Plug-in Electric Vehicle Battery Research Articles

A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry

The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system.

Multi-objective optimal energy management of microgrids including plug-in electric vehicles with the vehicle to grid capability for energy resources scheduling

As more battery electric vehicles and plug-in hybrid electric vehicles are connected to the microgrid, plug-in electric vehicles have a major impact on the microgrid. This paper proposes a multi-objective optimization energy management model including plug-in electric vehicles and other distributed generations. By analyzing the powertrain structure of different kinds of plug-in electric vehicles, the engine fuel consumption model, the charging model, the discharge model, and the battery state of charge model of plug-in electric vehicle in microgrids are given. The proposed model considers the plug-in electric vehicle battery state of charge constraints to prevent the battery from overcharging and over-discharging and gives the state of charge curve in microgrids. Simultaneously, an improved gray wolf algorithm, introducing optimization control factors and greedy strategies to better balance the mining and exploration capabilities of the gray wolf algorithm, is proposed to solve this multi-objective optimization energy management model. Compared with particle swarm optimization and traditional gray wolf algorithm, the improved algorithm further improves the accuracy and convergence speed. Besides, the improved algorithm is applied to three scheduling schemes, and the results show that plug-in hybrid electric vehicles have more advantages in energy economy in some special cases.

Charging Coordination of Plug-In Electric Vehicle for Congestion Management in Distribution System Integrated With Renewable Energy Sources

This article presents a novel strategy for congestion management in a distribution system, which occurs due to the uncoordinated charging of plug-in electric vehicles (PEVs). The large proliferation of PEVs has been significantly creating impact on the transportation and power sector in recent times. However, uncoordinated charging of PEVs leads to congestion in distribution feeders when the penetration of PEVs is reasonably high. A charging coordination strategy of grid-to-vehicle (G2V) and vehicle-to-grid (V2G) is formulated considering the large PEVs penetration in the distribution system. In this article, the popular ensemble method, i.e., gradient boosting regression tree method is employed to predict the state-of-charge for PEVs batteries. The charging coordination of G2V-V2G mode is developed in order to study the maximum PEVs penetration in the distribution system with an objective function of minimizing overall cost. The strategy is implemented in workplace car park powered by grid and grid-connected photovoltaic generation located in industrial areas of the IEEE 38 bus radial distribution system. An analytical study is presented to evaluate the maximum possible PEV penetration that the distribution network can accommodate without violating the grid constraints. Moreover, the performance of proposed strategy with or without the presence of solar powered car park is analyzed to assess the maximum possible PEVs penetration in the network so that no congestion occurs in the distribution lines.

Resilient Distributed Coordination of Plug-In Electric Vehicles Charging under Cyber-Attack

The coordinated scheduling of plug-in electric vehicle (PEV) charging should be constructed in distributed architecture due to the growing population of PEVs. Since the information and communication technology makes the adversary more permeable, the distributed PEV charging coordination is vulnerable to cyber-attack which may degrade the performance of scheduling and even cause the failure of scheduler task. Considering the tradeoff between system-wide economic efficiency, distribution level limitations and PEV battery degration, this paper investigates the resilient distributed coordination of PEV charging to resist cyber-attack, where the steps of detection, isolation, updating and recovery are designed synthetically. Under the proposed scheduling scheme, the misbehaving PEVs suffering from cyber-attack are gradually marginalized and finally isolated, and the remaining well-behaving PEVs obtain their own optimal charging strategy to minimize the total system cost in distributed architecture. The simulation results verify the effectiveness of theoretical method.

Techno-economic evaluation of PEVs energy storage capability in wind distributed generations planning

Plug-in electric vehicles (PEVs) can be used for renewable energy dispatching in both operation and planning of distribution networks. In this paper, wind distributed generations (WDGs) are planned based on vehicle to grid (V2G) operation mode of PEVs. The optimal long-term planning is based on optimal short-term scheduling considering all related uncertainties. A comprehensive model of the batteries degradation is considered in the objective function. A two-layer optimization procedure is proposed in which the optimal location and capacity of WDGs are determined in first layer, while optimal charge and/or discharge of PEVs are scheduled in the second layer, subject to technical constraints. The numerical results delineate the capability of PEVs in V2G operation mode for wind energy dispatching in WDGs planning. The simulation results show the storage capability of PEVs will increase by their number growing. Therefore, the operation and planning costs of WDGs will be reduced if the PEVs penetration to be high. The capacity of installed WDGs in smart charging scenario is less than uncoordinated charging scenario as the PEVs properly dispatch WDGs in smart charging strategy. Moreover, the results show that the PEVs battery degradation cost limit the participation rate of PEVs in WDGs dispatching.

