Control Of Electric Vehicle Charging Research Articles

A novel bald eagle search optimised ANFIS controlled high gain sepic converter-based efficient regenerative charging control for electric vehicles

A novel bald eagle search optimised ANFIS controlled high gain sepic converter-based efficient regenerative charging control for electric vehicles

Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles

Today, wind and solar energy sources have opened their place in the power system due to their environmental appeal. With the presence of these renewable energy sources (RESs), forecasting net load (NL) and its ramp are of huge importance owing their fundamental role in evaluating and improving the flexibility of the power grid. However, it is challenging to analysis flexibility and NL ramps using NL forecasts due to the uncertainty of RESs. Furthermore, the surge in the number of electric vehicles (EVs) as notable electricity consumers, and how their charging strategy can affect the NL curve are matters of concern. Most NL forecasting and flexibility analyses overlook the concurrent integration of wind and solar sources. Moreover, the impact of controlled EVs charging on grid flexibility remains largely unexplored. Existing studies also fail to assess the proper combination of wind and solar energy sources for future flexibility needs. This paper presents a framework for forecasting NL and detecting ramps with the simultaneous presence of EVs and RESs to investigate the impact of controlled charging of EVs on flexibility. Investigating charging of EVs as a solution is delved deeply in this paper to reduce NL ramps and improve flexibility. The numerical results show that controlled EVs charging may lead to 3% ramp reduction and 52% increment in the system flexibility. This paper also proposes to forecast and find the proper combination of RESs for the next years, leading to a 75% increment in the system flexibility compared to the business as usual RESs penetration.

Techno-economic modelling for energy cost minimisation of a university campus to support electric vehicle charging with photovoltaic capacity optimisation

Workplace charging of Electric Vehicles (EV) is a promising approach for transport decarbonization while addressing issues emerging from renewable energy growth. For workplaces, the decision to invest in EV charging infrastructure depends on the overall cost of providing this service. This study proposes a novel modelling approach to minimize the net annual energy cost of a university campus providing EV charging service using optimum capacity solar photovoltaic (PV) systems. The research includes an innovative approach to determine the campus EV charging demand and a novel net annual energy cost minimisation method combining PV size optimisation and EV charging control. A comprehensive analysis is presented to illustrate the influence of EV penetration, charging strategy, charging fees, charger cost and PV generation cost on the campus net annual energy cost. The influence of these parameters are also analysed on the optimal PV capacity, power and energy demand, and PV self-utilisation. Results show that, by using the proposed method, for 25 % EV penetration, the campus's peak demand is reduced by around 12 % and net annual energy cost is reduced by up to 9.2 % while providing free EV charging. The net annual energy cost reduction increases to over 20 % for 100 % EV penetration.

Communicational aspects in hierarchical real‐time control of electric vehicle charging: Available information and its value

AbstractSmart electric vehicle (EV) charging is a widely studied topic, and multiple different solutions have been proposed in the scientific literature to execute various demand response objectives. However, most studies use assumptions and simplifications that may not hold in real‐life situations, and thus, the proposed solutions may have a diminished practical value. Here, communicational aspects of hierarchical EV charging control are studied. The study considers the availability, acquisition, and value of certain information in case of real‐time control. The paper illustrates potential pitfalls when using certain assumptions or simplifications in the simulations and proposes a novel algorithm to deal with the lack of preliminary information that exists in real‐life situations. The objective of the proposed algorithm is to allow a lower‐layer controller to track the real‐time controllability of the EVs. This information is necessary in hierarchical control solutions with multiple lower‐layer controllers controlled by an upper‐layer controller to ensure efficient operation. The simulation results shows that a lower layer controller can estimate the controllability of the EV charging in realistic conditions within 1%–4 % accuracy despite the initially unknown characteristics of EVs.

Wireless Charging Control for Electric Vehicles

Abstract: Wireless power transmission is a way to transmit power without a wire. Wireless power transmission helps connect areas where people do not have access to an adequate power source. Anyone can get clean and green wireless power. From now on, all devices will be connected to the power source wirelessly. Wireless charging for electric vehicles has been in development for several years before the widespread use of these vehicles. Today, wireless charging systems offer an efficient and flexible way to charge electric vehicles of several categories and different capacities from a common base source. Standardization work is well underway to ensure system compatibility between vehicles and locations. In this paper, we presented successful experimental experiments for wireless power transmission and the future scope of wireless power transmission in electric vehicles

Transactive Charging Control of Electric Vehicles Considering Voltage Unbalance and Transformers’ Loss of Life

