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We study the problem of phase optimization for electric-vehicle (EV)charging. We formulate our problem as a non-convex mixed-integer programmingproblem whose objective is to minimize the charging loss. Despite the hardnessof directly solving this non-convex problem, we solve a relaxation of theoriginal problem by proposing the PXA algorithm where "P", "X", and "A" standfor three variable matrices in the formed phase optimization problems. We showthat under certain conditions, the solution is given by the PXA preciselyconverges to the global optimum. In addition, using the idea of modelpredictive control (MPC), we design the {PXA-MPC}, which is an onlineimplementation of the PXA. Compared to other empirical phase balancingstrategies, the PXA algorithm significantly improves the charging performanceby maximizing energy delivery, minimizing charging price, and assisting futureenergy planning. The efficacy of our algorithm is demonstrated using datacollected from a real-world adaptive EV charging network (ACN).

Towards Balanced Three-phase Charging: Phase Optimization in Adaptive Charging Networks