Stochastic systems: estimation, identification and adaptive control
Stochastic systems: estimation, identification and adaptive control
Machine Learning
Learning action strategies for planning domains
Artificial Intelligence
Multidimensional binary search trees used for associative searching
Communications of the ACM
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Using learned policies in heuristic-search planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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The high cost, limited capacity, and long recharge time of batteries pose a number of obstacles for the widespread adoption of electric vehicles. Multi-battery systems that combine a standard battery with supercapacitors are currently one of the most promising ways to increase battery lifespan and reduce operating costs. However, their performance crucially depends on how they are designed and operated. 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.