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Vehicle to Vehicle (V2V) energy sharing is emerging as an alternate solution to range anxiety and limited charging infrastructure challenges associated with electric vehicles (EVs). The existing off-board DC fast charging options for V2V ap- plication found in the literature are not effective due to the additional weight, size and cost of the external charger or interface. In this paper, a new V2V interface is proposed in which both EVs motor winding neutrals and negative rails of on-board drivetrains are directly connected to each other. This further helps in forming an integrated dual bidirectional DC-DC converter to control the direction of power flow. The proposed approach does not require reconfiguration of motor winding or mechanical clutch to arrest the motor from rotating and/or the reconfiguration of drivetrain inverter connections. The major hardware requirement for the proposed approach is, access to the already existing neutral connection of the EV motor phase windings. Furthermore, Finite Element Analysis is presented for 150 kW EV motor model and its performance validation with the high frequency DC current flowing in the motor windings during the V2V operation. A scaled lab prototype is developed to demonstrate the energy sharing between two Lithium-ion battery banks through proposed direct V2V power transfer concept.

The population of electric vehicles (EVs) has grown rapidly over the past decade due to the development of EV technologies, battery materials, charger facilities, and public charging services. Many governments have implemented plans to ban fossil fuel vehicles considering the significance of EVs in reducing greenhouse gas emissions. However, due to the battery material characteristics and charger power limitations, the EV charging process requires more time than is needed to fill a non-EV with fuel at a gasoline station, causing drivers to experience range anxiety and impeding the promotion of EVs. Hence, the battery swapping station (BSS) model has been proposed as an alternative method. Recently, researchers have studied the BSS approach by proposing various operation systems and optimization methods, and BSS service operators have successfully implemented swapping at commercial and private stations. This paper reviews the state-of-the-art BSS literature and business models, where the BSS offers a recharged battery to an incoming EV with a low state-of-charge. First, four operation modes are presented: a single BSS, multiple BSSs, an integrated BSS and battery charging station (BCS), and multiple BSSs and BCSs. Then, the BSS decision scenarios are surveyed in relation to five operational areas, i.e., charging schedule, service policy, construction and planning, dispatching and routing, and power management, where the scenarios are compared in terms of the BSS mode, decision maker, EV category, number of battery types, vehicle to grid, and focus and objective. Finally, the survey concludes with a discussion of several future research directions for EV BSSs.

The increasing environmental awareness paired with the rise of global warming effects has led, in the past few years, to an increase in the sales of electric vehicles (EVs), partly but not only, caused by governmental incentives. A significant roadblock in the mass transition to EVs can be found in the so-called range anxiety: not only do EVs have, generally, considerably shorter ranges than their internal combustion engine vehicle (ICEV) equivalents, but recharge takes significantly longer than does filling up a gas tank, and charging stations are less widespread than are petrol stations. To counteract this, EV manufacturers are developing route planners which select the best route to go from A to B according to the range of the vehicle and the availability of charging stations. These tools are indeed powerful but do not account for the state of health (SoH) of the battery or for temperature conditions, two factors which may severely degrade the range of an EV. This article presents an innovative route planning method which takes into account SoH, temperature and driving style and selects, along the planned route, the charging stations among those which can be reached with the energy of the battery. To verify its proper operativity, simulations were conducted, highlighting the risk of running out of battery before destination, considering if the route is planned based on the declared range, and taking into account battery SoH, external temperature and driving style.

In the last decade, car manufactures invested a lot of effort to align their products to the latest energy directives which encourage the production and usage of electrified vehicles to reduce the greenhouse gases production. This resulted in several important developments, which enhanced the advantages of electric vehicles in terms of local emissions (zero tailpipe emissions), efficiency, convenience in urban areas and others and ultimately led to their ever-increasing adoption. However, there are still some challenges that need to be addressed. One example is the negative influence of low (winter) and high (summer) atmospheric temperatures on electric vehicle range due to the cabin temperature heating and cooling. This requires more efficient ways of using energy to avoid sacrificing the passenger thermal comfort for an increased vehicle range. The present study proposes a new strategy for heating the seats in electrically powered vehicles using an uneven distribution of the heating elements. The uneven positioning of the heating elements is based on the thermal sensitivity of the human skin measured data and scientific literature. For this, a thermal sensitivity test device was developed to map the human skin thermal sensitivity. To test the new solution, a vehicle seat was equipped with heating pads (arranged according to the position of the relevant human skin thermal sensitivity points). For the next step, comparative measurements (power consumption, temperature distribution—with an IR camera—and human subjectivity test) were carried out between a classical vehicle seat heating system and the newly proposed heating solution. The outcome of the study revealed that the proposed heating system will supply at least the same thermal comfort sensation as the standard vehicle seat but using only half of the energy consumption, which translates in an increase of the electrically powered vehicle range between 1.2% and 1.5%, depending on the climate and driving conditions (over the WLTC). For example, a vehicle with a 16 kWh battery driving over the WLTC in Frankfurt climate conditions can gain in 1 year between 139.6 and 164.5 km.

Household vehicle miles traveled (VMT) has traditionally been studied in the context of gasoline vehicles. In this study, we analyze VMT of Plug-in Electric Vehicles (PEVs) to understand PEV use and the factors that influence their use in a household. We use data from a unique repeat survey of PEV owners in California who were originally surveyed when they first bought their vehicle. Having two survey responses per household allows us to obtain more reliable VMT data and analyze the change in VMT over the vehicle ownership period. The results show that PEV VMT is correlated with traditional factors like population density, built environment, attitudes towards technology, and lifestyle preferences. Specific to PEVs, electric driving range and access to infrastructure, have a major influence on PEV VMT. Moreover, while lower electricity price at home may lead to a higher share of PEV VMT in total household VMT, we do not identify the presence of “rebound effect”. Overall, we observe that factors influencing PEV VMT are like those observed for conventional gasoline vehicles. We find that PEVs drive a similar amount as conventional vehicles, not less (due to range anxiety) as some have suggested. The results have implications for emissions impact assessments and travel demand models that depend on assumptions of average annual VMT for policymaking.

