Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

Combining Market-Based Control with Distribution Grid Constraints when Coordinating Electric Vehicle Charging

The charging of electric vehicles(EVs) impacts the distribution grid, and its cost depends on the price of electricity when charging. An aggregator that is responsible for a large fleet of EVs can use a market-based control algorithm to coordinate the charging of these vehicles, in order to minimize the costs. In such an optimization, the operational parameters of the distribution grid, to which the EVs are connected, are not considered. This can lead to violations of the technical constraints of the grid(e.g., undervoltage, phase unbalances); for example, because many vehicles start charging simultaneously when the price is low. An optimization that simultaneously takes the economic and technical aspects into account is complex, because it has to combine time-driven control at the market level with eventdriven control at the operational level. Diff erent case studies investigate under which circumstances the market-based control, which coordinates EV charging, conflicts with the operational constraints of the distribution grid. Especially in weak grids, phase unbalance and voltage issues arise with a high share of EVs. A low-level voltage droop controller at the charging point of the EV can be used to avoid many grid constraint violations, by reducing the charge power if the local voltage is too low. While this action implies a deviation from the cost-optimal operating point, it is shown that this has a very limited impact on the business case of an aggregator, and is able to comply with the technical distribution grid constraints, even in weak distribution grids with many EVs.

Vehicle-to-grid power system services with electric and plug - in vehicles based on flexibility in unidirectional charging

With proper power scheduling and dynamic pricing,a unidirectional charger can provide benefits and regulation services to the electricity grid,at a level approaching that of bidirectional charging.Power scheduling and schedule flexibility of electric and plug-in hybrid vehicles are addressed.The use of electric vehicles(EVs)as flexibility resources and associated unidirectional vehicle-to-grid benefits are investigated.Power can be scheduled with the EV charger in control of charging or via control by a utility or an aggregator.Charging cost functions suitable for charger-and utility-controlled power scheduling are presented.Ancillary service levels possible with unidirectional vehicle-to-grid are quantified using sample charging scenarios from published data.Impacts of various power schedules and vehicle participation as a flexibility resource on electricity locational prices are evaluated.These include benefits to both owners and load-serving entities.Frequency regulation is considered in the context of unidirectional charging.

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.

Communication-less Management Strategy for Electric Vehicle Charging in Droop-controlled Islanded Microgrids

Adopting high penetration levels of electric vehicles(EVs) necessitates the implementation of appropriate charging management systems to mitigate their negative impacts on power distribution networks. Currently, most of the proposed EV charging management techniques rely on the availability of high-bandwidth communication links. Such techniques are far from realization due to(1) the lack of utility-grade communication systems in many cases such as secondary(low-voltage) power distribution systems to which EVs are connected, rural areas, remote communities, and islands, and(2) existing fears and concerns about the data privacy of EV users and cyber-physical security. For these cases, appropriate local control schemes are needed to ensure the adequate management of EV charging without violating the grid operation requirements. Accordingly, this paper introduces a new communication-less management strategy for EV charging in droop-controlled islanded microgrids. The proposed strategy is autonomous, as it is based on the measurement of system frequency and local bus voltages. The proposed strategy implements a social charging fairness policy during periods when the microgrid distributed generators(DGs) are in short supply by allocating more system capacity to the EVs with less charging in the past. Furthermore, a novel communication-less EV load shedding scheme is incorporated into the management strategy to provide relief to the microgrid during events of severe undervoltage or underfrequency occurrences due to factors such as high loading or DG outages. Numerical simulations demonstrate the superiority of the proposed strategy over the state-of-the-art controllers in modulating the EV charging demand to counteract microgrid instability.

Optimized speed control for electric vehicles on dynamic wireless charging lanes: An eco-driving approach

As the adoption of Electric Vehicles(EVs)intensifies,two primary challenges emerge:limited range due to battery constraints and extended charging times.The traditional charging stations,particularly those near highways,exacerbate these issues with necessary detours,inconsistent service levels,and unpredictable waiting durations.The emerging technology of dynamic wireless charging lanes(DWCLs)may alleviate range anxiety and eliminate long charging stops;however,the driving speed on DWCL significantly affects charging efficiency and effective charging time.Meanwhile,the existing research has addressed load balancing optimization on Dynamic Wireless Charging(DWC)systems to a limited extent.To address this critical issue,this study introduces an innovative eco-driving speed control strategy,providing a novel solution to the multi-objective optimization problem of speed control on DWCL.We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs.Three objective functions are formulated to tackle the challenges at hand:reducing travel time,increasing charging efficiency,and achieving load balancing on DWCL,which corresponds to four control strategies.The results of numerical tests indicate that a comprehensive control strategy,which considers all objectives,achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing.Furthermore,by defining the energy demand and speed range through an upper operation limit,a relatively superior speed control strategy can be selected.This work contributes to the discourse on DWCL integration into modern transportation systems,enhancing the EV driving experience on major roads.

