1. Introduction
In recent years, due to the increasingly significant shortage of non-renewable resources, such as oil and coal, excessive consumption, and the consequent environment pollution, electric vehicles (EVs) have gradually drawn people’s attention and become favored as a type of clean energy vehicle [
1]. The key issue for the effective operation of EVs is energy replenishment. It is known that there are two main ways to solve this problem: EV charging and battery swapping [
2,
3]. In general, EV charging requires a long charging process. Thus far, due to policy and money constraints, the charging stations, charging piles and other charging infrastructure are not widely deployed. The abovementioned reasons make it probable that EV users will be forced to stop and wait, which results in waiting anxiety. In addition, EV users trade-off between the remaining battery energy, the location distribution of charging facilities and their travel plans, which easily results in range anxiety [
4,
5]. Therefore, more researchers and EV operators are turning their attention to battery swapping [
6,
7,
8]. Battery swapping can provide a new fully charged battery, which does not require depleting the energy of the old battery. Range anxiety is eased, and to some extent infinite mileage is obtained. Because battery swapping only requires a few minutes, waiting anxiety is significantly eased.
However, before the benefit of battery swapping becomes a reality, two problems need to be solved. One is the EV battery technology, which is fundamental for battery swapping. A standardized EV battery with the characteristic of high mileage, high energy density, high recycling ratio, high recovery ratio, environmentally friendly ability and security needs to be developed [
9]. Currently, the development of zinc air batteries and zinc nickel batteries can initially meet the abovementioned demand [
10]. Therefore, the EV battery technology is not discussed in this paper. The other problem is a reasonable and effective EV battery swapping architecture that can support the effective operation of EV battery swapping.
The current EV battery swapping systems and infrastructure are mainly based on battery swapping stations and battery charging factories. A larger number of batteries are centrally-charged and transported to different battery swapping stations via a logistics system [
11]. Most research is this area is focused on the following issues: battery logistics strategy, battery swapping station planning and construction strategy, and battery charging strategy for the battery swapping stations [
12,
13]. The abovementioned research intends to improve the coverage and service of a battery swapping system. However, these approaches do not realize the objective of “get energy replenishment anytime and anywhere.” EV users must drive to a battery swapping station for battery swapping. Due to the obvious constraints of location and number of the existing battery swapping stations, there may still be a queueing and waiting phenomenon [
14]. Therefore, a more reasonable and effective EV battery swapping architecture is needed.
To solve this problem, one effective idea is to switch from the existing passive battery swapping mode to the active battery swapping mode. Recently, a new fast EV battery swapping device has been developed. The patents [
15,
16] indicate that this device can be installed on a van, which transforms the van into a mobile EV battery swapping station. This device has the advantages of accurate positioning and convenient swapping due to its components, such as a positioning pin, positioning hole, positioning track, positioning slot and PLC control system. The operations of removing and installing a battery occur at the same time. Thus, the entire battery swapping process is very fast and lasts only a few minutes (in the experimental environment, it is approximately 3 min). EVs drive into the interior predetermined position of the battery swapping van through the folding slope steel plate to perform an automatic battery swap. Thus, the actual transaction of battery swapping occurs inside the battery swapping van. The mobility of the battery swapping van removes the constraints of location and quantity of EV battery swapping stations. The battery swapping locations are more flexible. When the EV cannot drive due to energy depletion, the battery swapping location is undoubtedly generated based on the EV’s location. Otherwise, the battery swapping location can be generated based on the specific battery swapping service scheduling strategy or the driver’s actual requirements. Using the battery swapping van, the active battery swapping can be achieved anytime and anywhere. The battery swapping van will provide a fast, convenient, and flexible battery swapping service, and it will ease the pressure of battery logistic system. Based on a battery swapping van, we mainly study the following problem in this paper:
• EV battery swapping architecture based on a battery swapping van
For the effective and efficient operation of an EV battery swapping service based on a battery swapping van, a reasonable EV battery swapping architecture needs to solve the specific process of battery production, charging, transportation, storage, swapping, communication and others and identify the specific functions of each participant of the entire EV battery swapping system and the relationship between them. Moreover, profit mode and some details and practical factors should be discussed. Thus, our first contribution is to establish an EV battery swapping architecture based on a battery swapping van.
