EnergySequential decision-making and control optimization under dynamic operating conditionsproposed research-stage control strategy demonstrated in the context of urban rail supercapacitor energy storage management.

Deep reinforcement learning controller for supercapacitor energy management in urban rail transit

An AI controller learns when a rail system’s supercapacitor should store or release electricity so trains use energy more efficiently.

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Executive Brief

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Technical Analysis

Model Strategy

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Data Strategy

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Part of Application

AI Supercapacitor Management

13 use cases in this application

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Source

https://ieeexplore.ieee.org/document/8956070