Out-of-Distribution-Aware Electric Vehicle Charging

We tackle the challenge of learning to charge electric vehicles (EVs) with out-of-distribution (OOD) data. Traditional scheduling algorithms typically fail to balance near-optimal average performance with worst case guarantees, particularly with OOD data. Model predictive control (MPC) is often too conservative and data-independent, whereas reinforcement learning (RL) tends to be overly aggressive and fully trusts the data, hindering their ability to consistently achieve the best-of-both-worlds. To bridge this gap, we introduce a novel OOD-aware scheduling algorithm, denoted OOD-Charging. This algorithm uses a dynamic "awareness radius," which updates in real-time based on the temporal difference (TD)-error that reflects the severity of OOD. The OOD-Charging algorithm allows for a more effective balance between consistency and robustness in EV charging schedules, thereby significantly enhancing adaptability and efficiency in real-world charging environments. Our results demonstrate that this approach improves the scheduling reward reliably under real OOD scenarios with remarkable shifts of EV charging behaviors caused by COVID-19 in the Caltech adaptive charging network (ACN)-Data.