Developing of the EV charging and parking shed of BIPV

Building-integrated photovoltaic(BIPV) is an important application way of solar photovoltaic power. The electric vehicle(EV) charging and parking shed of BIPV is the regeneration energy intellectual integration demonstration application system collection of photovoltaic(PV) grid power,PV off- grid power,EV charging and parking shed,and any part of the functions and their combination will be engaged in practical application on demand. The paper describes the PV shed system structure and design in detail with the present of its actual photos. The shed is 50 m long and 5.5 m wide and capable of parking 18 cars. Under the control of system intellectual controller,the power produced by PV from sunlight will charge the parking EV car prior to charging the storage battery,charging the storage battery prior to grid power,grid power at last,and charge the EV by utility grid when it is a cloudy or rainy day.

Edge Impulse Based ML-Tensor Flow Method for Precise Prediction of Remaining Useful Life (RUL) of EV Batteries

Electric Vehicle (EV) adoption is rapidly increasing, necessitating efficient and precise methods for predicting EV charging requirements. The early and precise prediction of the battery discharging status is helpful to avoid the complete discharging of the battery. The complete discharge of the battery degrades its lifetime and requires a longer charging duration. In the present work, a novel approach leverages the Edge Impulse platform for live prediction of the battery status and early alert signal to avoid complete discharging. The proposed method predicts the actual remaining useful life of batteries. A powerful edge computing platform utilizes Tensor Flow-based machine learning models to predict EV charging needs accurately. The proposed method improves the overall lifetime of the battery by the efficient utilization and precise prediction of the battery status. The EON-Tuner and DSP processing blocks are used for efficient results. The performance of the proposed method is analyzed in terms of accuracy, mean square error and other performance parameters.

Analysis of transmitter-side control methods in wireless EV charging systems

The wireless electric vehicle(EV) charging system is highly safe and flexible. To reduce the weight and cost of EVs, the wireless charging system, which simplifies the structure inside an EV and utilizes the transmitter-side control method, has become popular. This study investigates the transmitter-side control methods in a wireless EV charging system. First, a universal wireless charging system is introduced, and the function of its transfer power is derived. It is observed that the transfer power can be controlled by regulating either the phase-shift angle or the DC-link voltage. Further, the influence of the control variables is studied using numerical analysis. Additionally, the corresponding control methods, namely the phase-shift angle and the DC-link voltage control, are compared by calculation and simulation. It is found that:(1) the system efficiency is low with the phase-shift control method because of the converter switching loss;(2) the dynamic response is slow with the DC-link voltage control method because of the large inertia of the inductor and capacitor;(3) both the control methods have limitations in their adjustable power range. Therefore, a combined control method is proposed, with the advantages of high system efficiency, fast dynamic response, and wide adjustable power range. Finally, experiments are performed to verify the validity of the theoretical analysis and the effectiveness of the proposed method. This study provides a detailed and comprehensive analysis of the transmitter-side control methods in the wireless charging system, considering the sensitivity of parameters, converter losses, system efficiency,and dynamic performance, with the dead-time effect taken into consideration. Moreover, the proposed control method can be used to realize the optimal combination of the phase-shift angle and the DC-link voltage with good dynamic performance, and it is useful for the optimal operation of the wireless charging system.

Access Control Method for EV Charging Stations Based on State Aggregation and Q-Learning

This paper presents intelligent access control for a charging station and a framework for dynamically and adaptively managing charging requests from randomly arriving electric vehicles(EVs),to increase the revenue of the station.First,charging service requests from random EV arrivals are described as an event-driven sequential decision process,and the decision-making relies on an eventextended state that is composed of the real-time electricity price,real-time charging station state,and EV arrival event.Second,a state aggregation method is introduced to reduce the state space by first aggregating the charging station state in the form of the remaining charging time and then further aggregating it via sort coding.Besides,mathematical calculations of the code value are provided,and their uniqueness and continuous integer characteristics are proved.Then,a corresponding Q-learning method is proposed to derive an optimal or suboptimal access control policy.The results of a case study demonstrate that the proposed learning optimisation method based on the event-extended state aggregation performs better than flat Q-learning.The space complexity and time complexity are significantly reduced,which substantially improves the learning efficiency and optimisation performance.

