Electric Vehicle Drivers Research Articles

Network equilibrium of battery electric vehicles considering drivers’ resting behavior

Driving fatigue cost is a major component of vehicle drivers’ travel costs in an intercity or regional network. The charging behavior of electric vehicle (EV) drivers, which is generally synchronized with drivers’ resting behavior, can contribute to the mitigation of drivers’ fatigue, especially after a prolonged driving period. Overlooking the impact of driving fatigue on the travel cost may overestimate the side-effects of the charging behavior for EV drivers, and result in biased flow and charging demand distribution. In this study, by considering the fatigue as part of the travel cost, we make the first attempt to investigate the impact of EV drivers’ charging and resting behaviors on their fatigue cost, and thus their travel plans and resultant flow distribution across the network. To this end, a novel network equilibrium modeling framework is first developed to capture the interaction among EV drivers’ travel plans, which specify the routing, recharging, and resting plans on a general intercity or regional network where charging stations and rest stops are deployed. When traveling between their origins and destinations, EV drivers are assumed to determine their travel plans to minimize their individual travel cost composed of driving time, rest time, charging cost, and fatigue cost, while preventing their batteries from being exhausted. The equilibrium model is then formulated as a variational inequality and transformed into a nonlinear optimization problem. An efficient solution algorithm integrating column generation and Benders decomposition approach is proposed to solve the established optimization problem. Numerical examples are presented to demonstrate the performance of the proposed models and solution algorithm. Numerical results validate that considering the impact of driving fatigue on the travel cost emphasizes the need for en-route charging for EV drivers with long-distance trips, and has an appreciable impact on the network flow and charging demand distribution. In addition, large-sized batteries and fast chargers may not necessarily reduce drivers’ travel costs for long-distance travel since charging behavior can be synchronized with drivers’ resting behavior and thus contribute to the mitigation of drivers’ fatigue.

Large Scale EV Charging Allocation in Unbalanced Multi-Microgrid System

The improvement of charging infrastructure has greatly relieved the range anxiety of electric vehicles (EV) drivers, leading to the rapid increase in EV ownership. Due to the uncertainty of EV charging behavior, a large number of EVs connected to microgrids (MGs) without coordination will aggravate the unbalanced load distribution in the multi-microgrid system. Contrariwise, EVs with appropriate coordination can be recommended to suitable charging stations. It is beneficial to the safe operation of the power grid. Considering the load distribution among MGs during the whole EV charging period, we propose a charging station recommendation strategy to dispatch EVs to suitable charging stations. The simulation results show that the proposed strategy can optimize the EV charging behavior and effectively balance the load distribution among MGs, which can alleviate the impact of a mass of EV charging behavior on the safe operation of the multi-microgrid system.

Location Selection of Battery Swap Station using Fuzzy MCDM Method: A Case Study in Indonesia

The rise of Electric Vehicles (EVs), supported by battery swap systems, brings various advantages, including reduced waiting times, lower upfront costs, and alleviating range anxiety. Battery Swap Stations (BSS) enhance green transportation by providing convenient options for EV users, especially in regions with limited fast-charging infrastructure. Many EVs, especially two-wheelers, need battery recharging after reaching their driving range. BSS availability can eliminate charging inconveniences for busy EV drivers. However, selecting BSS locations is often challenging due to budget constraints. This study aims to understand the criteria for selecting BSS locations in Indonesia. Potential location alternatives were identified using a fuzzy multi-criteria decision-making approach and input from government officials and industry experts. Factors like driving range, EV capacity, and budget availability were considered in determining the order of BSS establishment. The study found that technological and social aspects were the top criteria, suggesting that BSS development should prioritize established locations like mini markets and petrol stations.

