The massive installation and deployment of electric vehicle (EV) charging infrastructures requires designing efficient and convenient testing tools for the field testing. This paper presents a novel testing system for the EV off-board charger, in an application such as field testing and maintenance. The authors discuss the design theory and hardware implementation of the system in first. Two typical charger testing scenarios using the proposed tester are discussed. In addition, the modular structure of the system provides the user with a high degree of flexibility, which allows them to configure their own testing systems according to their applications. Three digital processing approaches for constraining high-frequency components and harmonics are introduced. The authors demonstrate that the digitalised traditional filter has an overwhelming advantage than the statistical filters in the experimental results. They also validate the function of the proposed testing system with a DC 60 kW charger, by finishing testing items such as the regulation accuracy, current imbalance and extreme temperature testing. Finally, they present a comparative study of recent EV charger testing systems.

Having the means to study the impact of electrical vehicle (EV) recharge on the power distribution network is one key aspect needed to manage the development of this technology. Power distribution grid and EVs are strongly connected elements that require to be wisely integrated to avoid that the limitations of the distribution network may hinder vehicle diffusion or that rapid growth of recharge requirements may put the distribution network in critical situations. In this study, a data-driven methodology is presented that aims at obtaining power requirement models that can be used to foresee the behaviour of the grid. The key to this methodology is the observation of charging profiles of a fleet of EVs over one year. The data collected defines a scenario representative of a generic fleet of commercial or sharing vehicles. The data is progressively loaded onto an existing database infrastructure and processed to obtain charge distributions that are then simulated in small sample networks in order to test the methodology. Starting from these data, a stochastic model is proposed to forecast the behaviour during the day and used to simulate by means of Monte Carlo techniques the impact on the power grid.

The growth of electric vehicles (EVs) market is an important step towards achieving carbon-free urban mobility. The coupling with renewable energy production according to an optimised model of energy management within an urban microgrid responds to tomorrow's challenges of networks and smart cities. This study aims to define an intelligent infrastructure dedicated to the recharge of EVs (IIREVs) in an urban area as a charging station empowered by photovoltaic (PV)-based microgrid. This system can facilitate interactions between the IIREVs, the public power distribution network, the users of EVs, and the surrounding building. The study provides key elements to encourage the stakeholders to develop IIREVs within societal expectations, urban planning, and in adequacy with the sustainable development goals. In addition, the study highlights multidisciplinary research position that demonstrates the need of a systemic approach to remain centred on users demand and needs, to asses efficiency at various scales of IIREVs, associated services, and power grid. By using a multidisciplinary framework leading to a technical–economic–environmental evaluation methodology for IIREVs and presenting a study case, the main result of this study focuses on requirements and feasibility of IIREVs implantation within the best fitting urban areas.

Energy recovery by means of braking with the electric machine helps to extend the cruising range of electric vehicles. By optimising the recuperation system, the total energy efficiency is increased. In general, using a usual brake force distribution the capability for recuperation is not equal for every type of drive train. Usually, four-wheel drives are advantageous in comparison to drive trains with only one driven axle. Regarding the single drives, generally, a front-wheel drive provides a higher capability for recuperation than a rear-wheel drive. This is mainly due to the dynamic axle load shift, which occurs during decelerations. In order to increase the energy recovery of electric vehicles with single drive, an adaptive brake force distribution is presented and compared with other brake force distributions in this study. It uses the knowledge of the currently available adhesion coefficient between tyre and road. The positive effect on the recuperation without impairing the braking performance on a straight road is demonstrated based on simulations. In curves, the lateral forces additionally need to be considered to guarantee the stability of the vehicle, especially relating to electric vehicles having a rear-wheel drive.

Speed controllers may be employed to provide safer along with more secure vehicles. They may also be used to minimise environmental pollution, e.g. speed controllers can be employed to track speed optimal velocity profile based on energy consumption minimisation. Consequently, accurate speed tracking is important. However, despite soft-computing techniques have been proved successful in controller tuning, there is a limited amount of research on these techniques applied to speed controller optimisation. Therefore, this study performs a comparison study on PI cruise controller tuning for an off-road electric vehicle. A cost function is designed to reach an accurate EV speed tracking while considering safety aspects, such as no reverse speed. The ACO-NM algorithm has been demonstrated to be the most efficient compared to GA, ALO, DE, and PSO. Indeed, ACO-NM reached high-quality solutions for lower computational cost for three driving cycles. Moreover, contrary to the majority of published work on the subject, experimental validations have been carried out with the optimised PI cruise controllers. The experimental results have validated the ACO-NM efficiency with a maximum overshoot average <10% for the hardest acceleration of the real driving cycle.

