Electric Vehicle Drivers Research Articles

Electric vehicle drivers’ choices of expressway usage and peak avoidance: an empirical analysis considering the random effects among individuals

ABSTRACT Traffic congestion is a persistent challenge in urban areas, particularly with increased private car ownership post-COVID-19. Traditional administrative measures to manage traffic demand have proven unsustainable, necessitating more effective strategies. This study examines trajectory data from 3,064 commuting (CMT) and 2,690 non-commuting (Non-CMT) electric vehicles in Shanghai to analyze how travel purposes, distances, directions, and departure times influence expressway usage and peak avoidance decisions. It identifies significant differences in choices between CMT and Non-CMT users, highlighting the need for personalized travel management methods based on vehicle usage patterns. Route management strategies should focus on commuting trips for CMT vehicle users, while spatial control measures considering the trip distance and direction can reduce Non-CMT vehicle users’ dependence on expressways during peak hours. This research contributes to enhancing our understanding of the heterogeneity of travelers’ behaviors and offers personal and practical insights for managing traffic congestion sustainably in metropolitan cities.

Advancing sustainable EV charging infrastructure: A hybrid solar-wind fast charging station with demand response

This study aims to design an efficient hybrid solar-wind fast charging station with an energy storage system (ESS) to maximize station efficiency and reduce grid dependence. The research employs Monte Carlo simulation to capture renewable energy source (RES) uncertainties, models stochastic electric vehicles (EVs) driver behavior and fleet diversity, and utilizes an Erlang B queuing model for EV load demand estimation. A hybrid optimization approach combining the Binary-Gravitational Search Optimization Algorithm (B-GOA) and the Non-dominated Crowding Sort Optimization Algorithm (NCSOA) is implemented for efficient optimization in a combined binary-continuous solution space. Results demonstrate superior performance of the proposed approach compared to existing methods, with high-RES penetration significantly reducing grid reliance. This study contributes to advancing sustainable EV charging infrastructure development and enhancing overall grid stability through improved load flexibility and demand response management. The analysis reveals Scenario IV as the most economically viable, with the highest Net Present Value of 1,025,895.32€. Most scenarios favor 5 chargers (44–46 kW) and 4 Type 3 wind generators. Battery capacity varies widely (108–372 kWh), as does grid connection (0–292 kW). Despite varying initial investments, Scenarios II-VII show a consistent 4-year Internal Rate of Return, indicating good economic potential across different configurations.

EvPowerStop - Electric Vehicle Station Finder and Slot Scheduler

With the increasing adoption of electric vehicles (EVs) worldwide, a significant challenge has emerged for EV drivers: locating compatible charging stations in real-time and securing slots for efficient use. The transition to electric mobility has created a demand for seamless infrastructure that supports the dynamic needs of EV drivers. Currently, the lack of centralized information on charging station availability and compatibility with specific vehicles leads to inconveniences and delays, reducing the overall efficiency of electric vehicle usage. This project addresses this gap by developing an EV station finder and slot scheduling application—EV Power Stop—designed to simplify the charging process and ensure that EV drivers can plan their trips with confidence. The broader context of this work lies in the realm of smart cities and sustainable transportation. As nations push toward carbon-neutral goals, the role of electric vehicles becomes crucial in reducing emissions and lowering dependency on fossil fuels. For this shift to be effective, smart solutions that integrate transportation with infrastructure are vital. This project aligns with the goals of smart urban mobility by offering a system that leverages real-time data to provide EV drivers with the most up-to-date information on charging station availability. The proposed system uses Flutter as a cross-platform framework for the frontend and Firebase as the backend for managing user data, station information, and slot booking in real-time. APIs like Google Maps are integrated to offer navigational support, ensuring users can easily find and access nearby stations. Key features include user authentication, slot booking, real-time updates on availability, and route guidance. The system is designed with scalability, performance, and security in mind, ensuring that it can handle a growing user base while maintaining quick response times. Initial testing shows that the app responds within 2 seconds for most user interactions, meets usability standards, and securely manages personal data. Future developments will focus on integrating additional functionalities like iOS support, predictive scheduling, and automated slot management to improve efficiency further. This project demonstrates the potential for integrating real-time systems with urban mobility to enhance the electric vehicle user experience and support broader sustainability goals..