Parameter tuning of EV drivers' charging behavioural model using machine learning techniques

The charging behaviour of electric vehicle (EV) drivers significantly influences planning of the future deployment of public charging stations (CSs). Thus, identifying the EV drivers' charging behaviour plays a major role in CS management and development. In this regard, some parametric behavioural Markov models (BMMs) introduced in the literature have acceptable performance with tuned parameters. Enjoying the benefits of these BMMs needs accurate and feasible parameter tuning. To address this challenge, we propose a machine learning-based method to tune the parameters of such a BMM dynamically. A Deep Q-Network (DQN) algorithm is an appropriate solution in which the reward function is designed based on the statistical resemblance between the EV plug-in and charging times derived from CS simulation with their equivalents derived from the CS charging data. The evaluation results based on the real charging data demonstrate the convergence of the proposed algorithm and validate the accuracy of the adapted behavioural parameters. Accurately adapting the model parameters is an essential prerequisite for designing a system that identifies the EV drivers' behaviour. This novel system helps control the CS congestion and predict the CS requirements when the EV population grows in the future.

Multi-Criteria assessment of the user experience at E-Vehicle charging stations in Germany

We propose an assessment framework for the user experience at public electric vehicle charging stations in Germany, which enables a structured analysis from a driver’s perspective. The assessment criteria were derived from 20 interviews with battery electric vehicle drivers and corroborated by existing literature. Seven criteria are considered as particularly relevant: ease of use, green power, comfort, functionality, price, price transparency, and availability. Using Saaty’s Analytic Hierarchy Process approach, the weights assigned to these criteria were determined in an online survey with 108 drivers of battery electric vehicles. The results indicate the particular importance of availability and functionality. The assessment framework includes value scores that indicate the match between specific charging stations and the assessment criteria. The conceptual framework can help to support operators of public charging points and electric mobility providers in the design of their services and products, as well as guide policy-makers.

Identifying charging zones to allocate public charging stations for electric vehicles

In recent years, several techniques have been presented in the specialized literature to identify the best location for installing public charging stations, considering the demand for charging the batteries of electric vehicles that travel along the main roads in urban areas. However, in cities with different travel patterns for electric vehicle drivers, such consideration may result in charging equipment serving few electric vehicles during high recharging demand. Thus, planning by zone can determine more chargers per station with greater utilization during the operation of charging stations. For this, this work presents a methodology that uses the concept of a charging zone, defined as a circular geographic space with charging equipment that will satisfy the charging demand of electric vehicles in their surroundings. Identifying the centroids of these zones is formulated as a p-median problem, solved by the Teitz-Bart algorithm to provide more coverage to the demands for electric charging in large urban areas. The proposed methodology was applied in a Brazilian city with approximately 3 million inhabitants to find the spatial distribution of public charging zones, considering six scenarios of the global penetration of electric vehicles. Furthermore, we compared the allocation of the centroids in these zones with the solution determined by commercial geoprocessing software. This comparison shows that the proposal determines a spatial distribution of 10% more of these zones with a load factor closer to 0.5 than the results of the commercial geoprocessing software. More zones with this load factor value contribute to better use of the power distribution network installations. In addition, the proposed methodology finds an average reduction of 319 kW of peak demand in regions with a low flow of electric vehicles to meet their charging needs in each analyzed scenario. This peak-demand reduction may allow less investment in connecting future charging stations to the power distribution network. Therefore, the proposed methodology can help public and private agents to disseminate electric mobility, finding public charging areas with greater utilization of chargers per station during the operation of charging stations.

