# Vulnerability assessment model integrating outcome and characteristic-based metrics for electric motorcycle battery swapping and charging stations

## TL;DR

This paper introduces a model to assess the vulnerability of electric motorcycle battery swapping and charging stations using IoT data.

## Contribution

A novel vulnerability assessment model integrating outcome and characteristic-based metrics for battery swapping and charging stations.

## Key findings

- The model classifies station vulnerability into four categories.
- Significant differences in vulnerability were observed among stations in Jakarta.
- Most stations were found to be not vulnerable to moderately vulnerable.

## Abstract

Battery swapping and charging stations are essential for increasing the adoption of electric motorcycles. The stations address the range anxiety issue and quickly obtain a fully recharged battery. However, operational issues with swapping and charging activities drive operational vulnerability. Therefore, this study proposes a vulnerability assessment model utilizing the IoT Platform data of electric motorcycle battery swapping and charging stations. The model computes a vulnerability score by integrating vulnerability indicator metrics of the system outcome and characteristic. The system outcome uses performance data representing vulnerability impact. The system characteristic uses data from the vulnerability driver and exposure factors. The driver factor represents mitigation ability, and the exposure factor represents conditions that may affect both the mitigation ability and performance. The model also classifies the vulnerability of stations in four categories: not vulnerable, potentially vulnerable, moderately vulnerable, and vulnerable. The model was implemented in a case in Jakarta. The result reveals significant differences in vulnerability among stations, although most stations fall into the not vulnerable to moderately vulnerable categories. The findings facilitate identifying station characteristics that potentially affect performance quantitatively.