Plug-in electric vehicles in China and the USA: a technology and market comparison

As the top two plug-in electric vehicle (PEV) markets in the world, China and the United States of America (USA) have developed different market structures that are influenced by government policies, test procedures, customer acceptance, and vehicle performance. There are differences in PEV test procedures and vehicle class definitions as well. This paper quantifies such differences and compares PEV characteristics and markets in the two countries using actual data collected over several years. First, although China surpasses the USA in annual PEV production because of generous PEV policies and higher charging infrastructure availability, the USA has a higher PEV adoption rate per capita (2.3 versus 0.81 per 1000 people at the end of 2017). Second, the most popular vehicle classes are A00 battery electric vehicles (BEVs) in China but long-range mid-size BEVs in the USA. Moreover, China’s electric and plug-in hybrid sport utility vehicle market is growing quickly. Third, the electricity consumption rated under Chinese test procedures is 20%–40% lower than that rated under US procedures. The sales-weighted electricity consumption of an average BEV in China is 23% lower than that in the USA because of larger proportions of small and micro BEVs in China. Fourth, PEV battery cost in the two countries is currently close to $210–$220 kWh. Finally, China has higher numbers of charging infrastructure, and the ratio of PEVs to public chargers is 9.0 in China versus 17.9 in the USA at the end of 2018.

Cybersecurity of Plug-In Electric Vehicles: Cyberattack Detection During Charging

While large scale deployment of plug-in electric vehicles (PEVs) offers promising advantages, such as environmental benefits, energy security, and economic stability, it also poses certain cybersecurity related challenges. Unlike power grid security, PEV cybersecurity is significantly underexplored. However, cyberattacks on PEVs may lead to disastrous situations such as out-of-service EVs via denial-of-charging (DoC) or battery pack damage via overcharging. In this article, we attempt to address this issue by exploring control-oriented approaches for PEV cybersecurity. Specifically, we focus on designing algorithms for detecting cyberattacks that can potentially affect PEV battery packs during charging. We discuss two algorithms: first, static detector that utilizes only measured variables, and, second, dynamic detector that utilizes the knowledge of system dynamics along with the measurements. Furthermore, we propose a filter-based design approach for the dynamic detector that considers a multiobjective criterion including stability, robustness, and attack sensitivity. We perform theoretical analysis and simulation studies to evaluate the effectiveness of the algorithms under DoC and overcharging attacks that indicate the superiority of dynamic detector in terms of attack detectability.

Contract-based utilization of plug-in electric vehicle batteries for day-ahead optimal operation of a smart micro-grid

Abstract The development of smart micro-grids makes the use of new and green technologies like plug-in electric vehicles more suitable. The plug-in electric vehicles can play a role in storing energy due to their network connection capability. The smart micro-grid can use this stored energy to reduce its operating cost. In this paper, a new mechanism based on the contractual agreements between the owners of plug-in electric vehicles and the smart micro-grid is proposed to provide the system's energy during the operating day. The proposed model enables plug-in electric vehicles parked in official parking to be integrated into the operation of smart micro-grid and earn revenue. The operation optimization is formulated as a two-stage stochastic mixed-integer linear problem and is implemented on energy management of a typical smart micro-grid as a case study. The simulation results confirm the effectiveness of the proposed design for both smart micro-grid and plug-in electric vehicles in the case of expected cost reduction. Also, the performance of the stochastic model for uncertainty handling is shown by the value of the stochastic solution.

