Plug-in Electric Vehicles Charging Research Articles

Modified repetitive control design for two stage off board Electric Vehicle charger

In this paper, a three-phase, two stage, bidirectional off board plug-in Electric Vehicle (PEV) charger controller has been designed and implemented in real time. The proposed PEV charger is able to work in all four quadrants in active–reactivepower plane. Generally, power electronics converters are controlled by PI regulators in d-q reference frame, where the signals to be regulated are inherently DC in nature. These controllers are implemented in two loops i.e., Slow (outer) control loop and fast (inner) control loop. Setting the gains of outer loops are little bit easier in comparison to fast acting inner loops besides the involvement of decoupling terms to have independent operation of active and reactive current components. Therefore, a repetitive controller (RC) is designed to regulate the inner sinusoidal reference current having both active–reactive​ components as per the control requirement without involving any decoupling terms. The RC is well known for their ability to track periodic signal and inherently based on internal model control theory. However, the major problem with RC is its sensitivity due to which it provides high gains at all frequencies levels. Therefore, in proposed controller the sensitivity of RC is modified by squaring it. This results the deep notches at higher frequency levels This approach is found to be very much suitable for the compensation of reactive power and load current harmonics. This also helps to exclude unwanted frequencies which results reduction in THD level. Therefore, the main contribution of this paper is adapting such robust EV charger controller for harmonic compensation. Moreover, from the implementation, it is found that the conventional RC is capable to compensate load current harmonic to some extent but it takes around 140 μs computational time. However, the proposed RC takes around 100 μs computational time, this results the grid current purely sinusoidal. The MATLAB/Simulink environment has been used to simulate proposed PEV charger controller, further it has been verified on scaled down experimental prototype.

Second-Order Ripple Minimization in Single-Phase Single-Stage Onboard PEV Charger

This article presents a single-phase, single-stage onboard plug-in electric vehicle (PEV) charger. The single-phase PEV chargers have an inherent problem of introducing a second-order ripple component on dc side. This problem is further exaggerated during the vehicle-to-grid (V2G) mode of operation where it may be normally controlled to supply both active as well as reactive power. During the V2G reactive power compensation, the second-order harmonics ripple component at dc side will increase, which further reduces the life cycle and performance of battery pack. Therefore, a second-order ripple component compensation technique is proposed by precompensating the reference current itself. The resultant reference current is periodic in nature, and accordingly, a repetitive controller (RC) is used, which is well known for its capability of tracking the periodic signals. The PEV charger is controlled in both grid-to-vehicle (G2V) and V2G modes. In this article, the performance of the controller has been tested in eight different modes on active-reactive (P-Q) power plane and found that ripple minimization technique successfully reduced second-order ripple component on dc side. The PEV charger controller has been simulated in MATLAB/Simulink environment and implemented on the scaled down experimental setup.

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

The effect of plug-in hybrid electric vehicle charging on fuel consumption and tail-pipe emissions

Plug-in hybrid electric vehicles (PHEV) have an electric motor and an internal combustion engine and can reduce greenhouse gas emissions (GHG) from transport. However, their environmental benefit strongly depends on the charging behaviour. Several studies have analysed the GHG emissions from upstream electricity production, yet the impact of individual charging behaviour on PHEV tail-pipe carbon emissions has not been quantified from empirical data so far. Here, we use daily driving data from 7,491 Chevrolet Volt PHEV with a total 3.4 million driving days in the US and Canada to fill this gap. We quantify the effect of daily charging on the electric driving share and the individual fuel consumption. We find that even a minor deviation from charging every driving day significantly increases fuel consumption and thus tail-pipe emissions. Our results show that reducing charging from every day to 9 out of 10 days, increases fuel consumption on average by 1.85 ± 0.03 l/100 km or 42.7 ± 0.8 gCO2 km−1 tail-pipe emissions (± on standard error). Charging more than once per driving day has less impact in our sample, this must occur during at least 20% of driving days to have a noteworthy effect. Even then, a 10% increase in frequency only has moderate effect of decreasing fuel consumption on average by 0.08 ± 0.02 l/100 km or 1.86 ± 0.46 gCO2 km−1 tail-pipe emissions. Our results illustrate the importance of providing adequate charging infrastructure and incentives for PHEV users to charge their vehicles on a regular basis in order to ensure that their environmental impact is small as even long-range PHEVs can have a noteworthy share of conventional fuel use when not regularly charged.

