Computation of energy efficient driving speeds in conveying systems

Author:

Windmann Stefan1,Niggemann Oliver1,Stichweh Heiko2

Affiliation:

1. Fraunhofer IOSB-INA , Lemgo , Germany

2. Lenze SE , Aerzen , Germany

Abstract

Abstract This article addresses the automatic optimization of driving speeds in conveying systems. Electric drives in existing conveying systems are usually accelerated and decelerated according to predetermined movement profiles. Such an approach is inflexible for conveying applications with changing constraints and, in many cases, not optimal with respect to energy efficiency. In the present work, a method for automatic computation of energy efficient movement profiles is proposed. The proposed method is based on accurate models for electric drives and several types of conveying applications such as roll conveyors, belt conveyors and vertical conveyors. Furthermore, joint energy efficiency optimization for two drives, which are attached to an intermediate circuit, is investigated. Thereby, additional constraints on the energy flow between the drives are imposed in order to reduce load peaks and energy feedback into the grid. The resulting optimization problem is a mixed integer quadratic program (MIQP), which can be solved in a few milliseconds. Experimental results show that energy losses of electric drives are cut down by using the obtained non-trivial movement profiles instead of standard trapezoid movement profiles. The additional constraints on the energy flow between two drives lead to further significant improvements with respect to the overall energy losses.

Publisher

Walter de Gruyter GmbH

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimization of a High Storage System with two Cranes per Aisle;2023 IEEE 21st International Conference on Industrial Informatics (INDIN);2023-07-18

2. PLC-Informed Distributed Game Theoretic Learning of Energy-Optimal Production Policies;IEEE Transactions on Cybernetics;2022

3. First Approaches to Automatically Diagnose and Reconfigure Hybrid Cyber-Physical Systems;Machine Learning for Cyber Physical Systems;2020-12-24

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