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Reinforcement Learning for Hybrid and Plug-In Hybrid Electric Vehicle Energy Management: Recent Advances and Prospects
Fast Optimal Energy Management with Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles
In this paper we demonstrate a novel alternating direction method of
multipliers (ADMM) algorithm for the solution of the hybrid vehicle energy
management problem considering both power split and engine on/off decisions.
The solution of a convex relaxation of the problem is used to initialize the
optimization, which is necessarily nonconvex, and whilst only local convergence
can be guaranteed, it is demonstrated that the algorithm will terminate with
the optimal power split for the given engine switching sequence. The algorithm
is compared in simulation against a charge-depleting/charge-sustaining (CDCS)
strategy and dynamic programming (DP) using real world driver behaviour data,
and it is demonstrated that the algorithm achieves 90\% of the fuel savings
obtained using DP with a 3000-fold reduction in computational time
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