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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.

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.

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.

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.

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.

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.

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.

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].

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.

Electric vehicles have the potential to change the global driving paradigm, significantly reducing the environmental impact of transportation systems. However, charging infrastructure cost and range anxiety impose challenges on rapid technology adoption. This paper proposes an onboard integrated dc charger, leveraging the traction inverter, and motor winding inductance to ensure the minimum incremental mass. The proposed circuit allows the vehicle to connect directly to emerging dc microgrids, utility storage, or renewable energy resources. The system is compatible with power supply voltages higher or lower than the vehicle battery, offering the possibility to charge 400-V batteries from emerging 1000-V supplies, as well as 800-V battery from the existing 600-V dc interfaces. The presented method also provides bidirectional fault blocking capability and bidirectional power transfer, suitable for V2G, G2V, or V2V operation. In addition, this paper proposes simultaneous driving and charging, especially useful for semitrailer trucks applications, significantly increasing operational range. The solution presented in this paper offers a simple and safe charging scheme with the potential to substantially reduce charging infrastructure cost and address range anxiety.

Limited range and charging infrastructure leads to range anxiety of electric vehicle drivers. Current range estimation algorithms are deemed unreliable and large safety margins are reserved to prevent the risk of stranding. Range estimation in general depends on two factors: current battery energy content and the energy consumption forecast on the route to destination. This paper aims at improving the latter by enhancing the forecast with a notion of uncertainty. The prediction algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts. Thereby, a central part of the algorithm is the explicit evaluation of the traffic situation by classifying the traffic phases. With the help of this methodology, individual forecasts can be made more precise since they are highly dependent on surrounding traffic. To demonstrate the validity of the algorithms, the performance is evaluated using real test drive data comprising multiple drivers. On the basis of the performance evaluation, both the superiority of stochastic algorithms over deterministic predictions and the improvement of predictive performance by evaluating explicit traffic phases can be shown. Implementing the proposed methodology in modern day electric vehicles could reduce range anxiety and ultimately increase acceptance of electric mobility worldwide.

The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user's attitude toward them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one-third of potential European users are deterred by this problem when considering the move to an EV. A similar consideration applies to aerial EVs for commercial use, where a careful planning of the flying range is essential not only to guarantee the service but also to avoid the loss of the EVs due to charge depletion during the flight. Therefore, route planning for EVs for different purposes (range estimation, route optimization) and/or application scenarios (terrestrial, aerial EVs) is an essential element to foster the acceptance of EVs as a replacement of traditional vehicles. One essential element to enable such accurate planning is an accurate model of the actual power consumption. While very elaborate models for the electrical motors of EVs do exist, the motor power does not perfectly match the power drawn from the battery because of battery non-idealities. In this paper, we propose a general methodology that allows to predict and/or optimize the operation range of EVs, by allowing different accuracy/complexity tradeoffs for the models describing the route, the vehicle, and the battery, and taking into account the decoupling between motor and battery power. We demonstrate our method on two use cases. The first one is a traditional driving range prediction for a terrestrial EV; the second one concerns an unmanned aerial vehicle, for which the methodology will be used to determine the energy-optimal flying speed for a set of parcel delivery tasks.

With the widespread growth in the use of electric vehicles (EVs), there is an urgent demand for fast-charging battery charger so as to alleviate the range anxiety for car users. A novel multipulse flexible-topology thyristor-based rectifier (mPFTTR) is proposed in this paper, which can be a suitable candidate for high-power fast-charging battery charger of the EVs. The proposed architecture has five operating modes: Series mode with 24 pulses (SM24P), parallel mode with 24 pulse, hybrid modes with 24 pulses (HM24P), series mode with 12 pulses, and parallel mode with 12 pulses. To meet the practical requirement of fast charging, the predictive current control strategy combined with multistep constant current charging (MSCCC) is also proposed, and followed by the detailed implementation and analysis. Simulation and experimental verifications are carried out, and both results show that the predictive current control strategy combined with the MSCCC method can charge the EVs with wide-range input voltage, and fast charging is also achieved. Finally, efficiency evaluation and comparisons of total harmonic distortions of the input current are developed between the proposed mPFTTR which operates in HM24P and the conventional thyristor-based rectifier that operates in SM24P.