Parametric analysis and prediction of energy consumption of electric vehicles using machine learning

Emission regulations for all automobiles have been introduced to reduce global warming caused by vehicles. Hybrid electric vehicles (HEVs) are being developed to address consumer demand for environmentally friendly automobiles with more power and better fuel efficiency. HEVs are propelled by a combination of an internal combustion engine (ICE) and one or more electric motors that draw power from a secondary battery, which is commonly a lithium-based battery. The fuel economy of such a hybrid drivetrain system can be enhanced above that of traditional ICE automobiles. Although the global world is now focussing on electric vehicles (EVs) over HEVs due to environmental pollution. In this study, a 1-dimensional model was developed for an electric vehicle (EV), and a parametric analysis was made for the eight different cycles using GT-Suite software. The parameters included motor power, state of charge of the battery, vehicle speed, distance travelled, and energy consumption. In light of the parametric analysis obtained using GT-Suite software, this paper also predicts the energy consumption of EV batteries using a neural network-based machine learning (ML) method. After selecting input parameters through a correlation coefficient index (CI) process, the proposed neural network-based prediction model has achieved 89% accuracy in predicting battery energy consumption which will help EV drivers to plan. It will also help automobile engineers to design more efficient and scalable EVs.

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.

Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations

The rapid development of electric vehicles (EVs) has increased the demand for public fast-charging stations (FCSs). Multiple self-interested charging service operators (CSOs) in an urban transportation network compete with each other through pricing signals to maximize their respective profits, where the information about customer demands and heterogeneous preferences work as important decision criteria. With the prevailing data transaction, the information superiority differences between CSOs may significantly affect the competitive pattern in the charging market. In this study, a Nash-Stackelberg-Nash game framework is established to investigate the strategic pricing of CSOs under different information distribution scenarios. The customer response pattern, namely, the routing and charging choices of EV drivers to pricing signals, is described via a computationally efficient extended stochastic user equilibrium model comprehensively considering heterogeneous customer preferences and decision stochasticity. Then, aiming at different data forms, both parameter-driven and sample-driven CSO pricing models are proposed. The pricing models are reformulated as a tractable single-level mixed-integer linear program, and the game equilibrium is solved alternately via Gauss-Seidel iteration. Numerical simulations are conducted to analyze the impact of single CSO information superiority and double CSO information distribution on CSO profitability and customer experience in both data forms. The influences of endogenous factors like CSO data amount and processing ability, as well as exogenous scenario parameters like customer preference distribution and perception error distribution, are discussed.

Feasibility of electric two and three-wheelers in Africa

Due to the less stringent emission technology requirement, motorized two and three-wheelers (2&3W) generate more pollutants than cars. About 20% of the world's registered motorcycles are known to be in Africa. Vehicle pollution is one of the major causes of death and health problems in Africa. The electrification of transportation provides an opportunity for E2&3W in Africa. To assess this transition, this study quantifies the number of E2&3W present in some African countries. Surveys of electric vehicle start-ups, drivers, and owner experiences are used to determine the E2&3W's technical feasibility and ability to compete with petrol scooters (p-scooters). SimaPro 9.4 software together with Ecoinvent 3.8 database was used to conduct a cradle-to-the-grave analysis of the environmental impact of using electric scooters (e-scooters). The research found that Africa's E2&3W's have a 0.2% market share compared to gasoline versions. The main disadvantage of e-scooters is their limited range and battery life. The average range and speed are 50 ​km and 50 ​km/h, respectively. Overloading of E2&3W caused damage to traction motors and was of major concern to distributors. The main advantages of E2&3W are their low operating costs and low environmental impact. In South Africa, the total environmental impact of e-scooters outweighed that of p-scooters. Emissions that have a direct impact on human health, were significantly lower for e-scooters than for p-scooters. The lack of battery performance standards, battery swapping station standards, and charging station standards negatively affects the quality of imported e-scooters. African countries need to implement electric vehicle standards, and battery recycling policies, and establish electric vehicle training and research centers.