An adequate charging infrastructure advocates to ameliorate the range anxiety to propel the disparaged EV market. But, the high installation cost, requirement of suitable places and anticipated immense load on the grid during peak times hinder to elongate the charging station network. Fortunately, the bidirectional energy transferring capability between vehicles may act as an auxiliary solution to charge an EV at any place and at any time without leaning on a stationary charging infrastructure. In this work, we assume a market where charging providers each has a number of charging trucks equipped with a larger battery and a fast charger to charge a number of EVs at some particular parking lots. A provider intends to maximize the served EVs using its limited charging trucksAll charging requests are assumed to be received by an agent which provisions a route and schedule for each charging truck and all trucks should return to the depot after serving EVs. We formulate this combinatorial problem as an ILP to maximize the served EVs . We present a solution methodology by decomposing the problem using Dantzig-Wolfe decomposition approach; we divide the problem into one master and a set of pricing problems and achieve the solution iteratively.

Electric buses are usually designed with various energy management features to overcome the limited cruising range problem. This paper reveals the impact of different energy management systems applied in the electric bus intended for rapid transit usage on battery state of charge and energy consumption. Different systems can be distinguished based on combinations of range extender usages and regenerative braking strategies. Simulations have been done using models developed from the fundamental theory of vehicle engineering and the bus’s design parameters. The results show that the minimum energy consumption can be achieved by an energy management system, adopting a serial regenerative braking strategy without the use of a range extender. It is also found that the serial regenerative braking strategy can save up to 15 % energy compared to the absence of the regenerative braking system. In comparison, the absence of a range extender contributes to a 7 % reduction in energy consumption. However, considering the range anxiety of the driver, the designed electric bus for rapid transit is recommended to utilize both the range extender and parallel regenerative braking strategy. The developed model in this work can be implemented to redesign or further optimize specific components and operating procedures of the bus.

Range anxiety of electric vehicles (EVs) still poses a major barrier in their adoption in the logistics operations despite the advancements in the battery technology. The need for recharging the battery during the day brings additional complexities to the operational planning of commercial EVs in last mile deliveries. The driving range of an EV may vary according to different factors including ambient temperature, weight, speed, acceleration/deceleration, and the road profile. In this study, we revisit the well-known electric vehicle routing problem with time windows by taking into account the weight of the load carried. Cargo weight may play a crucial role in the operational efficiency of the EVs since it may affect the energy consumption significantly. We first present two alternative mathematical formulations of the problem and test their performances on small-size instances that can be solved using a commercial solver. Next, we develop a matheuristic approach that integrates an optimal repair procedure in the large neighbourhood search method and validate its performance. Then, we present an extensive numerical study to investigate the influence of load on the routing decisions. Our results show that cargo weight may create substantial changes in the route plans and fleet size, and neglecting it may cause severe disruptions in service and increase the costs.

In this study, a secure and coordinated blockchain based energy trading system for Electric Vehicles (EVs) is presented. The major goals of this study are to provide secure and efficient energy trading between EVs and Charging Stations (CSs), and to ensure efficient coordination between EVs. In this study, a consortium blockchain based energy trading algorithm is presented that handles the essential energy requests, discards the redundant requests and calculates the distance between EVs and CSs. Moreover, Matching Pool (M-Pool) and Pairing Pool (P-Pool) are used to store energy and payment requests. Furthermore, a blockchain based mechanism is proposed for ensuring efficient coordination between EVs at the unsignalized crossroads and intersections. The scheme involves efficient communication between EVs and the timely sharing of important messages. In the proposed work, EVs are authenticated using a Registration Authority (RA) before they become part of a Vehicular Energy Network (VEN). The increase in the number of EVs results in an increase in the number of messages leading to data redundancy issue. To solve the issue, message filtration is performed. The delays incurred in the VEN are also mathematically formulated in this study. In addition, the range anxiety issue is discussed in the proposed work. Besides, a Local Aggregator (LAG) is used as an energy broker to manage energy trading events and transaction validation. To promote the users to take part in the proposed network, they are provided with incentives. The proposed model is tested against selfish mining attack and the security analysis is performed through Oyente tool. The simulation results show that the proposed study excels in providing a secure, efficient and coordinated energy trading and data sharing system for EVs. The results show that due to proper coordination, the risk factor is reduced by almost 25–30%. Moreover, almost 40–50% reduction in time is observed when storing less redundant data.

International audience; Rethinking electric mobility in terms of user needs, vehicle characteristics and affordability, as well as actual driving conditions is the motivation for this paper. Studies and experience have highlighted that users' range anxiety may be alleviated by intelligent technological solutions. In this work, a comprehensive investigation of several such solutions and their impact on overall trip time for longdistance journeys has been carried out. In particular, the impact of battery size, driving speed, energy consumption, as well as charging strategy (i.e. sequence of events and charging power) has been evaluated. The goal is to understand how to best influence driving operation (in terms of driving speed and charging strategy) for given physical design parameters (battery size, maximum charging power, etc.) and make long-distance trips feasible even with smaller battery capacities.

