learning policies for battery usage optimization in electric vehicles

in this paper, we formalize the problem of optimizing real-time energy management of multi-battery systems as a stochastic planning problem, and we propose a novel solution based on a combination of optimization, machine learning and data-mining techniques. we evaluate the performance of our intelligent energy management system on various large datasets of commuter trips crowdsourced in the united states. we show that our policy significantly outperforms the leading algorithms that were previously proposed as part of an open algorithmic challenge. further, we show how to extend our approach to an incremental learning setting, where the policy is capable of improving and adapting as new data is being collected over time.