Plug-in Electric Vehicles Fleet Research Articles

Enhancing Electric Vehicle Reliability and Integration with Renewable Energy: A Multi-Faceted Review

Electric vehicles (EVs) are gaining attention due to their zero carbon emissions, but concerns about their reliability, especially critical components, persist. Previous research has primarily focused on EV drive motor reliability, neglecting the motor controller. To address this gap, this study assesses the reliability of the entire motor system in electric vans, including both drive motor and motor controller components. It predicts failure rates for these components, highlighting vulnerabilities and shortcomings in existing research, which can inform future design and maintenance. In addition, the integration of EVs and renewable energy resources has garnered attention, but concerns about component reliability have arisen. A novel approach called " Innovative Incentive-Driven Fuzzy Fault Tree Analysis " (IIFFTA) is introduced for power systems incorporating EVs and renewable energy, addressing vague and imprecise events and data deficiencies in conventional fault tree analysis. IIFFTA considers different component failure rates and probability values of fault occurrences, offering a more effective risk assessment method. Lastly, plug-in electric vehicles (PEVs) impact distribution systems, and this paper explores distribution feeder reconfiguration (DFR) as a reliability-enhancing strategy for coordinating vehicle-to-grid (V2G) services from PEV fleets within a stochastic framework. The study accounts for uncertainties related to network demand, energy prices, wind power generation, and PEV fleet behavior, employing a self-adaptive evolutionary algorithm (SOS) to address the stochastic optimization problem.

Advanced control strategies to manage electric vehicle drivetrain battery health for Vehicle-to-X applications

The demand for local power resilience is promoting the installation of renewable-based microgrids. Plug-in electric vehicles (PEVs) are rapidly increasing and can play a critical role in microgrid service provision. The capability of bi-directional charging and discharging will transition PEVs from a simple means of transportation to multi-purpose value generating assets. They can balance the inherently fluctuating generation of renewable energy resources and shape customer’s electricity demand, while still providing the core service of transportation. Value stacking through those additional services allows PEV owners to draw from a number of revenue streams to offset investment costs. However, accelerated battery degradation is often cited as concern when using PEV batteries for purposes other than driving, which presents a major barrier for broad market adoption. This study seeks to assess the economics and trade-offs of bi-directional use of PEVs when utilized as fleet vehicles. In particular, the usage patterns and benefits for PEV fleet deployment at three U.S. military bases is presented. The results show operational cost benefits for bi-directional application of up to 60.8 % annual return on investment without managing battery health, and up to 106.0 % when actively managing battery health through advanced control strategies. The one year payback period far exceeds the expected installation lifespan of at least ten years. Given the results, utilizing PEV fleets for additional services at U.S. military bases can largely offset the additional cost of bi-directional charging hardware and software compared to standard uni-directional equipment.

Bi-level deep reinforcement learning for PEV decision-making guidance by coordinating transportation-electrification coupled systems

The random charging and dynamic traveling behaviors of massive plug-in electric vehicles (PEVs) pose challenges to the efficient and safe operation of transportation-electrification coupled systems (TECSs). To realize real-time scheduling of urban PEV fleet charging demand, this paper proposes a PEV decision-making guidance (PEVDG) strategy based on the bi-level deep reinforcement learning, achieving the reduction of user charging costs while ensuring the stable operation of distribution networks (DNs). For the discrete time-series characteristics and the heterogeneity of decision actions, the FEVDG problem is duly decoupled into a bi-level finite Markov decision process, in which the upper-lower layers are used respectively for charging station (CS) recommendation and path navigation. Specifically, the upper-layer agent realizes the mapping relationship between the environment state and the optimal CS by perceiving the PEV charging requirements, CS equipment resources and DN operation conditions. And the action decision output of the upper-layer is embedded into the state space of the lower-layer agent. Meanwhile, the lower-level agent determines the optimal road segment for path navigation by capturing the real-time PEV state and the transportation network information. Further, two elaborate reward mechanisms are developed to motivate and penalize the decision-making learning of the dual agents. Then two extension mechanisms (i.e., dynamic adjustment of learning rates and adaptive selection of neural network units) are embedded into the Rainbow algorithm based on the DQN architecture, constructing a modified Rainbow algorithm as the solution to the concerned bi-level decision-making problem. The average rewards for the upper-lower levels are ¥ -90.64 and ¥ 13.24 respectively. The average equilibrium degree of the charging service and average charging cost are 0.96 and ¥ 42.45, respectively. Case studies are conducted within a practical urban zone with the TECS. Extensive experimental results show that the proposed methodology improves the generalization and learning ability of dual agents, and facilitates the collaborative operation of traffic and electrical networks.