We document non-linear stock effects in the relationship linking emerging technology adoption and network infrastructure increments. We exploit 2010-2017 data covering nascent to mature electric vehicle (EV) markets across 422 Norwegian municipalities together with two complementary identification strategies: control function regressions of EV sales on flexible polynomials in the stock of charging stations and charging points, and synthetic control methods to quantify the impact of initial infrastructure provision in municipalities that previously had none. Our results are consistent with indirect network effects and the behavioral bias called "range anxiety," and support policies targeting early infrastructure provision to incentivize EV adoption., Environmental and Resource Economics, 83 (3), ISSN:0924-6460, ISSN:1573-1502

Determination of appropriate battery ranges is critical for developing and utilizing electric cars, which remains an active research topic. In particular, the issues of range anxiety have not been well studied concerning the battery design. Towards these research gaps, this study firstly investigates the baseline battery ranges based on the actual travel data collected from a large-scale longitudinal naturalistic driving study in the Midwestern USA. The occurrences and severity levels of range anxiety are then studied given the baseline, which leads to an augmented optimization model to eliminate such issues. Results show that in the baseline model, 60% of drivers can replace their gas cars entirely with 400-mile battery ranges, and less than 40% can do so with 200-mile battery ranges. Even when all the travel needs are satisfied, the optimal battery ranges can still cause range anxiety issues for all the drivers. An additional 25 miles of battery range can help solve the problem based on the improved optimization results.

Range anxiety remains one of the main hurdles to the widespread adoption of electric vehicles (EVs). To mitigate this issue, accurate energy consumption prediction is required. In this study, a hybrid approach is proposed toward this objective by taking into account driving behavior, road conditions, natural environment, and additional weight. The main components of the EV were simulated using physical and equation-based models. A rich synthetic dataset illustrating different driving scenarios was then constructed. Real-world data were also collected using a city car. A machine learning model was built to relate the mechanical power to the electric power. The proposed predictive method achieved an R2 of 0.99 on test synthetic data and an R2 of 0.98 on real-world data. Furthermore, the instantaneous regenerative braking power efficiency as a function of the deceleration level was also investigated in this study.

Range anxiety seriously restricts the development of electric vehicles (EVs). To address the above issue, a hybrid battery swapping system (HBSS) is developed in this paper. In the system, EVs can swap their battery at battery swapping stations or by the roadside via battery swapping vans. The proposed scheduling strategy aims to achieve the best service quality for the HBSS by controlling the mobile swapping service fee. In the model, the uncertainty of EV selection is managed by leveraging the Sigmoid function. Based on proving the uniqueness of the solution, the particle swarm optimization algorithm is used to solve the problem. Simulations validate the effectiveness of the proposed strategy in alleviating range anxiety. Moreover, the impacts of maximum service capacity and the operating rule have been analyzed.

The objective of this study is to identify factors that influence actual electric vehicle (EV) drivers’ acceptance of Vehicle-to-Grid (V2G) charging. The study takes a qualitative approach in order to provide insight into actual EV users’ perceptions of V2G technology and their underlying motivation to accept or not accept V2G. The Theory of Planned Behaviour is adopted to create a basic conceptual model of the potential factors influencing users’ acceptance of V2G. Twenty semi-structured interviews are conducted among Dutch EV drivers, including both regular EV drivers, as well as participants who had previously taken part in V2G projects. The factors that are found to be most important for fostering acceptance are financial compensation, transparent communication and reliable control of the system by the user. On the other hand, the factors that are found to have a negative effect on acceptance are range anxiety, discomfort experienced while participating and battery degradation. Our study shows that the majority of our interview participants accept V2G albeit with some reservations and caution. As EVs and V2G are new technologies, our sample of twenty actual EV users consists of early adopters. As such, their attitudes may not reflect those of the majority of future users. However, our study suggests that there are EV users who are willing to use V2G charge points and will continue to do so. The reasons behind such user acceptance are further described in the study together with additional insights and ideas for future research.

Transportation electrification has been gaining momentum in the past two decades. Ground vehicle electrification is the front-runner of this paradigm shift as electric vehicles slowly but steadily penetrate the consumer market. Besides higher costs, one of the main concerns related to vehicle electrification is range anxiety, which is the result of the limited accessibility of charging ports and stations as well as the relatively low energy density and cycle life of batteries. Lower cost, higher power, and more accessible battery chargers are among potential solutions to address the expense and range anxiety issues.

The pursuit of lower vehicle emissions has allowed the market share of electric vehicles (EVs) to rapidly increase, which has motivated researchers to develop several charging techniques, aiming to minimize the charging downtime and reduce the range anxiety of EV drivers. Of those techniques, dynamic wireless charging (DWC) has gained significant traction due to the substantial amount of work that has been done in wireless charging. However, with DWC, some unforeseen challenges have risen. These include identifying the optimum locations of those DWCs with the objective of maximizing the system efficiency while minimizing the system cost by minimizing the required lengths of charging lanes. This paper focuses on formulating the deployment optimization problem to find the optimum locations, along with the optimum length of DWC lanes at each location, by utilizing traffic simulations and non-linear optimization methods, aiming to maximize the total energy transfer of the system while meeting a given budget.

The ever-growing battery electric vehicle market (BEV) have reached several millions of sales annually, as a result of significant electrification of transport efforts globally. Accurate estimation of energy consumed by BEVs is of major importance especially for charging station selection, route planning and development of energy-efficient driving strategies in both manual and autonomous driving. One of the ways of achieving this is to conduct experiments on real BEVs, which is costly and labor intensive. An alternative to real life experimentation is to design high-fidelity mathematical models for energy consumption of BEVs and feed this model with experimental standardized driving cycles. This study develops and investigates the capabilities and advantages of a generic, detailed, modular BEV model. The effects of wide range of parameters on energy consumption of a BEV are analyzed comprehensively. The study is concluded by discussing the promising potential areas of use of the developed model.

