A novel strategy for emergency power supply by electricity migration in cooperation with a battery-swapping electric vehicle service

Abstract

In recent years, battery electric vehicles (BEVs), which do not emit any CO₂, have attracted considerable attention globally as a countermeasure against global warming. The plug-in BEV, a common BEV variant, is limited by the long charging time. The battery swapping electric vehicle (EV), however, allows for a relatively short fulﬁlling time in terms of electricity as its battery can be swapped for a pre-charged spare battery at a battery swapping station (BSS) in a few minutes. This paper presents an examination of the use of stored electricity as a power supply in emergencies. We propose both electricity migration for maintaining a suﬃcient amount of electricity among BSSs and methods that enable the reduction of the overheads incurred by EV users. The performance evaluations that validate the eﬃcacy of the proposed methods are detailed, and the future research scope is indicated.

Keywords

electric vehicle battery swapping battery energy storage system electricity migration emergency energy supply

1 Introduction

Decarbonization through the use of nonfossil fuels is expected to ﬁnd application across a wide array of domains as a countermeasure against global warming. A notable instance of the adoption of this process is the migration from vehicles powered by traditional engine systems to battery electric vehicles (BEVs), which do not emit any CO₂, in the transportation domain. The widespread deployment of electric vehicles (EVs) is projected to contribute to a signiﬁcant reduction in CO₂ emissions and the improvement of the capacity of the battery energy storage system (BESS).

The plug-in EV (PEV) is the most widely used BEV variant. The PEV requires a long time to charge, thereby limiting its operating time and its utility for long-distance driving. The newly developed battery swapping electric vehicle (BSEV) addresses these challenges by realizing a short electric power fulﬁlling time through the incorporation of a battery that can be swapped for a charged unit at a battery swapping station (BSS) into its design. It has been reported that the time required to execute this swap is approximately 3 to 5 min.¹

However, in order to deploy BSEVs on a wide scale, BSSs would ﬁrst need to be built in the target areas. The potential costs of establishing this service infrastructure hinder the use of these vehicles.

As the number of BSSs increases, the capacity of BESSs would be improved, as each BSS would require multiple spare batteries. Using the electricity stored in the spare batteries in each BSS is expected to reduce the overhead of the initial cost of BSEV service.

Recent times have witnessed the occurrence of an unprecedented number of natural disasters the world over, frequently resulting in power failures. Prolonged power failure can serve as a signiﬁcant impediment to the usage of communication infrastructure and medical facilities, rendering emergency power facilities essential.

In this context, a BSS could be considered a major candidate for an emergency power source in times of crisis; however, ensuring that it harbors a suﬃcient supply of electrical energy, ready for use during a contingency, is challenging. The electrical energy reserves in a BSS depend entirely on the usage of the station by EV users. For example, a BSS in a densely populated area would be used with a higher frequency and would, consequently, have batteries with lower energy levels than a station in a sparsely populated area. Therefore, stations in densely populated areas would make poor candidates for emergency power facilities, indicating the necessity for a method to maintain adequate electricity reserves outside of emergency scenarios, ensuring that the facility is suﬃciently supplied in the eventuality of a natural disaster.

In this paper, we propose a novel method for regulating the electricity reserves available in a BSS by migrating batteries between stations using the battery swapping mechanism designed for BSEV servicing. Our proposal entails the migration of batteries between any two BSSs using the regular activity of a volunteer BSEV user. As this method depends on volunteers, we also propose additional methods for minimizing user load. We evaluate our proposal by using a simulation of urban mobility² to validate its eﬃcacy and feasibility, demonstrating that our method effectively realizes electricity regulation in BSSs and user load reduction.

The rest of this paper is organized as follows. Related works are described in Section 2. Section 3 presents a simulation model. A proposed method and three improved methods are described in Sections 4 and 5. Simulation results and discussion are given in Section 6, and ﬁnally, Section 7 concludes this paper.

2 Related works

Strategic installations of BSSs are an important issue in the deployment of the BSEV service. Koirala et al.³ have referred to the proper selection of BSS location and analyzed it using multiple criteria decision making. Wang et al.⁴ have discussed the strategy under the mixed environment of hybrid EVs, PEVs, and BSEVs. They described that the required number of BSS depends on the proportion of penetration.