Coordination of Charging Plug-in Electric Vehicles (PEV) in Electric Distribution Networks to Minimize Power Losses and Voltage Deviations

Plug-in Electric Vehicle (PEV) is a vehicle with a drive in the form of an electric motor and gets its energy source from a rechargeable battery. PEV (grid-to-vehicle, or G2V) charging activity can pose a risk to the distribution system when PEV is injected into the system. Charging PEV in the distribution system will cause the demand for electrical power to increase. Without a proper and optimal charging coordination system for the many PEVs that exist in the distribution network system, the following technical problems will arise in the network such as: occurrence of unusual and unwanted peak loads in power consumption, voltage sag, transformers or conductors overload, as well as the occurrence of high power system losses in the distribution system. This study discusses the optimization of PEV charging coordination scheduling in a distribution system by considering power losses and voltage deviations. To optimize the coordination of PEV charging, this study used a metaheuristic method called the Binary Particle Swarm Optimization (BPSO) which was then compared with the Binary Gray Wolves Optimization (BGWO) method. This scheme is simulated on the 20kV Pujon Feeder distribution system in Malang, Indonesia. The initial simulation results, at the time when the most loads were presented, showed 80 percent of PEV penetration, increase in power losses of 54.51 percent and voltage deviation of 9.82 percent. By using the BPSO method, the results showed power loss increase of 27.2 percent and voltage deviation of 7.75 percent. Meanwhile, by using the BGWO method the increase in power losses was 31.09 percent and the voltage deviation was 7.98 percent.

Inducing Human Behavior to Maximize Operation Performance at PEV Charging Station

Plug-in electric vehicle (PEV) charging station service capability is physically limited by the charger availability and local transformer capacity. However, the station operation performance has become an increasingly important factor for enhancing charging service accessibility. In this work, we propose an innovative station-level optimization framework to operate charging station with optimal pricing policy and charge scheduling. This model incorporates human behaviors to explicitly and effectively capture drivers' charging decision process. We propose a menu of price-differentiated charging services, which differ in both the per-unit prices and the energy delivery schedule. Involving human in the loop, the operation model also exploits the capability to alleviate the overstay issue that occurs when a PEV's charging session has completed. We then propose a muli-block convex transformation methodology to reformulate the resulted non-convex problem via Fenchel-Young Inequality; the Block Coordinate Descent algorithm is applied to solve the overall problem and the efficiency enables real-time implementation. As a result, our simulation demonstrates that the proposed control polices can realize benefits in three aspects: (i) net profits gains, (ii) overstay reduction, and (iii) quality-of-service increase.

Efficient Real-Time EV Charging Scheduling via Ordinal Optimization

The surge of plug-in electric vehicles (PEV) on the roads poses the issue to handle the substantial charging demand. Particularly, the operation of charging stations requires an efficient and scalable real-time scheduling method to accommodate the dramatic charging requests in an economical (i.e., to best utilize the local renewable generation) and friendly (i.e., to reduce the impact on the electric grid) manner. This paper fulfills the objectives and makes the following main contributions. First, we develop a parameterized aggregated PEV charging model using the energy boundaries to express the charging flexibility. We propose to parameterize the aggregated charging policy by the incomplete Beta function based on the problem structures. The proposed model can scale down the decision variables from O(NH) to O(2) where N is the number of PEVs and H is the number of prediction horizon. Second, we develop an ordinal optimization (OO) based method (denoted as OO-P) to search for good enough charging policies within seconds while still providing probabilistic performance guarantee. Third, we demonstrate the performance of the proposed OO-P via simulations. The numerical results show that the solution of OO-P is only 4% worse than the optima. However, OO-P shows high computation efficiency and scalability. Compared with the existing real-time PEV charging scheduling method, OO-P can reduce 6% of the operation cost for the charging station. OO-P is also shown to outperform the existing heuristic rule.

Comparative Analysis of Proposed Photovoltaic-Grid and Grid-Only Systems for Uninterrupted Charging of Plug-in Electric Vehicles

Currently, uninterrupted charging of Electric Vehicle (EV) is not possible due to grid overloading during day-time. The interruptions during charging causes uncertainty for users. This work proposes a solution to this vehicle charging problem by introducing a combination of Energy Storage Unit (ESU) and Photovoltaic (PV) panel charging along with the grid. The proposed solution is efficient in providing charging for EVs without any interruption in an office parking scenario. The important benefit of the proposed system is the rapid fulfillment of the charging demand of all EVs in parallel to lessening the added grid burden. The control algorithm is designed according to deterministic rule-based schemes. The proposed work is valid for the areas where parity level has already been reached. The efficacy of the proposed technique is evaluated under different charging situations using Matlab simulations. The results show that all connected EVs are charged fully without interruption. Moreover, the simulation results prove that EV charging using the proposed system lowers the added burden from the grid up to 98.6%. This work is envisioned to offer stimulating research prospects in the area of vehicle charging using grid-connected renewable sources.