Electric vehicles (EV) are widely seen as a sustainable alternative to gasoline vehicles to decrease emissions and dependence on fossil fuels. However, range anxiety, limited range, long charging t...

Owing to the limited range, battery electric vehicle drivers are likely to worry about being stranded in range‐critical situations, known as range anxiety. Research has revealed that the provision of accurate information through range‐related in‐vehicle information systems establishes a solution to overcome range anxiety. However, an important range‐related information i.e. the battery state of health, is seldom being considered in the research of range anxiety due to the difficulty in daily‐use acquisition. Therefore, this paper aims to explore the influence of state of health on the range anxiety of battery electric vehicle drivers by performing a driving experiment. Under the condition of state of health degradation, participants used simplified battery electric vehicle simulators equipped with different types of in‐vehicle information systems to finish a designated trip in a range‐critical situation. During and after the trip, they measured their experienced range anxiety level. Results show that participants perceive less range anxiety when the in‐vehicle information system provides the remaining range corrected by the state of health, and range anxiety can be further reduced when the extra correlated information (the state of health) is displayed on the in‐vehicle information systems. The findings provide useful inspirations that range anxiety can be reduced if the range‐related in‐vehicle information system is well designed in information content and presentation considering the state of health.

The pursuit of energy density has driven electric vehicle (EV) batteries from using lithium iron phosphate (LFP) cathodes in early days to ternary layered oxides increasingly rich in nickel; however, it is impossible to forgo the LFP battery due to its unsurpassed safety, as well as its low cost and cobalt-free nature. Here we demonstrate a thermally modulated LFP battery to offer an adequate cruise range per charge that is extendable by 10 min recharge in all climates, essentially guaranteeing EVs that are free of range anxiety. Such a thermally modulated LFP battery designed to operate at a working temperature around 60 °C in any ambient condition promises to be a well-rounded powertrain for mass-market EVs. Furthermore, we reveal that the limited working time at the high temperature presents an opportunity to use graphite of low surface areas, thereby prospectively prolonging the EV lifespan to greater than two million miles. Ternary layered oxides dominate the current automobile batteries but suffer from material scarcity and operational safety. Here the authors report that, when operating at around 60 °C, a low-cost lithium iron phosphate-based battery exhibits ultra-safe, fast rechargeable and long-lasting properties.

Electric Vehicle (EV) public charging is important to meet the exploding charging demand and to address the range anxiety issue. In this paper, we focus on the EV public charging market with heterogeneous charging stations (CSs) under the time-based billing model. We jointly consider the charging time optimization for EVs, the EV-CS pairing, and the pricing mechanism for CSs. A hierarchical game, which mathematically corresponds to an equilibrium problem with equilibrium constraints (EPEC), is then developed to formulate the three coupled problems. In the proposed hierarchical game, each CS sets the charging price to maximize its own revenue first, then the EVs choose their desired CSs and determine the charging time. We analyze the optimal charging time strategies for EVs, and a many-to-one matching algorithm is applied to solve the EV-CS pairing problem. Besides, a block coordinate descent (BCD) based algorithm is applied for each CS to solve the pricing problem. Simulation results show that our proposed schemes can achieve the performance improvement of the charging system.

The integration of electrification and intelligence is of great significance to alleviating range anxiety of electric vehicles. Predictive cruise control (PCC), which optimizes the longitudinal driving strategies by using the upcoming road traffic information, can further improve the vehicle economy. The paper gives the comparison of different solution methods regarding PCC problem of electric vehicles. The car-following optimization problem is formulated as a constrained nonlinear optimization problem. For ease of presentation, the car-following optimization problem is reformulated as a standard form of the optimization problem in continuous time domain. Then, the standard form of the optimization problem is transformed from a continuous form to a discrete form by using Euler method and Gauss pseudospectral method. Two common solution methods, that is dynamic programming and sequential quadratic programming, are used to solve the optimization problem in discrete form. Simulations are performed to demonstrate the comparison of different solution schemes.

This paper explores the movement of connected vehicles in Indiana for vehicles classified by the NHTSA Product Information Catalog Vehicle listing as being either electric (EV) or hybrid electric (HV). Analysis of trajectories from July 12-18, 2021 for the state of Indiana observed nearly 33,300 trips and 267,000 vehicle miles travelled (VMT) for the combination of EV and HV. Approximately 53% of the VMT occurred in just 10 counties. For just EVs, there were 9814 unique trips and 64,700 Electric Vehicle Miles Traveled (EVMTs) in total. A further categorization of this revealed that 18% of these EVMTs were on Interstate roadways and 82% on non-interstate roads. Proximity analysis of existing DC Fast charging stations in relation to interstate roadways revealed multiple charging deserts that would be most benefited by additional charging capacity. Eleven roadway sections among the 9 interstates were found to have a gap in available DC fast chargers of 50 miles or more. Although the connected vehicle data set analyzed did not include all EV’s the methodology presented in this paper provides a technique that can be scaled as additional EV connected vehicle data becomes available to agencies. Furthermore, it emphasizes the need for transportation agencies and automotive vendors to strengthen their data sharing partnerships to help accelerate the adoption of EV and reduce consumer range anxiety with EV. Graphics are included that illustrate examples of counties that are both overserved and underserved by charging infrastructure.