Planning and Real-time Pricing of EV Charging Stations Considering Social Welfare and Profitability Balance

This paper presents a planning and real-time pricing approach for EV charging stations(CSs).The approach takes the form of a bi-level model to fully consider the interest of both the government and EV charging station operators in the planning process.From the perspective of maximizing social welfare,the government acts as the decision-maker of the upper level that optimizes the charging price matrix,and uses it as a transfer variable to indirectly influence the decisions of the lower level operators.Then each operator at the lower level determines their scale according to the goal of maximizing their own revenue,and feeds the scale matrix back to the upper level.A Logit model is applied to predict the drivers’preference when selecting a CS.Furthermore,an improved particle swarm optimization(PSO)with the utilization of a penalty function is introduced to solve the nonlinear nonconvex bi-level model.The paper applies the proposed Bi-level planning model to a singlecenter small/medium-sized city with three scenarios to evaluate its performance,including the equipment utilization rate,payback period,traffic attraction ability,etc.The result verifies that the model performs very well in typical CS distribution scenarios with a reasonable station payback period(average 6.5 years),and relatively high equipment utilization rate,44.32%.

An Optimization Model of EVs Charging and Discharging for Power System Demand Leveling

This paper presents an advanced methodology for optimizing a UK network load demand with various uncertainties which are related to individual driving behaviours. Without the optimized regulation for traditional power system demand, EVs （electric vehicles） would have an adverse impact on the stability of power systems. This becomes more significant for large-scale EVs plugging into the power grid. Traditional optimized methodologies are effective only for EV charging. The proposed techniques improve the system flexibility and stability through an advanced optimization model and flexible bidirectional charging/discharging control. Three scenarios with different charging and discharging power levels and various penetration levels of EVs are discussed in detail in this paper. Simulation results demonstrate that bidirectional EV power flow control has vast potentials to improve the load demand profile, with increased proportion of EVs, and charging/discharging power levels.

Coordinating EV Charging via Blockchain

The increasing electric vehicle(EV) penetration in a distribution network triggers the need for EV charging coordination. This paper firstly proposes a hierarchical EV charging coordination model and an algorithm based on Lagrangian relaxation. A barrier to the implementation of the coordination algorithm is that there usually does not exist a reliable coordinator of charging stations. This paper shows that an unreliable coordinator may collude with some charging stations and behave dishonestly by disobeying the coordination algorithm. Thus, the collusion coalition can gain more profits while lowering the profits of others and the total social welfare. To provide reliable coordination of charging stations, a novel blockchain-based coordination platform via Ethereum is established, including a coordination structure and a smart contract. A mathematical analysis is given to show that the proposed platform can mitigate the collusion behaviors in the coordination. Simulation results show the consequence of collusion and how blockchain can prevent the collusion.

A Two-stage Autonomous EV Charging Coordination Method Enabled by Blockchain

Increasing electric vehicle(EV) penetration in distribution networks necessitate EV charging coordination. This paper proposes a two-stage EV charging coordination mechanism that frees the distribution system operator(DSO) from extra burdens of EV charging coordination. The first stage ensures that the total charging demand meets facility constraints,and the second stage ensures fair charging welfare allocation while maximizing the total charging welfare via Nash-bargaining trading. A decentralized algorithm based on the alternating direction method of multipliers(ADMM) is proposed to protect individual privacy. The proposed mechanism is implemented on the blockchain to enable trustworthy EV charging coordination in case a third-party coordinator is absent. Simulation results demonstrate the effectiveness and efficiency of the proposed approach.

Python Supervised Co-simulation for a Day-long Harmonic Evaluation of EV Charging

To accurately simulate electric vehicle DC fast chargers'(DCFCs')harmonic emission,a small time step,i.e.,typically smaller than 10μs,is required owing to switching dynamics.However,in practice,harmonics should be continuously assessed with a long duration,e.g.,a day.A trade-off between accuracy and time efficiency thus exists.To address this issue,a multi-time scale modeling framework of fast-charging stations(FCSs)is proposed.In the presented framework,the DCFCs'input impedance and harmonic current emission in the ideal grid condition,that is,zero grid impedance and no background harmonic voltage,are obtained based on a converter switching model with a small timescale simulation.Since a DCFC's input impedance and harmonic current source are functions of the DCFC's load,the input impedance and harmonic emission at different loads are obtained.Thereafter,they are used in the fast-charging charging station modeling,where the DCFCs are simplified as Norton equivalent circuits.In the station level simulation,a large time step,i.e.,one minute,is used because the DCFCs'operating power can be assumed as a constant over a minute.With this co-simulation,the FCSs'long-term power quality performance can be assessed time-efficiently,without losing much accuracy.

