Hyper Space Exploration A Multicriterial Quantitative Trade-Off Analysis for System Design in Complex Environment

Palm, Herbert; Holzmann, Jörg

doi:10.1109/syseng.2018.8544435

Cited by 5 publications

(10 citation statements)

References 14 publications

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“…Figure 5 provides an overview of the steps in the workflow of the HSE as well as an overview of the different subspaces of the hyperspace. [21] In the first step, the hyperspace is spanned as the Cartesian product of the design space D, the use case space U, and the target space T. The design space contains all possible design variants for the system that are considered for optimization. The use case space includes the possible use cases, in which the system to be optimized can operate, while the target space consists of the desired target variables that are part of the objective functions of the system.…”

Section: Hyperspace Explorationmentioning

confidence: 99%

“…is a function family that approximates the relation between the elements d and u of the design and use case space to the elements t of the target space. [21] The SOCEMO algorithm uses radial basis functions (RBFs) to build the surrogate. For each objective inside the target space, a separate RBF with the general form…”

Section: Hyperspace Explorationmentioning

confidence: 99%

“…The HSE is applicable in this context, since it was developed for analyzing and solving complex optimization problems. [21] It is an evolution of the MOO methods introduced by the groups mentioned before and presents a holistic approach with high potential for automation. With this automated process, the filter covariance parameters no longer require manual tuning and the method presents a unique way for the evaluation of filter parameters and their influence on the estimation results.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to that, it makes the evaluation of trade-offs accessible through the utilization of a surrogate model. [21] To the best of the authors' knowledge, this work is the first that employs the HSE framework to the optimal tuning of a KF.…”

Section: Introductionmentioning

confidence: 99%

“…The HSE is applicable in this context, since it was developed for analyzing and solving complex optimization problems. [ 21 ]…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Kalman Filter Tuning for State Estimation of Lithium‐Ion Batteries by Multi‐Objective Optimization via Hyperspace Exploration

Mößle,

Tietze,

Danzer

2023

Energy Tech

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For the estimation of the state of charge of lithium‐ion batteries Kalman filters are the state of the art. To ensure precise and reliable estimations these filters use covariance matrices, which need to be tuned correctly by the developer. This process is time consuming and depends largely on the experience and skill of the developer. Hence, filter tuning is not reproducible and not optimal with regard to goals as accuracy and convergence speed. In this paper a multi‐objective optimization framework called hyper space exploration is used for the first time to automate the filter tuning procedure for an extended Kalman filter and two versions of adaptive extended Kalman filters. Four key performance indicators, including the maximum error in the estimation of the state of charge and the according root mean square error, are used to describe, validate, and compare the filter performance. Consequently, filter parameters are found, which improve the estimation behavior regarding the objective functions for all load profiles. This automated process enables optimal usage of the degrees of freedom in filter tuning and no longer requires manual tuning while the whole hyper space, including different use cases and validation scenarios, is considered in the optimization. Furthermore, the proposed approach yields a novel method for the evaluation of filter parameters and their influence on the estimation behavior.This article is protected by copyright. All rights reserved.

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Section: Hyperspace Explorationmentioning

confidence: 99%

Section: Hyperspace Explorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The HSE is applicable in this context, since it was developed for analyzing and solving complex optimization problems. [ 21 ]…”

Section: Introductionmentioning

confidence: 99%