Energy Management of Multi-mode Hybrid Electric Vehicles based on Hand-shaking Multi-agent Learning

TL;DR

This paper introduces a multi-agent deep reinforcement learning framework with a novel hand-shaking strategy for energy management in multi-mode hybrid electric vehicles, achieving over 2.4% energy savings compared to traditional methods.

Contribution

It proposes a new MADRL framework with a hand-shaking strategy and independence ratio, enabling simultaneous control of multiple outputs for vehicle energy management.

Findings

MADRL with independence ratio 0.2 is optimal.

Achieves 2.4% energy savings over conventional DRL.

Demonstrates effectiveness of multi-agent approach in vehicle energy control.

Abstract

The future transportation system will be a multi-agent network where connected AI agents can work together to address the grand challenges in our age, e.g., mitigation of real-world driving energy consumption. Distinguished from the existing research on vehicle energy management, which decoupled multiple inputs and multiple outputs (MIMO) control into single-output(MISO) control, this paper studied a multi-agent deep reinforcement learning (MADRL) framework to deal with multiple control outputs simultaneously. A new hand-shaking strategy is proposed for the DRL agents by introducing an independence ratio, and a parametric study is conducted to obtain the best setting for the MADRL framework. The study suggested that the MADRL with an independence ratio of 0.2 is the best, and more than 2.4% of energy can be saved over the conventional DRL framework.