Battery Charger for Electric Vehicles based on a Wireless Power Transmission

In this paper,the case of a battery charger for electric vehicles based on a wireless power transmission is addressed.The specificity of every stage of the overall system is presented.Based on calculated and measured results,relevant capacitive compensations of the transformer and models are suggested and discussed in order to best match the operating mode and aiming at simplifying as much as possible the control and the electronics of the charger.

Modelling of electric vehicle charging station and controlled by fuzzy logic controller with different modes of operation

The main objective of implementing charging stations is to ensure the good charging to theElectric Vehicles by using a solar PV array which is interconnected to the battery energy storagesystems. The charging station regulates the supply voltage and frequency without the use of amechanical speed governor. It also assures that energy gained from grid or by the DG set willhave the unity power factor (UPF) when the load is nonlinear. Besides this, the Point of CommonCoupling (PCC) voltage is synchronised with the grid/generator voltage in order to providecontinuous charging. In order to increase the optimal efficiency of the charging stations, thecharging stations will perform the active/reactive power transfer from the vehicle to grid, vehicleto house and vehicle to vehicle (V2V) power transfer. The operational experiment of the chargingstation is simulated and verified by using MATLAB/SIMULINK.

Day-ahead optimal charging/discharging scheduling for electric vehicles in microgrids

Microgrid as an important part of smart grid comprises distributed generators(DGs),adjustable loads,energy storage systems(ESSs)and control units.It can be operated either connected with the external system or islanded with the support of ESSs.While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar,unregulated electric vehicle(EV)charging demand will deteriorate the unbalance between the daily load curve and generation curve.In this paper,a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge(SOC)of EV batteries.The optimization problem is proposed to obtain the economic operation for the microgrid based on this model.In dayahead scheduling,with the estimated power generation and load demand,the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming.With the optimal charging/discharging scheduling of EVs,the daily load curve can better track the generation curve.The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.

Composite Braking Control Strategy for Electromechanical-Hydraulic Vehicles

Short driving ranges and low braking energy recovery efficiencies are two recognized technical bottlenecks to be overcome in electric vehicles. In this paper, a novel electromechanical-hydraulic coupling system is proposed and integrated as a powertrain for electric vehicles, which can assist the electric vehicle to fully utilize its braking energy. The hydraulic regenerative braking force and electric regenerative braking force can provide all the braking needs using the medium and small braking intensities. Furthermore, an improved compound brake control strategy based on the braking force distribution is proposed and simulated. The results show that under the premise of ensuring braking stability, the electromechanical-hydraulic coupling driving electric vehicle can adapt to various working conditions with excellent energy-saving results. The hydraulic accumulator recovery efficiency is above 99%, and the state of charge consumption rate of the battery pack can be reduced by more than 9%. More importantly, the proposed hybrid power system can significantly improve the driving range and energy efficiency, as well as reduce the consumers' mileage anxiety in electric vehicles.

Impact Analysis of EV Charging with Mixed Control Strategy

EVs （electric vehicles） have been widely accepted as a promising solution for reducing oil consumption, air pollution and greenhouse gas emission. The number of EVs is growing very fast over the years. However, the high adoption of EVs will impose a burden on the power system, especially for neighborhood level network. In this paper, we propose a mixed control framework for EV charging scheduling to mitigate its impact on the power network. A metric for modeling customer＇s satisfaction is also proposed to compare the user satisfaction for different algorithms. The impacts of the proposed algorithms on EV charging cost, EV penetration and peak power reduction are evaluated with real data for a neighborhood level network. The simulation results demonstrate the effectiveness of the proposed algorithms.

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.

A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system

This study focuses on the potential role of plugin electric vehicles(PEVs) as a distributed energy storage unit to provide peak demand minimization in power distribution systems. Vehicle-to-grid(V2 G) power and currently available information transfer technology enables utility companies to use this stored energy. The V2 G process is first formulated as an optimal control problem.Then, a two-stage V2 G discharging control scheme is proposed. In the first stage, a desired level for peak shaving and duration for V2 G service are determined off-line based on forecasted loading profile and PEV mobility model. In the second stage, the discharging rates of PEVs are dynamically adjusted in real time by considering the actual grid load and the characteristics of PEVs connected to the grid. The optimal and proposed V2 G algorithms are tested using a real residential distribution transformer and PEV mobility data collected from field with different battery and charger ratings for heuristic user case scenarios. The peak shaving performance is assessed in terms of peak shaving index and peak load reduction. Proposed solution is shown to be competitive with the optimal solution while avoiding high computational loads. The impact of the V2 G management strategy on the system loading at night is also analyzed by implementing an off-line charging scheduling algorithm.

