  Adaptive charging can charge electric vehicles (EVs) at scale cost
effectively, despite the uncertainty in EV arrivals. We formulate adaptive EV
charging as a feasibility problem that meets all EVs' energy demands before
their deadlines while satisfying constraints in charging rate and total
charging power. We propose an online algorithm, smoothed least-laxity-first
(sLLF), that decides the current charging rates without the knowledge of future
arrivals and demands. We characterize the performance of the sLLF algorithm
analytically and numerically. Numerical experiments with real-world data show
that it has a significantly higher rate of feasible EV charging than several
other existing EV charging algorithms. Resource augmentation framework is
employed to assess the feasibility condition of the algorithm. The assessment
shows that the sLLF algorithm achieves perfect feasibility with only a 0.07
increase in resources.
