Risk Analysis of Operational Disruptions in Public Electric Vehicle Charging Stations Using the Failure Mode and Effects Analysis (FMEA) Method

  • Teddy Maulana Putra Faculty of Economics and Business, Universitas Indonesia, Salemba, Jakarta Pusat, 10440, Indonesia (ID)
  • Ruslan Prijadi Faculty of Economics and Business, Universitas Indonesia, Salemba, Jakarta Pusat, 10440, Indonesia (ID)
Keywords: PEVCS, FMEA, Disruptive mitigation

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The Indonesia government is actively promoting the adoption of electric vehicles, as detailed in the 2021-2030 Electricity Supply Business Plan. The state-owned electricity provider, PLN, is responsible for establishing Public Electric Vehicle Charging Stations (PEVCS). However, several of these stations have encountered malfunctions; notably, 82 of the 567 stations are classified as Unavailable, indicating they are non-functional. Research literature points to a financial loss of $34,000 from operational issues at PEVCSs. This research aims to helps management understand and prioritize disruption that leads to failures or damages at these stations. Method used is the Failure Mode and Effects Analysis (FMEA) method along with logistic regression to examine the disruptions at PEVCSs labeled as Unavailable. The data for this research comes from a six-month historical record of PEVCS disruptions. The variables utilized for logistic regression analysis include foundational variables from the FMEA methodology—Severity, Occurrence, and Disturbance—complemented by two supplementary variables: the speed and age of the PEVCS. Result was found that three out of twelve types of disruptions have a high likelihood of failure, specifically issues with Device Communication, Connectivity, and Emergency Stop functions. A disruption is deemed likely to cause failure if its probability exceeds 50%.


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How to Cite
Putra, T. M., & Prijadi, R. (2024). Risk Analysis of Operational Disruptions in Public Electric Vehicle Charging Stations Using the Failure Mode and Effects Analysis (FMEA) Method. Quantitative Economics and Management Studies, 5(3), 541-559. https://doi.org/10.35877/454RI.qems2585