Electric vehicles (EVs) have been considered as a feasible solution to deal with the high fuel consumption and greenhouse gas emissions caused by conventional vehicles. However, long charging times and drivers’ range anxiety are the main disadvantages of EVs. A key factor that is expected to mitigate these problems and facilitate the wide adoption of EVs will be the effective operation of fast charging stations (FCSs). In this paper, the operation of a FCS is evaluated, in terms of operator’s profits and customers’ waiting time in the queue. The FCS contains both dc and ac outlets that provide high power levels, while the various EV models are classified by their battery size and the fast charging option they use (dc or ac). The operator’s daily profits and the queue waiting time are initially computed by considering that the EVs recharge under a flat-rate pricing policy. In order to avoid a long queue build-up at the FCS, a new pricing policy is then proposed. The intuition behind the scheduled pricing policy is that users are deterred to charge more than an arranged energy threshold, thus reducing the load and the waiting time at the FCS.

Knowledge of the remaining driving range is crucial for drivers of battery electric vehicles (BEVs), because maximum range and charging infrastructure are limited. This and the fact that current implementations of range displays are deemed unreliable leads to range anxiety and ultimately less acceptance of BEVs. Every range estimation algorithm depends on various uncertain inputs and hence, its result is also uncertain. A more reliable range estimation could be achieved by assessing uncertainties of the underlying predictions and measurements and their influence on the calculation result. This paper presents a method for accurately calculating the probability of reaching the destination of a given route. For this, uncertainty of future velocity and battery state is analyzed using the example of an actual test vehicle. The error propagation through a physical model of the powertrain is evaluated by means of Monte-Carlo method and Taylor series expansion. Range uncertainty is investigated for different sets of input parameters, yielding a necessary safety margin between 12% and 23% battery charge. It is also shown that uncertainty more than doubles over lifetime of the vehicle and that neglecting this uncertainty can lead to unpredictable stranding for disadvantageous initial conditions. Knowledge of range uncertainty can reduce the necessary safety margin and avoid stranding of vehicles. In addition, displaying the uncertainty information to the driver alleviates range anxiety of drivers and consequently, acceptance of electric vehicles can be raised in society.

A vehicle-to-vehicle (V2V) energy swapping strategy can provide an alternative fast charging way for plug-in electric vehicles (PEVs) to relieve the charging overload problem in the power system during peak-demand hours. The main challenges in designing an efficient V2V energy swapping strategy are: 1) to stimulate mobile PEVs to participate in an energy swapping transaction that balances supply with demand at aggregators and 2) to achieve optimal energy utilization and individual PEVs’ profits. In this paper, we present a novel smart grid architecture with enhanced communication capabilities for mobile PEVs, via a heterogeneous wireless network-enhanced smart grid. We propose an online V2V energy swapping strategy based on price control. Specifically, mobile PEVs with surplus energy are motivated by getting paid to contribute to a V2V energy swapping transaction at aggregators with energy-hungry PEVs. To evaluate the performance of the proposed V2V energy swapping strategy, a realistic suburban scenario is developed in VISSIM to track the PEVs’ mobility using the generated simulation traces. Extensive simulation results are given to demonstrate the efficacy of the proposed V2V energy swapping strategy.

