<p>This paper aims to analyze consumer behavior in laptop lithium-ion battery consumption throughout their life cycle. As the demand for battery-powered products continues to grow, helping consumers select batteries that align with their actual usage patterns is important for promoting sustainable consumption. However, there is limited research on whether consumers' choices of battery features, such as capacity, are compatible with their real consumption needs. To investigate this, a multinomial logistic regression model is developed to predict battery health status over time. The study uses a dataset of 719 records collected from student laptop users in Chicago, IL. The dataset includes technical specifications and usage metrics such as charging cycles, full and design capacities, and battery age. The findings show that each additional cycle increases the likelihood of degradation by 0.022. On the other hand, batteries with larger design capacities tend to be more durable, with each additional unit of capacity reducing the likelihood of degradation by 0.0011. Next, an optimized consumption scenario is suggested to demonstrate how aligning battery choice with real usage needs can lead to more sustainable outcomes. The results show a nearly 60% reduction in the likelihood of battery degradation, achieved by better matching battery capacity with consumer' actual needs. Finally, we discuss the sustainability benefits of the proposed scenario.</p>

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Aligning lithium-ion battery durability with consumer needs: A data-driven analysis of consumption behavior

  • Mostafa Sabbaghi,
  • Sara Behdad

摘要

This paper aims to analyze consumer behavior in laptop lithium-ion battery consumption throughout their life cycle. As the demand for battery-powered products continues to grow, helping consumers select batteries that align with their actual usage patterns is important for promoting sustainable consumption. However, there is limited research on whether consumers' choices of battery features, such as capacity, are compatible with their real consumption needs. To investigate this, a multinomial logistic regression model is developed to predict battery health status over time. The study uses a dataset of 719 records collected from student laptop users in Chicago, IL. The dataset includes technical specifications and usage metrics such as charging cycles, full and design capacities, and battery age. The findings show that each additional cycle increases the likelihood of degradation by 0.022. On the other hand, batteries with larger design capacities tend to be more durable, with each additional unit of capacity reducing the likelihood of degradation by 0.0011. Next, an optimized consumption scenario is suggested to demonstrate how aligning battery choice with real usage needs can lead to more sustainable outcomes. The results show a nearly 60% reduction in the likelihood of battery degradation, achieved by better matching battery capacity with consumer' actual needs. Finally, we discuss the sustainability benefits of the proposed scenario.