This paper introduces a novel approach for dynamically detecting the lowest speed threshold at which regenerative braking is effective in Electric Vehicles (EVs). The control approach is based on real-time sensing of the motor controller DC link current and disabling regenerative braking when current changes direction while the motor is operating as a generator. Various factors influencing regenerative braking capability of EVs at low speed are discussed and simulation studies are carried out to illustrate the effect of each factor on the displacement of the low-speed threshold. Based on the results obtained from the simulation studies, a dynamic Low-Speed Cutoff Point (LSCP) detection method is proposed. This method requires no hardware modification to the vehicle braking architecture and can be implemented solely by modifying the brake controller. The proposed method is tested on an experimental EV test platform for a predetermined drive cycle. It is shown that in comparison to considering a constant low-speed threshold during braking, the amount of energy recaptured through the regenerative braking process can be improved by taking advantage of the proposed method.

This paper proposes a novel approach to efficiently distribute braking force of an electric vehicle (EV) between friction and regenerative braking with an ultimate goal of maximizing harvested energy during braking. The regenerative braking performance of an EV depends on various factors influenced by the driver behavior and driving conditions, which are challenging to measure or predict in real-time. In the proposed method, the performance map of the traction motor (TM) and its controller is used to define a boundary in which blending of regenerative and friction braking is performed with the goal of maximizing recaptured energy through the regenerative braking process. The proposed method is validated on an experimental EV hardware-in-the-loop (HIL) test bench setup for a predetermined drive cycle of Urban Dynamometer Driving Schedule (UDDS). It is shown that the amount of recaptured energy through the regenerative braking process can significantly increase by taking advantage of the proposed method compared to a case, which considers a constant boundary for brake distribution.

In this paper, an effective objective function is proposed to minimize the cost of operation of a microgrid with large-scale plug-in electric vehicles and renewable energy resources. The profit of consumers is taken into account by utilizing the incentives in the demand response programs, and vehicle-to-grid feature of the plug-in-electric vehicles integrated into the grid. The optimization is performed using genetic algorithms. Also, reliability indices of the economically optimized microgrid are computed for various operation configurations in both the grid-tied and islanded modes. Numerical studies are conducted on a microgrid testbed to validate the performance of the proposed strategy.

In this study, a double-loop control strategy is proposed for power grid frequency and voltage regulation using plug-in electric vehicles (PEVs) connected to the grid through a three-level capacitor clamped inverter. The frequency and voltage regulation problem is first formulated using vector space analysis and phasor diagrams to find the boundaries and constraints in terms of the system parameters. The derived formulas are then utilized to design a double-loop controller using an exclusive phase detector control loop and a novel pulse width modulation (PWM) scheme to effectively regulate the frequency and voltage of the grid. The effectiveness and feasibility of the proposed control strategy are evaluated through simulation and experimental studies. This approach can benefit both the customers and the grid operator, as it facilitates utilizing the batteries of the connected PEVs to supply a portion or all of the active and reactive power demand, hence regulating the frequency and voltage of the grid. The extent to which active and reactive power can be supplied depends on the number of PEVs connected to the local grid.

In this study, a novel non‐linear sliding‐mode controller with a boundary layer solution and a redefined sliding manifold is proposed for a bidirectional converter utilised in plug‐in electric vehicles. The proposed method employs a Lyapunov function to formulate the stability condition of the non‐linear controller. The proposed switching regime and control strategy enforce a pseudo‐resistive relation at the input terminals of the converter to ensure the unity power factor in power conversion. The input resistance of the converter is regulated and controlled to positive and negative values to facilitate both grid‐to‐vehicle and vehicle‐to‐grid features, respectively. Simulation and experimental results are provided to evaluate performance of the proposed modelling and feedback control scheme.

A nonlinear sliding-mode controller for a three-phase converter, utilized in plug-in electric vehicles (PEVs), is proposed in this paper. The proposed controller enables the utilized converter to perform multiple functions during different operating modes of the vehicle, i.e., grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes. The bidirectional three-phase converter and the proposed controller operate as a power factor correction circuit, bridgeless boost converter, and rectifier during G2V mode (i.e., plug-in charging), and it operates as a conventional single-stage inverter during V2G mode. The stability analysis of the proposed controller is performed by defining a proper Lyapunov function. The functionality of the proposed nonlinear controller is first evaluated through simulation studies. The feasibility and effectiveness of the proposed control strategy is then validated using an industrial control card through a hardware-in-the-loop (HIL) experimental testbed.