Online parameters identification and state of charge estimation for lithium-ion batteries based on improved central difference particle filter

Accurate SOC is of great significance to alleviate the endurance anxiety of electric vehicle drivers. To improve the accuracy of SOC estimation, a method of combining FFRLS and GPR for parameter identification and an improved particle filter for lithium battery SOC estimation are proposed. The proposed collaborative parameter identification method uses the forgetting factor recursive least squares (FFRLS) to identify the parameters in the middle and high SOC region. In the low SOC region, the parameters identified by the FFRLS are used as the training data of Gaussian process regression (GPR) for model parameter prediction to improve the parameter identification accuracy in the low SOC region; in the SOC estimation stage, the central difference Kalman filter is used to generate the importance density function to overcome the particle degradation, and the step size h of the central difference is calculated suboptimal to further improve the prediction accuracy. Finally, the proposed method is compared with other methods under different working conditions and different temperatures, and the effectiveness and adaptability of the proposed method are verified.

Redesign of sharing charging system for electric vehicles using blockchain technology

Electric vehicles (EVs) have great potential in improving environmental sustainability. However, current charging infrastructures could not meet the heightening demand due to the rapid expansion of EVs. Sharing private charge posts with others during idle time, as a win–win solution for charge post owners and non-owner EV drivers, is a proactive response to the dilemma. To build a trusted environment for the peer-to-peer charge post rental service, we explore a blockchain-enabled sharing charging system with redesigned operating procedures and a tailored non-myopic charge post match strategy. The new system can leverage the advantages of blockchain in privacy-preserving and misbehavior-auditing while fully utilizing shared charging facilities. The proposed system model is validated on Ethereum, and test results suggest that the redesigned system can provide high quality services with affordable computational resource consumption. Moreover, the results also report that, compared to the myopic strategy, the non-myopic strategy can improve the overall matching successful rate of charging requests by considering the potential impact of each assignment, especially when charge post resources are scarce.

Economic emission dispatch considering electric vehicles and wind power using enhanced multi-objective exchange market algorithm

Due to the environmental concerns and the high penetration of wind power (WP) and electric vehicles (EVs), reducing the generation costs and emissions, coordinating EV charging in the distribution networks, and addressing the WP uncertainty, are among the most challenging issues in power system control and operation. Hence, the multi-objective dynamic economic emission dispatch problem (MDEEDP) integrated with EVs and wind farms has become one of the hottest topics in this research area. In this paper, the MDEEDP in the presence of EVs, the random behavior of EV drivers, and WP uncertainty is solved using a new multi-objective evolutionary algorithm called the enhanced multi-objective exchange market algorithm. In the proposed algorithm, the novel point-to-point distance technique is introduced to find the Pareto front solutions set (PFSS) with a uniform and diverse distribution. Also, smart strategy management for charging and discharging EVs is proposed to smooth the load curve. The effectiveness of the proposed method and performance of the proposed smart strategy are investigated on two single-objective and multi-objective test systems, including EVs and wind farms, considering uncertainty and system constraints such as ramp rate limits. The simulation results show that the PFSS of the MDEEDP is well extracted by the proposed method while maintaining its uniformity and diversity. The presence of EVs and the application of the proposed smart strategy lead to a reduction of 2,926,497 ($) in the operation costs for test system 1, and, 38,694,698.28 ($) and 7,832,876.64 (kg), respectively in the operation costs and emissions for test system 2, assuming the same load curve throughout the year. In addition, the load factor in test systems 1, and 2 is improved by 11 and 19 percent, respectively.

Public charging infrastructure for EVs: A comprehensive analysis of charging patterns & real-world insights—Case study of Rabat City, Morocco

The introduction of electric vehicles (EVs) will contribute to decarbonizing our cities and make them more sustainable, which will help mitigate climate change. To ensure a successful transition to e-mobility, charging infrastructure development is considered an essential pillar. This paper presents a comprehensive analysis of electric vehicle supply equipment (EVSE) usage. It aims to understand charging patterns in urban environments and retrieve some real-world insights. By collecting a 2-years historical dataset (from 2019 to 2021) of a public EVSE based in Rabat, the methodology presented in this study covers time evolution assessment, energy delivery analysis, and users’ behavior characterization. The analysis of about 2835 events showed that the average energy delivery is 12 kWh corresponding to an average charging duration of 48 min. Also, more than 95% of charging sessions consumed less than 25 kWh, which corresponds on average to 60km. The performed analyses resulted in various findings that led to a set of recommendations that could be insightful for different types of stakeholders (policymakers, grid operators, charging operators, EV drivers, etc.). Furthermore, the methodology followed in this work is reproducible and may be used as a reference for other EVSE usage analyses.