• Battery swapping service request scheduling
There are clear battery energy differences between the battery swapping requests of different EV users. Furthermore, the locations of the moving battery swapping van and EVs vary, which leads to the consequent change of battery energy consumption. Therefore, to improve the efficiency and effectiveness of battery swapping service, battery swapping requests need to be distinguished and scheduled based on the advanced management system. Our second contribution is to propose a minimum waiting time based on priority and satisfaction scheduling strategy (MWT-PS) to distinguish and schedule the battery swapping requests. First, we define the battery swapping service request and set its priority according to the State of Charge (SOC). Second, we establish a battery swapping service request queuing model according to the specific battery swapping service mode based on a battery swapping van. Then, we discuss the satisfaction of EV users based on waiting time and request priority and establish the scheduling model. Finally, the MWT-PS is proposed based on the abovementioned analysis.
The rest of this paper is organized as follows: in
Section 2, a related work is introduced. In
Section 3, the EV battery swapping architecture based on a battery swapping van is established. The battery swapping service request is discussed in
Section 4. In
Section 5, the battery swapping request scheduling mechanism MWT-PS is proposed. Simulation results are analyzed in
Section 6.
Section 7 draws the conclusion.
2. Related Work
Currently, the body of work related to EVs is rapidly increasing. For the realization of EVs, many studies have looked at the potential impact, adoption limitation, potential operational pattern and actual usage simulation of EVs in current electricity grids [
17,
18,
19,
20]. Most research indicates that energy replenishment is a significant factor in making EVs more competitive because of range anxiety among the EV users [
4,
5].
To help alleviate concerns of range anxiety among the EV users, many effective methods of energy replenishment are analyzed, including location sites of energy replenishment stations, how many stations to construct, energy replenishment strategies, and so on. For both the maximal coverage and minimal costs, the siting of charging stations is analyzed using a case study of Penghu in Taiwan [
21]. An ordered EV charging strategy in a charging station to improve charging effectiveness is proposed [
22]. The stochastic programming model, which takes into account price variation and the stochastic behavior of vehicle staying patterns, is put forward to achieve the optimal management of EV charging points [
23]. A previous study [
24] has proposed an integrated EV charging navigation framework that takes into consideration the impact of both the energy and transportation systems to save time of EV users and provide effective charging. A heuristic charging strategy is proposed to improve the real-time charging performance by optimizing the charging rate feasible searching region and saving searching time [
25].
In addition to energy replenishment methods that are based on battery charging, the methods that are based on battery swapping have been widely studied. A vehicle flow-interception model is proposed, and the optimal number of battery swapping stations, which included retrofitting of the existing petrol station, is analyzed and specifically evaluates a case study in Alexandria (VA, USA) [
26]. Reference [
27] presents an integer programming model to determine the location strategy of battery swapping stations and the routing plan of a fleet of EVs that are under a battery driving range limitation. The optimal placement and sizing of the battery swapping stations are studied using the Artificial Bee Colony algorithm [
6]. The EV battery swapping station strategy model is proposed that considers frequency and distribution of EV users’ arrivals, cooperation of users, and grid load demand curves [
28]. References [
29,
30], respectively, studied a service and operation scheduling model and an economic scheduling model for EV battery swapping stations to succeed in the rolling out of EVs.
