Table 3 The vulnerability driver factors.

VDF₁

State of charge (SoC)

The State of Charge (SoC), reflecting a battery’s remaining energy, varies substantially among incoming batteries and impacts vulnerability. Its variability is measured by MAD, where higher MAD values imply greater vulnerability.

Emeric⁵⁷

VDF₂

SoC below 15%

This attribute is the total number of batteries swapped with an initial State of Charge (SoC) below 15% over the observation period T. The 15% threshold is determined by the EM-BSCS operator. This indicator is treated as a static feature \(x_{T}\), where higher values of \(x_{T}\) signify increased operational vulnerability.

Emeric⁵⁷

VDF₃

Customer inter-arrival time.

Customer inter-arrival time is defined as the interval between successive battery swap customers. This variability is quantified via MAD.

.

VDF₄

Swap completion time

Swap completion time measures the duration from compartment opening to closure during a battery exchange. Variability in this time, influenced by both technical (e.g., signal strength, battery condition) and behavioral factors, is quantified using MAD.

⁵⁵

VDF₅

Charging duration

Charging duration is the time a customer spends recharging a private battery, influenced by initial SoC, target SoC, and other factors. This variability is quantified via MAD.

Helmus⁵⁵

VDF₆

The SoC of the recharged battery

This attribute captures the initial SoC of batteries entering recharging services, and it is assessed via MAD.

⁵⁵

VDF₇

Battery charging throughput

Battery charging throughput is the daily number of batteries recharged (charging service). This attribute is classified as a driver factor rather than a performance indicator because charging remains a secondary service in EM-BSCS with relatively low transaction volumes. This attribute is assessed using a static feature \(x_{T}\); greater charging throughput indicates higher operational vulnerability.

.

VDF₈

The distance between station

Travel distance from neighboring battery swapping stations is evaluated as a static feature \(x_{T}\); greater distances indicate higher operational vulnerability.

.

VDF₉

Activated compartment

The number of active compartments at a station, assessed via the static feature \(x_{T}\); lower \(x_{T}\) values signify greater operational vulnerability.

Prianjani⁶¹