Adaptive Congestion Control for Electric Vehicle Charging in the Smart Grid

This article proposes an adaptive control algorithm for plug-in electric vehicle charging without straining the power system. This control algorithm is decentralized and merely relies on congestion signals generated by sensors deployed across the network, e.g., distribution-level phasor measurement units. To dynamically adjust the parameter of this congestion control algorithm, we cast the problem as multi-agent reinforcement learning where each charging point is an independent agent which learns this parameter using an off-policy actor-critic deep reinforcement learning algorithm. Simulation results on a test distribution network with 33 primary distribution nodes, 1760 low voltage end nodes, and 500 electric vehicles corroborate that the proposed algorithm tracks the available capacity of the network in real-time, prevents transformer overloading and voltage limit violation problems for an extended period of time, and outperforms other decentralized feedback control algorithms proposed in the literature. These results also verify that our control method can adapt to changes in the distribution network such as transformer tap changes and feeder reconfiguration.

A Stochastic Optimal Planning Model for Fully Green Stand-Alone PEV Charging Stations

Electric vehicle users need access to recharging services within the all-electrical driving range of their vehicles. However, public charging stations to be located on remote highways are in many cases far from the power grid, where a connection to the power grid may be impossible, costly, or impractical. This article formulates a stochastic mixed integer linear programming (MILP) planning model for energy storage and renewable energy systems to cover the energy demand of stand-alone charging stations for plug-in electric vehicles (PEVs) entirely using green energy generated by renewable energy sources (RESs). The proposed model accounts for the stochastic behavior of PEVs and RESs as well as the technical, economic, and operational characteristics of a variety of energy storage technologies. A customer satisfaction criterion is introduced to allow for a tradeoff between performance and investment costs. The proposed model can also be used for designing fully green PEV charging stations in urban and rural settings to capitalize on PEV environmental benefits by avoiding the use of fossil-fuel-generated electric power in their charging or to reduce the main grid load. A set of numerical studies have been performed in General Algebraic Modeling System (GAMS) environment to demonstrate the feasibility and effectiveness of the proposed model.

How much smart charging is smart?

The threat of climate change motivates the expansion of variable renewable energy sources (VRES) as well as the transition to plug-in electric vehicles (PEV). Both technologies present unique challenges to electricity systems, potentially leading to high grid expansion cost. In this paper, we present a detailed assessment on how smart-charging of PEVs can mitigate these problems and reduce cost on the distribution grid level. To this end, we propose a heuristic policy that dynamically decides on charging of PEVs, curtailment of VRES, and subsequently on infrastructure investments trading off fixed investments with variable operational costs. The main inputs are modeled as stochastic and the proposed policy is non-anticipative. We conduct a comprehensive case study for Germany in the year 2035 using detailed descriptions of existing distribution grids, realistic driving patterns, and real-world VRES feed-in data. Potential savings of €6.2 billion in investment costs lead to a reduction in total distribution grid cost of around 19%. This result is achieved by upgrading 7 million (21%) PEV chargers all over Germany. Upgrading 100% of chargers to smart-chargers as is proposed in the extant literature is clearly sub-optimal as it leads to significantly higher total cost. A closer investigation for single grids reveals that savings as well as the optimal share of smart chargers varies widely between grids. In particular, the potential of smart charging is much greater in rural areas than in urban centers. Furthermore, the results suggest that curtailment of VRES production is economically only in rare circumstances.

A Stochastic Multi-Criteria Decision-Making Algorithm for Dynamic Load Prioritization in Grid-Interactive Efficient Buildings

Abstract Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of grid-interactive efficient buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners, and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

Eco-environmental planning of various energy storages within multi-energy microgrid by stochastic price-based programming inclusive of demand response paradigm

