Battery Switch Station Research Articles

Aggregate demand response strategies for smart communities with battery-charging/switching electric vehicles

Demand response (DR) plays an important role in the peak shaving and load-shifting of a community, but the avalanche effect creates a huge obstacle in DR with uncoordinated usage of appliances and charging of electric vehicles (EVs). Recognizing the remarkably rising deployment of EVs, there is great potential to coordinate EVs' charging in a charging station or a battery switching station for providing DR. However, the potential benefits of the battery-switching EVs for providing DR and their impacts on the aggregate load of the community have not been fully studied. In this regard, the present study proposes two novel aggregate community-level DR strategies, i.e., a strategy employing battery-charging EVs and a strategy employing battery-switching EVs. The proposed strategies are compared with the conventional strategy that only incorporates community battery energy storage for enabling peak shaving. The cost optimization is conducted with optimization parameters including demand limits during different periods and the power and energy capacity of the battery energy storage system. A stochastic bottom-up demand model for households, a probabilistic model for predicting loads of EVs, and a redistribution time-of-use tariff are implemented in the optimization. A group with 20 households and 20 EVs in New York, USA is used in the case study. The results in the case study show that the total annual costs (electricity tariff cost and battery energy storage cost if any) are reduced by 63.3 %, 54.0 %, and 14.7 % under the battery-switching EV strategy, the battery-charging EV strategy, and the battery energy storage strategy compared to the base case (i.e., DR-free strategy), respectively. Additionally, the battery-switching EV strategy enables DR throughout the day with the maximum temporal flexibility. Last, the developed two EV strategies can provide a good stability of the household loads on the grid and avoid the avalanche effect.

Toward Efficient Mobile Electric Vehicle Charging under Heterogeneous Battery Switching Technology

The fast increase in adoption and development of Electric Vehicles (EVs) has invited a significant challenge to the existing charging management techniques and infrastructure. It is necessary to efficiently manage a large number of mobile EVs. As compared to fuel and gasoline type vehicles, the EV has a limited driving range and needs to recharge its battery frequently during long journeys. Hence, with plug-in charging services one major concern is the long duration of battery recharging. In this paper, we employ heterogeneous BS (Battery Switching) technology to provide an alternative charging option to minimize the charging duration of EV. Furthermore, enabling BS reservation in a centralized manner for mobile EVs, load balancing algorithm and optimal selection of Battery Switching Station (BSS) across the network are proposed. In addition, we suggest a scheduling technique for depleted batteries to recharge effectively in BSS to minimize power loss and queuing time at selected BSS. We have conducted a performance evaluation by comparing the proposed scheme with other benchmarks, in terms of average trip duration, total trip energy consumption, etc. Finally, it is proven that the battery stock is managed across the network efficiently through the proposed scheme.

Cruising with a Battery-Powered Vehicle and Not Getting Stranded

The main hindrance to a widespread market penetration of battery-powered electric vehicles (BEVs) has been their limited energy reservoir resulting in cruising ranges of few hundred kilometers unless one allows for recharging or switching of depleted batteries during a trip. Unfortunately, recharging typically takes several hours and battery switch stations providing fully recharged batteries are still quite rare – certainly not as widespread as ordinary gas stations. For not getting stranded with an empty battery, going on a BEV trip requires some planning ahead taking into account energy characteristics of the BEV as well as available battery switch stations. In this paper we consider very basic, yet fundamental problems for E-Mobility: Can I get from A to B and back with my BEV without recharging in between? Can I get from A to B when allowed to recharge? How can I minimize the number of battery switches when going from A to B? We provide efficient and mathematically sound algorithms for these problems that allow for the energy-aware planning of trips.

