Smart management of distinct plug‐in hybrid electric vehicle charging stations considering mobility pattern and site characteristics

Management of plug‐in hybrid electric vehicles (PHEVs) is an important alternative energy solution to accord the prevailing environmental depletion. However, adding PHEVs to the existing distribution network may stimulate issues such as increase in peak load, power loss, and voltage deviation. Addressing the aforementioned issues by incorporating distinct mobility patterns together will develop an attractive energy management. In this paper, suitable location of the charging station is presented for a novel 2‐area distribution system following distinct mobility patterns. A comprehensive study by considering the optimal, midst, and unfit site for placing the charging station is incorporated. For managing the charging sequence of PHEVs, a meta‐heuristic solving tool is developed. The main contribution of this programming model is its ability to schedule the vehicles simultaneously in both the areas. The efficiency of the proposed energy management framework is evaluated on the IEEE 33‐bus and IEEE 69‐bus distribution systems. The test system is subjected to different scenarios for demonstrating the superior performance of the proposed solving tool in satisfying the convenience of vehicle owner along with reducing the peak demand. The results show that charging at low electricity price period and discharging at high electricity price period enables the minimum operational cost.     

Smart utilization of renewable energy sources in a microgrid system integrated with plug‐in hybrid electric vehicles

Mass roll‐out of plug‐in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi‐zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real‐time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm‐based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously.     

A centralized vehicle‐to‐grid scheme with distributed computing capacity engaging internet of smart charging points: Case study

In order to accommodate additional plug‐in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle‐to‐grid (V2G) technology has been regarded as a cost‐effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users’ privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub‐problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak‐shaving and valley‐filling scheduling algorithm is built up. The proposed algorithm can be conducted in real‐time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak‐shaving and valley‐filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak‐shaving and valley‐filling strategy is near‐linear, which verifies the proposed scheme can be carried out very efficiently.     

An energy management strategy based on dynamic power factor for fuel cell/battery hybrid locomotive

In order to efficiently absorb more regenerative braking energy which sustains much longer compared with the conventional vehicle, and guarantee the safety of the hybrid system under the actual driving cycle of locomotive, an energy management control based on dynamic factor strategy is proposed for a scale-down locomotive system which consists of proton exchange membrane fuel cell (PEMFC) and battery pack. The proposed strategy which has self-adaption function for different driving cycles aims to achieve the less consumption of hydrogen and higher efficiency of the hybrid system. The experimental results demonstrate that the proposed strategy is able to maintain the charge state of battery (SOC) better than Equivalent Consumption Minimization Strategy (ECMS), and the proposed strategy could keep the change trend of SOC, which the final SOC is closed to the target value regardless of the initial SOC of battery. Moreover, the hydrogen consumption has been reduced by 0.86g and the efficiency of overall system has been raised of 2% at least than ECMS under the actual driving cycle through the proposed strategy. Therefore, the proposed strategy could improve the efficiency of system by diminishing the conversion process of energy outputted by fuel cell.     

Smart grid adds to renewable resources hosting capacity: Collaboration of plug‐in hybrid electric vehicles in Volt/VAr control process

This paper develops an efficient energy management approach to increase the renewables share in energy provision of smart distribution grids (SDGs). Voltage violation ends in curtailment of renewables generations and, hence, decreases the economic success of distribution companies. To avert such deficits, this study fosters the collaboration of SDG components in an intelligent Volt/VAr control process. The investigated SDG is characterized with high penetration of photovoltaics (PVs), dispatchable distributed generations (DDGs), plug‐in hybrid electric vehicles (PHEVs), and infield control devices say as under‐load tap‐changing transformers (ULTCs). In charge stations, PHEVs are coupled to the SDG through bidirectional inverters which are offering simultaneous exchanges of active and reactive powers. Thus, regarding the PHEV aggregators, optimal schedules of active power charge/discharge signals with their inductive/capacitive reactive power provisions are determined. This notion effectively increases PV power injections and, consequently, provides significant monetary savings. Besides, this mechanism reduces ULTC tap operations in Volt/VAr control process maintaining its nominal lifetime. The proposed approach is formulated as a mixed‐integer non‐linear programming (MINLP) and solved based on DICOPT solvers in general algebraic modeling system (GAMS). Effectiveness of the proposed approach is explored on a typical distribution test system. The obtained results show 8.94% increment in harvested PVs power and hence 5.24% reduction on daily operation cost of SDG.     