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

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

Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on Türkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09 % across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77 %. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72 %.

Personalised electric vehicle charging stop planning through online estimators

AbstractIn this paper, we address the problem of finding charging stops while travelling in electric vehicles (EVs) using artificial intelligence (AI). Choosing a charging station is challenging, because drivers have very heterogeneous preferences in terms of how they trade off the features of various alternatives (for example, regarding the time spent driving, charging costs, waiting times at charging stations, and the facilities provided at the charging stations). The key problem here is eliciting the diverse preferences of drivers, assuming that these preferences are typically not fully known a priori, and then planning stops based on each driver’s preferences. Our approach to solving this problem is to develop an intelligent personal agent that learns preferences gradually over multiple interactions. This study proposes a new technique that utilises a small-scale discrete choice experiment as a method of interacting with the driver in order to minimise the cognitive burden on the driver. Using this method, drivers are presented with a variety of routes with possible combinations of charging stops depending on the agent’s latest belief about their preferences. In subsequent iterations, the personal agent will continue to learn and refine its belief about the driver’s preferences, suggesting more personalised routes that are closer to the driver’s preferences. Based on real preference data from EV drivers, we evaluate our novel algorithm and show that, after only a few queries, our method quickly converges to the optimal routes for EV drivers [This paper is an extended version of an ECAI workshop short paper (Shafipour Yourdshahi et al., in: ECAI 2023 workshops, Kraków, Poland, 2023)].

Geographic Factors Impacting the Demand for Public EV Charging: An Observational Study

The practicality and substitutability of electric vehicles depend on there being a fast, reliable way to recharge on round trips beyond the range of a single charge. Grouping such infrastructure into charging hubs benefits developers and operators through economies of scale and electric vehicle drivers in terms of travel logistics and passed-through cost savings. The need for charging capacity at en-route charging hubs is impacted by the following four identifiable geo-social parameters: (a) highway travel volumes, reflecting the rate at which electric vehicles are expending energy in the area; (b) local population, reflecting both the increased needs of electric vehicle owners without dedicated home chargers and the reduced needs of those commuting into a metropolitan center; (c) the quantity of competing charging stations; and (d) being on a critical interprovincial route. Twelve charging stations located in diverse locations around Nova Scotia, Canada, were evaluated in terms of these four parameters, and their recorded use was investigated from a dataset of 26,000 charging events between April 2022 and April 2024. The regression reveals that there are strong positive correlations between demand for fast charging and (a) traffic volumes (45%) and (c) being on an interprovincial route (42%), while there is only a very weak correlation with (b) local population (2%). Interestingly, there is only a weak negative correlation with (c) the number and capacity of nearby competing chargers (−6%), suggesting that either in short-term route choice or longer-term vehicle choice, the presence of chargers encourages electric vehicles.

Using Electric Vehicle Driver’s Driving Mode for Trip Planning and Routing

With the increasing adoption of electric vehicles worldwide, some limitations have emerged in their usage. The main limitations include low autonomy and a scarcity of charging points. In this work, we describe a software architecture for planning a stop at charging stations along a trip, by prediction of battery charge to be spent along the path. We describe the main components of this architecture and evaluate regression methods for the car consumption prediction module. We also use a real dataset built from an electric vehicle usage to validate the architecture concept and its viability analyzing multiple linear regression machine learning models. To further validate the architecture, we make comparisons between simulated and a real trips.