Experiment Study on the Ending Criteria of Charge and Discharge of Nickel-Hydride Battery

Charge and discharge characteristics of Ni/MH batteries are investigated with experiments. During battery’s working, the voltage, capacity, temperature and internal resistance were recorded, corresponding curves were depicted. Variations of the aforementioned four parameters are differently obvious. Ending criteria of charge and discharge of Ni/MH batteries are discussed on the basis of the curves. Voltage, capacity and temperature of a battery can be used as ending criteria during charge. When discharge takes place, voltage, capacity and internal resistance can be chosen as ending criteria. As a whole, capacity is more suitable for being used as ending criteria of charge and discharge than the other three parameters. At last, the capacity of a battery is recommended to be ending criteria of charge and discharge. The conclusions will provide references to different capacity Ni/MH batteries for electric vehicles.

Wind-integration benefits of controlled plug-in electric vehicle charging

Flexibility in plug-in electric vehicle(PEV) charging can reduce the ancillary cost effects of wind variability and uncertainty on electric power systems. In this paper, we study these benefits of PEV charging, demonstrating that controlled PEV charging can reduce costs associated with wind uncertainty and variability. Interestingly, we show that the system does not require complete control of PEV-charging loads to mitigate the negative cost impacts of wind variability and uncertainty. Rather, PEV owners giving the system a two-hour window of flexibility in which to recharge their vehicles provides much of the benefits that giving full charging control does.

Operation-area-constrained Adaptive Primary Frequency Support Strategy for Electric Vehicle Clusters

Due to their fast response and strong short-term power throughput capacity, electric vehicles(EVs) are promising for providing primary frequency support to power grids. However, due to the complicated charging demands of drivers, it is challenging to efficiently utilize the regulation capacity of EV clusters for providing stable primary frequency support to the power grid. Accordingly, this paper proposes an adaptive primary frequency support strategy for EV clusters constrained by the charging-behavior-defined operation area. First, the forced charging boundary of the EV is determined according to the driver's charging behavior, and based on this, the operation area is defined. This ensures full utilization of the available frequency support capacity of the EV. An adaptive primary frequency support strategy of EV clusters is then proposed. The output power of EV is adaptively regulated according to the real-time distance from the EV operating point to the forced charging boundary. With the proposed strategy, when the EV approaches the forced charging boundary, its output power is gradually reduced to zero. Then, the rapid state-of-charge declines of EVs and sudden output power reductions in EV clusters caused by forced charging to meet the driver's charging demands can be effectively avoided. EV clusters can then provide sustainable frequency support to the power grid without violating the driver's charging demands. Simulation results validate the proposed operation-area-constrained adaptive primary frequency support strategy, which outperforms the average strategy in terms of stable output maintenance and the optimal utilization of regulation capacities of EV clusters.

考虑奖励机制的电动汽车充电优化引导策略

随着电动汽车(electric vehicle,EV)的大规模推广,其无序充电严重威胁电网的安全稳定运行,积极引导EV用户参与充电优化策略,对于提高电网的安全稳定性具有重要意义。为此,基于充电优化管理调度思路,提出一种考虑奖励机制的EV充电优化引导策略,在分时电价的基础上,计入用户在降低电网负荷波动中的奖励机制,考虑充电位置固定、不确定用户的出行需求,确定EV的充电时间及充电位置,达到用户满意度最高的目的;利用EV动态响应的实时优化算法,对所提的优化调度模型进行求解。仿真结果验证了所提策略的有效性和可行性,该优化调度策略不仅能有效改善负荷低谷时段集中充电形成新的负荷高峰的问题,而且可明显降低用户的充电成本及电网负荷波动。

考虑电动汽车充放电的输配协同能量-灵活性市场出清机制

为应对输电网对灵活性资源的迫切需求,挖掘配电网分布式资源的灵活性支撑潜力,首先,构建市场环境下电动汽车的车-网互动响应模型,分析不同充放电方式下电动汽车的需求响应能力,为电动汽车参与市场环境下电网调度控制提供模型基础;然后,计及配电网运营商对需求侧灵活性资源的聚合作用,以购买电力和灵活性资源总成本最低为目标,构建考虑电动汽车充放电方式的输配协同能量-灵活性市场出清模型,通过协调控制输配电网中各类灵活性资源来满足输电网的灵活性需求;最后,通过修改的IEEE 33节点输电网与2个IEEE 33节点配电网耦合的测试系统对所提输配协同能量-灵活性市场出清方法的有效性进行分析与验证。结果表明,所提方法可提升电力系统的运行经济性和灵活性。

基于深度强化学习的住宅区电动汽车充电策略

为降低电动汽车(Electricvehicle,EV)充电成本、缓解用户里程焦虑,提出了一种基于深度强化学习算法的EV充电控制策略。首先,使用数学模型定量分析EV用户的充电成本、里程焦虑以及电池老化;然后,将EV充放电问题转化为状态转移概率未知的马尔可夫决策过程,使用强化学习算法得到离散的充放电决策。与传统的基于模型预测控制算法相比,在用该策略得到充电策略时可以不依赖充电环境的精确模型以及EV用户行为等不确定性因素的预测结果。实验结果表明,应用该策略可以在满足用户充电需求的同时降低充电成本、保护电池,其控制性能相比基于模型预测控制的充电策略也更为优异。