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Abstract It is a critical problem to improve energy efficiency for electric vehicle platooning systems. Moreover, different from internal combustion engine vehicles, the electric engine has higher efficiency, and further regenerating braking is widely used to recycle part of the energy in the electric vehicle when it is braking. What is more, if vehicles take a formation to drive, they can save more energy. Combining all the favorable factors, this paper presents a two‐layer energy‐efficiency optimization strategy for electric vehicle platooning. The upper layer presents an optimization method to find the optimal velocities and distances between vehicles under different road conditions during the cruise status of the electric vehicle platooning. Due to the nonconvex cost function and considering regenerative braking, the optimization problem is addressed by the dynamic programming method combined with the successive convex approximation method. Further, the lower layer presents a real‐time Model Predictive Control (MPC) strategy, and it directly introduces the battery pack state of charge consumption as the input, which not only finishes the control mission but also consumes minimal energy. Finally, simulation results are provided to verify the effectiveness and advantages of the proposed methods.

It is a critical problem to improve energy efficiency for electric vehicle platooning systems. Moreover, different from internal combustion engine vehicles, the electric engine has higher efficiency, and further regenerating braking is widely used to recycle part of the energy in the electric vehicle when it is braking. What is more, if vehicles take a formation to drive, they can save more energy. Combining all the favorable factors, this paper presents a two‐layer energy‐efficiency optimization strategy for electric vehicle platooning. The upper layer presents an optimization method to find the optimal velocities and distances between vehicles under different road conditions during the cruise status of the electric vehicle platooning. Due to the nonconvex cost function and considering regenerative braking, the optimization problem is addressed by the dynamic programming method combined with the successive convex approximation method. Further, the lower layer presents a real‐time Model Predictive Control (MPC) strategy, and it directly introduces the battery pack state of charge consumption as the input, which not only finishes the control mission but also consumes minimal energy. Finally, simulation results are provided to verify the effectiveness and advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract In vehicle‐to‐grid (V2G) networks, electric vehicle (EV) batteries have significant potential as storage elements to smooth out variations produced by renewable and alternative energy sources and to address peak demand catering to smart grids. State estimation and management are crucial for assessing the performance of EV batteries. Existing approaches to these tasks typically do not include the effect of various parameters like route type, environmental conditions, current, and torque to estimate the state of charge (SoC) of EV batteries. In experiments, it is observed that the overall driving cost is affected by these parameters. A new method based on deep reinforcement learning is proposed to estimate and manage the SoC of nickel‐metal hybrid batteries, with an emphasis on the realisation of the parameters that affect a battery's health. The proposed deep deterministic policy gradient‐based SoC estimation and management for EV batteries, under the effect of battery parameters, are compared with the existing state‐of‐the‐art models to validate their usefulness in terms of overall battery life, thermal safety, and performance. The proposed method demonstrates an accuracy of up to 98.8% in SoC estimation and overall driving cost with less convergence time as compared to the state‐of‐the‐art models for EV batteries.

Temperature monitoring is critical for estimating the available capacity of Lithium‐ion batteries. In electric vehicle applications using large‐scale battery packs, monitoring individual cell temperature is challenging due to difficulties in sensor management. To address this issue, a sensor‐less battery temperature prediction technique is proposed that ensures both accuracy and rapid runtime execution using deep learning. A deep neural network‐based temperature prediction model is introduced that utilizes short sequences of battery voltage and discharge current. An adaptive sequence length strategy is then devised to ensure high accuracy and responsiveness, covering the non‐identically distributed nature of the data. The proposed technique is experimentally validated with commercial batteries, verifying its accuracy and rapid execution.Here, a sensor‐less battery temperature prediction technique is proposed that ensures both accuracy and rapid runtime execution using deep learning. A DNN model is designed that utilizes short sequences of battery information to predict temperature. Additionally, an adaptive sequence length strategy is introduced to ensure accurate predictions under various discharge conditions. [ABSTRACT FROM AUTHOR]

Currently, there is an increased global climate change awareness, and the world is tending towards net zero emissions. The transportation sector is leading in Green House Gas emissions (GHG). Furthermore, the diminishing reserves of fossil fuels, the need for cheaper maintenance vehicles, more efficient drive trains, and better performance vehicles presents a challenge in ICE. Electric Vehicles (EVs) offer the perfect mobility solution which can replace the conventional ICE in the near future. This article comprehensively reviews the components and advances in the various technologies employed in electric vehicles to achieve efficiency in motion and optimise energy management in electric vehicles. This article gives an analysis of the current EV scenario globally. It then details the different configurations of electric vehicle architectures available. The battery is discussed, and the various electrochemical technologies are analysed. Battery Management Systems (BMS) to efficiently manage energy are discussed. The charging methods, voltage levels, and relevant standards are outlined in detail. The traction motors and power conversion technologies are reported with advancements in electric vehicle applications. Furthermore, this article gives a comprehensive overview of various emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI) & Machine Learning (ML), to improve EVs' performance through digitalisation. On the other hand, cybersecurity has become an ever‐challenging issue in EVs due to the increasing use of digital technologies. This article thoroughly overviews cybersecurity challenges in EV applications and explains possible proactive measures. Interoperability arising from various EV manufacturers' many different charging designs is discussed. [ABSTRACT FROM AUTHOR]

Abstract The demand for energy is a relevant topic in the field of science and engineering, which has been discussed throughout the last years due to the challenges of climate change and environmental concerns around the world. Currently, electric vehicles (EVs) offer a source of mobility that emphasises the use of energy storage devices to reduce CO2 emissions. The growing development of advanced data analytics and the Internet of Things has driven the implementation of the Digital Twin (DT), all to improve efficiency in the build, design and operation of the system. Regarding the components of EVs, the batteries are considered as the most expensive elements to analyse according to the State of Health and the State of Charge, which lead to implement the most optimal models, along with a DT for battery systems. The present article provides a literature review about the current development trends of EVs' energy storage technologies, with their corresponding battery systems, which gives an overview to understand different type of models and to identify future challenges in the industrial sector. Additionally, a solid explanation of the DT focussed on battery systems for EVs is discussed, highlighting some study cases, characteristics, and technological opportunities.

