Congratulation to Amna, her hard work has fruited into her first publication at the FedEdge Workshop of ACM MobiCom 2022. In this work, we consider the energy consumption across battery-constrained devices in federated learning which is largely unexplored and a limitation for the wide adoption of FL. To address this issue, we develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices. EAFL is a power-aware training algorithm that cherry-picks clients with a higher battery level in conjunction with its ability to maximize system efficiency. Our design jointly minimizes the time-to-accuracy and maximizes the remaining on-device battery levels. EAFL improves the testing model accuracy by up to 85% and decreases the drop-out of clients by up to 2.45X