An electric-recharging planning algorithm was developed in this study to accompany a dispatching algorithm that assigns real-time trip requests to vehicles of a shared taxi fleet owned by an operator. The algorithm decides on when, where, and how much each vehicle should charge, in real-time. It will also relocate idle vehicles if needed. The approach, to designing the algorithm, was to allow maximum flexibility for the dispatcher, not forcing charging on vehicles ahead of time, and restricting their chance of picking up a customer meanwhile, having limited empty routing cost (going to charger and back, and relocating trips), of course while providing enough charge for the expected level of demand to be met. Three sequential mixed linear integer programming (MLIP) optimizations were designed to achieve a pro-active charging planner, that can use aggregated prediction data, run in manageable time, and remain scalable with respect to the fleet-size.