This paper presents a novel transactive energy (TE) management model for real-time coordination of plug-in electric vehicles (PEVs) in the distribution network. The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and distribution transformers’ loss of life. In the proposed TE market model, the PEV owners send the information of the required reimbursement for the response and the target level of state-of-charge at departure to the retailer. Then, the retailer runs an optimization problem to determine the scheduling of PEVs by minimizing the total cost including the cost of exchanging energy with upstream grid and payment to PEVs to use their flexibility. Besides, the model predictive control approach is used to effectively integrate the upcoming system operation conditions and dealing with system uncertainties. Moreover, linearization techniques are employed to reduce the computational complexity and improve the precision of the solutions. Finally, the proposed framework is applied to the unbalanced IEEE 33-bus distribution network, and the numerical analyses illustrate the effectiveness of the proposed model.

A Cooperative Charging Control Strategy for Electric Vehicles Based on Multiagent Deep Reinforcement Learning

The growth of electric vehicles (EVs) significantly increases the residential electricity demand and potentially leads to the overload of the transformer in the distribution grid. Aiming to coordinate the charging control of EVs, this article formulates the EVs charging problem as a Markov game with an unknown transition function and proposes a cooperative charging control strategy based on the multiagent deep reinforcement learning. The uncertainties from the dynamic electricity price, non-EV residential load consumption and drivers’ individual behaviors are considered to construct the dynamic charging environment. Each agent contains a collective-policy model and an independent learner. The collective-policy model is introduced to model other agent's behaviors by approximating their power consumption. The independent learner is used to learn the optimal charging strategy by interacting with the environment. The soft-actor-critic framework is adopted to train the independent learner, enabling the proposed method to address the continuous state and action. Agents are trained with only the local observation and approximation, indicating that the proposed approach is fully decentralized and scalable to the problem with multiple agents. Finally, several numerical studies constructed based on the real-world data demonstrate the effectiveness and scalability of the proposed approach.

Fuzzy Logic Strategy for Priority Control of Electric Vehicle Charging

The study of future scenarios where transport systems are highly based on electric propulsion constitutes a key point for the world sustainable development. In particular, the integration between electric vehicles and smart grids makes possible reaching new goals on energy savings, efficiency of energy generation and distribution. This paper proposes a controller integrated into the electric vehicle, based on fuzzy logic, providing the car user with the necessary information regarding the charging/discharging stations located nearby. In particular, for each station a priority level is reported, representing the degree of economic convenience/necessity that the user has in using this charging station. The final objective is to easily integrate a fleet of electric vehicles into smart grids with users willing to charge and discharge their electric vehicles during periods and modalities that highly favors the smart grid operation, energy savings, and economic efficiency for the car owners.

Electric Vehicle Charging Station: a Review of Energy Management Systems and Control Type

Due to their multiple strengths (total absence of exhaust emissions, silent engine, low operating costs, etc.) and the particularly favorable environment they have enjoyed in recent years, electric vehicles provide an effective and concrete response to reduce the environmental footprint of transport. Indeed, the current deployment rate and large-scale development of electric vehicles require the development of energy management and control strategies to better manage the charge/discharge of a large number of electric vehicles and energy exchange in a charging station, while ensuring optimal and economical operation, and minimizing stresses on the electric grid. The main contribution of this paper is an extensive review and classification of the different energy management systems strategies of electric vehicle charging stations, various methods and approaches are presented and classified. Moreover, this also includes a study on the charging infrastructure of electric vehicles, a comparison of the types of charge control of electric vehicles, the benefits and limitations associated with each type are presented.

Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation

In recent years, the importance of electric mobility has increased in response to climate change. The fast-growing deployment of electric vehicles (EVs) worldwide is expected to decrease transportation-related CO2 emissions, facilitate the integration of renewables, and support the grid through demand–response services. Simultaneously, inadequate EV charging patterns can lead to undesirable effects in grid operation, such as high peak-loads or low self-consumption of solar electricity, thus calling for novel methods of control. This work focuses on applying deep reinforcement learning (RL) to the EV charging control problem with the objectives to increase photovoltaic self-consumption and EV state of charge at departure. Particularly, we propose mathematical formulations of environments with discrete, continuous, and parametrized action spaces and respective deep RL algorithms to resolve them. The benchmarking of the deep RL control against naive, rule-based, deterministic optimization, and model-predictive control demonstrates that the suggested methodology can produce consistent and employable EV charging strategies, while its performance holds a great promise for real-time implementations.

