Reducing energy consumption is a major challenge that mobile computing needs to deal with. Smartphones are constantly evolving to match traditional computers in many aspects, especially in terms of processing and memory. However, user experience is severely impacted by a rapid discharge of battery in smartphones. The purpose of this article is to identify which metrics and assessment techniques are applied to evaluate energy consumption on Android smartphones, by summarizing factors that cause high energy consumption through a Systematic Literature Review (SLR). The methodology of this SLR consisted in performing searches on the digital libraries ACM, IEEE and Scopus. Fifty-four articles were obtained, of which 14 were identified as relevant for this study. Among the main methods identified, there is the collection of energy consumption at time intervals based on information on battery voltage/current or even based on information from features such as Wi-Fi, cellular networks, screen brightness, screen duration on, Bluetooth usage and others. The main application for collecting this information is Trepn Profiler, which is present in 5 of the 14 articles. Generated data is mainly analyzed by techniques such as clustering (21.42\%), variance (21.42\%), Bayesian classification and decision trees with 14.28\%. From these techniques, it was identified that user profile is the main factor affecting battery performance, being present in 28.57\% of the articles, followed by cellular networks and Wi-Fi (21.43\%), in addition to applications and services in background present at 14.29\%. However, only two papers in this SLR provide direct recommendations to reduce consumption from user actions such as turning off unused network interfaces, reducing screen brightness and stopping background services. This review concludes that user habits are the most responsible factor for energy consumption, as usage time, preferred apps, and custom settings are all determining factors in battery drain. Moreover, the analysis of the articles reviewed points out to a growing number of artificial intelligence techniques to automatically adapt smartphone usage settings in order to save energy.
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