All the abovementioned studies assume that EVs conduct most of their energy replenishment at static location energy replenishment stations. However, in practical implementations, mobile energy replenishment options would need to be provided to minimize response times and key indicators, such as latencies and miss ratios, and further help alleviate concerns of range anxiety. Reference [
31] proposed a mobile charging platform as an alternative implementation of those static battery recharge options, the implementation could either be in the form of a mobile plug-in charger or a mobile battery-swapping station. The mobile energy replenishment system possesses similar properties to mobile service systems such as ambulance and mobile data collection in WSN. The key factors are usually the location of mobile servers, coverage areas, service queuing models, and so on.
There are many previous studies about such mobile service systems. References [
32,
33,
34] determined that the location of ambulance bases and coverage areas are usually the key parameters. The reasonable and accurate queuing models of mobile servers are well studied through the analysis of mobile data collection in WSN with mobile data collectors. The most fundamental service queueing discipline of first-come–first-serve (FCFS) is explored in [
35,
36]. The nearest-job-next (NJN) discipline is explored in [
37]. It is clear that battery swapping vans in our EV battery swapping service system can be viewed as a special type of mobile servers. Therefore, we can adopt a similar approach based on a queue theory to analyze the mobile battery swapping process for EVs.
4. Battery Swapping Service
In this paper, an EV battery swapping service that is based on a battery swapping van is discussed. When an EV user wants to use the battery swapping service, a battery swapping service request needs to be sent to the battery swapping service management system. Then, the battery swapping service management system assigns this request to one battery swapping van, which is in a working state in its patrolling area. After the chosen battery swapping van confirms this request, it will drive to the EV user and complete this battery swapping service.
4.1. Service Request Priority
Theoretically, EV users can launch a battery swapping service request anytime and anywhere no matter how much the SOC is. However, this will increase the workload of the entire battery swapping system as well as usage loss of batteries and devices if a battery with a relatively high SOC is swapped. Moreover, there is no doubt that this will seize the opportunity of other EV users with an eager demand for battery swapping. Therefore, it is recommended that EV users should launch the battery swapping service request when the SOC is relatively low. Therefore, a battery swapping request should first contain the information about SOC.
During one service period, the battery swapping van keeps moving, and EVs can stop to wait or keep moving until they meet each other to save time. To provide an accurate battery swapping service as soon as possible, real-time position, driving direction and speed of both the battery swapping van and EV need to be exchanged. Thus, a battery swapping request should also contain this information.
We denote as the EV user with an ID id. The EV user launches a battery swapping service request via the battery swapping service mobile APP at time t. We, respectively, denote pos, dir as the real-time position and driving direction at time t.
Due to the range anxiety, it is highly unlikely that EV users will launch a battery swapping request when the battery energy is exhausted. When they consider that SOC is as low as possible according to the mileage requirement, convenience, and other factors, they will choose to launch the battery swapping request. However, different EV users launch their battery swapping request with different SOCs. Therefore, to complete as many battery swapping services as possible before the battery energy is exhausted, it is necessary to distinguish different battery swapping requests and prioritize them. For EV users, the fundamental requirement is battery energy; thus, this parameter used to prioritize the battery swapping requests according to SOC. In this paper, the method of setting thresholds and dividing battery energy into several intervals is used.
Table 2 shows the battery swapping request priority.
Currently, the mileage of many EVs such as the BYD-E series (305 km), BJEV-E series (200–260 km), and so on is approximately 200–300 km. The mileage of some EVs such as Tesla Model 3 (345 km), Tesla Model S (372–572 km), and so on is beyond 300 km. Without loss of generality, we choose 300 km as the mileage of EVs for the purpose of calculation in this paper. When the SOC is not greater than 5%, the EV can only drive for approximately ten kilometers or ten minutes at a speed of approximately 50 km/h. Thus, this type of battery swapping request should be responded to in a very short time and has the highest priority. When the SOC is within 5%–10%, the battery energy will not be exhausted immediately but within a relatively short time. This type of battery swapping request is reasonable and should have the second priority. When the SOC is higher than 20%, the EV can drive for at least tens of kilometers more. In most cases, this type of battery swapping request is not urgent and necessary. The priority is obviously the lowest. Generally, it will not be responded to if the battery swapping vans are busy. Moreover, if there is no convincing reason for launching the battery swapping request, the EV user will be given some price penalty or credit penalty.