Abstract Temperature monitoring is critical for estimating the available capacity of Lithium‐ion batteries. In electric vehicle applications using large‐scale battery packs, monitoring individual cell temperature is challenging due to difficulties in sensor management. To address this issue, a sensor‐less battery temperature prediction technique is proposed that ensures both accuracy and rapid runtime execution using deep learning. A deep neural network‐based temperature prediction model is introduced that utilizes short sequences of battery voltage and discharge current. An adaptive sequence length strategy is then devised to ensure high accuracy and responsiveness, covering the non‐identically distributed nature of the data. The proposed technique is experimentally validated with commercial batteries, verifying its accuracy and rapid execution.

Abstract Currently, there is an increased global climate change awareness, and the world is tending towards net zero emissions. The transportation sector is leading in Green House Gas emissions (GHG). Furthermore, the diminishing reserves of fossil fuels, the need for cheaper maintenance vehicles, more efficient drive trains, and better performance vehicles presents a challenge in ICE. Electric Vehicles (EVs) offer the perfect mobility solution which can replace the conventional ICE in the near future. This article comprehensively reviews the components and advances in the various technologies employed in electric vehicles to achieve efficiency in motion and optimise energy management in electric vehicles. This article gives an analysis of the current EV scenario globally. It then details the different configurations of electric vehicle architectures available. The battery is discussed, and the various electrochemical technologies are analysed. Battery Management Systems (BMS) to efficiently manage energy are discussed. The charging methods, voltage levels, and relevant standards are outlined in detail. The traction motors and power conversion technologies are reported with advancements in electric vehicle applications. Furthermore, this article gives a comprehensive overview of various emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI) & Machine Learning (ML), to improve EVs' performance through digitalisation. On the other hand, cybersecurity has become an ever‐challenging issue in EVs due to the increasing use of digital technologies. This article thoroughly overviews cybersecurity challenges in EV applications and explains possible proactive measures. Interoperability arising from various EV manufacturers' many different charging designs is discussed.

Abstract This study proposes an approach for finding the optimal size of a high‐speed flywheel in an energy storage system based on battery‐flywheel cooperation, called battery‐flywheel energy storage system (BFESS), supplying an electric vehicle. Considering an energy‐based model for the flywheel and battery, including the losses, the optimal control theory is utilised as a two‐dimensional Pontryagin's minimum principle for solving the problem. The objective function of the flywheel sizing problem includes two terms, in which its maximum interaction with the system from the energy and filtering aspects are taken into consideration by a desirable weighting. While energy interaction is a common task, filtering the fast dynamics of the system such as the harsh conditions for the battery is the key role of the considered lightweight and high‐speed flywheel. A trade‐off between the energy contribution and filtering is made, targetted for the long‐term and short‐term dynamics in the drive cycles, respectively. The proposed methodology is presented in detail using some simple dynamics. Also, the sizing is carried out for the ECE‐15 urban drive cycle, the WLTC class‐3 and FTP‐75 drive cycles. The results confirm that by using the determined optimal size of the flywheel energy storage system (FESS), most of the dynamics could be handled from the filtering and energy interaction aspects.

Abstract The proposed study reports the essential parameters required for the battery charging schemes deployed for Electric Vehicle (EV) applications. Due to efficient power delivery, cost‐effectiveness, and environmental acclimation, EVs have emerged as a suitable alternative to the Internal Combustion (IC)‐based engines. However, prominent challenges for leveraging the EVs are the suitable availability of battery charging infrastructure for high energy/power density battery packs and efficient charging topologies. Despite the challenges, EVs are gradually being implemented across the globe to avoid oil dependency, which currently has a 5%–7% decline rate of post‐peak production. The vast deployment of EVs as private and commercial vehicles has created a major challenge for the grids in maintaining the power quality and peak load demand. This study, therefore, reviews the various battery charging schemes (battery charger) and their impact when used in EV and Hybrid EV applications. The available constituents of the battery chargers such as ac‐dc/dc‐dc converter topologies, modulations, and control techniques are illustrated in detail. The comprehensive study classifies the charging topologies depending upon the power and charging level. Some appropriate battery charging converter topologies that are suitable for domestic, industrial, and commercial applications like EVs are suggested in the study. In addition, a decision‐making inference is developed through a flow chart that decides on the suitable selection of the converter topology based on the required applications. Furthermore, the charging infrastructures along with the converters' design standards are also discussed concisely, which adds a significant contribution to the review article.

Abstract Lithium‐ion batteries have become a core component of electric vehicles (EVs) because of their high energy density. However, several issues in lithium‐ion batteries usage, such as safety, durability, charging time, and driving range, limit the development of EVs. Meanwhile, with the emergence of Industry 4.0, the digital twins technology has received widespread attention in the manufacturing industry because it provides real‐time monitoring and intelligent management of the production process. The authors propose a framework based on digital twins, which can be used for real‐time monitoring, intelligent management, and autonomous control of battery packs. The framework covers all aspects of a battery pack's lifecycle, including design, manufacturing, operation monitoring, and second use options. Such a framework can solve some critical issues inhibiting the usage of batteries. A case study of the application of the proposed digital twins‐based framework to electric vehicle battery systems has been conducted. The results show that deploying digital twins into the battery packs of EVs will improve the safety and service life of the battery packs.