Fast-charging station location problem: A two-phase approach with mathematical program with equilibrium constraints considering charging choice behaviour

Electric vehicles (EVs) produce zero emissions, which makes them an essential solution for reducing air pollution in urban areas. Fast-charging station (FCS) planning is crucial for ensuring a successful transition to EVs. Therefore, to address the FCS location problem (FCSLP) for EVs in urban areas, a two-phase approach consisting of data processing and model optimization is proposed. During data processing, the spatiotemporal charging demand distribution, existing FCSs, and potential locations for new FCSs are sequentially identified. The results are then used in the model optimization phase, where a spatiotemporal mathematical program with equilibrium constraints (MPEC) model is developed to minimize the total social cost (TSC). The MPEC model integrates the maximal coverage location model and charging equilibrium model, which is formulated as a variational inequality to capture the charging choice behaviour of the EVs within the FCS service radius. To solve the MPEC model, a modified genetic algorithm is developed, in which the charging equilibrium model is iteratively solved. Finally, a case study is conducted in a practical area in Shenzhen, China. The results indicate that a trade-off exists between the TSC and the system service level concerning the optimal FCS locations under a specific FCS service radius. Moreover, the FCS service radius significantly influences EV drivers’ charging choice behaviour and FCS planning.

RLCharge: Imitative Multi-Agent Spatiotemporal Reinforcement Learning for Electric Vehicle Charging Station Recommendation

Electric Vehicle (EV) has become preferable choices in modern transportation system due to its environmental and energy sustainability. However, in many large cities, EV drivers often fail to find proper spots for charging because of the limited charging infrastructures and spatiotemporally unbalanced charging demands. Indeed, the recent emergence of deep reinforcement learning provides great potential to improve charging experience over long-term horizons. In this paper, we propose RLCharge for intelligent EV charging station recommendation by jointly considering various long-term spatiotemporal factors. Specifically, by regarding each charging station as an agent, we formulate the problem as a multi-objective multi-agent reinforcement learning task. We first develop a multi-agent actor-critic framework with centralized training decentralized execution. Particularly, we propose a tailor designed centralized attentive critic with the delayed access strategy to coordinate the recommendation between geo-distributed agents during centralized training. Besides, we propose the spatio-temporal heterogeneous graph convolution module to handle the partial observability problem during decentralized execution. After that, to effectively optimize multiple divergent objectives, we develop a dynamic gradient re-weighting strategy to adaptively guide the optimization direction, and propose an adaptive imitation learning scheme to further accelerate and stabilize the policy convergence. Finally, extensive experiments on two real-world datasets demonstrate that RLCHARGE achieves the best comprehensive performance compared with ten baseline approaches.

Low-Carbon Charging Facilities Planning for Electric Vehicles Based on a Novel Travel Route Choice Model

Promotion of charging facilities (CFs) can ameliorate range anxiety and facilitate long-distance travel by electric vehicles (EVs). In this context, a multistage low-carbon EV CFs planning model is proposed in this paper for the coupled transportation and power systems. This model not only takes into account the construction costs of newly-built CFs and their adverse impacts on the distribution system, but also considers the carbon emissions of CFs and the penalty due to the inconvenience for EVs to be recharged. In addition, a rational travel route choice model is significantly important to accurately evaluate the service capability of CFs to be constructed and thereby to obtain an optimal CF planning result. Therefore, a novel travel route choice model developed in this work allows EV drivers to take detours according to the CF locations to charge their EVs multiple times and then finish their trips. Carbon emission flow (CEF) model is innovatively employed to precisely calculate CFs’ carbon emission amount from the perspective of consumption side. Subsequently, uncertainties involved in CF planning, i.e., distribution and growth rate of traffic flow and electric load, popularity of different types of EVs, as well as location of the connected node of clean electricity, are fully considered to obtain a robust CF planning scheme that can achieve a good performance in the current stage and exhibit robustness for the uncertainties in the future stage. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model. The impacts of future carbon price and EV cruising range on the planning results are also comprehensively evaluated.