The penetration of electric vehicles (EVs) in the transportation sector is increasing but conventional internal combustion engine (ICE) based vehicles dominates. To accelerate the adoption of EVs and to achieve sustainable transportation, the bottlenecks need to be elevated that mainly include the high cost EVs, range anxiety, lack of EV charging infrastructure, and the pollution of the grid due to EV chargers. The high cost of EVs is due to costly energy storage systems (ESS) with high energy density. This paper provides a comprehensive review of EV technology that mainly includes electric vehicle supply equipment (EVSE), ESS, and EV chargers. A detailed discussion is presented on the state-of-the-art of EV chargers that include on-/off-board chargers. Different topologies are discussed with low-/high-frequency transformers. The different available power levels for charging are discussed. To reduce the range anxiety the EV chargers based on inductive power transfer (IPT) are discussed. The last part of the paper focuses on the negative impact of EV chargers along with the remedies that can be adopted. The international standards decided by different institutions and adopted universally are discussed in the latter part of this paper and finally, this paper concludes with the near to future advancement in EV technology.

Battery electric vehicle (BEV) has been thought as a key factor in decreasing global greenhouse gas emissions and energy conservation. Therefore, recent developments in BEVs have heightened the nee...

While battery electric vehicles (EVs) are on the advance, the broad customer basis is still concerned about battery electric range, a phenomenon commonly known as range anxiety. To tackle these concerns, the functionality of in-vehicle navigational systems must adapt to the new propulsion technology with a limited battery capacity. Central aim is to consider charging infrastructure in route planning. Furthermore, detailed powertrain models are required to accurately forecast an EV’s energy consumption. On the other hand, such detailed models are hardly applicable to large scale road networks that are usually handled by routing services for vehicle navigation. This study proposes a two-staged approach to compute time optimal routes for EVs. To this end, a reduced road network is obtained from a leading routing service. Subsequently, a detailed consumption model is applied and the resulting multiobjective shortest path problem is solved using an adapted Moore-Bellman-Ford algorithm. Within an experimental study, the consumption forecast is validated against measurement data and query times of the proposed methodology are assessed for generic routing problems. The former shows significant improvement of consumption forecast accuracy compared to state-of-the-art models while the latter indicates potential for application in car manufacturers vehicle backend services.

Usually, electric vehicle systems are based on various modules that should ensure the high power and stability of the vehicle on the track. The majority of these components are linked to the charging mechanism. In this regard, dynamic wireless power transfer is a practical method to solve electric vehicle range anxiety and reduce the cost of onboard batteries. Wireless recharging has long been common with pure electric vehicles and is designed to allow charging even when the vehicle is in motion. However, it is difficult to analyze this method since its operating philosophy is complex, particularly with the existence of several variables and parameters. Also, the state of the vehicle, whether it is in motion or not, defines several parameters such as the vehicle speed as well as the sizes and dimensions of the coil receivers. This paper presents a novel method to improve the performance of the dynamic wireless recharge system. In the proposed system, receiver coils have been added to maximize charging power by offering a dynamic mathematical model that can describe and measure source-to-vehicle power transmission even though it is in motion. In the proposed mathematical model, all physical parameters describing the model were presented and discussed. The results showed the effectiveness of the proposed model. Also, the experimental tests confirmed the validity of the simulation results obtained by providing two coil receivers under the vehicle.

To overcome range anxiety problem of electric vehicles (EVs), an accurate real-time energy consumption estimation is necessary, which can be used to provide the EV’s driver with information about the remaining range in real time. A hybrid CNN–BDT approach has been developed, in which convolutional neural network (CNN) is used to provide an energy consumption estimate considering the effect of temperature, wind speed, battery’s SOC, auxiliary loads, road elevation, vehicle speed and acceleration. Further, bagged decision tree (BDT) is used to fine-tune the estimate. Unlike existing techniques, the proposed approach does not require internal vehicle parameters from manufacturer and can easily learn complex patterns even from noisy data. The comparison results with existing techniques show that the developed approach provides better estimates with least mean absolute energy deviation of 0.14.

Range anxiety has remained a critical technological bottleneck in electric vehicles (EVs). The issue lies in the fact that the estimation of the EV's batteries state of charge (SoC) is still inaccurate and unreliable due to the complex and nonlinear characteristics of the batteries. To tackle the problem, this paper presents a novel real-time mixed SoC estimation algorithm for the EV's lithium-ion batteries for implementation in an advanced driver assistance system (ADAS). The mixed estimation algorithm combines: i) an improved Coulomb counting method (CCM) that takes into account the battery state-of-health, operating temperature, and aging effect, ii) a model-based method (MBM) that represents a real-time recursive structure of the battery, and iii) a bottom-up based method (BUBM) that takes into account a variety of the environmental conditions, traffic conditions, auxiliary loads, and driver's behavior. To validate the proposed algorithm, several laboratory tests under real-time driving cycles have been conducted on a Manganese-oxide Li-ion cell of a 2012 Nissan Leaf battery cell. Furthermore, the effectiveness of the model has been demonstrated by driving the Nissan Leaf along a selected route in Australia. The results demonstrate a great accuracy for the SoC estimation compared to previous models.

The adoption of electric vehicles has the potential to help decarbonizing the transport sector if they are powered by renewable energy sources. Limitations commonly associated with e-cars are their comparatively short ranges and long recharging cycles, leading to anxiety when having to travel long distances. Other factors such as temperature, destination or weekday may influence people in choosing an e-car for a certain trip. Using a unique dataset of 129 people who own both an electric vehicle (EV) as well as one powered by an internal combustion engine (ICE), we analyze tracking data over a year in order to have an empirically verified choice model. Based on a wide range of predictors, this model tells us for an individual journey if the person would rather choose the EV or the ICE car. Our findings show that there are only weak relations between the predictor and target variables, indicating that for many people the switch to an e-car would not affect their lifestyle and the related range anxiety diminishes when actually owning an electric vehicle. In addition, we find that choice behavior does not generalize well over different users., AGILE: GIScience Series, 1, ISSN:2700-8150, 23rd AGILE Conference on Geographic Information Science