Abstract The disordered charging of electric vehicles (EVs) may result in transmission congestion during the peak load period; therefore, a nodal dynamic charging price (NDCP) strategy is proposed to schedule the charging activity of regional electric vehicle agents (REVAs) to manage congestion. REVA is responsible for meeting the charging demand of users at a minimum charging cost, while the grid aims at minimising the congestion management cost and regulates the charging price of different nodes. The Stackelberg game theory is used to model the negotiation process of charging price, and the congestion contribution index is proposed to determine the congestion cost that the user side should be allocated. The proposed approach is implemented on a modified IEEE 30 bus system, and the results show that the congestion caused by EVs is reduced effectively, while the charging cost and congestion cost are also optimised.

Abstract With the tight interconnection of multiple energy flows in the integrated energy system, the security problem owing to the interaction between the electric network and the heating network has gained wide attention. To facilitate the security assessment of the integrated energy system, this article initially proposes the definition for the secure operation region (SOR) of the heat‐electricity integrated energy system under the exact AC power flow model and non‐linear heating formulation. The SOR of the integrated energy system is defined as a set of operation points for which the operational constraints of the electric network and the coupling heating network are satisfied. Next, complete characterization for the SOR of the integrated energy system is achieved based on the non‐linear dynamical quotient gradient system. After that, the evaluation criteria based on the integrated energy system (IES) SOR is defined to screen flexibility providers. Flexibility improvement by utilizing plug‐in hybrid electrical vehicle, installing energy storage, installing a heat storage tank, installing electric boiler, and utilizing building thermal inertia in the heat network is simulated and measured by application of the completely characterized IES SOR. Finally, numerical studies are given to verify the effectiveness of the proposed scheme.

Abstract The use of distributed energy resources (DERs) in a city contributes to the net zero CO2 of a city. However, the spatially uneven distribution of power demand and surplus electricity causes congestion in the grid system, making wide‐area operation difficult. The concept of local energy self‐sufficiency via energy management, in which batteries or electric vehicles are charged using power generated by DERs and discharged to neighbouring consumers, is expected to be a way to avoid grid conjunction while maximizing the use of DERs. For efficient local energy self‐sufficiency, it is necessary to identify where and when future power surpluses and shortages will occur within a city and optimize battery operation according to demand. Forecasts that focus only on representative points of a city may be less reproducible in diversity in the power demand transition for individual consumers in local parts of cities. Electricity smart meters that monitor power demand every 30 min from each consumer are expected to help predict the spatiotemporal distribution of power demand to achieve efficient local energy self‐sufficiency. The significance of reflecting regional characteristics in forecasting spatiotemporal distribution of power demand is demonstrated using actual data obtained by smart meters installed in Japanese cities. The results suggest that the forecast approach, which considers the daily periodicity of power demand and weather conditions, obtains high prediction accuracy in predicting power demand in meshed local areas in the city and derives results precisely reproducing the spatiotemporal behaviours of power demand.

Abstract Lithium‐ion battery is the commonly used energy storage technology in electric vehicles (EVs) because of its inexpensive manufacturing cost and high energy capacity. For optimal utilization of its capacity and lifetime, reliable state of health (SoH) monitoring solutions have to be included in the battery management system (BMS). SoH of a cell is affected by several reasons such as internal degradation or external damages that need to be estimated. This article analyses the current density in electrode and electrolyte of an EV lithium‐ion cell using a simulation assisted method that leads to improvement in SoH estimation accuracy. The experimental results are analysed through the fusion of the magnetic field images captured by quantum fluxgate magnetometers, installed on the surface of the cell, together with the real‐time simulation of the multi‐physics model of the cell. The magnetic field sensors measure the magnetic field intensity with an accuracy of ±2 mT. The real‐time simulation input data is updated from the measurements of both the magnetic field sensors and the battery cycler. The multi‐physics model of the cell is developed in COMSOL modelling software, and real‐time data fusion process is implemented on dSPACE Microlabbox real‐time simulator. Results confirm that the proposed monitoring solution provides useful insight that can be employed in ageing estimation of EV batteries.

Abstract Effective control of energy storage system (ESS), supplying an ancillary service to a grid, requires effective and critical calculation of state‐of‐charge (SoC). Charging and discharging values from battery operations are essential in calculating the efficiency and performance of a storage system. This information can also be a key to understand and forecast peak demand performance. Missing data is a real problem in any operations system, and it appears to be more common within powers systems due to sensor and/or network malfunctioning problems. Missing data imputation techniques have evolved in power systems research using smart meter data, but little research has gone into understanding how missing data can be best handled within storage management systems. This paper builds on a year's worth of charging and discharging data collected from a real 6MW/10MWh lithium‐ion storage battery deployed on the distribution network at Leighton Buzzard, UK. Using R Studio version (1.3.959‐1) open‐source software, eight selected imputation techniques were applied in identifying the best suited technique in replacing various missing data amounts and patterns. Findings from the study open up avenues for discussion and debate in identifying an appropriate imputation technique within the storage management context. The study also provides a pioneering lead in understanding the importance of decomposition in evaluating the right imputation technique.

Abstract Conventional road transport is a significant contributor to greenhouse gas emissions and air pollution. Electric mobile assets (E‐buses, E‐trucks, E‐taxis, and even E‐ferries) are an efficient, low noise, low emission alternative which could—if operated alongside significant renewable energy capacity—help to improve air quality and avoid catastrophic global warming. In this study, a Mixed Integer Linear Programming‐based solution for operating a fleet of E‐mobile assets has been proposed, considering technical and operational characteristics of different fleet elements. It takes into account customer (E‐fleet operator) objectives, such as load balancing and charging cost minimization. Furthermore, the E‐depot charging capacity increase achieved through integrating an energy storage system has been studied and discussed. The results of the optimization have been discussed and reported using a case study (demonstrating peak reduction of up to 50% and total charging reduction of 27%) and the benefits of operating an E‐fleet using the proposed solution have been discussed from economic and technical perspectives.

