# Fast Optimal Energy Management with Engine On/Off Decisions for Plug-in Hybrid Electric Vehicles

## TL;DR

This paper introduces a fast ADMM-based algorithm for optimal energy management in plug-in hybrid electric vehicles, effectively balancing fuel efficiency and computational efficiency with real-world data.

## Contribution

The paper presents a novel ADMM algorithm that efficiently solves the hybrid vehicle energy management problem considering engine on/off decisions, outperforming traditional methods in speed and near-optimality.

## Key findings

- Achieves 90% of DP's fuel savings
- Reduces computational time by 3000 times
- Effective in real-world driver scenarios

## Abstract

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.