Electric Vehicle Trip Chain Information-Based Hierarchical Stochastic Energy Management With Multiple Uncertainties

This paper proposes a vehicle trip chain information-based hierarchical stochastic energy management system (Hi-SEMS) for a microgrid integrated with highly uncertain plug-in electric vehicles (PEVs) fleet, renewable energy sources, and loads. The two-layer Hi-SEMS scheme aims at optimal scheduling of PEV fleet to counteract the operational uncertainties encountered in real-time operation and minimizing the microgrid daily operating cost comprising the fuel cost and the emission cost. In the proposed work, the realistic behavior of PEVs fleet is modeled using a stochastic trip chain information to accurately forecast the charge/discharge demand taking into account the driver’s behavior as foundation. Realistic statistical trip data, multi-location charge/discharge of PEVs fleet to include coupled spatial-temporal dynamics of heterogeneous PEVs fleet is considered for intelligent charge/discharge scheduling. The effectiveness of the proposed strategy is verified on modified LV CIGRE and modified IEEE 33 bus radial distribution test networks and is compared with four different cases on each of the two test systems. The simulation results exhibit that the proposed Hi-SEMS outperforms the other four cases in terms of daily operating cost, the incentive to the PEVs owner, and robustness to multiple uncertainties.

PDE-based modelling and observer design for PEV fleets in V2G charging stations

We are considering the problem of modelling and observer design for a fleet of Plug-in Electric vehicles (PEVs) in a public station connected to a smart grid. Both charging (Grid-to-Vehicle (G2V)) and discharging (Vehicle-to-Grid (V2G)) are focused on. We first develop models of fleet dynamics in both modes. The models are 1D hyperbolic-type partial differential equations (PDEs), and their well-posed is analysed using the method of characteristics, while their discretisation is performed using the θ-scheme. Then, we design observers that provide online estimates of the state variable along the PDE domain. The design and the analysis of observers are performed using Volterra integral backstepping transformation. Finally, numerical simulations are provided that confirm the theoretical convergence results in G2V and V2G modes.

Real Time Scheduling of a Microgrid Equipped with Ultra-Fast Charging Stations

Ultra-fast charging infrastructures are gaining increasing interest thanks to their ability to reduce the charging-time of plug-in electric vehicles to values comparable to those of the refueling of traditional vehicles in gas stations. This is a consequence of the increasing rated power of both on-board batteries and charging equipment. On the other hand, the increased values of charging power have led to an increased impact on the power distribution networks, particularly in terms of line currents and bus voltages. In presence of large penetration of ultra-fast charging devices, in fact, both currents and voltages are affected by larger variations whose values can exceed the admissible limits imposed by the technical constraints and by the levels of quality of service. In order to reduce the impact of this typology of vehicles’ charging on the electrical infrastructure, in this paper a methodology is presented which allows managing a microgrid in presence of ultra-fast charging stations by satisfying the constraints of the grid, while preserving the expected short charging-time for electric vehicles. To this end, a proper optimal strategy is proposed which coordinates the demands of electric vehicles and of the other loads of the microgrid with the power provided by the renewable energy generation resources. The proposed approach aims to optimally control the active and reactive power of charging stations and renewable generation units and to minimize the charging time of a fleet of plug-in electric vehicles while satisfying the constraints on the technical aspects and on the quality of service. The proposed approach has been tested on a test system and the results, proposed in the last part of the paper, demonstrate the feasibility of the proposed approach.