Review on techno-socio-economic studies of electric vehicles in electrical energy systems

Transportation electrification is a practical solution for the world's environmental, economic, and technical challenges. During the last decades, numerous studies have focused on electric vehicles (EVs) as one of the essential energy fields due to the growth rate of EV penetration into energy systems. Researchers have investigated different operation and long-term planning models of EV charging infrastructure and prediction and uncertainty-handling approaches for EV drivers’ behavior (i.e., the arrival/departure time to/from charging stations and the associated state-of-charge) as well as fixed and mobile charging stations (MCSs) and social equity access to EV charging infrastructure. This study provides a detailed analysis and discussion of the daily operation and long-term planning of EV charging stations with economic and technical aspects. Also, the prediction of EV drivers’ behavior and approaches to dealing with uncertain EV parameters are studied. Furthermore, the role of MCSs and social equity access in planning EVs’ charging station placement is investigated. The authors also assess the applications of permanent and MCSs and the social equity access to EV infrastructure. Finally, the current study discusses EVs future trends and their effect on electrical systems as future subjects of research studies.

Vehicle-to-grid as a competitive alternative to energy storage in a renewable-dominant power system: An integrated approach considering both electric vehicle drivers' willingness and effectiveness

Vehicle-to-grid (V2G) technology, which enables bidirectional power flow between electric vehicles (EVs) and power grids, is a possible solution for integrating EVs and renewable energy (RE) into the power system. While EV drivers are indispensable components for the V2G applications, the extant power system studies have underexamined the willingness of EV drivers to participate in V2G. In addition, few studies investigate whether the small-scale and time-varying V2G supply can replace utility-level energy storage. Given this background, this study combined discrete choice experiments with energy storage capacity expansion planning (ES-CEP) to simulate time-varying V2G participation and effectiveness. The choice experiments identified drivers’ preferences for V2G, which was used to simulate V2G participation under different compensation schemes. Time-varying V2G participation was integrated into ES-CEP for renewable-dominant power system to assess the potential of V2G to replace ES at the utility scale. The results showed that most drivers were unlikely to engage in V2G, although this reluctance decreased during the night. Higher guaranteed state-of-charge, accessible V2G facilities, and monetary enrollment compensation encouraged the participation. The ES-CEP results showed that, even with a limited V2G (4.5–15.3 % of EV drivers), V2G significantly reduced the optimal ES capacity (36.5–45.6 % for power and 39.6–60.6 % for energy), shaved peak netload by 18.5 %, and saved the total cost by 3.35 %, even after accounting for V2G enrollment compensation and battery degradation. EVs that charged during the daytime and participated in V2G at night were the most utilized, resulting in 0.92 % of annual battery aging, but generated a profit of 636,749 KRW (553 USD). The findings of this study underscore the importance of a suitable compensation scheme for V2G and supplementary measures for incentivizing EV charging during off-peak hours, as these elements are essential for the successful implementation of V2G.

A Novel Battery Temperature-Locking Method Based on Self-Heating Implemented with an Original Driving Circuit While Electric Vehicle Driving: A Numerical Investigation

In extremely cold environments, when battery electric vehicles (BEVs) are navigating urban roads at low speeds, the limited heating capacity of the on-board heat pump system and positive temperature coefficient (PTC) device can lead to an inevitable decline in battery temperature, potentially falling below its permissible operating range. This situation can subsequently result in vehicle malfunctions and, in severe cases, traffic accidents. Henceforth, a novel battery self-heating method during driving is proposed to maintain battery temperature. This approach is ingeniously embedded within the heating mechanism within the motor driving system without any necessity to alter or modify the existing driving circuitry. In the meantime, the battery voltage can be regulated to prevent it from surpassing the limit, thereby ensuring the battery’s safety. This method introduces the dead zone into the space vector pulse width modulation (SVPWM) algorithm to form the newly proposed dSVPWM algorithm, which successfully changes the direction of the bus current in a PWM period and forms AC, and the amplitude of the battery alternating current (AC) can also be controlled by adjusting the heating intensity defined by the ratio of the dead zone and the compensation vector to the original zero vector. Through the Simulink model of the motor driving system, the temperature hysteresis locking strategy, grounded in the field-oriented control (FOC) method and employing the dSVPWM algorithm, has been confirmed to provide controllable and sufficiently stable motor speed regulation. During the low-speed phase of the China Light Vehicle Test Cycle (CLTC), the battery temperature fluctuation is meticulously maintained within a range of ±0.2 °C. The battery’s minimum temperature has been successfully locked at around −10 °C. In contrast, the battery temperature would decrease by a significant 1.44 °C per minute without the implementation of the temperature-locking strategy. The voltage of the battery pack is always regulated within the range of 255~378 V. It remains within the specified upper and lower thresholds. The battery voltage overrun can be effectively avoided.