Quo Vadis Smart Charging? A Literature Review and Expert Survey on Technical Potentials and User Acceptance of Smart Charging Systems

Uncontrolled charging of plug-in Battery Electric Vehicles (BEV) represents a challenge for the energy system. As a solution, recent studies propose smart charging to avoid grid congestion and to integrate renewable energy. While financial benefits for smart charging schemes are currently quite low, there are other objectives for smart charging. However, it is unclear for which objectives smart charging can be used most effectively and which arguments are most likely to convince end users of BEVs to use smart charging schemes. To fill this gap, we conducted a literature review of the premises and the objectives of smart charging and how they fit the end-user’s motivation to use such smart charging systems. To evaluate the results, we present findings of 16 domain experts who evaluated various statements on smart charging according to their technical correctness and their persuasiveness towards end users. The results show that experts consider those smart charging objectives as most persuasive towards end users which they consider technically correct. Moreover, cost savings and integration of renewable energies are rated highest on both scales. On the contrary, experts do not expect a positive impact of smart charging systems on battery life and rate it as not very convincing.

Decentralized PEV Power Allocation With Power Distribution and Transportation Constraints

Plug-in Electric Vehicles (PEVs) keep on penetrating the automobile market. However, uncoordinated PEV charging can impair the reliability of power grid. In this paper, an interesting problem of PEV charging power allocation is investigated, in which both power distribution and transportation constraints are considered. A novel approach for PEV charging management based on optimal power flow (OPF) analysis is proposed to optimize PEV charging energy in a power distribution system. Firstly, spatial and temporal PEV demand scheduling is introduced to maximize PEV charging service capacity while considering the maximum traveling distance of PEVs. Secondly, to ensure the scalability of the OPF analysis, a distributed optimization technique, i.e., proximal Jacobian alternating direction multiplier method, is applied to attain the optimal power allocation in a decentralized manner. The resulting PEV charging service capacity in the power distribution system is improved without violating power distribution and transportation constraints. Furthermore, kernel density estimation method is adopted to identify the PEV range anxiety constraint without the PEV battery information. Simulation results are presented to validate the effectiveness of our approach with high PEV penetration.

Day-Ahead Optimal Control of PEV Battery Storage Devices Taking Into Account the Voltage Regulation of the Residential Power Grid

This paper first presents an optimization model to flexibly control available plug-in electric vehicle (PEV) battery charging/discharging power based on three-phase power flow and sensitivity approaches. This model can achieve one of the two goals: 1) minimizing both battery charging/discharging cost and extra battery degradation cost due to vehicle-to-grid (V2G) activities (cost-reduction strategy); 2) maximizing local peak load shifting and minimizing extra battery degradation cost due to V2G activities (peak-shifting strategy). The first strategy can determine the appropriate charging/discharging rates of an available PEV battery in order to benefit both the PEV owners and the distribution utilities for the day ahead. The second strategy can reduce the peak loads of the system. Both strategies can improve the power quality at the same time. With the help of a sensitivity method, most of the nonlinear constraints are transformed into linear constraints, and the number of constraints is reduced in the model. An interior point optimization approach is utilized to solve the optimization model. The optimization model is modified further to address unexpected PEV connections and travel scenarios during operation. The effectiveness and accuracy of the proposed method are demonstrated and verified on a 75-node test feeder.

A Game Theoretic Approach to Phase Balancing by Plug-In Electric Vehicles in the Smart Grid

With the increasing penetration of renewable energy resources and plug-in electric vehicles (PEV) in the smart grid, the problem of phase imbalance in a three-phase distribution system becomes more challenging. In this paper, we propose a phase balancing (PB) scheme performed by PEVs. With PB remunerations introduced as financial incentives, PEV owners are encouraged to assist the distribution system operator in reducing the phase imbalance. This is achieved by exploiting the charging flexibility of PEV batteries. We formulate this problem into a non-smooth non-cooperative game, where each PEV owner autonomously minimizes his/her charging cost minus the PB remuneration, considering decisions of other PEV owners. Through transforming this game into a smooth extended one, we are able to solve this challenging problem. Specifically, existence of its Nash equilibrium (NE) is demonstrated, and a distributed algorithm is developed to achieve the NE. Convergence proof of this algorithm is also provided. Simulation results validate our proposed scheme, and illustrate that both PEV owners and the distribution system operator will benefit financially. Moreover, the power quality and system reliability of the distribution network can also be improved.