Abstract Inductive Power Transfer (IPT) system is an appealing approach among researchers as well as industrial manufacturers of electric vehicle (EV) charging systems. IPT can be used to transfer power through the air gap by generating a high‐frequency current in the transmitter pad and inducing current magnetically in the receiver pad. A triple quadrature pad (TQP) has been proposed to enhance transferred power in larger misalignments and various air gaps. This pad excels in its previous three‐coil structures by implementing a fewer number of inverters and lower dimensions compared to other similar structures. All these features result in a higher coupling coefficient in low misalignments and higher tolerance to misalignment in horizontal displacements. A laboratory‐scale prototype has been designed and built for 26 kHz switching frequency and 150 mm air gap in order to deliver maximum power in different displacements. Simulations have been done by the finite element analysis (FEA) tool of ANSYS Maxwell. The prototype has been built to validate the simulation results.

Abstract A dynamic optimal transport problem of electric vehicles (EVs) over a network is investigated. The EVs are considered to be transported from their initial locations to the destination nodes for charging purposes. In our framework, the operators of charging stations are strategic, and each of them designs their charging pricing optimally to maximise the revenue. Since EVs play an essential role as power loads at the charging stations, the designed transport strategy by the EV operator has an impact on the market energy price which in turn influences the charging prices. Therefore, to design an efficient transport plan, the EV operator needs to take into account its influence on the charging pricing and the market energy price due to their complex interplay. To achieve this goal, a unified framework is proposed for optimal EV transportation by considering factors including the delay, charging cost, and real‐time social demand of EVs over a finite‐time horizon. The balanced dynamic optimal transport strategy is enabled through a combined quadratic and entropic regularisation. To compute the equilibrium pricing for all charging stations, an iterative particle‐swarm optimisation scheme is designed which addresses a high‐dimensional nonlinear optimisation problem. Finally, case studies are used to illustrate and corroborate the obtained results.

Abstract Three‐phase interleaved LLC resonant converters are preferably employed to realize high efficiency and high power density in the application of On‐board Charger. The parameter design of LLC resonant converter with wide range output has always been rigorous and difficult. Compared with the common time‐domain model, a high‐accuracy time domain analysis is adopted to analyze the interaction between three phases in detail. This method improves the accuracy of the design results by adding the interaction analysis. According to the operation mode analysis, transformer's turn ratio, resonant inductor, magnetic inductor and resonant capacitor are optimized based on the design principle of minimizing the conduction loss. Finally, the modified efficiency‐oriented optimal method of wide output range LLC resonant converter is proposed and its design results is validated by a 6.6 kW prototype. The efficiency of the design using proposed method is significantly improved than the results of the common time‐domain model.

One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users’ anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles’ owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs’ batteries during the off-peak hours and drawing it from the EVs’ batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs’ charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.

This study focuses on considering battery one time constant model in the design of controllers of a two‐stage AC/DC and DC/DC battery charger using the small‐signal approach. For this aim, small‐signal models of DC/DC and AC/DC converters and also the battery model are obtained. Moreover, the effect of disturbance terms are analysed to be omitted in controller loops. Simulation and experimental results of a 1 kW laboratory prototype show appropriate performance of designed controllers in tracking reference signals. Also, tuned controllers using this approach have guaranteed unity power factor and low total harmonic distortion. Furthermore, a complete charge cycle of a lithium‐ion battery evaluates the performance of the converter in different constant current/voltage controlling modes. In comparison with restive model consideration, results show a more accurate model with grater damping ration and better stability characteristics. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) is expected to grow significantly, which calls for effective planning of charging infrastructures. While the planning of the charging infrastructure relies on an accurate charging demands, the behaviours of EVs charging are not always predictable and can be sensitive to many uncertain future environmental factors. Considering such uncertainties, this study aims to robustly and optimally determine the chargers and main switch board (MSB) capacities without violating queuing time constraints and load flow constraints. The non-parametric estimations of charging demands are derived with data-driven charging behaviour analysis considering diverse social factors, including travelling patterns, queuing, and changes of charging facilities. Then, the impacts of the EV integration are modeled by a stochastic load flow program. The samples of the stochastic load flow stipulate the conditional value-at-risk constraints for the planning of chargers and MSBs, which consider the probabilities and scenarios in a box of ambiguity with bounds. Afterwards, by limiting the frequency and severity of constraints violation, the total investment cost is minimized with a distributionally robust optimisation program. Simulation based on a real-world residential community in Singapore is carried out to testify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Electricity network is leading to a low carbon future with high penetration of plug-in electric vehicles (EVs). However, it is extraordinarily difficult to acquire detailed information on regional EV electrification with an incomplete monitoring system for network operators. In this study, a flexible graph signal processing (GSP)-based non-intrusive monitoring on aggregated EVs is proposed to enhance the EVs visibility for operating power system safely and cost-efficiently. It can deduce the individual EV charging status with the highest possibility iteratively from the limited dataset using a GSP-based possibility calculation after processing a daytime EV characteristic charging patterns. The experiment is developed with realistic EV charging datasets collected in London, and the results show the daily EVs number in a specific region of 500 EVs daily aggregation can be estimated efficiently with an around 4.77% value of relative mean absolute deviation applying the proposed method. [ABSTRACT FROM AUTHOR]

The charging management of plug-in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle-to-grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four-minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum-inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results. [ABSTRACT FROM AUTHOR]

