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Stand-alone photovoltaic systems with battery storage are increasingly deployed in remote areas, particularly in Sub-Saharan Africa. However, battery lifetime under irregular cycling and local temperatures is rarely considered in economic analysis and system design. This study proposes a predictive model of battery lifetime based on manufacturer data, the Rainflow cycle-counting algorithm, Miner’s rule, and a thermal correction factor accounting for local climatic conditions. Three load profiles (diurnal, nocturnal, and hybrid) and two battery technologies (Lithium SuperPack and Lead-acid GEL) were simulated for a real site in Nagréongo, Burkina Faso. Results show that battery lifetime decreases by about 24% when the average annual temperature rises from 25 °C to 28 °C, which is significant for economic assessment, and that load distribution and storage autonomy also significantly affect aging. The model provides an effective tool for predicting battery lifetime in the techno-economic optimization and reliability assessment of stand-alone PV systems with storage.

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Predictive Assessment of Battery Aging Under Real Operating Conditions in Stand-Alone Photovoltaic Systems

  • 会议论文

  • 发表日期：

    2026年

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  • Guétinsom Jean Kafando,
  • Daniel Yamegueu Nguewo

摘要

Stand-alone photovoltaic systems with battery storage are increasingly deployed in remote areas, particularly in Sub-Saharan Africa. However, battery lifetime under irregular cycling and local temperatures is rarely considered in economic analysis and system design. This study proposes a predictive model of battery lifetime based on manufacturer data, the Rainflow cycle-counting algorithm, Miner’s rule, and a thermal correction factor accounting for local climatic conditions. Three load profiles (diurnal, nocturnal, and hybrid) and two battery technologies (Lithium SuperPack and Lead-acid GEL) were simulated for a real site in Nagréongo, Burkina Faso. Results show that battery lifetime decreases by about 24% when the average annual temperature rises from 25 °C to 28 °C, which is significant for economic assessment, and that load distribution and storage autonomy also significantly affect aging. The model provides an effective tool for predicting battery lifetime in the techno-economic optimization and reliability assessment of stand-alone PV systems with storage.