An MPC-Based Strategy for Managing Energy in Hybrid Powertrains of Fast Boats

Author:

Tordela Ciro1,Fornaro Enrico1

Affiliation:

1. Universita Degli Studi di Napoli

Abstract

<div class="section abstract"><div class="htmlview paragraph">In the shipbuilding industry, the employment of hybrid propulsion systems is increasingly common on-board vessels for making more eco-sustainable boat traffic in marine waters. Energy management systems are required to ensure the culling of fuel consumption and the preservation of batteries by monitoring their state of charge in hybrid powertrains, coupled with the possibility of performing the sea path desired by a driver unit. A Model Predictive Control (MPC) supervisor is proposed in the present work for managing a marine parallel-hybrid propulsion system in terms of handling the state of charge of batteries and the driving cycle imposed by the boat driver. Specifically, the MPC is employed to avoid excessive electric energy consumption observable as a reduced loss in terms of the state of charge of batteries by selecting the best amount of command torques related to two electric motors and one internal combustion engine of the considered powertrain. A lumped parameters model of a fast boat coupled with map-based motors belonging to the considered hybrid propulsion system is employed for making tests functional to evaluate the performance of the proposed supervisor based on an MPC in terms of energy management capabilities. The proposed approach can be employed for preliminary design purposes of hybrid propulsion systems for naval applications. Specifically, two propulsors featured by different hybridization factors are compared, demonstrating the possibility of recharging batteries only for a lower hybridization factor based on the chosen waterway. The low computational load related to the proposed MPC demonstrates its suitability to manage naval hybrid propulsion systems in real-time. Therefore, this type of supervisor can be included in electronic control units of fast boats.</div></div>

Publisher

SAE International

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3