Three-phase electric vehicle (EV) battery chargers are expected to not impact the unbalance of distribution networks; moreover, using active power factor correction topologies they are expected to behave like resistive loads not affecting the system harmonic distortion. In this study, unexpected unbalance characteristics of fundamental and harmonic currents of three-phase EV battery chargers are analysed by means of experimental tests performed on two EV cars of different technology. In order to quantify unbalance and harmonic distortion, proper comprehensive indices, in part developed in this study with specific reference to currents, are utilised. The unbalanced current absorbed even under ideal voltage balanced supply conditions by one of the two EV tested is shown and quantified constituting a real-life case study of endogenous unbalance. Then, the influence of voltage unbalance supply conditions on fundamental and harmonic currents is evaluated by means of a proposed testing procedure, obtaining case studies of exogenous unbalance. The experimental results obtained for the two EVs should be extended to a larger set of cars; anyway, they show the need, before moving forward to full grid EV connection, for utilities and for designers to improve the current standard certification process and the control performances of three-phase battery chargers. [ABSTRACT FROM AUTHOR]

This study introduces a type of solid‐state transformer (SST) for solar power station design and an energy management strategy (EMS) for the SST. The purpose of this study is to design a real efficient EMS for the photovoltaic‐assisted charging station in smart grid ancillary services and apply the optimal decision method. Also, the energy bound calculation (EBC) model is proposed to find the upper and lower bounds of flexible sources. Also, taking into account the EBC results and the power order from the aggregator, a charge power allocation algorithm is designed to distribute the power of flexible electric vehicles (EVs). With the help of a case study and laboratory analysis, the proposed EMS strategy is effective in real‐time energy management and is suitable for practical applications. The obtained results show the stable performance in the calculation of the energy range and real‐time power allocation which improves the efficiency of the photovoltaic‐based charging station. Also, the SST improves the operation of charge stations for supplying the sustaining power. [ABSTRACT FROM AUTHOR]

It is more convenient and safer to employ wireless power transfer (WPT) systems to charge the battery pack of electric scooters (ESs) than conventional plug‐in systems. A charging equaliser suitable for ESs is proposed to achieve constant current (CC) output without communication link between the transmitter side and the receiver side. The full‐bridge rectifier and select switches utilised at the receiver side are employed to be operated once to change from the balancing mode to charging modes. The characteristics of the load‐independent current output in the CC mode are achieved by properly selecting the parameters of the circuit so that no sophisticated control strategies are required to regulate the output as per the charging profile. The experimental results show that when the voltage of battery pack is charged at 32 or 4.2 V, the current is 1.02 or 1.12 A, respectively. Therefore, the proposed system can be considered as a CC system. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) technology proposed itself as a great solution for the predictable shortage in traditional energy sources. Using wireless power transfer (WPT) technology for charging the battery can be considered as a good solution to overcome the battery charging time problem. The considered WPT technique in this research is an inductive magnetic resonance (IMR). The problem now is to design a smart controller system that enables the EVs to obtain the optimum value of WPT by using the machine vision and the technology of Internet of Things (IOT). The utilisation technique of the maximum WPT in this research based on the simulation study of the magnetic flux density between the sending and receiving coils of the WPT system. Maximum efficiency of IMR method depends directly on the ideality of coils alignment. To achieve this alignment, a smart controller has been introduced with four integrated controlling layers. Coordinating an image processing technique with smart embedded sensing elements is considered the main solution key to better positioning. National instrument packages NI LabVIEW and NI vision are considered to fully control the system. NI Data Dashboard enables creating a wireless link using the concept of IOT to a smartphone running simple graphical user interface. [ABSTRACT FROM AUTHOR]

This study presents a novel quasi‐Z‐source converter integrated isolated multiport bidirectional DC–DC converter topology for a photovoltaic (PV) powered and battery/supercapacitor buffered electric vehicle (EV). The proposed topology can provide uninterrupted power to the electric motor of EV and regain the braking energy with the capability of bidirectional power flow. The system integrates quasi‐Z‐source and H‐bridge converters with an existing switch. Thus, a four‐port converter is achieved without any need for individual converters or additional switches. Besides, the high‐gain quasi‐Z‐source converter allows the reduction of the rated voltages of the battery and supercapacitor packs, as well as allows using a high‐frequency transformer (HFT) with a low turn ratio. The isolation between ports is provided by a secondary centre‐tapped HFT. The secondary side of the HFT is equipped with a controlled full‐wave rectifier to provide bidirectional power flow. In addition, a power flow management and corresponding control scheme are suggested. The performance of the proposed system has been evaluated for different operational changes. Results show that it properly performs the power flow between the ports under steady‐state and transient conditions. The power flow capabilities and efficiency values validate the viability and effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

The rapid rise in the grid integration of low‐carbon technologies, e.g. renewable energy sources (RESs) and plug‐in electric vehicles (PEVs), has led to several challenges in distribution networks (DNs). This is due to the intermittent generation of RESs and uncertain loads of PEVs, both of which necessitate enhancing the flexibility requirements at the distribution level so as to accommodate the high penetration of these clean technologies in the future. To address such issues, this study proposes a mixed‐integer linear programming‐based expansion planning model for DNs considering the impact of high RES and PEV penetration, the associated uncertainties, and providing flexibility requirements at the distribution level. In this respect, the spherical simplex unscented transformation, an analytical uncertainty modelling method, is implemented in the planning model to take into account the forecasting errors of the uncertain green technologies. Also, in order to estimate the electric vehicle parking lot demand at each load node of the network, a new approach for PEV‐charging model is suggested. To investigate the effectiveness and efficiency of the proposed probabilistic planning model, it is implemented on two test DNs, and the obtained results are thoroughly discussed. [ABSTRACT FROM AUTHOR]