Electric Vehicles Lithium-Polymer Ion Battery Dynamic Behaviour Charging Identification and Modelling Scheme

Lithium-ion batteries are considered the substantial electrical storage element for electric vehicles(EVs). The battery model is the basis of battery monitoring, efficient charging, and safety management. Non-linearmodelling is the key to representing the battery and its dynamic internal parameters and performance. This paperproposes a smart scheme to model the lithium-polymer ion battery while monitoring its present charging currentand terminal voltage at various ambient conditions (temperature and relative humidity). Firstly, the suggestedframework investigated the impact of temperature and relative humidity on the charging process using the constantcurrent-constant voltage (CC-CV) charging protocol. This will be followed by monitoring the battery at thesurrounding operating temperature and relative humidity. Hence, efficient non-linear modelling of the EV batterydynamic behaviour using the Hammerstein-Wiener (H-W) model is implemented. The H-W model is considered ablack box model that can represent the battery without any mathematical equivalent circuit model which reducesthe computation complexity. Finally, the model beholds the boundaries of the charging process, not affecting onthe lifetime of the battery. Several dynamic models are applied and tested experimentally to ensure theeffectiveness of the proposed scheme under various ambient conditions where the temperature is fixed at40°C and the relative humidity (RH) at 35%, 52%, and 70%. The best fit using the H-W model reached 91.83% todescribe the dynamic behaviour of the battery with a maximum percentage of error 0.1 V which is in goodagreement with the literature survey. Besides, the model has been scaled up to represent a real EV and expressedthe significance of the proposed H-W model.

An Optimization Model for Reliability Improvement and Cost Reduction Through EV Smart Charging

There is a general concern that the increasing penetration of electric vehicles(EVs)will result in higher aging failure probability of equipment and reduced network reliability.The electricity costs may also increase,due to the exacerbation of peak load led by uncontrolled EV charging.This paper proposes a linear optimization model for the assessment of the benefits of EV smart charging on both network reliability improvement and electricity cost reduction.The objective of the proposed model is the cost minimization,including the loss of load,repair costs due to aging failures,and EV charging expenses.The proposed model incorporates a piecewise linear model representation for the failure probability distributions and utilizes a machine learning approach to represent the EV charging load.Considering two different test systems(a 5-bus network and the IEEE 33-bus network),this paper compares aging failure probabilities,service unavailability,expected energy not supplied,and total costs in various scenarios with and without the implementation of EV smart charging.

Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic accidents.Various congestion pricing and electric vehicle charging policies have been introduced in recent years.Nonetheless,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system.In this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment.The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations.We propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees.The proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection stations.Through large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

Management of Charging Load of Electric Vehicles for Optimal Capacity Utilisation of Distribution Transformers

A de-centralised load management technique exploiting the flexibility in the charging of Electric Vehicles (EVs) is presented. Two charging regimes are assumed. The Controlled Charging Regime (CCR) between 16:30 hours and 06:00 hours of the next day and the Uncontrolled Charging Regime (UCR) between 06:00 hours and 16:30 hours of the same day. During the CCR, the charging of EVs is coordinated and controlled by means of a wireless two-way communication link between EV Smart Charge Controllers (EVSCCs) at EV owners’ premises and the EV Load Controller (EVLC) at the local LV distribution substation. The EVLC sorts the EVs batteries in ascending order of their states of charge (SoC) and sends command signals for charging to as many EVs as the transformer could allow at that interval based on the condition of the transformer as analysed by the Distribution Transformer Monitor (DTM). A real and typical urban LV area distribution network in Great Britain (GB) is used as the case study. The technique is applied on the LV area when its transformer is carrying the future load demand of the area on a typical winter weekday in the year 2050. To achieve the load management, load demand of the LV area network is decomposed into Non-EV load and EV load. The load on the transformer is managed by varying the EV load in an optimisation objective function which maximises the capacity utilisation of the transformer subject to operational constraints and non-disruption of daily trips of EV owners. Results show that with the proposed load management technique, LV distribution networks could accommodate high uptake of EVs without compromising the useful normal life expectancy of distribution transformers before the need for capacity reinforcement.

