-loop (CHIL) experimentation on the IEEE 33 bus system. To study the impact of EV charging, EV data is collected from the market and the literature to generate realistic EV load profiles to demonstrate the effectiveness of the proposed strategy on saving operating costs and maintaining power quality.

Data-driven spatial-temporal prediction of electric vehicle load profile considering charging behavior

Accurately predicting the spatial-temporal distribution of electric vehicles (EVs) load is of great significance to the optimal dispatching and safe operation of the power grid. This paper proposes a spatio-temporal distribution prediction method for EV charging loads, which considers the charging behavior characteristics of different types of EVs and the spatio-temporal coupling between EVs and charging stations. Firstly, an EV charging demand prediction model based on improved random forest (IRF) is established, in which the parameters of random forest (RF) prediction model for each type of EVs are optimized by harmony search (HS) with the principle of minimum prediction error to release the sensitivity of the EV charging prediction model to parameters. Then, a bottom-up method for predicting the spatial and temporal distribution of EV cluster charging loads based on IRF is proposed, which takes into account the spatial-temporal coupling between EVs and charging stations. In addition, a parallel computational method with data parallelization and task parallelization is proposed to enhance the efficiency and practicability of the proposed method. Finally, the accuracy level of IRF has been explored through rigorous case studies comparing with support vector machine (SVM), back propagation neural network (BPNN) and general random forest (RF). The case studies also reveal that the proposed method, compared with traditional methods, can not only improve the prediction accuracy of the total EV charging load but also obtain the spatial and temporal distribution of the charging load in the region.

Data-Driven EV Load Profiles Generation Using a Variational Auto-Encoder

Although the penetration of electric vehicles (EVs) in distribution networks can improve the energy saving and emission reduction effects, its random and uncertain nature limits the ability of distribution networks to accept the load of EVs. To this end, establishing a load profile model of EV charging stations accurately and reasonably is of great significance to the planning, operation and scheduling of power system. Traditional generation methods for EV load profiles rely too much on experience, and need to set up a power load probability distribution in advance. In this paper, we propose a data-driven approach for load profiles of EV generation using a variational automatic encoder. Firstly, an encoder composed of deep convolution networks and a decoder composed of transposed convolution networks are trained using the original load profiles. Then, the new load profiles are obtained by decoding the random number which obeys a normal distribution. The simulation results show that EV load profiles generated by the deep convolution variational auto-encoder can not only retain the temporal correlation and probability distribution nature of the original load profiles, but also have a good restorative effect on the time distribution and fluctuation nature of the original power load.

The Prediction of Electric Vehicles Load Profiles Considering Stochastic Charging and Discharging Behavior and Their Impact Assessment on a Real UK Distribution Network

Abstract Electric vehicle (EV) as one of the most promising solutions to reduce the greenhouse emission is developing faster than ever. With the increasing number of EVs, the additional load will cause technical issues on the existing distribution network. To cope with possible challenges, the reasonable prediction of EV load profile is fundamental to the evaluation of how the distribution network responds to the potential increasing EV penetration. This paper investigates the critical issues that EVs bring into the network at various penetration levels considering the uncertainties due to stochastic charging and discharging behaviour. To deal with these uncertainties, a Monte Carlo based simulation method is utilised to create EV charging and discharging profiles. Three scenarios are proposed and their impacts on a real UK distribution network are analysed by the simulation in OpenDSS and MATLAB. The simulation results imply that EV charging process has the negative effect in regard to thermal stress, voltage drop, system efficiency and power factor of the network. Conclusions are drawn to provide the guidance for the upgrading and reinforcement of the existing network assets.

Low-temperature aging mechanisms of commercial graphite/LiFePO4 cells cycled with a simulated electric vehicle load profile—A post-mortem study