A price‐regulated electric vehicle charge‐discharge strategy for G2V,V2H,and V2G

Electric vehicles (EVs) and smart grids are gradually revolutionising the transportation sector and electricity sector respectively. In contrast to unplanned charging/discharging, smart use of EV in home energy management system (HEMS) can ensure economic benefit to the EV owner. Therefore, this paper has proposed a new energy pricing controlled EV charging/discharging strategy in HEMS to acquire maximum financial benefit. EV is scheduled to be charged/discharged according to the price of electricity during peak and off‐peak hours. In addition, two different types of EV operation modes, ie, grid‐to‐vehicle (G2V) in off‐peak time and vehicle‐to‐home (V2H) in on‐peak time are considered to determine comparative economic benefit of planned EV charging/discharging. The real load profile of a house in Melbourne and associated electricity pricing is selected for the case study to determine the economic gain. The simulation results illustrate that EV participating in V2H contributes approximately 11.6% reduction in monthly electricity costs compared with G2V operation mode. Although the facility of selling EV energy to the grid is not available currently, the pricing controlled EV charging/discharging presented in the paper can be used if such facility becomes available in the future.     

Energy management strategy based on driving pattern recognition for a dual‐motor battery electric vehicle

To improve the economic performance of dual‐motor battery electric vehicles, a novel driving pattern recognition–based energy management strategy (NDPREMS) is proposed in this paper. The NDPREMS firstly employs principal component analysis method to reduce the dimension of characteristic parameters of driving patterns and uses hierarchical cluster method for classifying driving patterns to construct a database of typical driving patterns, based on which a driving pattern recognizer is achieved using generalized regression neural network (GRNN) and the accuracy of this recognizer reaches 96.08%. In order to reasonably allocate the power between two motors, on the basis of rule‐based energy management strategy (REMS), a dynamic programming–based energy management strategy (DPEMS) under typical driving patterns is formulated. By doing so, the logic thresholds of REMS are optimized, and thus, the NDPREMS is achieved. Comparison simulations of control effect concerning the REMS, DPEMS, and NDPREMS are performed under typical driving patterns. Results indicate that the proposed NDPREMS exhibits greater energy conservation compared with REMS, the economic improvement under urban driving pattern is the most obvious at 11.04%, the improvement under the comprehensive test driving pattern is 5.65%, and the performance of the NDPREMS is similar to that of DPEMS.     

Intelligent energy management strategy based on hierarchical approximate global optimization for plug-in fuel cell hybrid electric vehicles

The energy management strategy (EMS) is a key to reduce the equivalent hydrogen consumption and slow down fuel cell performance degradation of the plug-in fuel cell hybrid electric vehicles. Global optimal EMS based on the whole trip information can achieve the minimum hydrogen consumption, but it is difficult to apply in real driving. This paper tries to solve this problem with a novel hierarchical EMS proposed to realize the real-time application and approximate global optimization. The long-term average speed in each future trip segment is predicted by KNN, and the short-term speed series is predicted by a new model averaging method. The approximate global optimization is realized by introducing hierarchical reinforcement learning (HRL), and the strategy within the speed forecast window is optimized by introducing upper confidence tree search (UCTS). The vehicle speed prediction and the proposed EMS have been verified using the collected real driving cycles. The results show that the proposed strategy can adapt to driving style changes through self-learning. Compared with the widely used rule-based strategy, it can evidently reduce hydrogen consumption by 6.14% and fuel cell start-stop times by 21.7% on average to suppress the aging of fuel cell. Moreover, its computation time is less than 0.447 s at each step, and combined with rolling optimization, it can be used for real-time application.     