Boundary Observer Design for Fleets of Plug-in Electric Vehicles in Bidirectional Charging Stations

In this paper, we consider the problem of estimating the density profiles of fleets of Plug-in Electric Vehicles (PEVs) in charging stations. We put the focus on the case of bidirectional exchange of energy between the grid and PEVs i.e. both vehicle-to-grid (V2G) and grid-to-vehicle (G2V) modes are considered. In both modes, the energy exchange is assimilated to a transfer phenomenon that is modeled by using a first-order hyperbolic partial differential equation (PDE). We design a boundary observer that provides online estimates of charge/discharge density profiles along the spatial domain. The observer is designed based on the system PDE model. It only makes use of boundary measurements and involves measurement injection in the boundary equation. The exponential convergence of the observer is proved using a suitable Lyapunov functional. Simulation results are provided that highlight the satisfactory observer performances.

A Multiagent Federated Reinforcement Learning Approach for Plug-In Electric Vehicle Fleet Charging Coordination in a Residential Community

The increasing penetration of distributed renewable energy and electric vehicles (EV) in local microgrids/residential-community has brought a great challenge to balancing system stability and economic benefits. This paper proposes a decentralized framework based on an efficient federated deep reinforcement learning method for plug-in electric vehicle (PEV) fleet charging management in a residential community, which is equipped with a photovoltaic and battery energy storage system and connected to a local transformer. Firstly, the framework of PEV charging management is described as a virtual EV charging station coordinating charging tasks through sharing public information with distributed agents. Then, an individual preference model of PEV is developed considering heterogenous PEV charging anxiety, battery degradation, and collective penalty. Subsequently, we propose an attention-weighted federated soft-actor-critic method to efficiently seek the co-ordinational scheduling of the PEV fleet charging in a distributed way, where scalability and privacy protection can be ensured with attention-based information sharing. Finally, a real-world case study is conducted to validate the effectiveness and feasibility of the proposed approach.

Optimal fleet deployment for electric vehicle sharing systems with the consideration of demand uncertainty

This study addresses the optimal allocation of a fleet of plug-in electric vehicles (EVs) to the stations of an EV-sharing system. The objective is to maximize the profit of the system operator. A multi-layer time–space network flow technique is adopted to describe the movement of EVs in the system. We develop a mixed integer linear programming model for optimal fleet allocation in EV-sharing systems based on the multi-layer time–space network. This study applies robust optimization and chance-constrained techniques to deal with the fleet deployment problem with uncertain and stochastic demands, respectively. While small-scale instances of the problem can be optimally solved using commercial software such as Gurobi, a network decomposition-based mathheuristic is developed to efficiently solve large-scale instances. A set of computational experiments were conducted based on the data provided by the operator of the EV-sharing system deployed in Sun Moon Lake National Park in Nantou, Taiwan. The results show the proposed models and the heuristic are able to effectively and efficiently generate optimal fleet allocations under deterministic, uncertain or stochastic demand scenarios. Two measures of effectiveness, robust price and hedge value, are examined to verify the price-paid and value-gained by applying the robust solution. The proposed approach can be used as a decision support tool to assist operators of EV-sharing systems in effectively determining the deployment of their fleets to stations considering uncertain or stochastic demands.