Substantial research has focused on EV batteries for their applications in electricity storage systems, as well as for the extension of the cruising range of EVs. Most of these efforts have focused on combining renewable energy and the electric power grid. For the combinations, EVs and EV's batteries are dealt as BESS, and BESS is used for adjusting demand response by charging/discharging from/to the BESS. This framework is called vehicle-to-grid, enhancing both the degree of freedom of electric power and the contribution to grid stability. BSEV service also improves grid ﬂexibility and stability in cooperation with a photovoltaic (PV) system. Introducing the PV contributes to mitigating grid load in terms of charging the EV battery without interruption.

Ban et al.⁵ and Nyamayoka et al.⁶ propose methods to make the most of PV energy by using BSSs and BSEVs. Huang et al.⁷ proposed the PV-storage-charging integrated BSS, in which the BSS employs the PV function. Additionally, the achievement of both the load reduction of the power grid and the maximization of the income loss of the BSS was proposed by controlling the timing of the charging and discharging processes from and to the BSS.

Wang and Yang⁸ proposed a method employing dynamically changing electrical charging prices for BSSs in the interest of mitigating costs for both the stations and EV users. This scheme is based on time-of-use pricing, which entails determining the price of electric power charging depending on the time of day that the user is availing of the service.

Utilizing EV energy contributes to the recovery of the grid. Guo et al.⁹ have proposed the post-disaster load restoration model of the distribution system (DS) considering both the biased battery swapping demands and multiple costs. Results of the evaluation demonstrate that the proposed system realizes fast reconstruction of a DS while minimizing cost. BSSs have also been considered for use as an emergency power supply (EPS) source, leveraging the portability of EV batteries to supply electricity to the target area. Kikuta et al.¹⁰ proposed one such method, entailing the creation of an EV cruising route from a non-power failure area to a power failure area.

To the best of our knowledge, few studies have explored leveraging both the mobility and swapping functions of BSEVs in order to use a BSS as an EPS source.

3 Assumptions

3.1 Model

The traﬃc model assumptions and parameters as presented in Figure 1 and Table 1, respectively. As shown in the ﬁgure, the ﬁeld consists of a square of 25 km side lengths, and the length between any two successive intersections is 2.5 km. All roads have a total of four lanes, with two lanes on each side.

Figure 1.

Simulation model (2BSS).

Table 1.

Parameters.

Item	Value
Number of POI	6
POI stay time	1 s
Number of BSS	2
Number of spare batteries on a BSS	50
Time required for swapping the battery on BSS	300 s
Battery charging speed on a BSS	3 kW/h
EV	50
Battery capacity	40 kW
Electricity consumption by EV cruising	8 km/kWh
Battery swapping threshold α	0.3

POI: point of interest; BSS: battery swapping station; EV: electric vehicle.

Table 2.

M-SOC_BSS and the number of battery swaps on each BSS (with/without electricity migration).

	BSS₀	BSS₁
SOC_BSS	0.569	0.667
Number of battery swaps	1116	769

BSS: battery swapping station.

For the following discussion, we consider two areas with one BSS each in the ﬁeld. The areas have four and two points of interest (POIs), respectively. Here, the locations of the BSS and POIs in each area are randomly selected. An example is shown in Figure 1.

In the model, we consider a POI to be a home, oﬃce, or commercial facility visited by an EV user in the course of their daily activities. Hence, an area with several POIs is deemed a highly populated area, and the electricity level of the corresponding BSS may be lower than that of a station in an area with fewer POIs. The level to which the batteries are charged is indicated using the state of charge (SOC). The SOC is a ratio of the charged electricity to the maximum capacity of the EV or BSS battery(s), and its value is in the range 0 to 1.

Figure 2 shows the EV travel process used in the model without battery swapping operations. An outline of the electricity charging operation of the BSEV service, as depicted in the ﬁgure, is given below. (1)

After leaving the current POI, an EV moves toward a destination POI selected randomly using the shortest route. When the EV arrives at the destination, the EV selects the next destination randomly and continues to travel. As it travels, the SOC of the EV's battery (SOC_EV) is consumed. If the SOC_EV falls under the predeﬁned battery swapping threshold α(0 ≤ α ≤ 1), go to the next step (2).

(2)

The EV moves to the nearest BSS using the shortest route. Go to the next step (3).

(3)

When the EV arrives at the nearest BSS, its battery is swapped for a suﬃciently charged spare battery. Go to the next step (4).

(4)

After the swap, the EV leaves the current BSS for the destination BSS and then continues to travel. Go to step (1).

Figure 2.

Normal swap driving mode.