This work improves the performance of an electric vehicle (EV) traction system by using non-linear fractional-order proportional–integral (FO-PI) controller. The functionality of FO-PI controller is benchmarked against integer-order proportional–integral (IO-PI) controller. A 400 V, 6.6 Ah Li-ion battery bank is designed to power an indirect field-oriented induction motor driven prototype EV traction system. The appeal of this study is to reveal the simplicity, robustness, and effectiveness of FO-PI speed regulator for EV traction system. The efficacy of FO-PI speed regulator for EV applications is validated through rigorous experimentation. The FO-PI controller due to its non-linear nature proves to be more robust and effective under variation of a system's parameters and external disturbances as compared to IO-PI controller. Furthermore, the experimental results show that FO-PI control strategy provides higher torque per amp output while maintaining better voltage and state-of-charge profiles of the Li-ion battery bank.

The design of space vector control (SVC) systems suitable for flux-weakening operation of permanent magnet brushless DC machines (PMBDCMs) is presented in this study. The proposed design approach enables overcoming the critical issues arising from the non-linearities of PMBDCM voltage and torque equations; these issues derive from the trapezoidal shapes of back-emfs and affect PMBDCM constraint management significantly. The SVCs presented in this study have been developed within two different synchronous reference frames, both of which enable distinguishing torque and demagnetising current components clearly. Therefore, reference torque current component is determined in accordance with PMBDCM torque demand, while reference demagnetising current component is computed through a voltage follower PI regulator, which processes the voltage deficit detected on the DC-link. In this regard, a novel synchronous reference frame is proposed in this study, which improves PMBDCM constraint management and results into a wider constant-power speed range, but at the cost of some torque ripple. The enhanced performances achievable by SVC approaches are highlighted by numerical simulations, which regard the comparison among the SVCs and an SVC with no flux-weakening capability, at different operating conditions.

This study presents a novel energy management strategy (EMS) which outperforms the published strategies developed for an international technology challenge, IEEE Vehicular Technology Society (VTS) Motor Vehicles Challenge 2017. The objective of the strategy is to minimise the cost of ownership of a low-power (15 kW) fuel cell (FC)-battery electric vehicle. Both the fuel consumption cost and power sources degradation costs are combined to represent the total cost of ownership. The simple adaptive rule-based strategy optimises the FC operation during low-traction power operation and switches to battery charge-sustaining operation for high traction power operation. This minimises fuel consumption and increases the lifetimes of the FC and of the battery. The strategy is then compared with the EMS of the 2015 Toyota Mirai, and the challenge vehicle model is modified to capture the learnings from the Mirai. Finally, a cost-benefit analysis for a plug-in FC vehicle is considered in order to improve FC lifetimes and to reduce costs for short drive cycles.

In the past, the diffusion of electric vehicles (EVs) has been hindered by energy storage limits. In fact, these are the reason for the limited EV range and the consequent range anxiety of their drivers. Thanks to constant improvements in storage system technologies over the years, in terms of both energy and power density, lithium-ion batteries (LiBs) now guarantee vehicle ranges higher than 150 km for small vehicles. Another important improvement has been achieved by the hybridisation of LiBs with other storage technologies such as electric double-layer capacitors or lithium-ion capacitors. By adding an additional storage unit (ASU) to the EV battery system, the overall efficiency increases, with a consequent gain in the vehicle's expected range. In a previous paper, an optimal sizing procedure was proposed, through which it is possible to calculate the optimal ASU mass that maximises the EV range for a given vehicle, ambient conditions, and driving cycle, which was considered to be known a priori. In the present work, a real-time implementation of the control strategy on which the optimal sizing procedure was based is proposed and analysed using the results of simulation tests.

In this study, an active battery balancing system is proposed, which allows direct energy transfer between arbitrary cells within a cell stack with simultaneous cell monitoring. The energy transfer utilises only one energy storage for the balancing process of the whole stack. Furthermore, the design enables the reduction of required sensors to only one voltage and one current sensor for full individual cell monitoring. The proposed system can be implemented in any battery application where cell balancing is mandatory, e.g. in lithium-based battery systems due to their strict operation limits. The proposed balancing system is validated through simulation and experimental measurements. The results confirm the proposed concept design.