Are electric vehicles riskier? A comparative study of driving behaviour and insurance claims for internal combustion engine, hybrid and electric vehicles

Electric vehicles (EVs) differ significantly from their internal combustion engine (ICE) counterparts, with reduced mechanical parts, Lithium-ion batteries and differences in pedal and transmission control. These differences in vehicle operation, coupled with the proliferation of EVs on our roads, warrant an in-depth investigation into the divergent risk profiles and driving behaviour of EVs, Hybrids (HYB) and ICEs. In this unique study, we analyze a novel telematics dataset of 14,642 vehicles in the Netherlands accompanied by accident claims data. We train a Logistic Regression model to predict the occurrence of driver at-fault claims, where an at-fault claim refers to First and Third Party damages where the driver was at fault. Our results reveal that EV drivers are more exposed to incurring at-fault claims than ICE drivers despite their lower average mileage. Additionally, we investigate the financial implications of these increased at-fault claims likelihoods and have found that EVs experience a 6.7% increase in significant first-party damage costs compared to ICE. When analyzing driver behaviour, we found that EVs and HYBs record fewer harsh acceleration, braking, cornering and speeding events than ICE. However, these reduced harsh events do not translate to reducing claims frequency for EVs. This research finds evidence of a higher frequency of accidents caused by Electric Vehicles. This burden should be considered explicitly by regulators, manufacturers, businesses and the general public when evaluating the cost of transitioning to alternative fuel vehicles.

On the value of orderly electric vehicle charging in carbon emission reduction

In this study, a bi-level model is developed to quantify the value of orderly electric vehicle (EV) charging in carbon reduction. Specifically, the upper-level model optimizes each EV driver’s charging schedule to diminish the total carbon emissions without impacting their travel plans, and the lower-level problem aims to fulfill electricity demands with minimal electricity dispatch cost. Based on real-world operation data obtained from 3,777 battery EVs (BEVs) in Shanghai over 11 months and local power plant data, the total carbon emissions generated by BEVs in Shanghai is calculated as 1,176,637 tons over this period, averaging 73 gCO2/km per BEV. By administering charging control to all BEVs in Shanghai, the above emission could be curtailed by 39%. Sensitivity analyses uncover that augmenting battery capacity and integrating wind power can significantly enhance emission reductions, while increasing the flexibility of the power plant might diminish the effectiveness of orderly EV charging.

A two-layer planning method for location and capacity determination of public electric vehicle charging stations

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.

Energy-Efficient Anomaly Detection and Chaoticity in Electric Vehicle Driving Behavior.

Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies-Bouldin index, and Calinski-Harabasz index, and the potential energy recovery rates were also determined. Two driving datasets were evaluated in terms of chaotic aspects using the Lyapunov exponent, Kolmogorov-Sinai entropy, and fractal dimension metrics. The developed hybrid models are superior to the sub-methods in anomaly detection. Hybrid Model-2 had 2.92% more successful results in anomaly detection compared to Hybrid Model-1. In terms of potential energy saving, Hybrid Model-1 provided 31.26% superiority, while Hybrid Model-2 provided 31.48%. It was also observed that there is a close relationship between anomaly and chaoticity. In the literature where cyber security and visual sources dominate in anomaly detection, a strategy was developed that provides energy efficiency-based anomaly detection and chaotic analysis from data obtained without additional sensor data.