3.2 Features of the model

Characteristics of SOC_BSS (without any electricity migration) of each BSS, based on the model depicted in Figure 1, are shown in Figure 3. Table 2 lists the number of battery swaps, and the M-SOC_BSS derived from the average of the period for which the simulation was conducted. In this study, we derived all values from 20 trials. These results conﬁrm that the SOC_BSS of BSS₀ for a densely populated area is low owing to the frequent use of the BSS for battery swaps.

Figure 3.

Transition of SOC_BSS of each BSS (with/without electricity migration).

4 Regulating stored electricity among BSSs

This section describes the battery migration between BSSs using the BSEV battery swapping mechanism for regulating the amount of electricity available for use in an emergency at each station. Assuming that the number of spare batteries is the same among all BSSs, we compared the electricity stored in the BSSs by SOC_BSS.

Figure 4 shows the process of adjusting the electricity reserves across BSSs so that their SOC_BSS values are equal.

As shown in the ﬁgure, migration of electricity using a battery swapping mechanism, higher SOC_{BSS_i} and lower SOC_{BSS_j} close to each other.

As described in Section 1, our method relies on volunteer BSEV users migrating batteries between BSSs during the course of their daily activities. A BSEV user may decide to make an electricity migration when travelling to a POI in a certain area. When a BSEV user is travelling to a POI in a certain area, they can choose to migrate electricity from a BSS in their current area to a BSS in the destination area if the SOC_{BSS_cur} of the BSS in their current area is larger than the SOC_{BSS_dest} of the BSS in the destination area. The two ratios are calculated as an average of the last 3600 s.

Table 3.
Difference/average of M-SOC_BSS and number of battery swaps.

Item With/without migration With/without migration (NT)

Difference M-SOC_BSS among BSSs 0.099 0.031

Average M-SOC_BSS 0.618 0.747

Number of battery swaps 1855 2815

Item	With/without migration	With/without migration (NT)
Difference M-SOC_BSS among BSSs	0.099	0.031
Average M-SOC_BSS	0.618	0.747
Number of battery swaps	1855	2815

BSS: battery swapping station; NT: naive transport.

Figure 4.

Adjusting SOC_BSS values (equalization): (a) before equalization and (b) after equalization.

The electricity migration between BSSs by battery swapping can be described as follows: (1)

If the next destination POI is in the current area, the EV user travels to the POI using the shortest route. Go to step (2). Otherwise, go to step (3).

(2)

When arriving at the POI, the EV user selects the POI randomly and goes back to step (1).

(3)

Before leaving for the destination, the EV user travels to the BSS in the current area. Go to step (3).

(4)

The EV battery is swapped for the spare battery with the highest SOC in the BSS. Go to step (4).

(5)

The EV user travels to the BSS in the same area as the destination POI using the shortest route. Go to step (5).

(6)

The EV battery is swapped for a spare battery from the destination BSS. Here, the SOC of the spare battery must satisfy two conditions: it must be the lowest at the BSS, and it must be larger than the battery swapping threshold α. After the swap, the EV user leaves for the destination POI. Go to step (2).

A schematic of the process is shown in Figure 5. We refer to the above method as the naive transport (NT) method.

Figure 5.

Electricity migration process using the naive transport (NT) method.

5 Methods for reducing user overhead

The previous section describes the NT method for electricity migration. Although the NT method maximizes the amount of electricity that can be migrated between two BSSs in a single migration, the EV user often takes a detour to the nearest BSS whenever it leaves for a different area. This detour results in both redundant power loss and redundant cruising distance. This increases the EV user's overhead.

Therefore, this section discusses the mitigation of the overhead incurred by the EV user by considering the EV's current battery condition SOC_{BSS_EV}.

5.1 Method for reducing the electricity shipping process

This section proposes an omission of battery migration (OBM) method for reducing the electricity consumption in the NT method.

OBM reduces this overhead by reducing the number of battery swaps at the BSS in the current location by considering SOC_{BSS_EV}. Although the NT method requires the volunteer to travel to the initial BSS and swap their battery for a spare battery to ship to the destination BSS, the shipping process means little if SOC_{BSS_EV} is high. The process simply increases the EV user's overhead because the change indicated as ① in Figure 4(b) would be small.

OBM addresses this overhead through the following process. First, EV estimates the expected SOC_{BSS_EV}. If the estimated value is larger than the SOC_BSS, the EV user skips the detour and leaves for the destination directly.