This paper investigates the problem of a combined study on both electric vehicle (EV) drivers’ behavior in charging station selection (DSS) and station’s behavior in charging service management (CSM) that are considered in the proposed DSS-CSM algorithm. The motivation behind the proposed algorithm is to comprehensively describe a charging market from both drivers’ point of view to reduce range anxiety and enhance customer satisfaction and charge station providers’ point of view to manage charging station properly and improve profitability. The proposed DSS algorithm obtains all the required data to find the best station in terms of the influential parameters given by the candidate stations. A fuzzy multi-criteria decision-making (FMCDM) method that is utilized in the proposed DSS algorithm is deployed for evaluating the parameters and deciding for the best charging station. Moreover, the proposed CSM algorithm of each charging station is utilized to maximize its profitability by taking into account the charge, reserve, and discount prices with respect to the reaction of EV drivers. The Monte Carlo simulations are applied to demonstrate the effectiveness of the proposed algorithm in comparison with a designed benchmark algorithm.

Range anxiety is one of the problems that hinder the large-scale application of electric vehicles (EVs). We propose a driving-behavior-based State-of-Charge (SoC) prediction (DBSP) algorithm to overcome this problem. This algorithm can determine whether drivers can reach their destinations while also predicting the SoC if drivers were to return the trip. First, two supercapacitor equivalent circuit models are established with one based on the historical average power and the other based on the equivalent current, which is proposed in this algorithm. Then, based on the equivalent transformation of the two models, an analytical expression relating the historical average power and the predicted SoC is derived by using the equivalent current as a “bridge.” Therefore, the predicted SoC can be dynamically adjusted in response to recorded historical data, including the output power, speed, and distance of EVs powered by supercapacitors. The simulation results demonstrate that the total prediction error is less than 0.5% of the real SoC at different initial SoC and temperature, which represents idealized behavior-based driving. In contrast, in actual driving experiments, the total prediction error is less than 3% of the real SoC at different initial SoC and temperature.

This study addresses the location problem of electric vehicle charging stations considering drivers’ range anxiety and path deviation. The problem is to determine the optimal locations of EV charging stations in a network under a limited budget that minimize the accumulated range anxiety of concerned travelers over the entire trips. A compact mixed-integer nonlinear programming model is first developed for the problem without resorting to the path and detailed charging pattern pre-generation. After examining the convexity of the model, we propose an efficient outer-approximation method to obtain the e-optimal solution to the model. The model is then extended to incorporate the charging impedance, e.g., the charging time and cost. Numerical experiments in a 25-node benchmark network and a real-life Texas highway network demonstrate the efficacy of the proposed models and solution method and analyze the impact of the battery capacity, path deviation tolerance, budget and the subset of OD pairs on the optimal solution and the performance of the system.

Vehicle-to-Grid (V2G) describes the concept of using the battery packs of parked electric vehicles (EVs) as distributed battery energy storage to support the power grid. The bi-directional power flow in a V2G network is managed by a so-called aggregator which has to consider the needs of both the power grid and connected EVs. ‘Range anxiety’ is a major factor holding back the EV market as many drivers consider range limitations as a major disadvantage of EVs over more traditional vehicles with internal combustion engines. Implementing V2G could potentially worsen this perception as connected EVs could actually lose charge over short periods when providing grid services. Enabling EV owners/users to state charging preferences via simple user interfaces might increase the acceptance of V2G and reduce range anxiety. The inclusion of user charging preferences puts further constraints on an aggregator and complicates charge/discharge scheduling. Taking such preferences into account and weighting them against (potentially conflicting) needs of the power grid is a major challenge for V2G scheduling and adds computational costs through increased complexity. However, depending on its application, the responsiveness of a V2G network is crucial and the scheduling process is hence time-sensitive. In this research, a method of capturing user charging preferences and accounting for the resulting constraints is presented. The impact of added complexity on the execution time of the scheduling process is addressed by resolving user inputs prior to the actual scheduling. The ability of each EV to either charge or discharge is scored taking into account user inputs and the current state of the battery pack. This scoring system informs the selection of suitable EVs before the scheduling process.

The goal of this work is to reduce driver’s range anxiety by estimating the real-time energy consumption of electric vehicles using deep convolutional neural network. The real-time estimate can be used to accurately predict the remaining range for the vehicle and hence, can reduce driver’s range anxiety. In contrast to existing techniques, the non-linearity and complexity induced by the combination of influencing factors make the problem more suitable for a deep learning approach. The proposed approach requires three parameters namely, vehicle speed, tractive effort and road elevation. Multiple experiments with different variants are performed to explore the impact of number of layers and input feature descriptors. The comparison of proposed approach and five of the existing techniques show that the proposed model performed consistently better than existing techniques with lowest error.

The global pressure to cut carbon dioxide emissions from automobiles is driving more and more buyers toward electric vehicles (EVs) and hybrid EVs (HEVs). As the market for EVs and HEVs slowly grows with lithium-ion (Liion) as the battery technology of choice, for reasons well known, there is another technology emerging. Researchers are developing a solid-state (SS) version of Li-ion batteries for EVs that promises to charge and discharge rapidly, offer longer lifecycles, provide a much higher energy density, cost less, and provide greater safety. Besides the performance improvement, safety is a major factor driving automakers toward SS technology. In SS batteries (SSBs), the flammable liquid electrolyte, which passes the charge that carries Li ions during charge and discharge cycles, is replaced by a solid electrolyte. These ongoing improvements in battery technologies will pave the way for an installed EV base of 100 million vehicles by 2028, according to global technology market advisory firm ABI Research, Oyster Bay, New York. In fact, ABI's principal analyst James Hodgson says, "Lithium-silicon and SS are the future EV battery technologies that will improve performance, hold more energy, and last longer at a lower cost. The addition of silicon alone over the next seven years will grow the EV installed base from 8 million vehicles in 2019 to 40 million in 2025, as consumers' range anxiety slowly eases" [1].