MEC Intelligence Driven Electro-Mobility Management for Battery Switch Service

As a key enabler in the green transport system, the popularity of Electric Vehicles (EV) has attracted attention from academia and industrial communities. However, the driving range of EVs is inevitably affected by the insufficient battery volume, as such EV drivers may experience trip discomfort due to a long battery charging time (under traditional plug-in charging service). One feasible alternative to accelerate the service time to feed electricity is the battery switch technology, by cycling switchable (fully-recharged) batteries at Battery Switch Stations (BSSs) to replace the depleted batteries from incoming EVs. Along with recent advance of vehicle cooperation through emerging Information Communication Technology (ICT), in this paper we propose a Mobile Edge Computing (MEC) driven architecture to gear the intelligent battery switch service management for EVs. Here, the decision making on where to switch battery is operated by EVs in a distributed manner. Besides, the Vehicle-to-Vehicle (V2V) communication in line with public transportation bus system is applied to operate flexible information exchange between EVs and BSSs. Dedicated MEC functions are positioned for bus system to efficiently disseminate BSSs status and aggregate EVs’ reservations, concerning the massive signalling exchange cost. The Global Controller (GC) is positioned as cloud server to gather BSSs (service providers) status and EVs’ reservations (clients), and predict the service availability of BSS (e.g., whether/when a battery can be switched). We conduct performance evaluation to show the advantage of MEC system in terms of reduction of communication cost, and BSS service management scheme regarding reduction of service waiting time (e.g., how long to wait for battery switch) and increase of service satisfaction rate (e.g., how many batteries to switch for EVs).

Joint Operation of Renewable Energy and Battery Switch Station Considering the Benefits of Different Subjects

The collaboration between renewable energy (RE) and an electric vehicles battery switch station (BSS) is a win-win strategy for both. In order to effectively coordinate and manage RE and BSS belonging to different investors, a new cooperative operation mode considering a multi-stakeholder scenario is proposed. By analyzing the relationship of cooperation and competition between them, their pursuit of maximizing their own interests is modeled as a Stackelberg game model. As the leader, the RE company determines the charge/discharge prices to guide the demand response of BSS. As the follower, the BSS optimizes its charge/discharge strategy according to the prices to maximize its own profits. The proposed model no longer requires that the RE company and BSS belong to a same interest subject, and there is no need for unified coordinated control between them. To solve the game equilibrium, a solution combining the backward-induction method and genetic algorithm is proposed by comparing and analyzing the characteristics of the strong and weak Stackelberg equilibriums. Finally, the effectiveness of the proposed model was validated by case studies. The simulation results show that both the RE company and the BSS have the motivation to participate in the game, and that a win-win outcome can be achieved automatically in the process of pursuing their own interest maximization in the game.

Stackelberg Game Model of Wind Farm and Electric Vehicle Battery Switch Station

In this paper, a cooperation method between wind farm and Electric vehicle battery switch station (EVBSS) was proposed. In the pursuit of maximizing their own benefits, the cooperation between wind farm and EVBSS was formulated as a Stackelberg game model by treating them as decision makers in different status. As the leader, wind farm will determine the charging/discharging price to induce the charging and discharging behavior of EVBSS reasonably. Through peak load shifting, wind farm could increase its profits by selling more wind power to the power grid during time interval with a higher purchase price. As the follower, EVBSS will charge or discharge according to the price determined by wind farm. Through optimizing the charging /discharging strategy, EVBSS will try to charge with a lower price and discharge with a higher price in order to increase its profits. Since the possible charging /discharging strategy of EVBSS is known, the wind farm will take the strategy into consideration while deciding the charging /discharging price, and will adjust the price accordingly to increase its profits. The case study proved that the proposed cooperation method and model were feasible and effective.

Robust DED based on bad scenario set considering wind, EV and battery switching station

With the increasing penetration of wind power and electric vehicle (EV) into the power system, system operators face new challenges for system reliability and generation cost due to the intermittent of wind power. Furthermore, the randomly connected EVs at different time periods and locations add more uncertainty to the power system. In this study, uncertainties in wind power generation and EV charging load are modelled into the day-ahead dynamic economic dispatch (DED) problem, solution feasibility and robustness are discussed, and the bad scenario set is formulated for the day-ahead DED problem. In the obtained model, parameters can be used to adjust the positive bias or conservative bias, charging/discharging power of battery switch stations are also controlled to optimise the total cost of power system. To solve the optimisation problem, the multi-agent bacterial colony chemotaxis algorithm and a mutation strategy based on the cloud theory are developed. The simulation results show that the proposed method is effective, and battery switching station can help to reduce total cost by charging and discharging batteries.