Optimal integration of Demand Response Programs and electric vehicles into the SCUC

Abstract Security Constrained Unit Commitment (SCUC) is carried out by power system operators to determine the optimal participation of generating units to meet the load demand while satisfying units’ and systems’ constraints as well as the desired level of security. With technological advances in the realm of communications and development in the infrastructure, a proper environment for the active contribution of customers has been formed. This new structure has enabled the consumers to play a vital role in the market via Demand Response Programs (DRPs) and coordinated aggregated Plug-In Electric Vehicle (PEV) fleets. These new resources could be a merit for the power system provided that they are employed correctly and in coordination with the rest of the system. The SCUC is where DRPs and PEVs could be optimally addressed to exploit their full potential. This paper tackles the problem of SCUC in the presence of DRPs and aggregated PEV fleets. The objective is to lessen the operation cost provided that all constraints are satisfied. Different constraints including those related to the system, units, transmission system, DRPs, and PEVs as well as the security constraints are taken into consideration. The SCUC is modeled as a two-stage stochastic mixed-integer programming problem and an appropriate model of PEVs is derived to facilitate the integration of these resources into the grid while considering their charging and discharging characteristics as well as efficiency. Moreover, a suitable model of DRPs is introduced in this study based on the concept of price elasticity of demand. The proposed approach is practiced over the IEEE 24-bus Reliability Test System (RTS) to analyze the effect of the smart grid on SCUC. Obtained results demonstrate that with proper deployment of DRPs and PEVs the operation cost could be lessened while the security of the system is improved.

Charging management of plug‐in electric vehicles in San Francisco applying Monte Carlo Markov chain and stochastic model predictive control and considering renewables and drag force

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.

A Distributed Dual Proximal Minimization Algorithm for Constraint-Coupled Optimization Problems

We address constraint-coupled optimization for a system composed of multiple cooperative agents communicating over a time-varying network. We propose a distributed proximal minimization algorithm that is guaranteed to converge to an optimal solution of the optimization problem, under suitable convexity and connectivity assumptions. The performance of the introduced algorithm is shown on a numerical example of a charging scheduling problem for a fleet of plug-in electric vehicles.

Stochastic two-stage reliability-based Security Constrained Unit Commitment in smart grid environment

In the realm of the smart grid, there are new issues that are needed to be addressed. However, these new concerns may have an adverse effect on the complexity of the operation of the network, they present great opportunities to the network operators for a better service, in terms of lower costs and enhanced security. In this paper, the integration of the coordinated aggregated Plug-in Electric Vehicle (PEV) fleets and Demand Response Programs (DRPs) into power systems is studied using the stochastic reliability-based Security Constrained Unit Commitment (SCUC). The proposed SCUC program minimizes network operation costs while determining the best strategy for deployment of PEVs and DRPs. Moreover, the proposed approach analyze the effect of these resources on the adequacy of the power system by considering reliability metrics Being a mobile power storing device enables the PEVs to provide their energy to the network when called upon. Moreover, DRPs can mitigate power consumption during the peak load or in case of a contingency. Optimal integration of these resources could reduce the network operating costs and improve security. A two-stage stochastic mixed-integer programming model is employed for power system modeling in a smart grid environment. In the proposed model, PEV fleets are modeled as virtual power plants that can provide electric power while DRPs are assumed to only affect the power consumption of the customers. The proposed paradigm is applied to the IEEE 6-bus and IEEE Reliability Test System (RTS) to illustrate its effectiveness. Numerical analyses confirm the effectiveness of the proposed strategy in lessening the cost of network operations, better performance in case of contingencies and a higher level of reliability.

Constructing a V2G-enabled regional energy Internet for cost-efficient carbon trading

The increasing penetration of plug-in electric vehicles (PEVs) has highlighted the importance of coordinating ubiquitous distributed energy resources (DERs) via the internet of things (IoT). With the help of vehicle-to-grid (V2G) technology, PEVs can be aggregated to behave as a storage system, yielding both economic and environmental benefits. In this paper, we propose an optimal bidding framework for a V2G-enabled regional energy internet (REI) to participate in day-ahead markets considering carbon trading. The REI operator aims to maximize the net profits from day-ahead markets while anticipating realtime adjustments. A detailed battery model is developed to depict the charging and discharging capability of V2G-enabled PEVs. A two-stage stochastic optimization model is formulated to schedule the operation of PEV fleets against various sources of uncertainties, e.g., the arrival and departure time of PEVs, solar power and real-time prices. Case studies undertaken based on realistic datasets demonstrate that the coordination of the V2G-enabled PEVs and other DERs can facilitate the accommodation of renewable energy, thus improving the REI's revenues in energy and carbon markets.

