Efficiency-Optimization Control of Extended Range Electric Vehicle Using Online Sequential Extreme Learning Machine

2013 IEEE Vehicle Power and Propulsion Conference (VPPC) Pub Date : 2013-10-01 DOI:10.1109/VPPC.2013.6671680

Bumin Meng, Yaonan Wang, Yimin Yang

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引用次数: 8

Abstract

This paper describes the application of an Online Sequential Extreme Learning Machine(OS_ELM) for online efficiency-optimization control of Extended Range Electric Vehicle (EREV also called REEV). Efficiency-optimization control of EREV is formulated as a nonlinear constrained multi-objective problem with competing and non-commensurable objectives of fuel consumption, emissions, driving performance, battery life and driving range. To get real-time Pareto optimal solutions, an Offline Extreme Learning Machine and OS_ELM are hanged together. ELM is used to describe nonlinear system of EREV. When work status of gasoline engine or load change, optimum work status can be sought out by OS_ELM. Finally, the optimization is performed over the following three typical driving cycles that are currently used in the U.S. and European communities: 1) the Federal Test Procedure (FTP); 2) Extra Urban Driving Cycle (EUDC); and 3) Urban Dynamometer Driving Schedule (UDDS). The results demonstrate the capability of the proposed approach to generate well optimal solutions of the on-board charger optimization of EREV.

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基于在线序列极限学习机的增程式电动汽车效率优化控制

本文介绍了在线顺序极限学习机(OS_ELM)在增程式电动汽车(EREV)在线效率优化控制中的应用。将电动汽车的效率优化控制表述为油耗、排放、行驶性能、电池寿命和续驶里程等目标相互竞争且不可通约的非线性约束多目标问题。为了得到实时的Pareto最优解，将Offline Extreme Learning Machine和OS_ELM挂在一起。用极限向量机描述了非线性电涡流控制系统。当汽油机的工作状态或负载发生变化时，可以通过OS_ELM找到最优的工作状态。最后，在美国和欧洲目前使用的三个典型驾驶循环中进行优化:1)联邦测试程序(FTP);2)城市外行驶周期(EUDC);3)城市测功机驾驶时间表(UDDS)。结果表明，该方法能够很好地求解车载充电器优化问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2013 IEEE Vehicle Power and Propulsion Conference (VPPC)

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