Effectiveness of smart charging of electric vehicles under power limitations

This article investigates charging strategies for plug‐in hybrid electric vehicles (PHEV) as part of the energy system. The objective was to increase the combined all‐electric mileage (total distance driven using only the traction batteries in each PHEV) when the total charging power at each workplace is subject to severe limitations imposed by the energy system. In order to allocate this power optimally, different input variables, such as state‐of‐charge, battery size, travel distance, and parking time, were considered. The required vehicle mobility was generated using a novel agent‐based model that describes the spatiotemporal movement of individual PHEVs. The results show that, in the case of Helsinki (Finland), smart control strategies could lead to an increase of over 5% in the all‐electric mileage compared to a no‐control strategy. With a high prediction error, or with a particularly small or large battery, the benefits of smart charging fade off. Smart PHEV charging strategies, when applied to the optimal allocation of limited charging power between the cars of a vehicle fleet, seem counterintuitively to provide only a modest increase in the all‐electric mileage. A simple charging strategy based on allocating power to PHEVs equally could thus perform sufficiently well. This finding may be important for the future planning of smart grids as limiting the charging power of larger PHEV fleets will sometimes be necessary as a result of grid restrictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Investigation of battery end-of-life conditions for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) capable of drawing tractive energy from the electric grid represent an energy efficient alternative to conventional vehicles. After several thousand charge depleting cycles, PHEV traction batteries can be subject to energy and power degradation which has the potential to affect vehicle performance and efficiency. This study seeks to understand the effect of battery degradation and the need for battery replacement in PHEVs through the experimental measurement of lithium ion battery lifetime under PHEV-type driving and charging conditions. The dynamic characteristics of the battery performance over its lifetime are then input into a vehicle performance and fuel consumption simulation to understand these effects as a function of battery degradation state, and as a function of vehicle control strategy. The results of this study show that active management of PHEV battery degradation by the vehicle control system can improve PHEV performance and fuel consumption relative to a more passive baseline. Simulation of the performance of the PHEV throughout its battery lifetime shows that battery replacement will be neither economically incentivized nor necessary to maintain performance in PHEVs. These results have important implications for techno-economic evaluations of PHEVs which have treated battery replacement and its costs with inconsistency.     

Real-time optimization strategy by using sequence quadratic programming with multivariate nonlinear regression for a fuel cell electric vehicle

The energy management of a plug-in FCEV (Fuel Cell Electric Vehicle) strictly depends on the control of SOC (State of Charge) over a given trip distance. The SOC may be varied with the trip distance by updating an EF (Equivalent Factor), which is derived from ECMS (Equivalent Consumption Minimization Strategy). However, the EF is too complicated to estimate accurately in real-time with traditional method. A real-time optimization strategy by using SQP (Sequence Quadratic Programming) with MNLR (Multivariate Nonlinear Regression) is proposed for a plug-in FCEV. First, the real-time hydrogen consumption optimization problem for SOC trip distance adaptive is formulated by using ECMS. The EF is adjusted according to the trip distances and predefined SOC. Then, in order to improve the accuracy of EF, SQP method is utilized to optimize the fuel cell and battery efficiency. Thus, the MNLR is applied to construct the fuel cell and battery efficiency response surface models for real-time optimization application. Finally, numerical verification and hardware in loop experiments are conducted to validate the proposed strategy. The results indicate that the combination of SQP with MNLR made it possible to develop the proposed strategy capable of significantly improving the hydrogen economic performance of this FCEV.     

A comprehensive optimized model for on‐board solar photovoltaic system for plug‐in electric vehicles: energy and economic impacts