OBM estimates the value using equation (1):

A -SO C_{EV} = C -SO C_{EV} - \frac{DistC- A}{Cost}

(1)

where A-SOC_EV is the estimated expected SOC_EV at the initial BSS, C-SOC_EV is the current SOC_EV, DistC-A is the length of the travel trajectory between the current POI and the initial BSS, and Cost is the battery consumption by EV cruising.

5.2 Method for reducing unneeded battery swaps after migration

This section proposes a method for reducing unneeded battery swaps after electricity migration. As aforedescribed, the NT method selects the battery with the lowest SOC among the batteries whose SOCs are larger than the battery swapping threshold α.

Except for migration activities, lower SOC leads directly to battery swapping for electricity charging within a short period. An increasing number of battery swaps serves to increase overheads for the EV user. Moreover, if the EV user swaps their battery in order to charge their vehicle at the same BSS where they have previously delivered electricity, it voids the purpose of the migration. The effects indicated as ② in Figure 4(b) would be lost as a consequence of these swaps.

For reducing unneeded battery swaps after migration, we propose a method called SBP, which employs a parameter β (>α) to deﬁne a lower limit condition that must be met in order for an EV user to swap their battery at a BSS. By setting an appropriate value for β, proper battery selection (PBS) effectively avoids unneeded battery swapping for electricity charging within a short period. The value of β is set as x × γ. Here, γ is the average electricity consumed by an EV between two successive BSS visits. SBP refers to γ from the travel history of the EV. x is a coeﬃcient that is given by the operator, and it should be set according to the variance of the electricity consumed by an EV between two successive BSS visits. If the variance is large, larger x would reduce the unnecessary battery swaps.

5.3 Method for priority use of higher SOC BSS

This section proposes frequently use of energy-satisfied BSS (FESB), a method that uses a higher SOC BSS in preference to a lower one. FESB reduces the user overhead by addressing the effect indicated as ① in Figure 4(b).

The EV user swaps the vehicle’s battery at the initial BSS (higher SOC BSS), and then directly leaves for the destination POI without detouring to the BSS within the same area of the destination POI. Here, FESB selects a spare battery at the initial BSS in the same way as OBM.

6 Performance evaluation

This section presents an evaluation of the effects of electricity migration and the effects of reducing user overhead by the proposed methods using simulation. In this study, to simply discuss the performance of the proposed method, the goal of the electricity adjustment was to equalize the electricity reserves among the BSSs.

6.1 Performance of electricity level regulation

Figure 6 shows the characteristics of SOC_BSS. Table 3 lists the values of M-SOC_BSS and the number of battery swaps. Upon comparing the result of Figure 6 with Figure 3, we can conﬁrm that the difference in SOC_BSS decreases upon improving the SOC_BSS₀.

Figure 6.

Characteristics of SOC_BSS (naive transport (NT)).

Figure 7.

Transition of SOC_BSS of each BSS (reduce overhead). (a) OBM; (b) PBS; (c) Combination of OBM/PBS; (d) FESB.

Table 3 demonstrates the eﬃcacy of electricity migration. In the table, M-SOC_BSS represents the average of SOC_BSS through the entire simulation. Then, the difference in M-SOC_BSS among BSSs shows the bias of the two BSSs. This value becomes smaller upon using the NT method, indicating that electricity migration can equalize the electricity reserves across BSSs.

However, it is evident from the table that the average M-SOC_BSS becomes large compared with that without migration. This indicates an increase in the total electricity stored across BSSs.

In the BSEV service, spare batteries continue to be charged at each BSS until they reach the upper limit of SOC (100% is the common upper limit). In a BSS in a sparsely populated area, a relatively large number of spare batteries reach this upper limit owing to the lower frequency of BSS use. In a BSS in a densely populated area, spare batteries are used frequently through battery swapping. The SOC_BSS is low, and almost all spare batteries are always charging, and not as many spare batteries reach the upper limit before they are used again.

For such circumstances, by applying the NT method, fully charged spare batteries would be carried to BSSs in densely populated areas that are frequently used with lower SOC_BSS values. Consequently, almost every battery, regardless of the BSS location, is charged for a longer time and stores more electricity.

However, the number of battery swaps increased in the case of the NT method. The increase in the battery swaps also increases the EV user overhead; this should be solved.

6.2 Effects of methods for reducing EV users' overhead

Characteristics of SOC_BSS of the methods for reducing the EV user's overhead, presented in the previous subsection, are summarized in Table 4. Speciﬁcally, Table 4 lists the values of M-SOC_BSS and the number of battery swaps. For both results, PBS used 2 as the value of the parameter x.