The priority of battery swapping request will change over time. After waiting for a period of time, the priority of a battery swapping request may increase. The fully charged battery can support driving
s km, and the speed of EV is
v km/h. Let
be the SOC threshold of the higher priority. Then, after time
, the priority of the battery swapping request will increase to the priority that is associated with
:
For example, if the initial priority of battery swapping request is third, 10% <
SOC ≤ 20%, then after
time, the priority will be converted into second. Furthermore, after the
time, the priority will be converted into the first. The conversion time of the battery swapping request priority is shown in
Table 3.
The content authenticity of the battery swapping request needs to be confirmed. Generally, high priority means that the battery swapping service will be provided to the requesting EV first. Thus, to seek a relatively high priority, some people will on purpose falsely report a lower SOC than the true values. In addition, some people will not cancel a battery swapping request immediately when they changed their battery swapping plans or decided to stop the current travel plans. Thus, we established the EV user’s credit model. Let
be the current credit of the EV user
. The credit calculation is shown in Equation (2):
where
is the positive constant,
is the positive decimal,
establishes the upper bound, and
decreases as
increases, which means that credit is easily destroyed but difficult to be rebuilt.
4.2. Service Request Queuing Model
The general battery swapping process is easily translatable into a service process of the queuing model, where the battery swapping service management system acts as the server, and the event of the EV user requesting a battery swapping service is modeled as the client arrival. Once the EV has completed a battery swap, the event is treated as a client departure.
From the previous analysis in
Section 3, to improve the service efficiency and guarantee the EV users’ satisfaction, the entire district will be divided into small service areas. Each service area has battery swapping vans that patrol it. One battery swapping van only serves the requests that belongs to its patrolling area. Therefore, each service area should have its own battery swapping service queue.
Moreover, the service area that one battery swapping request belongs to would change over time because of the mobility of EVs. When the EV drives out of one service area, its battery swapping request should be queued in the battery swapping service queue of the adjacent service area. The general battery swapping service queuing model is shown in
Figure 2.
In the battery swapping scenario, the utilization of individual EVs is normally independent from each other, which indicates that their battery swapping requests are launched independently. Therefore, for each single service area, the battery swapping service queuing model is the same. In this paper, we base our analysis on a single service area. The general battery swapping service queuing model for a single service area is shown in
Figure 3.
Based on this general battery swapping service queuing model for a single service area, there are several submodules that need to be specified to determine a representative model.
4.2.1. Request Arrival
The request arrival determines the number and frequency of the battery swapping request. For a single service area, the request arrival consists of two parts: the request launched in this area and the request transferred from the adjacent service area. Both of them are independent of each other. Commonly, we can assume a Poisson process to capture the request arrival.
4.2.2. Request Queuing Discipline
The request queuing discipline determines the order by which the requesting EVs will be served. It would refer to the next EV to swap its battery. The disciplines can include HPF (select the request with the highest priority first), FCFS (queuing requests according to the order of their request times), NJN (select the request that is spatially closest to the current position of the battery swapping van first), STDF (select the request that is the same driving direction to the battery swapping van first if the requesting EV is in a parked state), HCF (select the request with the highest credit first), etc.
The SOC is a fundamental factor for battery swapping. The HPF discipline must be considered first. To reduce the driving time and improve the service efficiency, the battery swapping van is regulated only to provide the battery swapping service in its patrolling area. According to this point, the SCF discipline seems to be adopted in our battery swapping service queuing process because it minimizes the driving distance for the battery swapping van to reach the target EV and thus reduces the time. Moreover, to reduce the risk of false reporting or to cancel the request by the EV user, the HCF discipline should also be considered. To make the driving route of the battery swapping van simpler, the STDF discipline needs to be considered. Thus, we should not queue the battery swapping service using an only one discipline but consider them together. In the next section, a further discussion will be given.