Both China’s national subsidy policies for plug-in electric vehicles (PEVs) purchasers and passenger cars corporate average fuel consumption and new vehicle credit regulation (dual-credit policy) favor long-range 300+ km battery electric vehicles (BEVs) and 80+ km plug-in hybrid electric vehicles (PHEVs). However, these electric vehicles tend to have lower energy efficiency and higher purchase and operation costs. Vehicle with larger batteries can also be less equitable because the subsidies are often provided to more expensive vehicles and wealthier owners. This study takes advantage of a novel dataset of daily driving data from 39,854 conventional gasoline vehicles in Beijing and 4999 PHEVs in Shanghai to determine the optimal range of BEVs and PHEVs within their respective cities. We simulate a model to explore ranges with which PEVs emit less GHGs than that of a baseline hybrid and conventional gasoline vehicle while ensuring that all daily travel demands are met. Our findings indicate that in both cities, the optimal ranges to balance cost and travel demand for BEVs are 350 km or less and for PHEVs are 60 km or less in Beijing and 80 km or less in Shanghai. We also find that to minimize carbon dioxide (CO2) emissions, the ranges are even lower 10 km in Beijing and 30 km in Shanghai. Our study suggests that instead of encouraging long-range PEVs, governments should subsidize PEV models with shorter ranges. Parallel efforts should also be made to both increase renewable energy over fossil fuels and expand charging facilities. Although individual mobility demand varies, the government could reduce occasional long-distance driving by subsidizing alternative transportation choices. Providing week-long driving trials to consumers before their purchases may help decrease the demand of very long range PEVs by alleviating the range anxiety through a learning process.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

Lithium-ion batteries have been treated as the most efficient energy storage candidates in electric vehicles. However, range anxiety hinders its popularity in daily transportation. When compared with the extensively discussed state-of-charge, state-of-energy (SoE) is a more appropriate indicator of remaining driving mileage by considering the voltage degradation in the discharging process. In this paper, an equivalent circuit model (ECM) based SoE estimator is proposed where a pseudo power definition is utilized to exclude the internal loss influence, and an unscented particle filter is employed to address system nonlinearities and noises. Additionally, to accommodate battery time-variant characteristics, the ECM parameters and variables are decoupled in complex-frequency domain, upon which the recursive least square algorithm is utilized for the on-line identification of parameters. Finally, a series of validation experiments on a LiFePO4 battery verify that the proposed methodology can track the real SoE closely with an error of less than 1.8%.

Electric vehicles (EVs) are emerging as a favorable strategy to meet the increasing environmental concerns and energy insufficiency, and this trend is expected to grow in the near future. However, the inadequate charging infrastructure is becoming a major barrier to the wide acceptance of EVs. Deployment of this infrastructure is expected to maximize the adoption of EVs to facilitate users' range anxiety. Therefore, connectivity between the charging stations (CS) is mandatory. Understanding the real-time status of CSs can provide valuable information to users such as availability of charging provisions, reserves and the time to reach the CS. The intent of this paper is to provide a better EV charging system by utilizing the advantages of the Internet of Things (IoT) technology. The IoT paradigm offers the present facilities a real-time interactional view of the physical world by a variety of sensors and broadcasting tools. This research article proposes a real-time server-based forecasting application: i) to provide scheduling management to avoid waiting time; and ii) to provide a real-time CS recommendation for EVs with an economic cost and reduced charging time. In addition, the proposed scheme avoids third-party intervention and protects EV user privacy and complex information exchange between the user and CS. The end users can easily use the CS based on their requirements. This synergetic application is built up through the PHP programming language in the Linux UBUNTU 16.04 LTS operating system, and all relevant information is processed and managed through Cloud Structured Query Language (CSQL) from a Google cloud platform. The effectiveness of this application is also validated through a low-cost test system using LTC 4150, ESP 8266 Wi-Fi module and Arduino.

‘Range anxiety’ is considered a barrier to market acceptance of electric vehicles (EVs), but perceptions of inadequate recharging infrastructure and limited battery range may be part of a more complex set of consumer concerns; experiences and practices of existing EV drivers – with reference to wider transport and energy systems – need to be understood in greater detail. Qualitative material from semi-structured interviews [n = 88] is used to formulate a chronological Typology of Electric Vehicle Driving which synthesises drivers’ decisions, actions and dynamic considerations before, during and after a journey. The typology outlines and tabulates spatio-temporal elements specific to driving an EV; describes adaptations and compromises in household routines and travel habits; draws out how or when drivers engage with external digital information sources – i.e. smartphone apps, social media – during journey-making; plots their intervention in behavioural changes; and could inform development of infrastructure, tools or services, policy or interventions to support EV uptake.

Alternate Energy vehicles mainly Battery Electric Vehicle (BEV) is becoming more commonplace in the mobility space globally and in India. BEVs are slowly picking up in Indian market and likely to grow due to stringent Government regulations, Green Mobility Initiatives; however, there are some challenges in implementing BEV in India on mass production like Driving Range, Battery cost, Lack of Charging Infrastructure, Customer Pain points in EV Purchase. This paper is focused on reviewing all the useful data available on EV trends in global and Indian scenario, EV Purchase Pain Points, Range Anxiety & factors Influencing EV Range, Technology/Strategies available for addressing Range Anxiety.