Table 4.
Difference/average of M-SOC_BSS and number of battery swaps (methods for mitigation of user overhead).

Item OBM PBS Combination of OBM/PBS FESB

Difference M-SOC 0.032 0.025 0.027 0.032

Number of battery swaps 2707 2573 2389 1683

Item	OBM	PBS	Combination of OBM/PBS	FESB
Difference M-SOC	0.032	0.025	0.027	0.032
Number of battery swaps	2707	2573	2389	1683

BSS: battery swapping station; OBM: omission of battery migration; PBS: proper battery selection; FESB: frequent use of energy-satisfied BSS.

Figure 7 shows the transition of SOC_BSS of each BSS reducing user overhead. As shown in the ﬁgure, compared with the results shown in Figure 3, the methods for overhead reduction effectively decreased the difference of SOC_BSS among BSSs. Furthermore, the results presented in Table 4 conﬁrm that all methods for overhead reduction achieved a lower number of battery swaps than without migration while maintaining a small difference in SOC_BSS among BSSs. Therefore, these methods for the reduction of overhead are effective.

Figure 8 shows the relationship between the number of battery swaps and coeﬃcient x on PBS. As is evident from the ﬁgure, the number of swaps is decreased in inverse proportion to x; however, the decrease stops when x becomes >3.

Figure 8.

Relationship between number of battery swaps and coeﬃcient x on proper battery selection (PBS).

A possible cause for this observation can be described as follows. As described in the previous section, PBS selects a spare battery at the destination BSS. The condition is that the selected spare battery must exhibit the appropriate value for β (= x × γ). Although a larger x value leads to selecting a spare battery enabling long cruising periods without additional battery swaps, a BSS cannot offer suﬃcient spare batteries when the β becomes larger than a certain level. This tends to occur at a destination BSS in a densely populated area. We posit that an EV did not pick up an adequate spare battery in that case, and the number of swaps stopped decreasing by increasing x. Furthermore, the amount of migrated electricity is reduced when x becomes large, owing to a spare battery having a higher SOC being migrated from the destination BSS. Hence, larger x contributes to a decrease in the number of battery swaps, although it decreases the effects of adjusting the electricity level if it exceeds a certain level.

7 Effects of multiple BSSs

This section presents an evaluation of the proposed methods for two, three, and four BSSs. Figure 9 shows the ﬁeld constructions of the cases where the number of BSSs is three and four. As in the two-BSS model, the positions of the BSS and POIs are randomly changed within each area for every trial.

Figure 9.

Multiple-battery swapping station (BSS) model. (a) number of BSSs = 3; (b) number of BSSs = 4.

Figure 10.

Characteristics of SOC_BSS (number of BSSs = 3). (a) with/without migration; (b) NT; (c) OBM; (d) PBS; (e) combination of OBM/PBS; (f) FESB.

Figure 11.

Characteristics of SOC_BSS (number of BSSs = 4). (a) with/without migration; (b) NT; (c) OBM; (d) PBS; (e) Combination of OBM/PBS; (f) FESB.

The results of the evaluations are presented in Figures 10 and 11 and Tables 5 and 6, respectively. We can conclude from these observations that the proposed method effectively achieved the electricity adjustment of average SOC_BSS for different numbers of BSSs. The resultant user overhead, when compared with that of the NT method, exhibited a signiﬁcant improvement on the application of these mitigation methods.

Table 5.

Difference/average of M-SOC_BSS and number of battery swaps (number of BSSs = 3).

	With/without migration	NT	OBM	PBS	Combination of OBM/PBS	FESB
Difference of M-SOC_BSS	0.148	0.056	0.055	0.053	0.055	0.063
No. of swaps	1606	3392	3320	2976	2805	1838

BSS: battery swapping station; NT: naive transport; OBM: omission of battery migration; PBS: proper battery selection; FESB: frequent use of energy-satisfied BSS.

Table 6.

Difference/average of M-SOC_BSS and number of battery swaps (number of BSSs = 4).

	With/without migration	NT	OBM	PBS	Combination of OBM/PBS	FESB
Difference of M-SOC_BSS	0.130	0.044	0.044	0.036	0.038	0.054
No. of swaps	1612	3987	3906	3482	3343	1992

BSS: battery swapping station; NT: naive transport; OBM: omission of battery migration; PBS: proper battery selection; FESB: frequent use of energy-satisfied BSS.