Abstract Reduced cycle life is one of the issues hindering the adoption of large lithium-ion battery systems in cold-climate countries. Thus, the aging mechanisms of commercial graphite/LiFePO4 (lithium iron phosphate) cells at low temperatures (room temperature, 0 °C and−18 °C) are investigated here through an extended post-mortem analysis. The cylindrical 2.3 Ah cells were cycled with a simulated battery electric vehicle load profile, and the aged cells were then disassembled inside an argon-filled glove box. A non-cycled cell was also dismantled as a reference. Half-cell testing was utilized to evaluate the degradation of the electrochemical performance of the electrodes, whereas X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, inductively coupled plasma optical emission spectroscopy and Raman spectroscopy were used to characterize the changes in the materials properties. The full-cell performance loss was mostly seen as capacity fade whereas significant changes in the cell impedance were not observed. Depending on the cycling temperature, loss of cyclable lithium due to solid electrolyte interphase growth and/or lithium plating on the graphite electrode were observed, and they are attributed as the main mechanisms responsible for the capacity loss. Furthermore, increased disordering of the graphite electrode was observed for the cell cycled at −18 °C. The graphite disordering was hypothesized to result from diffusion-induced stress and the mechanical stress caused by severe lithium plating. In contrast, the LiFePO4 electrodes showed only minimal signs of degradation regardless of the cycling temperature.

HPC-Based Probabilistic Analysis of LV Networks With EVs: Impacts and Control

Distribution network analyses have been traditionally carried out by sequentially processing computational tasks, i.e., without taking advantage of parallel processing now available in multi-core machines. However, future distribution networks require studies that cater for the uncertainties due to the location and behavior of loads and low carbon technologies, resulting in a much more computationally demanding environment. This paper investigates the adoption of high performance computing (HPC) to accelerate probabilistic impact and control analyses carried out on residential low voltage (LV) networks with electric vehicles (EVs). First, the impacts of uncontrolled charging of EVs are quantified using a Monte Carlo-based approach using 1000 time-series daily simulations per penetration level (i.e., 0%–100%). Then, to mitigate these impacts, the coordinated management of the on-load tap changer and EVs is proposed considering a preventive control approach that caters for the uncertainties ahead (1000 scenarios). Two real residential, underground U.K. LV networks considering realistic demand and EV load profiles (1-min resolution) are analyzed. Results show that the processing time for the impact analysis is reduced almost proportionally to the number of cores. From the control perspective, it is demonstrated that HPC can be a feasible and implementable alternative in the management of future smart grids.

Generating electric vehicle load profiles from empirical data of three EV fleets in Southwest Germany

The increasing demand for electricity caused by a growing number of electric vehicles (EV) might challenge future energy systems. For a smooth integration of the electricity demand from EV, a comprehensive knowledge of its characteristics is essential. The analysis of charging behavior patterns of EV and resulting load profiles become important premises for this crucial task. Three electric mobility studies in Germany's southwestern region (Get eReady, iZEUS, and CROME) deliver comprehensive data of EV use for this purpose. In this paper we analyze and discuss the mobility and charging characteristics of this data in detail. We derive empirical EV load profiles and show how they are affected by charging management. We present a model to simulate EV loads based on statistical characteristics of the conducted studies. The resulting charging load profiles show similar patterns as other EV studies. The developed simulation model and its results (see supplementary data available online) allow a realistic representation of EV demand in analyses of future energy systems.

電気自動車の移動目的地での充電可能性を考慮した充電負荷曲線の推計

電気自動車の移動目的地での充電可能性を考慮した充電負荷曲線の推計

Potential power system and fuel consumption impacts of plug in hybrid vehicle charging using Australian National Electricity Market load profiles and transportation survey data

Future electric vehicle (EV) deployment raises the potential for opportunities and challenges for policy makers in both the electricity and transportation sectors. This paper describes the development of a simple, time based simulation tool for assessing plug-in hybrid EV charging load and gasoline consumption under a range of standard charging infrastructure and charge control scenarios. This tool is intended for use by power system planners and other policy makers in evaluating a range of possible load outcomes arising from EV integration. Australian vehicle trip data from the New South Wales Household Transport Survey is used with the introduced model to assess the impact of EV charging load on the 2011 Australian National Electricity Market load profile. Results are presented which address a gap in existing literature with respect to EV load profiles in the Australian context with findings which include (1) that the provision of non-residential (public) charging infrastructure is beneficial for both vehicle owners and power system load profiles, (2) that fast charging in the residential context represents a significant risk to the power system if not accompanied by charging control, (3) that inappropriate Time-of-Use electricity tariffs may lead to poor outcomes at high penetration levels, and (4) that there are trade-offs between benefits for the power system and the amount of gasoline consumed by the vehicle fleet when charging is restricted to occur overnight. Given the current focus on Time-of-Use electricity tariffs as the primary mechanism for influencing EVs recharging, the finding that inappropriate Time-of-Use electricity tariffs lead to poor outcomes at high penetration levels is significant for long term EV integration planning.