Review of the Scalable Core-Shell Synthesis Methods: The Improvements of Li-Ion Battery Electrochemistry and Cycling Stability.

The demand for lithium-ion batteries has significantly increased due to the increasing adoption of electric vehicles (EVs). However, these batteries have a limited lifespan, which needs to be improved for the long-term use needs of EVs expected to be in service for 20 years or more. In addition, the capacity of lithium-ion batteries is often insufficient for long-range travel, posing challenges for EV drivers. One approach that has gained attention is using core-shell structured cathode and anode materials. That approach can provide several benefits, such as extending the battery lifespan and improving capacity performance. This paper reviews various challenges and solutions by the core-shell strategy adopted for both cathodes and anodes. The highlight is scalable synthesis techniques, including solid phase reactions like the mechanofusion process, ball-milling, and spray-drying process, which are essential for pilot plant production. Due to continuous operation with a high production rate, compatibility with inexpensive precursors, energy and cost savings, and an environmentally friendly approach that can be carried out at atmospheric pressure and ambient temperatures. Future developments in this field may focus on optimizing core-shell materials and synthesis techniques for improved Li-ion battery performance and stability.

The promise of EV-aware multi-period optimal power flow problem: Cost and emission benefits

Increased electric vehicle (EV) penetration brings considerable challenges to the daily planning of the power grid operations. A careful coordination of the grid operations and charging schedules is needed to alleviate these challenges, and turn them into opportunities. For this purpose, we study the Multi-Period Optimal Power Flow problem (MOPF) with electric vehicles under emission considerations. We integrate three different real-world datasets: household electricity consumption, marginal emission factors, and EV driving profiles. We present a systematic solution approach based on second-order cone programming to find globally optimal solutions for the resulting nonconvex optimization problem. To the best of our knowledge, our paper is the first to propose such a comprehensive model integrating multiple real datasets and a promising solution method for the EV-aware MOPF Problem. Our computational experiments on various instances with up to 2000 buses demonstrate that our solution approach leads to high-quality feasible solutions with provably small optimality gaps. In addition, we show the importance of coordinated EV charging to achieve significant emission savings and reductions in cost. In turn, our findings can provide quantitative insights to decision-makers on how to incentivize EV drivers depending on the trade-off between cost and emission.

Design Methodology and Circuit Analysis of Wireless Power Transfer Systems Applied to Electric Vehicles Wireless Chargers

In road transportation, the market for electric vehicles (EVs) is considered a potential solution for addressing issues related to gas emissions and noise pollution. Due to the limited driving range of the EV battery pack, the charging process must be fast and safe for EV drivers. Wireless charging technology for EVs has gained attention in recent years, and in this research, the authors explore the analysis and design of a resonant magnetic wireless system for charging electric vehicles. The authors propose a design methodology for a serial–serial (SS) wireless system, which outlines how to determine the appropriate pad dimensions for transferring power to the EV battery. The design approach is crucial to attaining the best possible coupling performance and efficiency. Additionally, the magnetic design of the pad is validated using Ansys Maxwell software, and the proposed design is co-simulated using Ansys Simplorer to analyze the performance of the system. Simulation results demonstrate that the proposed model can transfer over 3.7 kW of power with an efficiency of over 90.02%. The paper also discusses the bifurcation phenomenon at the resonance condition to ensure maximum efficiency.