— Energy planning and optimization constitutes one of the most significant challenges for high-mobility networks. This paper proposes a novel framework to share, retain and refine endto-end energy metrics in the joint memory of the nodes, over time scales over which this information can be spread to the network and utilized for energy planning decisions. We construct maps of end-to-end energy metrics that enable energy optimization in high-mobility networks. We show how to (1) compute the spatial derivatives of energy potentials in high-mobility networks, (2) construct energy maps on-demand via path integration methods, (3) distribute, share, fuse, and refine energy maps over time by information exchange during encounters, (4) allow the nodes to use energy maps for energy planning and optimization in delaytolerant, high-mobility networks.