This paper introduces an adaptive multiresolution framework for electric vehicle (EV) energy consumption estimation with real-time capability. Three key parameters, namely powertrain efficiency, wind speed, and rolling resistance, are adaptively estimated using a two-step nonlinear iterative algorithm. Based on this algorithm, a multichannel framework for high-resolution powertrain efficiency estimation is introduced. Employing the “connected vehicles” concept, more reliable trip level energy estimates are achieved by sharing sensed environmental information. In addition, state-of-charge aware energy cost prediction methods of different accuracy and complexity are introduced to combat range anxiety and reduce computational complexity during times of high energy reserves. A variety of detailed simulations illustrate the introduced concept and its benefits for future EV systems.

Electric vehicles (EVs) require a well-developed charging infrastructure. Particularly when used for the daily commute, most EV drivers will rely on a nightly charge in their garage, for instance. In typical European urban residential areas, however, private parking and charging resources are severely limited. Therefore, public on-street charging often is the only option. However, it faces several limitations that lead to an inefficient and unfair utilization of charging stations, or EV supply equipment (EVSE). For instance, EVSEs are often blocked by fully charged vehicles. We thus propose and evaluate a cooperative protocol for EVs that facilitates coordinated handovers of EVSEs. We integrate this protocol with the ISO 15118 standard and provide a detailed security analysis. In the evaluation, we show that coordinated handovers significantly improve both EVSE utilization (helping to amortize the expensive operating costs) and provide benefits for EV owners by providing sufficient charging resources. This reduces range anxiety and saves them from cruising for charging.

With the increasing popularity of electric vehicles, there is an urgent demand to shorten the charging time, so the development of high-power charging stations with fast chargers is necessary to alleviate range anxiety for drivers. The charging station based on the neutral-point-clamped (NPC) converter can bring many merits, but it has unbalanced power problems in the bipolar dc bus. To solve this issue, comprehensive dc power balance management (PBM) in conjunction with high-power three-level dc–dc converter based fast charger is proposed in this paper. The active dc power balance management (APBM) is proposed to assist the central NPC converter in balancing power so that the additional balancing circuit is eliminated; while the passive dc power balance management (PPBM) is proposed to eliminate the fluctuating neutral-point currents and to ensure the balanced operation of fast chargers. The principles of APBM and PPBM are researched, the efficient integration between them is studied, and the overall control scheme for the fast charger is proposed. The power balance limits of APBM are explored, while the circulating currents of PPBM are analyzed. Simulation and experimental results are presented to verify the effectiveness of the proposed fast charger with PBM functions.

Electric vehicles numbers are increasing but they still have issues with range between charges. Battery pack energy density is still low compared to the density of fossil fuel so it is advantageous to offer more recharging windows. This will also reduce consumer “range anxiety”. Automatic inductive charging technology can connect the vehicle to the grid during very short stops, or even during on-road use. The core system device is the charging coupler which transfers power from grid to vehicle. It is a medium-frequency loosely-coupled transformer and the efficiency is critical to the charging process. The loss in the coupler can be categorized into core loss and copper loss. Litz-wire can be used to reduce eddy current loss. This paper analyzes this loss and the AC resistance of multiconductor wires and calculates the magnetic field within the winding. The copper loss of the coupler is then estimated.

Coordinated charging is an effective charging plan for PEVs to improve the overall system energy utilization and prevent the overload of an electric power grid. On the other hand, PEVs, which have energy storage and controllable loads, can be discharged to help the grid to smooth the fluctuations, for example, introduced by distributed generators (DGs). Either to prevent overloading or to regulate the power grid, most existing charging/discharging plans focus on temporal charging/discharging coordination for parked vehicles. However, for moving vehicles, spatial coordination can also bring benefits to the grid. For spatial coordination, the range anxiety problem should be carefully handled, since PEVs cannot reach some charging stations due to the limited battery levels. In this article, by exploiting both spatial and temporal coordinations, we introduce an online PEV charging/discharging strategy considering range anxieties. To collect real-time information for the proposed online strategy, a heterogeneous wireless infrastructure is proposed by integrating cellular networks with vehicular ad-hoc networks (VANETs). Challenging issues are discussed in terms of modeling PEV mobility, network selection for real-time information delivery, balancing the trade-off between the grid power utilization and drivers’ preferences, and modeling business revenues for charging/discharging. Case studies demonstrate that joint spatial and temporal charging coordination can effectively improve power utilization and avoid overloading the power grid.

