Proposed Methodology for Battery Aging and Drainage Mitigation | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Volume 24, Issue 1, March 2024, Pages 42-54 PDF (451.27 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2024.271804.1322 | ||
| Authors | ||
| Yusuf Mamdouh Awad* 1; El-Sayed M. El-Horabty2; Islam Hegazy3 | ||
| 1Software Engineering, Computer and Information Science, Ain Shams University, Cairo, Egypt | ||
| 2Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University | ||
| 3Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University | ||
| Abstract | ||
| A longer battery life is a highly sought-after feature for most smartphone users when considering their next device. However, with the emergence of new hardware technology and software applications that require heavy processing, the demand for battery power has significantly increased. Unfortunately, the development of battery technology has not kept up with the rapid advancements in smartphone hardware and software, which rely heavily on battery power. To address this issue, several approaches have been proposed to regulate battery consumption and the charging process on smartphones. In this paper, we summarize the different approaches related to this problem that managed to achieve up to a 61% increase in battery daily usage in simulation testing, highlighting their strengths, limitations, and current challenges. Furthermore, we provide a comprehensive review of various open-source datasets that have the potential to be used in developing new approaches to improve battery drainage and degradation in smartphones. We also discuss the methodology for collecting each dataset. Finally, we propose a new approach to address the current limitations and challenges to solving the problem of battery drainage and degradation that could be developed using the currently available datasets. These new approaches may involve incorporating machine learning techniques to predict battery charging patterns and minimize battery drainage | ||
| Keywords | ||
| Smartphone battery; Battery drainage; Battery degradation; Charging prediction; Intelligent charging | ||
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