PV and Grid interfaced Plug-in EV Battery Charger operating in P-VG, P-V and V-G Modes

The proposed system facilitates uninterruptable charging of a photovoltaic (PV) fed plug-in electric vehicle (EV) battery charging system irrespective of solar irradiation conditions by integrating utility grid to the battery charging system. The system employs bidirectional cycloconverter (BDCC) in order to use utility grid as source or sink during different modes of operation which depends on the availability of solar power. During low irradiation condition, the utility grid acts as a backup source in order to facilitate uninterruptable charging of the EV battery. When surplus power is generated from the PV panel, it is fed to the utility grid, which acts as sink in this mode. For uninterruptable EV battery charging, the controller operates the switches and relays in the proposed system corresponding to solar irradiation level. The available literatures define complex control strategies which are solved in this proposed system by adopting a simple dynamic control algorithm. The simulation of the proposed system has been carried out using PSIM simulation software and experimental prototype has been designed, developed and tested for different modes of operations to validate the efficacy of the proposed system.

SOC estimation based on data driven exteaded Kalman filter algorithm for power battery of electric vehicle and plug-in electric vehicle

State of charge (SOC) estimation has always been a hot topic in the field of both power battery and new energy vehicle (electric vehicle (EV), plug-in electric vehicle (PHEV) and so on). In this work, aiming at the contradiction problem between the exact requirements of EKF (extended Kalman filter) algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period, a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed. The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery. Through the combination of data-based and model-based SOC estimation method, the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods, and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment (such as in an EV or PHEV). A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.

Resilient Scheduling Portfolio of Residential Devices and Plug-In Electric Vehicle by Minimizing Conditional Value at Risk

Home energy management systems (HEMSs) encourage participation of residential consumers into the demand response programs. This paper proposes a robust Conditional Value at Risk (CVaR) optimization approach for day ahead HEMS to reduce the effect of risk of real-time exposure to energy price and solar power generation uncertainties. Initially, the CVaR method is integrated with the two-point Estimation (2PE) analysis to approximate the solar power, modeled as Beta probability distribution function, in low computation effort compared to conventional Monte Carlo simulation (MCS) based CVaR approach. Then the optimization constraints are revised to their robust counterparts by accounting a certain amount of uncertainty in the energy prices from their nominal values. Unlike the previous literatures, the optimization problem is developed to minimize the risk value of the energy cost. Again to maximize the life of the plug-in electric vehicle (PEV) a pseudo cost function for the PEV battery degradation is proposed. The entire optimization portfolio is developed as a mixed integer linear programming for its easy execution. Simulation is demonstrated on a smart home, designed as an ac-dc microgrid, having practical appliance data sets, to prove the efficacy of the proposed method.

Stochastic flexible transmission operation for coordinated integration of plug-in electric vehicles and renewable energy sources

In this paper, the large-scale integration of plug-in electric vehicles (PEVs) with Vehicle-to-Grid (V2G) technology is taken as a promising way to promote the integration of renewable energy sources (RESs). But, it is a challenging task for the independent system operator (ISO) to manage the power flows in its system, especially in the large-scale integration of PEVs and RESs into the transmission system. The coordination between PEV, as distributed storage devices, and variable RESs create variable power flows and loop flows and quests for utilization of controllable devices to manage these flows. The On-load tap changing (OLTC) transformers and phase shifting transformers (PSTs) are two flexible transmission technologies that can be solved this challenge through controlling the power flows. In this paper, a novel model is developed for optimal operation of flexible transmission technologies for coordinated integration of PEVs and RESs in power transmission networks. The proposed model has been implemented on the six-bus and the IEEE 118-bus test systems. The simulation results imply that: (i) The V2G technology of PEV battery can ease the impact of variability of RES on power system operations and reduce operation cost and wind power spillage; (ii) substantial economic savings can be achieved through utilization of both OLTC transformer and PSTs beyond the independent capabilities of each technology.