On June 30th of 2019, China Association for Science and Technology announced the list of twenty questions that are considered either of guiding importance for frontiers of sciences or playing crucial roles in technological and industrial innovation. Among which the question 4 is “materials electrochemistry for high energy density power batteries”. This article is an interpretation of this question, including a basic overview of the current technology, industry and market of lithium ion battery for powering electric vehicles, the trends of future development, and where the scientific problems are. Along with the rapid economic development in the last a few decades, the vehicle market in China has grown dramatically in recent years. The total number of vehicles in China is already exceeding 0.2 billion, which makes twenty percent of the total number in the world. And this number keeps increasing at a fast pace since the number of vehicle per capital in China is still much lower than those of developed countries (the average number of vehicles per thousand person at year 2016: China-140, USA-797, Japan-591, Korea-376). The huge number of vehicles in China consume a large amount of fuel oil (about 50% of the total fuel oil consumption of China every year are attributed to vehicles), which is prepared from petroleum, a scarce resource in China. Fossil fuel consumption also causes severe air pollution that threatens people’s health and results in global environmental concerns considering the consequences arising from the excessive emission of Greenhouse Gases such as CO2. Development of electric vehicles (EVs) to replace fossil fuel consuming vehicles is a promising solution to tackle the fossil energy exhaustion, global warming, and environment pollution problems, realizing a low-carbon society, and is also an opportunity for China to develop the automobile industry. However, the energy density of the current power batteries used in the EVs is still much lower than that of the fossil energy, which causes the so-called “range anxiety” in EV drivers, hindering the mass-market penetration of EVs. Therefore, overcoming the “range anxiety” by developing higher energy density power batteries is a critical technological challenge for innovative revolution of the vehicle market. Usually, driving ranges of at least 500 km are required for EVs to achieve wide customer acceptance. However, the state-of-the-art lithium-ion battery, which has the highest energy density among all mass-produced rechargeable battery systems, barely meets this requirement when battery safety and cycle lifetime are also concerned. Since the energy density of rechargeable batteries is in principle determined by the active anode and cathode materials, the development of new materials chemistry and electrochemistry is the central scientific task for the popularization of EVs. A survey of currently or potentially available active materials is thus helpful in making a technologically sound forecast of battery energy density as well as in setting up EV development roadmap. In this paper, we briefly go through the materials electrochemistry for current and future active materials for lithium ion batteries and lithium metal batteries, thus including both intercalation and conversion reaction mechanisms, and assess the potential of these active materials in the coming decade to achieve energy density as high as 300 Wh/kg or even higher. The scientific questions and technological challenges that need to be addressed to achieve the goal are analyzed and emphasized. We hope this article will help the readers to unravel the complex power battery market, and potentially inspire researchers in the field to solve the “materials electrochemistry for high energy density power batteries” problem.

This paper proposes an energy-efficient predictive cruise control (PCC) system to cope with range anxiety of automated electric vehicles. The proposed approach is formulated as an optimal control problem to realize better energy efficiency and ensure safe inter-vehicle distance. To improve computational efficiency, a fast algorithm combining Gauss pseudospectral method (GPM) and moving horizon control (MHC) is introduced to solve this nonlinear optimal problem. The comparative simulation results reveal that the energy economy of the PCC system is improved about 4.1%, and its computation time is reduced compared with the Euler method while ensuing the same accuracy.

We report an integrated methodology (FMC-XGBoost) that mainly consists of a five-state nonhomogeneous Markov chain model (FMC) and XGBoost model. Unlike those existing methods in which capacity fading processes are assumed to be irreversible, the proposed integrated methodology can combine user-specific driving patterns (UDP) and capacity recovery effects (CRE) to predict battery fading dynamics even with partially available data for an individual battery. The parameters of the constructed FMC model are linked to the known physicochemical and material properties of Li-ion battery fading dynamics, which aims to cognize and predict the primary fading dynamics, and the proposed XGBoost model is to cognizes and predicts the fluctuation dynamics regarding UDP & CRE. To comprehensively verify the capabilities of the proposed integrated methodology, a series of cases and comparisons are conducted and analysed based on partial available fading data by selecting batteries to simulate situations of individual differences and different UDPs & CREs. The averages of MAE, MRE and RMSE are approximately 0.0128, 0.9251%, and 0.0153 respectively even when only 60% of the data are available. All verifications and comparison analyses reveal that the proposed integrated methodology provides an accurate, robust, stable, and general way to cognize and predict battery fading dynamics during usage, and subsequently to alleviate range anxiety for batteries in real applications.

During the transition from conventional towards purely electrical, sustainable mobility, transitional technologies play a major part in the task of increasing adaption rates and decreasing range anxiety. Developing new concepts to meet this challenge requires adaptive test benches, which can easily be modified e.g. when progressing from one stage of development to the next, but also meet certain sustainability demands themselves. The system architecture presented in this paper is built around a service-oriented software layer, connecting a modular hardware layer for direct access to sensors and actuators to an extensible set of client tools. Providing flexibility, serviceability and ease of use, while maintaining a high level of reusability for its constituent components and providing features to reduce the required overall run time of the test benches, it can effectively decrease the CO2 emissions of the test bench while increasing its sustainability and efficiency.

One of the most serious obstacles to massive deployment of electric vehicles is insufficient and cumbersome charging infrastructure. Both the number of charging points and their power capacity are often insufficient. On top of that, this new technology often suffers from many issues related to insufficient testing, immaturity and irregular handling. This paper summarizes the issues with the electric vehicle charging infrastructure and describes first-hand experiences with long-range electric vehicle trips originating from Zagreb, Croatia, during 2022. Issues with the charging points locations, power and availability are assessed. Based on these experiences, the paper provides some thoughts on the possible directions of the further development of the electric vehicle charging infrastructure in Croatia.