Electric Vehicles (EVs) are quickly gaining widespread interest as attractive alternatives to conventional petrol driven vehicles. If EVs can derive some or all of their electrical power from renewable sources, then they can significantly help to reduce global dependence on fossil fuels for transportation. Inductive power transfer (IPT) is a method that can transfer power to EVs over an air-gap without physical contact. If IPT systems are incorporated into the highway networks, then EVs can be charged dynamically as they travel. This will dramatically increase the range, convenience and safety of EV charging as well as reduce range anxiety and the required battery bank capacities. However one of the major difficulties involves accurately detecting an EV as it travels along the IPT highway. By detecting the approaching EV, the primary power supply can enable power transfer by energizing the primary IPT coupler buried in the roadway just before the EV is ready to efficiently couple power from the road. A three coil detection system is proposed to allow the primary power supply to detect the approaching secondary IPT coupler mounted on the EV. The proposed detection method is tested on a laboratory scale prototype and can detect EVs approaching at different speeds, ground clearances and horizontal misalignments.

This paper presents a mobile information system denominated as Vehicle-to-Anything Application (V2Anything App), and explains its conceptual aspects. This application is aimed at giving relevant information to Full Electric Vehicle (FEV) drivers, by supporting the integration of several sources of data in a mobile application, thus contributing to the deployment of the electric mobility process. The V2Anything App provides recommendations to the drivers about the FEV range autonomy, location of battery charging stations, information of the electricity market, and also a route planner taking into account public transportations and car or bike sharing systems. The main contributions of this application are related with the creation of an Information and Communication Technology (ICT) platform, recommender systems, data integration systems, driver profile, and personalized range prediction. Thus, it is possible to deliver relevant information to the FEV drivers related with the electric mobility process, electricity market, public transportation, and the FEV performance., Fundação para a Ciência e Tecnologia (FCT)

An optimization model is proposed to manage a residential microgrid including a charging spot with a vehicle-to-grid system and renewable energy sources. In order to achieve a realistic and convenient management, we take into account: (1) the household load split into three different profiles depending on the characteristics of the elements considered; (2) a realistic approach to owner behavior by introducing the novel concept of range anxiety; (3) the vehicle battery management considering the mobility profile of the owner and (4) different domestic renewable energy sources. We consider the microgrid operated in grid-connected mode. The model is executed one-day-ahead and generates a schedule for all components of the microgrid. The results obtained show daily costs in the range of 2.82 ${\rm C}\!\!\!\!{\scriptstyle ^{=}}$ to 3.33 ${\rm C}\!\!\!\!{\scriptstyle ^{=}}$ ; the proximity of these values to the actual energy costs for Spanish households validate the modeling. The experimental results of applying the designed managing strategies show daily costs savings of nearly 10%.

In this article, we have reviewed the state of the art of IPT systems and have explored the suitability of the technology to wirelessly charge battery powered vehicles. the review shows that the IPT technology has merits for stationary charging (when the vehicle is parked), opportunity charging (when the vehicle is stopped for a short period of time, for example, at a bus stop), and dynamic charging (when the vehicle is moving along a dedicated lane equipped with an IPT system). Dynamic wireless charging holds promise to partially or completely eliminate the overnight charging through a compact network of dynamic chargers installed on the roads that would keep the vehicle batteries charged at all times, consequently reducing the range anxiety and increasing the reliability of EVs. Dynamic charging can help lower the price of EVs by reducing the size of the battery pack. Indeed, if the recharging energy is readily available, the batteries do not have to support the whole driving range but only supply power when the IPT system is not available. Depending on the power capability, the use of dynamic charging may increase driving range and reduce the size of the battery pack.