A stochastic price-based planning model is proposed for a multi-energy microgrid (MEM) in this article. The MEM can supply the electricity, heating and cooling loads. The presented model can control the flexible demands and also can provide continuous control in the presence of smart and comprehensive programming of electricity, heating, ice, compressed air, and hydrogen energy storages. In the proposed procedure, all the energy carriers’ price is considered to be uncertain and the market prices are applied in the proposed modeling by some scenarios with appropriate probabilities. The features of the MEM parts like losses and amortization costs of electricity, heat and cool energy storages, also the operating area of the combined heat and power (CHP) units can fully be planned. The principle of convexity is considered related to the CHP unit operation area. The proposed formulation is applied on two days in the summer and winter seasons for a variety of studies including the storage effect, energy sales to network, charge and discharge of plug-in hybrid electric vehicles (PHEVs), and flexible devices planning. The outcomes represent that utilizing the proposed stochastic MEM plan and available demand planning with the proposed storage programming results in significant advantages for the power network and the consumer. One of the important outcomes is 6.6% and 50.9% cost saving for winter and summer days respectively, when the power is offered to the power distribution system. It is worth mentioning that the proposed plan can make the curve of demand optimally utilization from the demand response program and energy storage plan.

Brayton-Moser Passivity Based Controller for Electric Vehicle Battery Charger

In this paper Brayton-Moser passivity-based control (BM-PBC) methodology is developed for an on-board battery charger for plug-in electric vehicles(PHEVs). The main features of this electric vehicle (EV) charger include improved power quality, reduced filter size and voltage stress across the switches and fast dynamic response. In this paper, a dynamic model of the three-level (TL) boost power factor correction (PFC) converter is developed using the Brayton-Moser formulation. Then, the Brayton-Moser based control technique is designed by injecting a virtual resistor in series with the input inductor. Further, the stability analysis of the proposed controller is also carried out using energy balance approach. To improve the dynamic performance and reduce the steady state error, a PI controller is integrated with the aforesaid controller. Therefore, the controller comprises of BM-PBC and the PI controller is implemented for the TL boost PFC converter as a battery charger and its performances are investigated under various operating modes with the help of MATLAB/Simulink. Furthermore, power quality of charger is assessed by monitoring source current total harmonic distortion (THD) under different operating conditions. It is also observed that the proposed system provides THD less than 5% in source current which satisfies IEC 61000-3-2 Class C standard. The performance of the aforesaid controller is also compared with the conventional PI controller. In order to validate the proposed controller, a prototype model of same specifications is tested in hardware in loop and obtained test results are also presented.

Harmonic evaluation of Plug-in Electric Vehicle on distribution network system

The significant development of plug-in electric vehicle (PEV) is a challenge for distribution networks. The PEV charger is a power electronic device that generates large amounts of power over a period of time to charge EV battery. In addition, the EV on board charger (OBC) also produces harmonics due to the process of converting AC grid voltage to DC voltage. Converter as OBCs main device is a source of harmonics which directly connected to the distribution network. In this study, harmonics from single OBC, multiple OBC and PEV penetration scenario were evaluated and compared with the SPLN standard D5.004-1: 2012. From the study results, THD injected to the distribution network is influenced by the percentage of the base load and PEV penetration. The worst possible condition is occurred under low load conditions while high PEV penetration happens. From the simulation results using the distribution network model especially in residential area, the low base load scenario (10.47%) and the high PEV penetration (90.01%) is generating TDD 6.59% and THDv 2.61%, respectively. Therefore, it is known that the harmonic effects generated by PEV charging to the distribution network still meet SPLN D5.004-1: 2012

Plug‐in electric vehicles smart charging mechanisms for cost minimization and ancillary service provision

The increasing number of Plug-in Electric Vehicles (PEV) connected to the grid will create challenges to be addressed and opportunities that can be ceased. One challenge is the load shape dynamics, which might result in increasing the loading level on the distribution lines. On the other hand, there is an opportunity to use the PEV stored energy to provide ancillary services to the grid. Therefore, this paper proposes a new PEV charging mechanisms that not only aid in ancillary service provision, but also help in reducing the charging cost and avoiding the extra investment in peak generation units while maximizing the integrated PEVs. The proposed approach utilizes three stages to ensure the satisfaction of PEV owners with different preferences and to respect the grid limitations. The main objective is to develop different charging mechanisms for charging PEV's in the parking lots to ensure the maximum satisfaction of the PEV owners by fulfilling their charging requirements with the minimum possible cost. The developed algorithm relies on a moving time window to address the real-time changes and considers the participation of PEVs in the frequency regulation and peak shaving. The effectiveness of the proposed algorithm is verified through simulation using real data.