Two-Stage Multi-Objective Collaborative Scheduling for Wind Farm and Battery Switch Station

In order to deal with the uncertainties of wind power, wind farm and electric vehicle (EV) battery switch station (BSS) were proposed to work together as an integrated system. In this paper, the collaborative scheduling problems of such a system were studied. Considering the features of the integrated system, three indices, which include battery swapping demand curtailment of BSS, wind curtailment of wind farm, and generation schedule tracking of the integrated system are proposed. In addition, a two-stage multi-objective collaborative scheduling model was designed. In the first stage, a day-ahead model was built based on the theory of dependent chance programming. With the aim of maximizing the realization probabilities of these three operating indices, random fluctuations of wind power and battery switch demand were taken into account simultaneously. In order to explore the capability of BSS as reserve, the readjustment process of the BSS within each hour was considered in this stage. In addition, the stored energy rather than the charging/discharging power of BSS during each period was optimized, which will provide basis for hour-ahead further correction of BSS. In the second stage, an hour-ahead model was established. In order to cope with the randomness of wind power and battery swapping demand, the proposed hour-ahead model utilized ultra-short term prediction of the wind power and the battery switch demand to schedule the charging/discharging power of BSS in a rolling manner. Finally, the effectiveness of the proposed models was validated by case studies. The simulation results indicated that the proposed model could realize complement between wind farm and BSS, reduce the dependence on power grid, and facilitate the accommodation of wind power.

Research on the optimal planning of the battery switch station for electric vehicles

With the rapid development of the global economy, the energy crisis and the deterioration of the ecosystem are becoming more serious. In this context, the development of electric vehicles has received the attention of all countries. Research on charging and changing facilities has a very important significance for the future comprehensive promotion of electric vehicles. In this study, the authors first analyse the advantages and disadvantages of different charging electric network modes. Then, they introduce distribution planning requirements of electric vehicles’ changing stations, and propose an optimisation model meeting various requirements when selecting station sites. This model aims to minimise overall construction and transportation costs, and meet the charging demand of drivers. Changing stations’ service types and operating characteristics of the substation act as constraint conditions. Finally, they solve the model based on the improved graph algorithms. It not only calculates the optimisation location of the changing stations, but also obtains the corresponding optimal substation access scheme. The research of this study can significantly guide planning and construction of electric vehicles’ charging and battery switch stations.

Optimal Charging Schedule for a Battery Switching Station Serving Electric Buses

We propose a model of a battery switching station (BSS) for electric buses (EBs) that captures the predictability of bus operation. We schedule battery charging in the BSS so that every EB arrives to find a battery ready for switching. We develop an efficient algorithm to compute an optimal schedule. It uses dual decomposition to decouple the charging decisions at different charging boxes so that independent subproblems can be solved in parallel at individual charging boxes, making the algorithm inherently scalable as the size of the BSS grows. We propose a direct projection method that solves these subproblems rapidly. Numerical results illustrate that the proposed approach is far more efficient and scalable than generic algorithms and existing solvers.

Optimal Configuration for Batteries and Chargers in Battery Switch Station Considering Extra Waiting Time of Electric Vehicles

AbstractBattery switch stations (BSSs) are regarded as important infrastructure for the development of electric vehicles (EVs). However, studies on the optimal configuration of BSSs are still limit...

The electric vehicle touring problem

The increasing concern over global warming has led to the rapid development of the electric vehicle industry. Electric vehicles (EVs) have the potential to reduce the greenhouse effect and facilitate more efficient use of energy resources. In this paper, we study several EV route planning problems that take into consideration possible battery charging or swapping operations. Given a road network, the objective is to determine the shortest (travel time) route that a vehicle with a given battery capacity can take to travel between a pair of vertices or to visit a set of vertices with several stops, if necessary, at battery switch stations. We present polynomial time algorithms for the EV shortest travel time path problem and the fixed tour EV touring problem, where the fixed tour problem requires visiting a set of vertices in a given order. Based on the result, we also propose constant factor approximation algorithms for the EV touring problem, which is a generalization of the traveling salesman problem.

A Charging Strategy for PV-Based Battery Switch Stations Considering Service Availability and Self-Consumption of PV Energy

The photovoltaic (PV)-based battery switch station (BSS) is one of typical integration systems to implement a solar-to-vehicle system. The charging strategy is important for the operation of the PV-based BSS. Generally, instant charging strategy for swapped electric vehicle (EV) batteries can keep the availability of battery-swapping service at a high level. However, it is always accompanied with the possibility of bringing a negative effect on the utilization of PV energy. The contribution of this paper is mainly on a novel charging strategy for the PV-based BSS considering the service availability and self-consumption of the PV energy. First, considering the features of the PV-based BSS, evaluation indexes for the operation performance are defined, including the availability of battery-swapping service, self-consumption of the PV energy, and operation profit. Second, the charging strategy is proposed, including a battery-swapping service model and a power distribution model. In order to guarantee the service availability, the battery-swapping service model is used to decide the lower limit of charging power based on short-term forecasting results of EV requirements. The power distribution model is obliged to dispatch the charging power supplied by the PV system and power grid. Finally, in the case study, the operation of the BSS is simulated with the instant charging strategy and the proposed strategy under different scenarios. From the analysis of results, the proposed strategy can effectively improve the self-consumption of PV energy with the premise of guaranteeing the availability of the battery-swapping service.