Environmental concerns along with high energy demand in transportation are leading to major development in sustainable transportation technologies, not the least of which is the utilization of clean energy sources. Solar energy as an auxiliary power source of on‐board fuel has not been extensively investigated. This study focuses on the energy and economic aspects of optimizing and hybridizing, the conventional energy path of plug‐in electric vehicles (EVs) using solar energy by means of on‐board photovoltaic (PV) system as an auxiliary fuel source. This study is novel in that the authors (i) modeled the comprehensive on‐board PV system for plug‐in EV; (ii) optimized various design parameters for optimum well‐to‐tank efficiency (solar energy to battery bank); (iii) estimated hybrid solar plug‐in EVs energy generation and consumption, as well as pure solar PV daily range extender; and (iv) estimated the economic return of investment (ROI) value of adding on‐board PVs for plug‐in EVs under different cost scenarios, driving locations, and vehicle specifications. For this study, two months in two US cities were selected, which represent the extremities in terms of available solar energy; June in Phoenix, Arizona and December in Boston, Massachusetts to represent the driving conditions in all the US states at any time followed by assessment of the results worldwide. The results show that, by adding on‐board PVs to cover less than 50% (around 3.2 m²) of the projected horizontal surface area of a typical passenger EV, the daily driving range could be extended from 3.0 miles to 62.5 miles by solar energy based on vehicle specifications, locations, season, and total time the EV remains at Sun. In addition, the ROI of adding PVs on‐board with EV over its lifetime shows only small negative values (larger than ?45%) when the price of electricity remains below Environmental concerns along with high energy demand in transportation are leading to major development in sustainable transportation technologies, not the least of which is the utilization of clean energy sources. Solar energy as an auxiliary power source of on‐board fuel has not been extensively investigated. This study focuses on the energy and economic aspects of optimizing and hybridizing, the conventional energy path of plug‐in electric vehicles (EVs) using solar energy by means of on‐board photovoltaic (PV) system as an auxiliary fuel source. This study is novel in that the authors (i) modeled the comprehensive on‐board PV system for plug‐in EV; (ii) optimized various design parameters for optimum well‐to‐tank efficiency (solar energy to battery bank); (iii) estimated hybrid solar plug‐in EVs energy generation and consumption, as well as pure solar PV daily range extender; and (iv) estimated the economic return of investment (ROI) value of adding on‐board PVs for plug‐in EVs under different cost scenarios, driving locations, and vehicle specifications. For this study, two months in two US cities were selected, which represent the extremities in terms of available solar energy; June in Phoenix, Arizona and December in Boston, Massachusetts to represent the driving conditions in all the US states at any time followed by assessment of the results worldwide. The results show that, by adding on‐board PVs to cover less than 50% (around 3.2 m²) of the projected horizontal surface area of a typical passenger EV, the daily driving range could be extended from 3.0 miles to 62.5 miles by solar energy based on vehicle specifications, locations, season, and total time the EV remains at Sun. In addition, the ROI of adding PVs on‐board with EV over its lifetime shows only small negative values (larger than ?45%) when the price of electricity remains below $0.18/kWh and the vehicle is driven in low‐solar energy area (e.g. Massachusetts in the US and majority of Europe countries). The ROI is more than 148% if the vehicle is driven in high‐solar energy area (e.g. Arizona in the US, most Africa countries, Middle East, and Mumbai in India), even if the electricity price remains low. For high electricity price regions ($0.35/kWh), the ROI is positive and high under all driving scenarios (above 560%). Also, the reported system has the potential to reduce electricity consumption from grid by around 4.5 to 21.0 MWh per EV lifetime. A sensitivity analysis has been carried out, in order to study the impacts of the car parked in the shade on the results. Copyright © 2016 John Wiley & Sons, Ltd.     

Plug-in hybrid electric vehicle impacts on hourly electricity demand in the United States

In this study we explore the effects of different charging behaviors of PHEVs in the United States on electricity demand profiles and energy use, in terms of time of day and location (at home, the workplace, or public areas). Based on driving behavior statistics on vehicle distance traveled and daily trips (US DOT, 2003) in the US, we develop a simulation algorithm to estimate the PHEV charging profiles of electricity demand with plausible plug-in times and depth of discharge of the PHEVs.     

Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system

Fuel cell, a new kind of energy supply equipment, has several advantages such as high efficiency, low noise, and no emission. Proton exchange membrane fuel cell (PEMFC) is considered to have the potential to take the place of the conventional engine on unmanned underwater vehicle (UUV). Besides the power sources in the hybrid power system, the energy management system (EMS) is crucial to operating performance. In this paper, an on-line adaptive equivalent hydrogen consumption minimization strategy (ECMS) is proposed to solve the problem of prior knowledge demand and poor adaptability of current energy management algorithms. In this presented method, a battery state of charge (SOC) constituted penalty term is designed to calculate the equivalent factor (EF), and then the equivalent factor obtained by optimization is substituted into the original objective equation to realize the real-time energy regulation. In this paper, a typical UUV load curve is used to verify the control effect under different working conditions, and the performance is compared with three conventional algorithms’. Simulation results show that the hydrogen consumption of proposed algorithm is close to the optimal solution obtained in offline environment, and it is reduced by more than 3.79% compared with the traditional online methods.     

Optimal vehicle control strategy of a fuel cell/battery hybrid city bus

In this article, an optimal vehicle control strategy based on a time-triggered controller area network (TTCAN) system for a polymer electrolyte membrane (PEM) fuel cell/nickel-metal hydride (Ni-MH) battery powered city bus is presented. Aiming at improving the fuel economy of the city bus, the control strategy comprises an equivalent consumption minimization strategy (ECMS) and a braking energy regeneration strategy (BERS). On the basis of the introduction of a battery equivalent hydrogen consumption model incorporating a charge-sustaining coefficient, an analytical solution to the equivalent consumption minimization problem is given. The proposed strategy has been applied in several city buses for the Beijing Olympic Games of 2008. Results of the “China city bus typical cycle” testing show that, the ECMS and the BERS lowered hydrogen consumption by 2.5% and 15.3% respectively, compared with a rule-based strategy. The BERS contributes much more than the ECMS to the fuel economy, because the fuel cell system does not leave much room for the optimal algorithm in improving the efficiency.     