Design and Application of Early Warning Model for Electric Vehicle Charging Safety

In order to improve the charging safety of electric vehicles, it is necessary to design the early warning model of charging safety, so as to overcome the overload defects in the charging process of electric vehicles. An electric vehicle charging safety warning model was proposed based on contactless charging design. The electromagnetic induction coil and electromagnetic coupler installed on the charging pile are used to design the electromagnetic coupling induction with the driving electric vehicle tire wire, and the electromagnetic coupling control and charging stability adjustment of the electric vehicle are carried out by using the inductive power transmission technology. According to the overload regulation of electric vehicle during charging, the overload interruption control of electric vehicle charging is carried out by mutual inductance coupling method, and the parameters such as resonance frequency, mutual inductance coefficient and coil internal resistance during charging are analyzed. The parameter steady-state regulation method is used for non-contact charging, and the electric vehicle charging safety early warning control is realized, and the parameter optimization design of electric power transmission system in the process of electric vehicle charging is carried out. According to the parameter optimization result, the electric vehicle charging safety early warning test is realized. The test results show that the proposed method has good control performance and can effectively realize the wireless energy transmission of 100W in 100cm, the charging efficiency and the charging safety are improved.

Private versus Shared, Automated Electric Vehicles for U.S. Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration, and Cost Impacts.

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. While the convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, it requires careful integration with rapidly evolving electricity systems. Here, we examine these interactions using a U.S.-wide simulation framework encompassing private electric vehicles (EVs), shared automated EVs (SAEVs), charging infrastructure, controlled EV charging, and a grid economic dispatch model to simulate personal mobility exclusively using EVs. We find that private EVs with uncontrolled charging would reduce GHG emissions by 46% compared to gasoline vehicles. Private EVs with fleetwide controlled charging would achieve a 49% reduction in emissions from baseline and reduce peak charging demand by 53% from the uncontrolled scenario. We also find that an SAEV fleet 9% the size of today's active vehicle fleet can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Such an SAEV fleet would achieve a 70% reduction in GHG emissions at 41% of the lifecycle cost as a private EV fleet with controlled charging. The emissions and cost advantage of SAEVs is primarily due to reduced vehicle manufacturing compared with private EVs.

Assessment of Prediction Uncertainties in EV Charging Management

The share of electric vehicles (EVs) in the market has been growing rapidly during the past few years. Much research has been conducted in order to enable smart charging, which would fulfil EV user needs while considering technical limitations of the grid and incentives provided by the electricity market. However, the major part of the developed charging methods uses information of EV near-future driving profiles, e.g. departure time and energy need of the next trip, which can be problematic to achieve accurately enough. This kind of information will require either an extra communication link between the EV user and the EV charging controller, or EV tracking. Regardless of how the information is acquired, some uncertainty is likely in the predictions, which can cause undesired results. The aim of this paper is to assess these prediction error-related issues for distributing available charging capacity and to introduce alternative EV charging control methods that eliminate prediction error-related challenges. The results indicate that the EV battery state-based methods can distribute available charging capacity almost as effectively as the EV near future driving profile-based method with perfect predictions.

Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain

Abstracts In order to improve the consumption capacity of renewable energy, an ordered charging control method of electric vehicles based on Markov chain prediction is proposed considering the uncertainty of the electric vehicle charging load. The quantity of electric vehicles in different parking lots and different time periods is predicted by the Markov chain to calculate the electric vehicle charging load of the photovoltaic charging station. Taking the minimum power sum of a photovoltaic charging station, the minimum cost of users and the maximum consumption of the renewable energy as the objective functions, an ordered charging control model with the interest and willingness of electric vehicle users is established. The peak-to-valley time period is divided by the particle swarm optimization algorithm, and the genetic algorithm is used to solve the model. The simulation tests of the electric vehicle driving data provided by the Unite State Travel Survey website verify the correctness and effectiveness of the proposed method, the effects of different peak and valley periods and the responsiveness of electric vehicle users on the ordered charging control of electric vehicles are compared and analyzed. The simulation results show that the prediction accuracy of the charging load obtained by the Markov chain is significantly higher than that of Monte Carlo, and when the responsiveness of electric vehicle users reaches 90%, the total charging cost can be reduced by up to 16.89%. The ordered charging strategy of electric vehicles proposed in this paper can effectively reduce the peak-to-valley difference, increase the consumption of renewable energy, and reduce the charging cost of electric vehicle users.