Optimal Dispatch Strategy of a Virtual Power Plant Containing Battery Switch Stations in a Unified Electricity Market

A virtual power plant takes advantage of interactive communication and energy management systems to optimize and coordinate the dispatch of distributed generation, interruptible loads, energy storage systems and battery switch stations, so as to integrate them as an entity to exchange energy with the power market. This paper studies the optimal dispatch strategy of a virtual power plant, based on a unified electricity market combining day-ahead trading with real-time trading. The operation models of interruptible loads, energy storage systems and battery switch stations are specifically described in the paper. The virtual power plant applies an optimal dispatch strategy to earn the maximal expected profit under some fluctuating parameters, including market price, retail price and load demand. The presented model is a nonlinear mixed-integer programming with inter-temporal constraints and is solved by the fruit fly algorithm.

An intelligent battery information management system to support information sharing and vehicle routing planning for battery distribution in Hong Kong

Recently, the deployment of electric vehicles (EV) has been recognised as a key element for establishing sustainable transport systems within countries. Research studies have been conducted to tackle challenges regarding the commercial introduction of EV. However, the current plug–in EV infrastructure poses operational limitations, causing users refusing to change from fossil–fuelled vehicles to EV. To eliminate the limitations of long charging time and huge infrastructure costs of the current EV models, an intelligent battery information management system (IBIS) is designed for supporting battery switching logistics operations. The system shows the potential value of the battery switching management and the minimisation of the supply chain cost. A case study in launching the proposed model in Hong Kong is conducted. The result provides a practical solution to balance the level of battery charging efficiency in the battery management hub, service rate of battery switching stations, and the inventory cost of holding batteries.

Electric Vehicles with a Battery Switching Station: Adoption and Environmental Impact

The transportation sector's carbon footprint and dependence on oil are of deep concern to policy makers in many countries. Use of all-electric drive trains is arguably the most realistic medium-term solution to address these concerns. However, motorist anxiety induced by an electric vehicle's limited range and high battery cost have constrained consumer adoption. A novel switching-station-based solution is touted as a promising remedy. Vehicles use standardized batteries that, when depleted, can be switched for fully charged batteries at switching stations, and motorists only pay for battery use. We build a model that highlights the key mechanisms driving adoption and use of electric vehicles in this new switching-station-based electric vehicle system and contrast it with conventional electric vehicles. Our model employs results from repairable item inventory theory to capture switching-station operation; we embed this model in a behavioral model of motorist use and adoption. Switching-station systems effectively transfer range risk from motorists to the station operator, who, through statistical economies of scale, can better manage it. We find that this transfer of risk can lead to higher electric vehicle adoption than in a conventional system, but it also encourages more driving than a conventional system does. We calibrate our models with motorist behavior data, electric vehicle technology data, operation costs, and emissions data to estimate the relative effectiveness of the two systems under the status quo and other plausible future scenarios. We find that the system that is more effective at reducing emissions is often less effective at reducing oil dependence, and the misalignment between the two objectives is most severe when the energy mix is coal heavy and has advanced battery technology. Increases in gasoline prices (by imposition of taxes, for instance) are much more effective in reducing carbon emissions, whereas battery-price-reducing policy interventions are more effective for reducing oil dependence. Taken together, our results help a policy maker identify the superior system for achieving the desired objectives. They also highlight that policy makers should not conflate the dual objectives of oil dependence and emissions reductions as the preferred system, and the policy interventions that further that system may be different for the two objectives. This paper was accepted by Yossi Aviv, operations management.