Innovative Services for Electric Mobility Based on Virtual Sensors and Petri Nets

About 60%of emissions into the earth’s atmosphere are produced by the transport sector,caused by exhaust gases from conventional internal combustion engines.An effective solution to this problem is electric mobility,which significantly reduces the rate of urban pollution.The use of electric vehicles(EVs)has to be encouraged and facilitated by new information and communication technology(ICT)tools.To help achieve this goal,this paper proposes innovative services for electric vehicle users aimed at improving travel and charging experience.The goal is to provide a smart service to allow drivers to find the most appropriate charging solutions during a trip based on information such as the vehicle’s current position,battery type,state of charge,nearby charge point availability,and compatibility.In particular,the drivers are supported so that they can find and book the preferred charge option according to time availability and the final cost of the charge points(CPs).To this purpose,two virtual sensors(VSs)are designed,modeled and simulated in order to provide the users with an innovative service for smart CP searching and booking.In particular,the first VS is devoted to locate and find available CPs in a preferred area,whereas the second VS calculates the charging cost for the EV and supports the driver in the booking phase.A UML activity diagram describes VSs operations and cooperation,while a UML sequence diagram highlights data exchange between the VSs and other electromobility ecosystem actors(CP operator,EV manufacturer,etc.).Furthermore,two timed Petri Nets(TPNs)are designed to model the proposed VSs,functioning and interactions as discrete event systems.The Petri Nets are synchronized by a single larger TPN that is simulated in different use cases and scenarios to demonstrate the effectiveness of the proposed VSs.

A data-driven framework for medium-term electric vehicle charging demand forecasting

The rapid phase-in of electric vehicles(EV)will cause unprecedented issues with managing the supply of electricity and charging stations.It is in the interest of utility providers and everyday consumers to be able to plan for peak charging times,and related congestion.While past work has been done for localized,short-term forecasting,it has not included longer term forecasting,or considered the relationships between multiple stations.Importantly,past work has also not offered a framework for dataset construction and evaluated different dataset features.We propose a methodology to forecast demand at public EV charging stations,and use it to explore the potential of data-driven models to predict demand up to one week in advance.Our strategy includes selecting parameters for formatting a dataset given a list of charging events,a way to consider flexible prediction horizons,and deployment of deep and supervised learning-based models.To the best of our knowledge,ours is the first study to propose machine learning to forecast medium-term public EV charging demand,to exploit weather and other features at public charging stations,and to forecast demand at multiple stations and the entire network.We validated our approach using data from eleven stations over three years from Scotland,UK.Our method outperforms the benchmark time series method,and predicts network demand with a symmetric mean absolute percentage error(SMAPE)of 5.9%and a mean absolute error(MAE)of 124.7 kWh,or less than twelve percent of average daily demand.

An optimization strategy of controlled electric vehicle charging considering demand side response and regional wind and photovoltaic

Renewable energy,such as wind and photovoltaic(PV),produces intermittent and variable power output.When superimposed on the load curve,it transforms the load curve into a‘load belt’,i.e.a range.Furthermore,the large scale development of electric vehicle(EV)will also have a significant impact on power grid in general and load characteristics in particular.This paper aims to develop a controlled EV charging strategy to optimize the peak-valley difference of the grid when considering the regional wind and PV power outputs.The probabilistic model of wind and PV power outputs is developed.Based on the probabilistic model,the method of assessing the peak-valley difference of the stochastic load curve is put forward,and a two-stage peak-valley price model is built for controlled EV charging.On this basis,an optimization model is built,in which genetic algorithms are used to determine the start and end time of the valley price,as well as the peak-valley price.Finally,the effectiveness and rationality of the method are proved by the calculation result of the example.

Analysis and Comparison of Two Wireless Battery Charger Arrangements for Electric Vehicles

The paper deals with wireless battery chargers(WBCs)for plug-in electric vehicles(PEVs)and analyzes two arrangements for the receiver of a series-series resonant WBC.The first arrangement charges the PEV battery in a straightforward manner through a diode rectifier.The second arrangement charges the PEV battery through the cascade of a diode rectifier and a chopper whose input voltage is kept constant.Figures of merit of WBCs such as efficiency and sizing factor of both the power source and the transmitter/receiver coils are determined.Afterwards,they are discussed and compared with reference to the case study of WBC for an electric city car.A proposal to optimize the efficiency of the second arrangement by a suitable selection of the chopper input voltage is presented.Measurements on the efficiency of the two arrangements are included to support the theoretical results.