Transport electrification is becoming the mainstream as a means to improve efficiency, performance, and sustainability of transportation systems. Electrical vehicles (EVs) can help to de‐carbonise the environment, but a downside is the technical issues presented to the low‐voltage distribution network. To quantify the stochastic nature of transport‐affected electrification, probabilistic load flow is employed. Monte Carlo‐based simulation is applied to accommodate the probabilistic uncertainties associated with variable EV charging patterns. This study considers high‐power charging (up to 11 kW) at the domestic level while monitoring power quality variations (voltage drop, voltage unbalance factor, voltage sag) standards. This work focuses on the Irish and UK, distribution system operator's–transmission system operator's perspectives, as it will help to identify the likely impacts due to high‐EV charger proliferation at household locations. The results indicate that if a 3.68 kW charger is used at the domestic level, it is possible for 40% of total household consumers to connect EVs directly to the distribution network without any power quality breaches. Furthermore, the proliferation of EV can be increased up to 100% if constrained to the start, and middle portions of the network (relative to the feeder substation transformer). For higher charger capacities (up to 11 kW), a bottleneck is presented regarding a resultant voltage unbalance factor. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) traction drives should be associated with flux‐weakening (FW) techniques wide‐range speed demands. In this study, the EV performance with an optimal FW strategy is studied in relation to the battery voltage variation caused by cell state‐of‐charge and temperature changes. The results show that the battery suffers from a voltage reduction by larger internal resistance as the temperature decreases. Moreover, the higher current is required for activating the FW process. However, the inner resistance growth produces more heat inside the cell that affects the battery electrical parameters as well as the system. To assess this effect by simulation, an improved electro‐thermal model of lithium‐ion battery ls dynamically coupled to the optimal FW strategy. In this model, all the electrical parameters are temperature‐dependent deduced from experimental measurements of an off‐road EV. The simulation results confirm the effect of the cell self‐heating on the battery voltage at sub‐zero temperatures. The higher battery voltage can support the FW operation at −10°C for more 1200 s under the modified NEDC driving cycle, whereas the motor drive voltage is saturated after 1118 s by using the simple battery model without thermal effects. [ABSTRACT FROM AUTHOR]

In order to obtain the compact and low‐cost on‐board battery‐charging system for the electric vehicles, the doubly salient electromagnetic (DSEM) with the cascade converter is employed as the driving system, the reactance of the phase windings and split‐field‐winding is reused as the filter inductance for the battery‐side DC/DC converter and grid‐side pulse‐width modulation (PWM) rectifier. The corresponding charging control schemes for the DC/DC converter and the double‐loop decoupling control for the PWM rectifier are given, and the demagnetisation control of DSEM is achieved to eliminate the output torque based on the in‐series split‐field‐winding control. Due to the coupled magnetic path of the flux linkage of field and phase windings of DSEM, the properties of the charging current fluctuation with respect to the mutual inductance and output torque ripple rated to the rotor position are given to achieve the best rotor location setting of DSEM in the charging mode. Finally, the co‐simulation model and experiment platform are built to verify the feasibility and coupling properties of the proposed system by the results in the steady and dynamic procedure. [ABSTRACT FROM AUTHOR]

The life loss of batteries caused by the daily operation implies a reduction in capital value, which is essential for the economic performance of storage‐containing systems. Most of current studies rarely considered it or simplified it to be proportional of throughput electricity, due to its multi‐factor dependence and complexity to be incorporated into dispatch models. This study presents a dynamic loss evaluation model for batteries that considers the cumulative effect of state of charge (SOC) changes. First, based on the results of battery aging test, the loss coefficient subject to SOC is derived. The general formulation of analytical battery life loss is further presented by integrating the damage effect during the change in SOC. Finally, by means of self‐optimal piecewise linearisation, the resultant life loss term is embedded in the online wind‐storage integrated scheduling. Case studies demonstrate the desirable computational complexity of the proposed evaluation method and the adaptability in general economic dispatch. [ABSTRACT FROM AUTHOR]

This work proposes a cost effective with a compact power electronic interface for plug‐in electric vehicles (PEVs), which has the ability to achieve the all modes of vehicle operation, including plug‐in charging, propulsion (PR) and regenerative braking (RB) for on‐board applications. The proposed converter for PEVs has minimum component count as well as step‐up and step‐down operation ability depending upon the system requirement. The capability of the proposed converter to work in both the modes allows wide range of battery parameters selection, efficient DC‐link voltage regulation and offers more flexibility in RB. Moreover, a current sensing based (without voltage sensor) non‐linear carrier control (NLCC) technique for power factor correction has also developed in this work. This control scheme improves the reliability and stability of system by reducing the feedback circuitry. A prototype of the proposed charging system is established and analysed to an authenticate the effectiveness of developed system under steady state and dynamic conditions. [ABSTRACT FROM AUTHOR]

To charge the battery of electric vehicles (EVs), inductive power transfer (IPT) system has been considered to be more appropriate than traditional plug‐in system especially for its security and convenience. This study proposes a hybrid IPT system for EVs charging applications adopting one transmitter coil with series compensation and two receiver coils with series compensation and LCL compensation. With the help of the two receiver coils and proper parameters design, the proposed system can naturally obtain constant current (CC) and constant voltage (CV) outputs and the maximum output power can be naturally limited which can prevent the system from possible overload. Therefore, the reliability of the proposed system is increased. Since the mode switching process can be automatically realised according to the value of battery equivalent load without extra control strategy and circuit, the fixed‐frequency control can be used. Therefore, not only zero phase angle condition, but also the soft switching can be achieved. The theoretical analysis is presented with the design process of the IPT coils and resonance parameters for required battery charging profile. The performance of the system is verified by a 3.3 kW experimental prototype. The experimental results coincide well with the theoretical analysis. [ABSTRACT FROM AUTHOR]

