The question “Where has my battery life gone?” remains a common source of frustration for many smartphone users. With the increased complexity of smartphone applications, and the increasing number of system settings affecting them, understanding and optimizing battery use has become a difficult chore. The present paper develops a novel approach for constructing energy models from crowdsourced measurements. In contrast to previous approaches, which have focused on the effect of a specific sensor, system setting or application, our approach can simultaneously capture relationships between multiple factors, and provide a unified view of the energy state of the mobile device. We demonstrate the validity of using crowdsourced measurements for constructing battery models through a combination of large-scale analysis of a dataset containing battery discharge and system state measurements and hardware power measurements. The results indicate that the models captured by our approach are both in line with previous studies on battery consumption and empirical measurements, providing a cost-effective way to construct energy models during normal operations of the device. The analysis also provides several new insights about battery consumption. For example, our analysis shows the energy use of high CPU activity with automatic screen brightness is actually higher (resulting in around 9 minutes shorter battery lifetime on average) than with a medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can result in a battery life loss of over 13%; and a smartphone sitting in the sun can experience over 50% worse battery life than one indoors in cool conditions.