Design and analysis of an aging‐aware energy management system for islanded grids using mixed‐integer quadratic programming

The rapid increase of renewable energy sources made coordinated control of the distributed and intermittent generation units a more demanded task. Matching demand and supply is particularly challenging in islanded microgrids. In this study, we have demonstrated a mixed‐integer quadratic programming (MIQP) method to achieve efficient use of sources within an islanded microgrid. A unique objective function involving fuel consumption of diesel generator, degradation in a lithium‐ion battery energy storage system, carbon emissions, load shifting, and curtailment of the renewable sources is constructed, and an optimal operating point is pursued using the MIQP approach. A systematic and extensive methodology for building the objective function is given in a sequential and explicit manner with an emphasis on a novel model‐based battery aging formulation. Performance of the designed system and a sensitivity analysis of resulting battery dispatch, diesel generator usage, and storage aging against a range of optimization parameters are presented by considering real‐world specifications of the Semakau Island, an island in the vicinity of Singapore.     

A comparison study of battery size optimization and an energy management strategy for FCHEVs based on dynamic programming and convex programming

Aiming to address the hydrogen economy and system efficiency of a fuel cell hybrid electric vehicle, this paper proposes comparison research of battery size optimization and an energy management strategy. One approach is based on a bi-loop dynamic programming strategy, which selects the optimal one by initializing the battery parameters in the outer loop and performs energy distribution in the inner loop. The other approach is a framework based on convex programming, which can simultaneously design energy management strategies and optimize battery size. In the dynamic programming algorithm, the influence of the different discrete steps of state variables on the results is analysed, and a discrete step that can guarantee the accuracy of the algorithm and reduce computational time is selected. The results based on the above two algorithms and considering the transient response limitations of the fuel cell are analysed as well. Finally, two driving cycles are chosen to verify and compare the performance of the proposed methodology. Simulation results show that the dynamic programming-based energy management strategy and battery size provide more accurate results, and the transient response of the fuel cell has little effect on the optimization results of the battery size and energy management strategies.     

Design,demonstrations and sustainability impact assessments for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) are hybrid electric vehicles that can draw and store energy from an electric grid to supply propulsive energy for the vehicle. This simple functional change to the conventional hybrid electric vehicle allows a plug-in hybrid to displace petroleum energy with multi-source electrical energy. This has important and generally beneficial impacts on transportation energy sector petroleum consumption, criteria emissions output, and carbon dioxide emissions, as well as on the performance and makeup of the electrical grid. PHEVs are seen as one of the most promising means to improve the near-term sustainability of the transportation and stationary energy sectors. This review presents the basic design considerations for PHEVs including vehicle architecture, energy management systems, drivetrain component function, energy storage tradeoffs and grid connections. The general design characteristics of PHEVs are derived from a summary of recent PHEV design studies and vehicle demonstrations. The sustainability impact of PHEVs is assessed from a review of recent studies and current research and development needs for PHEVs are proposed.     

Energy‐cost‐aware flow shop scheduling considering intermittent renewables,energy storage,and real‐time electricity pricing

The industrial sector is one of the major energy consumers that contribute to global climate change. Demand response programs and on‐site renewable energy provide great opportunities for the industrial sector to both go green and lower production costs. In this paper, a 2‐stage stochastic flow shop scheduling problem is proposed to minimize the total electricity purchase cost. The energy demand of the designed manufacturing system is met by on‐site renewables, energy storage, as well as the supply from the power grid. The volatile price, such as day‐ahead and real‐time pricing, applies to the portion supplied by the power grid. The first stage of the formulated model determines optimal job schedules and minimizes day‐ahead purchase commitment cost that considers forecasted renewable generation. The volatility of the real‐time electricity price and the variability of renewable generation are considered in the second stage of the model to compensate for errors of the forecasted renewable supply; the model will also minimize the total cost of real‐time electricity supplied by the real‐time pricing market and maximize the total profit of renewable fed into the grid. Case study results show that cost savings because of on‐site renewables are significant. Seasonal cost saving differences are also observed. The cost saving in summer is higher than that in winter with solar and wind supply in the system. Although the battery system also contributes to the cost saving, its effect is not as significant as the renewables.