Control, Optimization and Management of Electric Mobility
Speaker: Prasant Kumar Misra – Bangalore, IndiaTopic(s): Applied Computing
Abstract
Urban mobility is evolving from a fixed supply chain that delivers process-driven travel to a dynamic ecosystem that delivers on-demand services. This new mobility model requires control and optimization across multiple systems such as transportation, parking, electric vehicle charging and vehicle-to-grid services, etc. The complexity, therefore, arises from the large scale of operations; heterogeneity of system components; dynamic and uncertain operating conditions; and goal-driven decision making and control with time-bounded task completion guarantees.
This talk will cover classical optimization techniques (such as linear programming) and newer approaches based on L2O or learning to optimize (such as reinforcement learning); which can be applied to solve operational problems in electric mobility. L2O is an alternative paradigm that leverages machine learning to develop optimization methods. It is particularly useful for solving complex decision problems whose solutions are difficult to obtain. Examples include planning/scheduling charging operations for a large fleet of electric vehicles; dynamic pricing for real-time charging demand management; electric vehicle route planning for last-mile delivery of goods and other valued-added services such as energy sale; etc. With the help of these representative problems and solution methodology, the talk will also discuss the benefits of L2O.
About this Lecture
Number of Slides: 40Duration: 120 minutes
Languages Available: English
Last Updated:
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