This study presents the full‐bridge dc–dc converter (FBDC) operated with customised pulse width modulation (cPWM) for electric vehicle (EV) battery charging. The proposed cPWM retains all the benefits of the popular conventional phase shifted modulation (PSM) with minimal modification in the pulse width while further achieving the zero voltage switching for the active switches of the converter. The cPWM gating technique results in minimal circulating loss and switch conduction loss in comparison to PSM gating technique and higher efficiency. The detailed operation of the converter is presented with analysis. The theoretical waveforms of the converter and power loss analysis are discussed. A 540 W, 80 kHz experimental prototype is developed to validate the theoretical analysis. Finally, the comparison between PSM and cPWM is presented. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) are becoming a more promising means of transportation mainly because of environmental issues and depletion of fossil fuel resources. This study deals with the basic theoretical knowledge for describing their behaviour in acceleration, cruising, deceleration and control strategies. Preliminary design calculations for a series HEV bus are carried out in MATLAB environment using a backward model. In addition to this, the rule-based algorithm is implemented to compare fuel consumption, battery's state of charge (SOC) and energy-saving possibilities. The model is firstly tested for highway and city drive cycles for SOC limits of 0.6 and 0.7. Further, prolonged simulations are conducted for both the highway and city drive cycles for four different SOC limits and three parameters of the hybrid vehicle (SOC of battery, fuel power and average charging power) are observed and compared with each other. The performance indexes of both drive cycles are estimated and it is found that higher performance indexes are obtained using power-split mode and greater SOC lower limit as internal combustion engine is more efficient when operated in this mode.

This study presents a practical control method for electric vehicle (EV) charging optimisation for detached and attached houses. The developed EV charging control method utilises real-time measurements to minimise charging costs of up to two EVs in a single household. Since some Finnish distribution system operators have already launched peak power-based distribution tariffs for small-scale customers and because there is a lot of discussion on this kind of tariff development, the control method considers peak power-based charges. Additionally, the proposed smart charging control method utilises charging current measurements as feedback to reallocate unused charging capacity if an EV does not utilise the whole capacity allocated for it. The control method is implemented and tested with commercial EVs. The conducted hardware-in-the-loop simulations and measurements confirm that the control method works as intended. The proposed smart charging control reduces EV charging electricity distribution costs around 60% when compared to the uncontrolled EV charging.

Combined borehole (BH) heat storage systems, batteries and power-to-gas system have the potential to shift load, reduce carbon emissions, provide hydrogen for fuel cell cars and save energy costs for end customers on an extended scale. This study proposes an optimal operation strategy for a local multi-vector energy storage system, which includes batteries, BH thermal storage, the power to the gas system and the fuel cell cars system. These storage systems can be divided into the short-term storage system and inter-seasonal storage system or low capacity storage system and high capacity storage system. The optimisation problem is divided into a two-stage framework, (i) the first stage optimisation is seasonal optimisation, which gives an approximate optimal operation plan for BH heat storage systems in the following year; (ii) the second stage develops a day-ahead robust optimal plan for all storage systems. Finally, the algorithm will return to seasonal optimisation to update the operation plan for BH heat storage systems to make results more accurate. The test case of eight nodes illustrates that the combined energy system of photovoltaic, heat pump power to gas, BH and batteries can provide hydrogen to fuel cell cars and significantly save power costs for customers with the optimal operation.

As electric vehicles (EVs) are currently under-utilised, the features of deploying EVs as distributed energy resources (DERs), based on an EV as a service (EVaaS) framework, are exploited and a resource allocation scheme is proposed for optimum association of dispersed EVs with critical load for demand fulfilment in microgrids. The proposed approach is based on a combined economic emission (CEE) optimisation model where both energy costs and carbon emissions are taken into account. The CEE optimisation problem is then formulated as a bi-objective optimisation problem, considering a number of practical constraints, such as energy demand, cost budget, emission limit and charging station limit. Carbon price is introduced to convert the bi-objective problem into a single objective function. The authors included EV battery degradation cost to ensure EV owners are not worse off after EVaaS participation. The feasibility of the proposed model is demonstrated in simulation studies. The approach has been extended to evaluate the trade-off between EVaaS and conventional DERs. Numerical results demonstrate the efficiency of the proposed resource allocation scheme.

Fast charging is a promising way for plug-in electric vehicles (PEVs) to get recharged quickly and reduce the impacts of long-lasting charging process on PEV owners’ daily life. Decreasing time during charging PEVs also makes the decision of PEV owners choosing where to charge affected more by the time length of driving towards and waiting in charging stations, raising new requirements for charging facilities planning. In this study, a cost minimisation planning method of PEV fast-charging stations taking influences of queuing and driving time into consideration is proposed and solved by the genetic algorithm-based methodology. An iterative algorithm obtaining the equilibrium of the user's decision of place to charge is proposed to consider the impacts of waiting and driving time at different charging stations on PEV owners. The effectiveness of the proposed strategy is then verified through the case analysis based on trajectory data of taxis in Beijing, which shows that the proposed methodology has good performances in computation. Weight of time costs and investment restrictions such as the number of charging stations would also influence the planning result.

