Data-driven on-demand energy supplement planning for electric vehicles considering multi-charging/swapping services

The problem of energy and reserve exchange between electric vehicles (EVs) and fast charging station (FCS) is studied using a Stackelberg game. In this game, the FCS operator, who acts as a leader, needs to set its energy and reserve prices to optimize its revenue while ensuring EV users' charging demand. On the other hand, EV users, who act as the followers, needs to decide their charging and reserve strategies to optimize a tradeoff between the benefit from battery charging and reserves provision. It is shown that the proposed game possesses a social optimal Stackelberg equilibrium, in which the FCS operator optimizes its prices and the EV users choose their equilibrium strategies. A mathematical programming with equilibrium constraints (MPEC) reformulation of the game is proposed and can be solved efficiently by commercial software packages. The reformulation enables the FCS operator and EV users to reach the equilibrium and is assessed by extensive simulations.

The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the charging benefit for large quantities of depleted batteries in the warehouse. However, the challenge is that the charging schedule for depleted batteries must be made without knowledge of future battery arrivals because the EV behaviors are difficult to predict. To address this problem, this paper developed an online charging scheduling algorithm, which formulates the charging strategy as a two-dimensional rectangle packing problem. The proposed method can provide battery replacement services in real-time and solve quickly without any information about incoming depleted EV batteries. The proposed model and method have been tested on the system with different numbers of batteries to show the effectiveness. Besides, the online two-dimensional rectangle packing problem can provide an online decision for BSSs.

Battery recharging is an essential aspect of integration of Electric Vehicle (EVs) into the transportation framework. So far, charging stations have been considered as the primary source for recharging EVs. However, a significant time is is taken at the charging station to recharge a single EV. This problem is addressed by Battery Swapping Stations (BSS) that allow for swapping a depleted battery (DB) with a charged battery (CB). As with charging stations, the participating entities remain to be the grid, the aggregator, service station(s) and the EV owner. However, a variation exists in their interaction. In this work, a scheduling management scheme is presented for an aggregator to maximize its profit while managing BSS in a densely populated area. Furthermore, this has been explored for a varying number of requests. Furthermore, a PV assist has also been considered for BSS, where the depleted batteries may be charged through the PV assist. In both cases, with and without penalty for unscheduled EVs, in the case of no PV assist and with PV assist, results indicated that maximum profit was observed in the case of PV assist. Furthermore, more batteries were found to be available for swapping in the case with PV assist.

Electric vehicle (EV) is one of the preferred modes of transportation due to less emission of pollutants. The depleted batteries can be refueled by using a battery charging station (BCS), battery swapping station (BSS), and battery swapping van (BSV). Earlier, the depleted batteries were replenished using the different battery charging modes, but due to less flexibility, battery swapping (BS) was preferred over battery charging (BC). However, battery charging is not completely ruled out as it causes less damage to the battery and the swapped batteries have to be charged using battery charging. The forecasting on the arrival of EVs helps the station owner to serve the customers and in optimizing the various cost(s) associated with BSS. Metaheuristics help to arrive at the solutions at a faster rate when compared to the traditional optimization techniques. BSV is the active mode of replenishing energy which increases the effectiveness and efficiency of the battery swapping process. Further, a case study is carried out to understand the need to serve the customer for an unpredicted situation in the service station.

To realize optimal day-ahead operation of battery swapping and charging systems (BSCSs), a closed loop supply chain (CLSC) based management scheme is proposed, where the game theory is adopted for benefits allocation. The CLSC is used to depict the battery-swapping-charging process between the battery charging stations (BCSs) and battery swapping stations (BSSs). The arrival, departure and swapping service of electric vehicles (EVs) at BSSs is modeled as distinct queues based on the network calculus theory. The depleted batteries (DBs) and well-charging batteries (WBs) based interaction among BCSs and BSSs is formulated as a Stackelberg game. In the game, one BCS acts as the leader and the BSSs act as the followers. The BCS sets optimized prices to maximize its utility and the BSSs optimally demand WBs, supply DBs and provide battery swapping services to maximize their own utilities while guaranteeing the quality of service (QoS) needed for battery swapping. The existence of Stackelberg equilibriums (SEs) of the proposed game is proved. A differential evaluation based hybrid algorithm is proposed to compute an SE. The effectiveness of proposed method has been demonstrated by the simulation results, guaranteeing the QoS and balancing benefits among the BCS and BSSs while maximizing social welfare.