Residential Microgrids Energy Trading With Plug-In Electric Vehicle Battery via Stochastic Games

For microgrids (MGs) with electric vehicle prosumers, effective time-of-use based energy trading is important for multi-vehicles-to-MG system. In this paper, a Stochastic Stackelberg game (SSG) model is proposed. The model is based on the Stackelberg game, where the sellers act as leader and the buyers are considered as follower. First, according to the distributed generation's (DG) output and load distribution, MGs are classified as sellers and buyers, whose strategies set are established separately. Then the utility models of sellers and buyers are established, which include a stochastic variable model for electric vehicles (EVs). Moreover, with the proof of equilibrium and uniqueness of the Stackelberg equilibrium, sellers are obligated to coordinate the sharing of energy with maximization of the profit, while the buyers are autonomous to maximize their utilities with demands response to availabilities. Finally, the game equilibrium is solved to deal with the uncertainty of EV's energy and plugging-in time. By using the collected data from realistic EVs, the effectiveness of the model is verified in terms of seller profit, the utilities of buyers, and the net energy usage in MG. The results of the static pricing model and SSG model were compared to demonstrate the effectiveness of the SSG model.

A Consensus-Based Cooperative Control of PEV Battery and PV Active Power Curtailment for Voltage Regulation in Distribution Networks

The rapid growth of rooftop photovoltaic (PV) arrays installed in residential houses leads to serious voltage quality problems in low voltage distribution networks (LVDNs). In this paper, a combined method using the battery energy management of plug-in electric vehicles (PEVs) and the active power curtailment of PV arrays is proposed to regulate voltage in LVDNs with high penetration level of PV resources. A distributed control strategy composed of two consensus algorithms is used to reach an effective utilization of limited storage capacity of PEV battery considering its power/capacity and state of charge. A consensus control algorithm is also developed to fairly share the required power curtailment among PVs during overvoltage periods. The main objective is to mitigate the voltage rise due to the reverse power flow and to compensate the voltage drop resulting from the peak load. Overall, the proposed algorithm contributes to a coordinated charging/discharging control of PEVs battery which provides a maximum utilization of available storage capacity throughout the network. In addition, the coordinated operation minimizes the required active power which is going to be curtailed from PV arrays. The effectiveness of the proposed control scheme is investigated on a typical three-phase four-wire LVDN in presence of PV resources and PEVs.

Adaptive V2G Peak Shaving and Smart Charging Control for Grid Integration of PEVs

The stochastic nature of plug-in electric vehicle (PEV) driving behavior and distribution grid load profile make it challenging to control vehicle-grid integration in a mutually beneficial way. This article proposes a new adaptive control strategy that manages PEV charging/discharging for peak shaving and load leveling in a distribution grid. For accurate and high fidelity transportation mobility modeling, real vehicle driving test data are collected from the field. Considering the estimated total required PEV battery charging energy, the vehicle-to-grid capabilities of PEVs, and the forecasted non-PEV base load, a reference operating point for the grid is estimated. This reference operating point is updated once at the end of peak hours to guarantee a full final state-of-charge to each PEV. Proposed method provides cost-efficient operation for the utility grid, utmost user convenience free from range anxiety, and ease of implementation at the charging station nodes. It is tested on a real residential transformer, which serves approximately one thousand customers, under various PEV penetration levels and charging scenarios. Performance is assessed in terms of mean-square-error and peak shaving index. Results are compared with those of various reference operating point choices and shown to be superior.

Predictive control of plug-in electric vehicle chargers with photovoltaic integration

This paper presents a model predictive control (MPC) for off-board plug-in electric vehicle (PEV) chargers with photovoltaic (PV) integration using two-level four-leg inverter topology. The PEV charger is controlled by a unified controller that incorporates direct power and current MPC to dynamically control decoupled active-reactive power flow in a smart grid environment as well as to control PEV battery charging and discharging reliably. PV power generation with maximum power tracking is seamlessly integrated with the power flow control to provide additional power generation. Fast dynamic response and good steady-state performance under all power flow modes and various environmental conditions are evaluated and analyzed. From the results obtained, the charger demonstrates less than 1.5% total harmonic distortion as well as low active and reactive power ripple of less than 7% and 8% respectively on the grid for all power flow modes. The PEV battery also experiences a low charging and discharging current ripple of less than 2.5%. Therefore, the results indicate the successful implementation of the proposed charger and its control for PV integrated off-board PEV chargers.