Electric vehicle rental battery replacement and maintenance decisions

Jigang He, Xingyu Chen, Mingdi Ma and Ming Gao

Energy, 2025, vol. 333, issue C

Abstract: The rapid expansion of China's electric vehicle (EV) sector has precipitated the widespread adoption of battery leasing and replacement systems, presenting a distinct operational paradigm compared to conventional gasoline vehicle refueling procedures. This innovative approach achieves dual objectives: substantial reduction in initial acquisition costs for consumers and enhanced preservation of battery lifecycle integrity. For automotive manufacturers, the development of sophisticated maintenance protocols and circular economy strategies for aging battery units has become paramount to ensuring operational efficiency in battery replacement services. This investigation introduces a comprehensive intelligent maintenance decision framework specifically designed for leased EV battery replacement systems. The proposed architecture integrates two core analytical components: An asset-liability management (ALM) model that optimizes maintenance and recycling operations while analyzing profitability matrices for EV enterprises; A Markov Chain-based behavioral analysis module that models diverse battery replacement patterns among vehicle owners, enabling robust trend forecasting through stochastic process modeling. Empirical results demonstrate that strategic temporal optimization in maintenance scheduling can potentially double the profit margins of power swap stations. Furthermore, precise behavioral pattern forecasting significantly enhances decision-making accuracy in resource allocation. Through systematic sensitivity analysis and stress testing scenarios, EV manufacturers can effectively quantify risk exposure parameters and implement targeted risk mitigation strategies.

Keywords: Intelligent maintenance; Markov chain; Leasing scenario; Battery-replacement; Asset-liability management (ALM) (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225031020

DOI: 10.1016/j.energy.2025.137460

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