The advantages and disadvantages of Battery Swapping Stations (BSS) for heavy-duty trucks are poorly understood, relative to Fast Charging Stations (FCS) systems. This study evaluates these two charging mechanisms for electric heavyduty trucks, aiming to compare the systems efficiency and identify their optimal design. A model was developed to address the planning and operation of BSS in a charging network, considering in-station batteries as assets for various services. We assess performance metrics including transportation efficiency and battery utilization efficiency. Our evaluation reveals that BSS significantly increased transportation efficiency by reducing vehicle downtime compared to fast charging, but may require more batteries. BSS with medium-sized batteries offers improved transportation efficiency in terms of time and labor. FCS-reliant trucks require larger batteries to compensate for extended charging times. To understand the trade-off between these two metrics, a cost-benefit analysis was performed under different scenarios involving potential shifts in battery prices and labor costs. Additionally, BSS shows potential for significant CO2 emission reductions and increased profitability through energy arbitrage and grid ancillary services. These findings emphasize the importance of integrating BSS into future electric truck charging networks and adopting carbon-aware operational frameworks.

The placement of Electric Vehicle (EV) Charging Stations (EVCS) and Battery Swapping Stations (BSS), along with the coordinated scheduling of Electric Vehicles (EVs), is essential to meet growing charging demands of EVs while minimizing their impact on the power distribution network. In this study, a comprehensive framework is developed on a radial distribution network integrated with a road network to strategically allocate EVs to different charging infrastructures. A double auction-based mechanism is proposed to allocate EVs between EVCS and BSS, ensuring mutual profit for both EV owners and EVCS/BSS operators. This work focuses on two primary objectives: (1) minimizing energy loss in the distribution network through optimal placement of EVCS and BSS, and (2) maximizing the profit of EVCS and BSS operators via optimal scheduling of EVs considering game theory-based compromised electricity prices. Real-time uncertainties related to EV travel behavior are modeled using the 2 m -point estimation method to improve reliability and practicality. The proposed problem is solved using a newly developed hybrid optimization technique combining Differential Evolution (DE) and Prairie Dog Optimization (PDO).

The expansion of electric vehicles made the expansion of charging infrastructure rudimentary to keep up with this developing technology that helps people in a myriad of ways. The main drawback in electric vehicle charging, however, is the time consumed to charge a vehicle. The fast charging of electric vehicles solves this problem thus making it a lucrative technology for consumers. However, the fast charging technology is not without its limitations. In this paper we have identified the technology gaps in EV fast charging stations mostly focused on the extremely fast charging topology. It will help pave a path for researchers to direct their effort in a consolidated manner to contribute to the fast charging infrastructure. A thorough review of all aspects and limitations of existing extremely fast charging (XFC) stations have been identified and supporting data are provided. The importance of DC power network based on free fuel energy sources and silicon carbide-based power electronics are proposed to provide ultra-low cost and ultra-high speed XFC stations.