Privacy-preserving estimation of electric vehicle charging behavior: A federated learning approach based on differential privacy

With the popularity of connected electric vehicles, the openness and sharing of charging data between stakeholders allows a more accurate estimation of charging behavior, which is valuable for optimizing energy systems and facilitating travel convenience. However, to enable such an effective mechanism, the challenge of data security and privacy should be addressed. Federated learning in the vehicular network is appealing for utilizing individual vehicle data while preserving data privacy. We propose an improved local differential privacy-based federated learning approach for modeling charging session prediction problems while preserving user privacy against the threat from a honest-but-curious server. In this approach, all vehicles, within the coordination of a cloud server, collaboratively establish a global regression network through parameter exchange. Meanwhile, the servers may belong to third-party model owners and can be semi-honest when inferring private information on the collected model parameters. Hence, local differential privacy is adopted to perturb the parameters. Additionally, a combination of local and global models via elastic synchronization is proposed to improve the accuracy of the learned noisy global model. Through the test on a real data set, the results show the superiority of the proposed algorithm over traditional noisy federated learning methods. Furthermore, the practical value of the proposed method is validated with a real-world charging case. Such an accurate charging session prediction service for electric vehicle drivers facilitates charging and travel convenience in the green transportation world.

Potential of an Integrated Platform for EV Charging Station and Ancillary Services

Since the creation the first successful electric car in 1890, the world has seen a rapid increase in the advancement of Electric Vehicle (EV) cars along with increasing demand for such vehicles. However, despite multiple benefits that EV cars offer, such as their sustainable nature, potential car buyers are still hesitant on purchasing an EV car due to concerns about its relatively short travelling range. While the technology powering EV cars has made significant strides, petrol cars can travel much further on a single tank of fuel than EV cars can on a single charge: electric cars can only travel between 100-300 miles on a single charge, whereas gas cars have a range of 300-600 miles. Furthermore, infrastructure is another benefit that gas-powered vehicles have over electric ones. Particularly in rural areas, gas stations are far more common and easily accessible than charging stations. While some owners of electric vehicles might be able to charge their vehicles at home or at the office, they might have trouble finding charging stations when they are out and about. Hence, EV drivers or potential car buyers share a common concern that EV cars will run of gas frequently, leading to inconvenient trips to gas stations. Through our research, by abstaining user experience and feedback, we were able to identify substantial demand for a platform or service that improves the accessibility of EV charging stations for EV car drivers. This paper will include a thorough representation of relevant results and a deep analysis on the potential of a platform that provides easily accessible charging stations as well as other car-related services.

Measuring the effectiveness of incorporating mobile charging services into urban electric vehicle charging network: An agent-based approach

Mobile charging service (MCS) has emerged as a cost-efficient solution to compensate for the limitations of fixed charging stations (FCSs) in service capacity and coverage. Nevertheless, the impacts of incorporating the MCS into the charging infrastructure network have not been well understood. Therefore, this study introduces an exhaustive framework for devising Agent-based Modeling (ABM) to delineate electric vehicle (EV) drivers' daily travels and charging behaviors within the integrated charging network. Specifically, the developed ABM framework encompasses both internal attributes of individuals and external variables of charging and transport networks that potentially shape EV drivers’ public charging decisions. It generates authentic daily charging demands for FCSs and MCSs and provides a streamlined process to further incorporate more charging modes into the EV charging ecosystem. Subsequently, we opted for the Salt Lake City metropolitan area to implement our framework and showcase the effectiveness of MCS integration on the charging network over various EV adoption levels and network settings. The findings of the designed case study unravel the choice patterns of EV drivers when facing diverse charging options and pinpoint the strengths and weaknesses of MCS incorporating into the urban charging network at different stages.

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

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

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