This study presents a multidisciplinary end-to-end design, build, and test drive experience of a Formula Society of Automatic Engineers (FSAE) electric vehicle. The design team members’ background include computer engineering, electrical engineering, and mechanical engineering expertise. Each team member worked on a sub-team namely mechanical, drivetrain, supervisory control and data acquisition (SCADA), and battery management system (BMS). The mechanical team modified/redesigned a gasoline FSAE car frame to accommodate the battery pack, cooling systems, electric drive train, and SCADA hardware. The drivetrain team designed a combinational logic-based tractive system control board to enhance the safety and reliability of the drivetrain. The SCADA team used a model-view-controller software architecture and agile development practices to design and implement a custom electric vehicle operating system and the main brain hardware. The BMS team designed the sensing network and communication protocol to provide an update on the state of the health of the energy source. A thorough description of each sub-system is provided herein. System integration and field test results are also included.

This study proposes a method of coordinating electric vehicle charging to reduce losses in a distribution system, using only knowledge of the phase that each charger is connected to. Reducing network losses cuts costs and can be achieved through demand response mechanisms. However, directly minimising losses requires accurate values of the line impedances, which can be difficult to obtain. Flattening load over time and balancing load across phases have both been proposed as alternate solutions which indirectly reduce losses. Here, the practical differences between load flattening and explicitly minimising losses are quantified using simulations of residential charging in European style, three-phase distribution networks. Then, a new smart charging strategy, which incorporates phase balancing as a secondary objective to load flattening, is proposed. This requires only the knowledge of the phase that each load is on and achieves 30–70% of the potential reduction in losses.

The increasing penetration of electric vehicles (EVs) brings challenges and opportunities for power systems. One particular opportunity concerns the use of parked EVs to provide energy and associated services to the grid. In this work, the potential energy storage capacity of parking lots (PLs) of EVs is computed using the proposed stochastic model which considers the sporadic nature of the EV’ behaviours (i.e. arrival/departure, battery degradation, travel pattern, charge/discharge rates). The analysis was performed for two types of PLs with very different occupancy distributions, i.e. a shopping centre PL, and a workplace PL. In both cases, the available energy storage capacity of EVs was estimated hourly using real household travel data, i-MiEV data and car park occupancy records. The results show that the aggregated energy storage capacity closely follows the occupancy of EVs in the PLs, and is substantial, with little sensitivity to charging rate. The proposed stochastic modelling considered the variations in energy consumption, battery degradation, and user behaviour, predicted 13.4% less peak capacity than deterministic modelling. Moreover, the authors conclude that the shopping centre PL is a viable energy resource to the grid, with their scale and throughput compensating for the relatively low occupancy.

In contemporary days, the research and development enterprises have been focusing to design intelligently the battery swap station (BSS) architecture having the prospects of providing a consistent platform for the successful installation of the large-scale fleet of hybrid and electric vehicles (i.e. xEVs). The BSS may calibrate its subsystem for the electric vehicle (EV) deployment by accomplishing similar idea as in existing gasoline refuelling stations, in which the discharged batteries are being replaced or swapped by partially or fully charged ones by spending a few minutes. The BSS approach has arisen as a promising technology to the traditional EV recharging station approach as it provides a broader experience of business prospects for the specific stakeholders. This work deals with the introduction to BSS including infrastructure, techniques, benefits over charging station and key challenges associated with BSS. Furthermore, an S34X-smart swapping station for xEVs is proposed and finally, the key thrust is research for BSS is discussed. To the authors’ knowledge, this is the first kind of review work on BSS.

An increasing share of electric vehicles can mean excessive peak loads in low-voltage power distribution networks. Introducing peak shaving mechanisms to the charging systems, such overloads can be mitigated significantly. The first contribution of this study is to quantify the amount of flexibility that electric vehicles can contribute to peak load reduction so that the drivers can still fully charge the batteries of their vehicles. The second contribution is that the study presents and compares two optimisation strategies for peak load reduction. The work is based on real charging data covering about 25,000 charging sessions at various charging sites in the metropolitan area of the Finnish capital city. The main finding is that the peak loads at charging sites can be reduced by up to 55%. Another important result is that load reduction through low-power charging is achievable only if the average parking time at the charging site is >3 h, without affecting the user experience negatively. It is also found out that the average parking time is over 2 h longer than the average charging time, which indicates the enormous potential of electric vehicles in peak shaving.

The burning of fossil fuels and the emission of greenhouse gases motivates policymakers to think about the transition in their approach towards electric vehicles (EVs) from conventional ones. Transportation vehicles’ electrification drives the attention of various researchers and scientists towards the emergence of charging stations (CSs). CS placement is a matter of great concern for large scale penetration of EVs. Old infrastructure causes several challenges in planning the ideal placement of the CS since EVs have not prevailed in recent years. Recently, a lot of studies have been performed on CS placement, which attracts the attention of researchers. Various approaches, objective functions, constraints and range of optimisation techniques are addressed by researchers for optimal placement of CS. This study provides the research outcomes in respect of the placement of CS over the past few years based on objective functions, solution methods, geographic conditions and demand-side management.

With increased penetration of wind power units and plug-in electric vehicles (PEVs), their control flexibility and quick response potentially provide an alternative way to fulfil the need for rapid restoration. A two-stage restoration strategy optimisation approach is presented with the coordination of PEVs and wind power units considered. The optimisation model is to maximise the restored generation capability and minimise the fluctuation of cranking power. In the first stage, the aim is to provide reliable cranking power remotely for black start generators through coordinated dispatch of PEVs and wind power units and quadratic programming (QP) models for dispatching electric vehicle aggregators (EVAs) subject to wind power fluctuations, and for dispatching numerous PEVs within each EVA are developed. To ensure close coordination between these two dispatching procedures, bi-level programming-based hierarchical decomposition approach is used to solve the QP models in an iterative way. In the second stage, an integer linear programming model is proposed to optimise the restoration schedules through an effective transformation of the original non-linear formulation, so as to reduce the computing time and effort significantly. Finally, a case study is presented to demonstrate the effectiveness and essential features of the developed models and methods.