Modeling charging infrastructure impact on the electric vehicle market in China

The plug-in electric vehicle (PEV) is deemed as a critical technological revolution, and the governments are imposing various vehicle policies to promote its development. Meanwhile, the market success of PEVs depends on many aspects. This study integrates one’s use of charging infrastructure at home, public place and workplace into the market dynamics analysis tool, New Energy and Oil Consumption Credits (NEOCC) model, to systematically assess the charging infrastructure (home parking ratio, public charging opportunity, and charging costs) impact on PEV ownership costs and analyze how the PEV market shares may be affected by the attributes of the charging infrastructure. Compared to the charging infrastructure, the impact of battery costs is incontrovertibly decisive on PEV market shares, the charging infrastructure is still non-negligible in the PEV market dynamics. The simulation results find that the public charging infrastructure has more effectiveness on promoting the PEV sales in the PEV emerging market than it does in the PEV mature market. However, the improvement of charging infrastructure does not necessarily lead to a larger PEV market if the charging infrastructure incentives do not coordinate well with other PEV policies. Besides, the increase of public charging opportunities has limited motivations on the growth of public PEV fleets, which are highly correlated to the number of public fast charging stations or outlets. It also finds that more home parking spaces can stimulate more sales of personal plug-in hybrid electric vehicles instead of personal battery electric vehicles.

A hierarchical charging control of plug-in electric vehicles with simple flexibility model

The demand for plug-in electric vehicles (PEVs) is increasing exponentially, which could add a considerable amount of load onto the power grid, because charging one PEV is nearly equivalent to adding three houses to the power system. On the other hand, their capability to actively interact with the grid presents excellent opportunities for load management, demand response, and various other grid services. This paper proposes a hierarchical charging scheduling and control framework to enable PEVs for grid services while meeting vehicle owners’ travel needs. The proposed control framework consists of coordination and vehicle layers. In the vehicle layer, each set of electric vehicle supply equipment (EVSE) is equipped with a controller that estimates charging power and energy flexibility based on vehicle characteristics, charging equipment power rating, battery energy state, and upcoming trip information. The controller also controls vehicle charging power in real-time. In the coordination layer, with the charging flexibility model received from each EVSE controller, the central coordinator determines the optimal power allocation for a look-ahead time window based on grid services to be provided. The proposed charging coordination approach can help reduce computational complexity and communication requirement compared with existing methods. It is also scalable to the expanding PEV fleet and robust to uncertainties in upcoming vehicle trips and future system condition. The proposed method is studied using a prototypical feeder developed at Pacific Northwest National Laboratory and detailed trip information extracted from National Household Travel Survey.

Load Guided Signal-Based Two-Stage Charging Coordination of Plug-In Electric Vehicles for Smart Buildings

A novel plug-in electric vehicle (PEV) charging coordination scheme for smart buildings, processed in two separate stages bridged by a load guided signal, is proposed in this study. The goal of the proposed coordination is to minimize the overall energy cost of the building, while satisfying the desired target and operation range of the state of charge (SoC) of each PEV. As the PEV penetration level grows, uncoordinated charging may impact the stability of the building's energy system by increasing the peak demand and introducing uncertainty. Consequently, the charging decisions on the PEV fleet require considering the uncertainty of the drivers' behavior and the power demand pattern of the coupled building. In this study, a customized load guided signal is introduced for PEV fleet charging. It formulated and implemented through mixed integer linear programming in two separate stages. The first stage involves the extraction of the electric vehicle supply equipment (EVSE) based guide signal for the benefits and physical constraints of the building's energy system. The load guided signals are created by jointly investigating the charging/discharging flexibility of the EVSE using load prediction and the PEV fleet to minimize the electricity cost in the time-of-use energy market. In the second stage, the priority weight is exploited for distributing the charging/discharging decisions for individual PEVs. To evaluate the performance of the proposed method, numerical evaluations were conducted at various PEV penetration levels using a pair of energy consumption and vehicle parking datasets for the building. The case study demonstrates that the proposed scheme provides 12% load factor improvement and 13% cost reduction at a 50% PEV penetration level.