Next, we discuss the effects of the number of BSSs on the number of battery swaps. The results without migration are summarized in Tables 4 to 6 and demonstrate that the number of BSSs does not inﬂuence the number of battery swaps. This is because EV users visit a BSS only when their SOC falls below the predeﬁned battery swapping threshold β owing to the electricity consumed by the vehicle when it is cruising.

However, Figure 12 indicated that the number of battery swaps increased following the increase in the number of BSSs in the methods with migration.

Figure 12.

Relationship between the number of battery swaps and the number of battery swapping stations (BSSs).

The probable cause underlying this observation is as follows. There are two types of battery swaps examined: swaps relating to electricity migration (energy management system (EMS)) and those relating to charging an EV for cruising (CEC). As mentioned previously, in the case of the no migration method, CECs constitute the bulk of the swaps, and no effects are exhibited on changing the number of BSSs. Hence, a change is only exhibited for EMSs in the case of the proposed methods, with migration methods.

In the proposed methods, an EV user may choose to make an electricity migration when travelling outside of their current area. Therefore, the number of EMSs depends on the number of times the volunteer chooses to visit a POI in a different area.

The following opportunities may be considered for inter-area EV travel. First, an EV user randomly selects their next destination POI when arriving at the current destination POI. Here, any POI could be selected with equal probability. Increasing the number of BSSs (and POIs) degrades relatively the ratio of the POIs belonging to the POIs in the current area. Hence, the probability of the occurrence of inter-area EV travel increases. An increase in instances of inter-area EV travel introduces opportunities for EMS. The increase in the number of BSSs results in an increase in the number of swaps.

Next, we consider the frequency of occurrence of the opportunities for inter-area EV travel. In this study, we changed the number of BSSs while maintaining the ﬁeld size. Under such conditions, the size of any one area covered by one BSS becomes small if the number of BSSs increases. This means that the distance between any two BSSs is also shortened, and the opportunity to make a judgment on electricity migration is also increased. Therefore, it can be concluded that the number of battery swaps increases based on the number of BSSs.

8 Conclusion

To use EV battery service stations as an emergency electricity supply source, this study examined regulating the electricity reserves in BSSs using battery migration. Our method is intended to ensure that a BSS always has adequate electricity reserves available for use in the event of an emergency. We implemented this method with the help of volunteer EV users, making use of their everyday routines.

Hence, we devised methods intended to realize an eﬃcient and optimized electricity migration method and minimize the overheads incurred by the volunteer EV user during the migrations. As is evident from the performance evaluations, the proposed method effectively achieves the electricity adjustment and mitigates the EV user's overhead.

Although we examined a situation wherein we aimed to equalize the electricity reserves across BSSs, the energy demands for a disaster-affected area could vary. Hence, we intend to discuss the capability of the proposed methods for adaptive adjustment on the basis of the challenges presented in a real-life emergency.

Footnotes

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

ORCID iDs

Mayu Hatamoto

Tetsuya Shigeyasu

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A novel strategy for emergency power supply by electricity migration in cooperation with a battery-swapping electric vehicle service

Abstract

Keywords

1 Introduction

2 Related works

3 Assumptions

3.1 Model

Table 3. Difference/average of M-SOCBSS and number of battery swaps. Item With/without migration With/without migration (NT) Difference M-SOCBSS among BSSs 0.099 0.031 Average M-SOCBSS 0.618 0.747 Number of battery swaps 1855 2815

5.1 Method for reducing the electricity shipping process

5.3 Method for priority use of higher SOC BSS

6 Performance evaluation

6.1 Performance of electricity level regulation

Table 4. Difference/average of M-SOCBSS and number of battery swaps (methods for mitigation of user overhead). Item OBM PBS Combination of OBM/PBS FESB Difference M-SOC 0.032 0.025 0.027 0.032 Number of battery swaps 2707 2573 2389 1683

Footnotes

Funding

Declaration of conflicting interests

ORCID iDs

References

Table 3.
Difference/average of M-SOC_BSS and number of battery swaps.

Item With/without migration With/without migration (NT)

Difference M-SOC_BSS among BSSs 0.099 0.031

Average M-SOC_BSS 0.618 0.747

Number of battery swaps 1855 2815

Table 4.
Difference/average of M-SOC_BSS and number of battery swaps (methods for mitigation of user overhead).

Item OBM PBS Combination of OBM/PBS FESB

Difference M-SOC 0.032 0.025 0.027 0.032

Number of battery swaps 2707 2573 2389 1683