Multi-objective optimization for component capacity of the photovoltaic-based battery switch stations: Towards benefits of economy and environment

With the rapid development of EVs (electric vehicles), the integration of PV (photovoltaic) generation with EV charging infrastructure can effectively improve the efficiency of clean energy utilization and carbon emission reduction. How to optimize the capacities of components in the PV-based BSS (battery switch stations) is still unsettled. This paper focuses on the mathematical model of the problem. With consideration of battery swapping requirement and maximally utilizing PV energy, an energy exchange strategy is introduced for the PV-based BSS, including battery swapping service model and power distribution model. Towards benefits of economy and environment, objective functions of capacity optimization are modeled with the purpose of minimizing annual cost and maximizing the percentage of utilized PV energy in total energy. The constraints include the construction scale, power balance and battery swapping service etc. Based on the energy exchange strategy, the optimization model is solved by NSGA-II algorithm. Finally, taking the planning of a PV-based BSS in a certain district as example, optimized capacities of PV panels, EV batteries, EV chargers and grid-connected modules can be obtained. From the analysis of the results, the method can provide a foundation for the plan and design of the PV-based BSSs.

Battery Charger for Electric Vehicle Traction Battery Switch Station

This paper presents the functionality of a commercialized fast charger for a lithium-ion electric vehicle propulsion battery. The device is intended to operate in a battery switch station, allowing an up-to 1-h recharge of a 25-kWh depleted battery, removed from a vehicle. The charger is designed as a dual-stage-controlled ac/dc converter. The input stage consists of a three-phase full-bridge diode rectifier combined with a reduced rating shunt active power filter. The input stage creates an uncontrolled pulsating dc bus while complying with the grid codes by regulating the total harmonic distortion and power factor according to the predetermined permissible limits. The output stage is formed by six interleaved groups of two parallel dc-dc converters, fed by the uncontrolled dc bus and performing the battery charging process. The charger is capable of operating in any of the three typical charging modes: constant current, constant voltage, and constant power. Extended simulation and experimental results are shown to demonstrate the functionality of the device.

Simulation study on optimal placement and sizing of Battery Switching Station units using Artificial Bee Colony algorithm

The improvements on DC electrical motor and battery technologies have increased the industries’ and users’ interest in Electrical Vehicle (EV). In line with this, multiple types of Charging Stations (CS) have also been introduced to support the EV’s growth in the vehicle market. Three types of CS units that are available nowadays are Levels 1, 2 and 3 (DC Charging type). However, there are some drawbacks on these CS types either in terms of charging time (for Levels 1 and 2) or the impact to the system performance (Level 3). Thus, the Battery Switching Station (BSS) is used to tackle all these problems. In the BSS, the customers’ current batteries are swapped with fully charged batteries by the automated system and the charging process of those batteries will be done at a later time by the BSS. Therefore, the long charging time issue can be evaded. However, inappropriate placement of BSS in the distribution network will give enormous impact to the distribution network due to the lower voltage level in distribution system. Two methods have been used to place the BSS in this study, which are, analytical and meta-heuristic (Artificial Bee Colony (ABC)) methods. The ABC is chosen due to the simplicity of the algorithm in finding the optimal location of BSS. For further improvement, the ABC is employed to find the optimal size of BSS and DG output. The results show that the analytical and optimization methods gave similar results in finding the optimal location of BSS, which is better than randomization of BSS placement. Furthermore, the analysis on optimal size of BSS and DG output (simultaneously) gives the lowest power loss compared to other analyses.

Optimal recharging strategy for battery-switch stations for electric vehicles in France

Most papers that study the recharging of electric vehicles focus on charging the batteries at home and at the work-place. The alternative is for owners to exchange the battery at a specially equipped battery switch station (BSS). This paper studies strategies for the BSS to buy and sell the electricity through the day-ahead market. We determine what the optimal strategies would have been for a large fleet of EVs in 2010 and 2011, for the V2G and the G2V cases. These give the amount that the BSS should offer to buy or sell each hour of the day. Given the size of the fleet, the quantities of electricity bought and sold will displace the market equilibrium. Using the aggregate offers to buy and the bids to sell on the day-ahead market, we compute what the new prices and volumes transacted would be. While buying electricity for the G2V case incurs a cost, it would have been possible to generate revenue in the V2G case, if the arrivals of the EVs had been evenly spaced during the day. Finally, we compare the total cost of implementing the strategies with the cost of buying the same quantity of electricity from EDF. • Optimal strategies for buying/selling electricity through day-ahead auction market. • Given fleet size power bought and sold would change market price and volume. • New prices computed using aggregate offers to buy/sell power in 2010 and 2011. • Timing of arrival of EVs critical in V2G case. If evenly spaced BSS makes money. • Strategies are very robust even when French and German markets were coupled Nov. 2010.