Electrifying Heavy-Duty Trucks: Battery-Swapping Versus Fast Charging

The recent developments in electric vehicles (EVs) causes several issues that have not been satisfactorily addressed. One of the foremost problems is the charging–discharging processes of EV batteries with diverse characteristics. Although a charging station is the first choice in this regard, a battery swap station (BSS) is also a suitable alternative solution as it eliminates long waiting periods and battery degradation due to fast charging. BSS has the capability to ensure prompt and efficient service for electric vehicles. Since BSS has a large number of battery systems, optimum planning of the charging–discharging operations of the batteries is critical for both BSS and the grid. This study presents an optimal charging–discharging schedule for multiple BSSs based on the swap demand of privately owned EVs and electric bus (EB) public transportation system. In addition, BSSs reinforce the power grid by providing ancillary services such as peak shaving and valley filling with demand response programs. In order to increase the flexibility of the operation, the mobile swapping station (MSS) concept, an innovative and dynamic service, is introduced to the literature and added to the model. The results indicate that BSS is an essential agent in the ancillary services market and the MSS concept is a yielding solution for both BSSs and power networks. Last, the data utilized in the study for swap demand calculation and power grid analysis are real-world data from Berlin, Germany.

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

Towards a multimodal charging network: Joint planning of charging stations and battery swapping stations for electrified ride-hailing fleets

The problems faced in the electric vehicle (EV) charging station like time consumption and limited space can be overcome by battery swapping station (BSS), which not only saves time but also reduces the range anxiety problem among the users of EV. Batteries are the backbone of EV as it is power supply and essential to run the vehicle. Total price of EV has a good share in the form of battery, thus impacting a lot in the EV industry. Therefore, this paper presents a methodology for the selection of a sustainable battery supplier for BSS using fuzzy multi-criteria decision-making method. Firstly, four main criteria segregated into 12 supplementary criteria are identified considering several requirements for BSS. To cope with the uncertainty in the decision-making process, triangular fuzzy number (TFN) has been implemented. The TFNs give the relative weights among the considered criterion, and to make this framework more customer-centric, quality function deployment (QFD) is implemented, leading to final weights of the criteria. The obtained results give the hierarchy of the criteria, and top five weights after implementing the QFD methodology are as follows: battery price (0.2313), innovation ability (0.2188), safety assurance (0.12135), financial condition (0.1119), and cost of transportation (0.066). Subsequently, integrating fuzzy with complex-proportional assessment of alternatives (COPRAS) leads to rank the alternatives. As a case study, Kolkata city of India is chosen to demonstrate a viable selection. It was found that supplier based on Kolkata Municipal Corporation is the topmost priority, followed by North 24 Parganas, eastern part of Howrah, and South 24 Parganas, with the relative significance value of 0.3305, 0.2575, 0.2540, and 0.1578, respectively. This paper reveals an approach to the stakeholders for accessing the sustainable battery supplier of BSS choosing the most significant one.

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 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.

The introduction of battery electric vehicles (BEVs) could help to reduce dependence on fossil fuels and emissions from transportation and as such increase energy security and foster sustainable use of energy resources. However a major barrier to the introduction of BEVs is their limited battery capacity and long charging durations. To address these issues of BEVs several solutions are proposed such as battery swapping and fast charging stations. However apart from these stationary modes of charging, recently a new mode of charging has been introduced which is called inductive charging. This allows charging of BEVs as they drive along roads without the need of plugs, using induction. But it is unclear, if and how such technology could be utilized best. In order to investigate the possible impact of the introduction of such inductive charging infrastructure, its potential and its optimal placement, a framework for simulating BEVs using a multi-agent transport simulation was used. This framework was extended by an inductive charging module and initial test runs were performed. In this paper we present the simulation results of these preliminary tests together with analysis which suggests that battery sizes of BEVs could be reduced even if inductive charging technology is implemented only at a small number of high traffic volume links. The paper also demonstrates that our model can effectively support policy and decision making for deploying inductive charging infrastructure.

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

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.

The method of route optimization of electric vehicle

Electric vehicles (EVs) can contribute to reducing carbon emissions and facilitate renewable integration. However, EVs are not competitive with fuel-based vehicles, particularly for long distances, because of their limited range and long charging times. We propose a smart scheduling approach for EVs to plan charging stops on a highway with limited charging infrastructure. This approach aims to minimize the total travel time for each EV based on the A* algorithm with constraint verification and a peer-to-peer scheduling system. By considering the estimated state of the charging stations, we achieve indirect coordination between EVs. We introduce a simulation framework with trips generated using a data-driven approach and support for time-varying highway parameters. Furthermore, we apply our approach to a use case for the German highway A9 from Munich to Berlin. The computation and communication requirements of the proposed solution remain moderate and privacy preserving, contributing to its applicability. Results show that the smart scheduling approach significantly reduces the total travel times. In addition, by dynamically adjusting the schedules, the proposed approach can account for changing highway conditions, for example, slow traffic on a given segment. Our approach can be generalized beyond fast charging to different technologies, such as hydrogen or battery swapping stations.

BEV Fast Charging Strategy Optimization

Fast charging stations are critical infrastructures to enable high penetration of plug-in electric vehicles (PEVs) into future distribution networks. They need to be carefully planned to meet charging demand as well as ensure economic benefits. Accurate estimation of PEV charging demand is the prerequisite of such planning, but a nontrivial task. This paper addresses the sizing (number of chargers and waiting spaces) problem of fast charging stations and presents an optimal planning solution based on an explicit temporal-state of charge characterization of PEV fast charging demand. The characteristics of PEV charging demand are derived through a vehicle travel behavior analysis using available statistics. The PEV dynamics in charging stations is modelled with a Markov chain and queuing theory. As a result, the optimal number of chargers and waiting spaces in fast charging stations can be jointly determined to maximize expected operator profits, considering profit of charging service, penalty of waiting and rejection, as well as maintenance cost of idle facilities. The proposed solution is validated through a case study with mathematical justifications and simulation results.

Modeling of fast charging station equipped with energy storage

Compared with the traditional slow charging loads, random integration of large scale fast charging loads will exert more serious impacts on the security of power network operation. Besides, to maximize social benefits, effective scheduling strategies guiding fast charging behaviors should be formulated rather than simply increasing infrastructure construction investments on the power grid. This paper first analyzes the charging users’ various responses to an elastic charging service fee, and introduces the index of charging balance degree to a target region by considering the influence of fast charging loads on the power grid. Then, a multi-objective optimization model of the fast charging service fee is constructed, whose service fee can be further optimized by employing a fuzzy programming method. Therefore, both users’ satisfaction degree and the equilibrium of charging loads can be maintained simultaneously by reasonably guiding electric vehicles (EVs) to different fast charging stations. The simulation results demonstrate the effectiveness of the proposed dynamic charging service pricing and the corresponding fast charging load guidance strategy.