Optimal design of electric vehicle charging stations for commercial premises

Influx of plug-in electric vehicles (PEVs) creates a pressing need for careful charging infrastructure planning. In this paper, the primary goal is to devise a closed-form expression for the PEV charging station capacity problem. Two types of commercial charging stations are considered. The first problem is related to the calculation of the optimal service capacity for charging lots located at workplaces where PEV parking statistics are given as a priori. The second problem, on the other hand, is related to the optimisation of arrival rates for a given station capacity. In the second part, the mathematical models are expanded for the case where multiple charger technologies serve customer demand. This time the goal is to calculate the optimal customer load for each charger type according to its rate. Calculations are carried out for both social and individual optimality cases. Markovian queues are used to model the charging station system to capture the complex interactions between customer load, service waiting times, and electricity cost. The related optimisation problems are solved using convex optimisation methods. Closed-form expressions of station capacity and optimal arrival rates are explicitly derived. Both analytical calculations and discrete-event simulations are carried out and the results show that 60% of the waiting times and 42% of the queue length can be reduced by optimal capacity planning.

A Multiagent Fuzzy Reinforcement Learning Approach for Economic Power Dispatch Considering Multiple Plug-In Electric Vehicle Loads

Economic/environmental power dispatch goals were to determine the optimal power generation from thermal plants to satisfy the given demand while diminishing operational costs and also minimizing emissions due to burning of fossil fuels. In this paper, economic and environmental scheduling problems are mathematically formulated by considering various system constraints like the valve point loading effect, ramp rate constraints, plug-in electric vehicle charging constraints, and generator’s capacity constraints. In this work, multiagent fuzzy reinforcement learning (MAFRL) is implemented for an effective elucidation for the economic/environmental power dispatch (EEPD) problem with multiple charging scenarios of plug-in electric vehicle and valve point loading of concern thermal power generators. Here, the EEPD is framed as a multiagent fuzzy reinforcement learning (MAFRL) tasks in which specific plug-in electric vehicles and power generating units act as multiple players for minimizing emissions due to fossil fuels and costs of power generation also satisfying charging constraints of plug-in electric vehicles and several thermal unit’s constraints. To prove the superiority of multiagent fuzzy RL, two standard test functions containing five and fifteen thermal generating units integrated with multiple charging scenarios of plug-in electric vehicles have projected. Numerical consequences and assessment with a numerous current solution methodologies show the potentiality of multiagent fuzzy reinforcement learning method in solving the EEPD problem.

A New Framework for Plug-In Electric Vehicle Charging Models Supported by Solar Photovoltaic Energy Resources Un nouveau cadre pour les modèles de recharge du véhicule électrique rechargeable supporté par des ressources d’énergie solaire photovoltaïque

This article proposes a new framework for modeling plug-in electric vehicle (PEV) charging demand supported by solar photovoltaic (PV) energy resources in a distribution system. The proposed work focuses on modeling the stochastic nature of both PEV loads and PV generation while considering the effect of the temporal-spatial characteristics of the driver's behavior, as well as solar irradiation and temperature. A trip chain, based on the Markov Chain Monte Carlo process, is developed to properly model PEV daily driving activities and the PV uncertainty. Charging facilities are assumed available at home, work, and fast-charging stations, having charging levels of 3.7, 6.6, and 50 kW, respectively. The proposed framework is examined, considering the National Household Travel Survey global data, as well as the city of Buffalo and New York state. The impact of varying the penetration levels of PEV and PV resources is also investigated. This work strengthens the proposed models in the literature by integrating the temporal-spatial characteristics of PEV charging demand into PV stochastic models.

Smart V2G/G2V Charging Strategy for PHEVs in AC Microgrids Based on Maximizing Battery Lifetime and RER/DER Employment

Nowadays, the problem of the proper charging of plug-in hybrid electric vehicles (PHEVs) has become increasingly difficult because there is high uncertainty on both the demand and supply sides of microgrid (MG) systems, which may reduce efficiency and increase the cost of MG systems. Available solutions to these challenges are the application of energy storage systems and energy exchange with the upstream utility grid (UUG). However, uncoordinated PHEV charging could have adverse effects on MGs, such as increasing the amounts of energy exchanged between the MGs and the UUG as well as decreasing the battery energy storage systems (BESSs) lifetime. This article proposes a smart charging scheme for PHEVs in ac MGs that can simultaneously minimize the energy drawn from the UUG to charge PHEVs and maximize the lifetime of BESSs based on a multiobjective optimization algorithm. For proving the validity of the proposed power management strategy (PMS), offline digital time-domain simulation studies are conducted based on the modified version of the IEEE 33-bus test system. The obtained results are compared with the results of other previously reported PMSs, which show that by increasing the output power of DER units (even when the penetration level of PHEVs increases), the proposed PMS can ensure proper charging of PHEVs while improving the lifetimes of BESSs and decreasing the energy drawn from the UUG.