Heterogeneous Island Models and Their Application to Recommender Systems and Electric Vehicle Charging

In this paper we describe a general framework for parallel optimization based on the island model of evolutionary algorithms. The framework runs a number of optimization methods in parallel with periodic communication. In this way, it essentially creates a parallel ensemble of optimization methods. At the same time, the system contains a planner that decides which of the available optimization methods should be used to solve the given optimization problem and changes the distribution of such methods during the run of the optimization. Thus, the system effectively solves the problem of online parallel portfolio selection. The proposed system is evaluated in a number of common benchmarks with various problem encodings as well as in two real-life problems — the optimization in recommender systems and the training of neural networks for the control of electric vehicle charging.

Quantifying the benefits of electric vehicles on the future electricity grid in the midwestern United States

Rising numbers of electric vehicles (EVs) along with increasing renewable electricity will create challenges for electricity grids, potentially leading to significant increases in peak load, overgeneration of renewables during low load periods, and increased ramping requirements. However, controlled EV charging could largely mitigate these issues, transforming EVs from a grid liability into a valuable asset. In this study, we model the evolution of the Midcontinent Independent System Operator (MISO) grid, located in the midwestern United States (U.S.) with large numbers of forecasted EVs and renewable generation by the late 2030s, over the period 2019–2039. Uncontrolled charging leads to peak load increases of up to 10% or 8 GW, and exacerbated ramping requirements. In contrast, controlled charging significantly ameliorates these challenges, with unidirectional (“V1G”) charging largely avoiding peak load increases, filling valley loads and reducing ramp rates. Bidirectional (“V2G”) charging provides much larger load flexibility to dramatically reduce peak loads and ramp rates. This study investigates EV load growth in the midwestern U.S. and the impact that different types of controlled charging can have on mitigating the grid challenges presented both by large numbers of EVs and increasing levels of renewable generation. Moreover, above ~3 million EVs, V2G charging can exhibit multi-day optimization behavior and very high load flexibility, suggesting the possibility of new load planning tools extending over multiple days. Such capabilities would provide significant aid in optimally scheduling resources with a larger percentage of intermittent renewables.

Private Versus Shared, Automated Electric Vehicles for U.S. Personal Mobility: Energy Use, Greenhouse Gas Emissions, Grid Integration and Cost Impacts

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. While the convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, it requires careful integration with rapidly-evolving electricity systems. Here we examine these interactions using a U.S.-wide simulation framework encom- passing private electric vehicles (EVs), shared automated EVs (SAEVs), charging infrastructure, controlled EV charging, and a grid economic dispatch model to simulate personal mobility exclusively using EVs. We find that an SAEV fleet 9% the size of today’s active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly lowering GHG emissions and decreasing solar curtailment by about one- third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.

Evaluation of Optimization-Based EV Charging Scheduling with Load Limit in a Realistic Scenario

In the literature, optimization-based approaches are frequently proposed for the control of electric vehicle charging. However, they are usually evaluated under simplifying assumptions and are not compared to more simple approaches. The present work compares optimization-based approaches with rule-based ones in a simple but realistic scenario, in which a certain limit for the total load has to be satisfied. The scenario is based on the situation at an office building in Germany. In simulation experiments, different control approaches are evaluated not only in terms of pure performance but also from an economic perspective. The results indicate that, although the optimization-based approaches outperform the rule-based approaches, they are not always the right choice from an economic point of view.

Charging cost minimisation by centralised controlled charging of electric vehicles

Charging cost minimisation by centralised controlled charging of electric vehicles

Socio-technical smart grid optimization via decentralized charge control of electric vehicles

The penetration of electric vehicles becomes a catalyst for the sustainability of Smart Cities. However, unregulated battery charging remains a challenge causing high energy costs, power peaks or even blackouts. This paper studies this challenge from a socio-technical perspective: social dynamics such as the participation in demand-response programs, the discomfort experienced by alternative suggested vehicle usage times and even the fairness in terms of how equally discomfort is experienced among the population are highly intertwined with Smart Grid reliability. To address challenges of such a socio-technical nature, this paper introduces a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness. In contrast to related work, a novel autonomous software agent exclusively uses local knowledge to generate energy demand plans for its vehicle that encode different battery charging regimes. Agents interact to learn and make collective decisions of which plan to execute so that power peaks and energy cost are reduced system-wide. Evaluation with real-world data confirms the improvement of drivers’ comfort and fairness using the proposed planning method, while this improvement is assessed in terms of reliability and cost reduction under a varying number of participating vehicles. These findings have a significant relevance and impact for power utilities and system operator on designing more reliable and socially responsible Smart Grids with high penetration of electric vehicles.