Generalized Nash equilibrium problem based electric vehicle charging management in distribution networks

This paper presents a generalized Nash equilibrium problem (GNEP) approach for the management of plug-in electric vehicle (PEV) charging activities in a distribution network. The PEV charging pricing signal is designed to reflect the distribution network operational cost. The charging strategy of individual PEVs is allowed to respond price tariff to minimize its charging cost, while must also consider the charging requirement and grid facility constraints, such as vehicle on board charger, distribution node, and substation power limits. With this approach, the selected charging strategies are near socially optimal solutions under physical constraints. The PEV charging GNEP is reformulated by Nikaido-Isoda function, which converts a distributed decision-making problem to a constrained optimization problem. Due to the non-differentiability of individual objective functions in the GNEP, relaxation algorithm is employed for searching optimal solutions in an iteration process. To adapt to the uncertainty and randomness of PEV fleet and grid load dynamics, the PEV charging management algorithm updates the GNEP when there are newly connected/disconnected PEVs or the load variation in the distribution network exceeds a predefined value. To validate the presented method, two use cases are defined. Simulation results of these two use cases verify the effectiveness of this method in both regulating the PEV charging activities and achieving PEV charging customers' objectives. The effect of customer participation rate on the performance of the presented control scheme is also analyzed.

A model predictive control approach for matching uncertain wind generation with PEV charging demand in a microgrid

The matching between uncertain wind power supply and plug-in electric vehicles (PEVs) charging demand has the potential to reduce the greenhouse gas emission and the fossil fuel pollution. In view of this, we propose a hierarchical model predictive control approach to coordinate the wind generation and PEV charging in the context of microgrid. The proposed control approach consists of two control layers. In the top layer, a stochastic model predictive controller computes the optimal power references for the wind generator and the PEV fleet. These references are fed to the bottom layer, and are further executed by the wind generator controller and PEV fleet controller, respectively. A salient feature of this approach is that it comprehensively incorporates the uncertainties in both sides of supply and demand, i.e., the uncertainties associated with the maximum available wind generation, and the uncertainties associated with the PEVs charging demand. Using the Chebyshev inequality and the chance constraints, the corresponding stochastic optimization problem is approximated as a quadratic programming problem. By doing so, the proposed approach not only keeps the microgrid power balance, but also ensures the PEV users’ quality of experience. Furthermore, it can bring the power flow between microgrid and utility power system to a predefined trajectory. Simulation results based on real-world wind and PEV data validate the effectiveness of the proposed approach.

PDE Modeling and Control of Electric Vehicle Fleets for Ancillary Services: A Discrete Charging Case

This paper examines modeling and control of a large population of grid-connected plug-in electric vehicles (PEVs). PEV populations can be leveraged to provide valuable grid services when managed via model-based control. However, such grid services cannot sacrifice a PEV’s primary purpose—mobility. We consider an aggregator, which can control a fleet of PEVs with three possible charging rates: charging at a constant rate, discharging at a constant rate or idle. We develop a system of coupled partial differential equations for aggregating large populations of PEVs and transform its discretized version into a state space representation. We propose a linear quadratic regulator to track a power signal that provides load following services. We investigate the sensitivity of controller parameters, different bidding strategies and their impact on the performance of the provided balancing service. We examine this control design on a simulated case study and analyze sensitivity to a variety of assumptions and parameter selections.