4.2.3. Request Departure
The request departure describes the specific battery swapping service process. Two factors determine the request departure process: the service time of an individual battery swapping request and the number of battery swapping vans. When a battery swapping van is selected, the requesting EV is assigned to it. The former consists of two parts: the driving time that the battery swapping van requires to reach the target EV and the time to swap a fully charged battery for the EV, which is assumed to be constant . These two time periods are clearly independent of each other.
The driving time is determined by the distance between the real-time position of battery swapping van and that of the target EV, both driving speeds of the battery swapping van and target EV, and both driving directions. Moreover, the driving time is reduced with a larger number of battery swapping vans. This is because it is more likely for the requesting EV to be assigned a battery swapping van that is spatially closer.
In this section, the analysis provides a basic theoretical reference for the performance metrics. To better understand the battery swapping system that is based on a battery swapping van and to provide more efficient and reasonable battery swapping service, the battery swapping request scheduling mechanism is proposed that is based on further analysis.
6. Simulation Results
In this section, the battery swapping service is simulated, and the performance of MWT-PS is evaluated. We chose an urban environment that is approximated by the Haidian district in Beijing as the simulation scenario. The entire simulation scenario is divided into four service areas of equal size. We assume that the battery swapping vans are able to complete the battery swapping requests of EVs at any location.
In the design of a mobile battery swapping system, in general, there are some parameters that will significantly affect the performance of the system. We evaluate the system design using two main metrics that represent how well this system is operating. The first metric is the miss ratio
, which considers the number of requests that are not completed by the acceptable waiting time of the EV. We denoted
M as the total number of requests served during the mobile battery swapping process, and
as the number of requests that fail to be completed.
is calculated as:
The second metric is the average response time, which is defined as the time taken from when the EV sends out its battery swapping request to the time the request is completed.
In this paper, a fully charged battery is swapped with a fixed swapping time. Due to the limited carrying capacity of the fully charged batteries of each battery swapping van, the battery swapping van without any available fully charged batteries needs to return to battery swapping stations to replenish fully charged batteries.
Figure 7 presents their impact on the mobile battery swapping service, examines the number of 1–5 for battery swapping vans in each single service area and the fully charged battery capacities of 5–20 for each battery swapping van.
Larger battery swapping vans and larger fully charged capacity improve the performance in terms of both the miss ratio and average response time, which is intuitive. Although the small fully charged battery capacity seems to lead to large miss ratio and average response time because of a relatively frequent fully charged battery replenishment, the large fully charged battery capacity cannot clearly improve the performance metrics, especially when the number of battery swapping vans is larger than 2 in each single service area. Thus, the number of battery swapping vans and the performance of service queue scheduling are more important as long as the fully charged battery capacity is not too small.
We further evaluate the performance of service queue scheduling and compare between three service scheduling disciplines: FCFS strategy, NJN strategy, and our MWT-PS strategy with five battery swapping vans, each with a fully charged battery capacity of 20 in a single service area. In addition to the miss ratio and average response time, there is still an important metric. It is the satisfaction ratio with priority 1 and 2, which considers the number of requests with priority 1 and 2 that are completed by the satisfied waiting time of the EV. The calculation of satisfaction ratio is similar to the miss ratio.
Figure 8 shows the change of three performance metrics with the increasing battery swapping service requests in a commute hour. The results show that the battery swapping service system with our MWT-PS service scheduling strategy can provide a relatively high-quality battery swapping service for the EV users, especially those with more eager requirement for the battery swapping service. The average response time is obtained in the experimental scenario. In the actual battery swapping service scenario, the time may increase to a certain extent due to device standardization, proficiency, traffic, and other force majeure.
The realization of battery swapping systems that are based on mobile battery swapping vans is still a significant engineering challenge. However, there can be a design possibility for a mobile battery swapping system, and the simulation results show that this novel approach can be used as a reference for a future system.