Greenhouse gas emissions-related issues have been extensively discussed in the past years, with over 70 countries already committed to a carbon-neutral economy by 2050. The electrification of transportation modals has increased following these goals, where Electric Vehicles (EVs) are starting to take Internal Combustion Engine Vehicles (ICEV) market share all over the globe. Besides the particular complexity in comparing EVs and ICEVs, challenges involving the nature of EVs and their integration with cities, such as the lack of public locals for charging, are also critical and interfere with their development. In this context, this work aims at studying the problem of a Battery Swapping Station (BSS), a structure where the EVs users swap their depleted batteries for fully or partially charged ones. In order to simulate the BSS daily operations and batteries charging schedule, a novel Mixed Integer Linear Programming (MILP) model is proposed, taking into account battery heterogeneity, the use of local photovoltaic (PV) production and battery degradation based on charging profile. A collection of BSS operation metrics are designed to evaluate the solution quality of the proposed scheduling model. A numerical experiment comprising four case studies based on real data from the US power and transportation systems is presented, with insights and analyses on the PV and grid power use, as well as a BSS financial comparison against close-related benchmark scheduling approaches, together with sensitivities on BSS sizing plan and costumers attendance.

Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties

Electric vehicles are in vogue nowadays and it has been gaining some ground in India after Tesla entered into the Indian automobile market. Electric vehicles do not require conventional fuel sources such as fuel, oil, etc., so they do not pollute the environment. BLDC motors are used as the motor of electric vehicles, as they provide a higher speed as well as higher efficiency as compared to other motors. Since the vehicles are powered electrically, it is imperative for us to charge the vehicles just like cell phones. Just like fuel cars have fuel stations for filling up fuel for the operation of vehicles, there must also be battery charging or swapping stations for charging the battery of electric vehicles. This paper would determine how we must charge the electric vehicle using solar along with the traditional grid charging architecture. These two must work in tandem to provide the necessary electrical power to assuring the sufficiently charged battery at the swapping station for uninterrupted use of electric vehicles even in emergency cases. Here, the solar PV along with the traditional grid (Renewable and Conventional both respectively) is used to charge the battery at the swapping station, so it is named a hybrid battery swapping station (HBSS). Also, the concept of charging the battery packs in batches with scheduling is used which reduces the installation cost and the area requirement for the swapping stations compared to other existing battery swapping stations (BSS) or battery charging stations (BCS).

Electric Vehicles (EVs) have generated a lot of interest in recent years, due to the advances in battery life and low pollution. Similarly, the expansion of the Internet of Things (IoT) allowed more and more devices to be interconnected. One major problem EVs face today is the limited range of the battery and the limited number of charging or battery swapping stations. A solution is to not only build the necessary infrastructure, but also to be able to correctly estimate the remaining power using an efficient battery management system (BMS). For some EVs, battery swapping can also be an option, either at registered stations, or even directly from other EV drivers. Thus, a network of EV information is required, so that a successful battery charge or swap can be made available for drivers. In this paper two blockchain implementations for an EV BMS are presented, using blockchain as the network and data layer of the application. The first implementation uses Ethereum as the blockchain framework for developing smart contracts, while the second uses a directed acyclic graph (DAG), on top of the IOTA tangle. The two approaches are implemented and compared, demonstrating that both platforms can provide a viable solution for an efficient, semi-decentralized, data-driven BMS.

Dynamic operation model of the battery swapping station for EV (electric vehicle) in electricity market

Dynamic expansion planning in active distribution grid integrated with seasonally transferred battery swapping station and solar energy

The environment is the major concern of today’s world due to global warming; cities are getting more polluted day by day. To keep the environment clean and green, every vehicle manufacturer is thinking of making electric vehicles but charging EVs became a major issue for EV owners due to its range anxiety. In a busy schedule, even a fast charger takes up to 30-40 minutes to charge an EV which is much more as compared to battery swapping which takes only 3-4 minutes, which is analogous to refilling a conventional vehicle from petrol or diesel or gas in the gas station and it motivates to use more electric vehicles. Charging a battery during peak hours increases the load on the grid and also increases the cost. To reduce the charging cost, the concept of charging during off-peak hours is proposed in this paper using the genetic algorithm to minimize the overall cost of charging in a battery-swapping station.