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  • 1.
    Hansson, Jörgen
    et al.
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Rögnvaldsson, Thorsteinn
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Byttner, Stefan
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Remote Diagnosis Modelling2008Patent (Övrig (populärvetenskap, debatt, mm))
    Abstract [en]

    A diagnosis and maintenance method, a diagnosis and maintenance assembly comprising a central server and a system, and a computer program for diagnosis and maintenance for a plurality of systems, particularly for a plurality of vehicles, wherein each system provides at least one system-related signal which provides the basis for the diagnosis and/or maintenance of/for the system are provided. The basis for diagnosis and/or maintenance is determined by determining for each system at least one relation between the system-related signals, comparing the compatible determined relations, determining for the plurality of systems based on the result of the comparison which relations are significant relations and providing a diagnosis and/or maintenance decision based on the determined significant relations.

  • 2.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prytz, Rune
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Wisdom of Crowds for Intelligent Monitoring of Vehicle FleetsManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    An approach is presented and experimentally demonstrated where consensus among distributed self-organized agents is used for intelligent monitoring of mobile cyberphysical systems (in this case vehicles). The demonstration is done on test data from a 30 month long field test with a city bus fleet under real operating conditions. The self-organized models operate on-board the systems, like embedded agents, communicate their states over a wireless communication link, and their states are compared off-line to find systems that deviate from the consensus. In this way is the group (the fleet) of systems used to detect errors that actually occur. This can be used to build up a knowledge base that can be accumulated over the life-time of the systems.

  • 3.
    Vachkov, Gancho
    et al.
    School of Engineering and Physics, Faculty of Science, Technology and Environment, The University of the South Pacific, Laucala Bay, Suva, Fiji.
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Svensson, Magnus
    Volvo Technology, Göteborg, Sweden.
    Detection of Deviation in Performance of Battery Cells by Data Compression and Similarity Analysis2014Ingår i: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 29, nr 3, s. 207-222Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The battery cells are an important part of electric and hybrid vehicles, and their deterioration due to aging or malfunction directly affects the life cycle and performance of the whole battery system. Therefore, an early detection of deviation in performance of the battery cells is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for the detection of deviation of battery cells, due to aging or malfunction. The detection is based on periodically processing a predetermined number of data collected in data blocks that are obtained during the real operation of the vehicle. The first step is data compression, when the original large amount of data is reduced to smaller number of cluster centers. This is done by a newly proposed sequential clustering algorithm that arranges the clusters in decreasing order of their volumes. The next step is using a fuzzy inference procedure for weighted approximation of the cluster centers to create one-dimensional models for each battery cell that represents the voltage–current relationship. This creates an equal basis for the further comparison of the battery cells. Finally, the detection of the deviated battery cells is treated as a similarity-analysis problem, in which the pair distances between all battery cells are estimated by analyzing the estimations for voltage from the respective fuzzy models. All these three steps of the computational procedure are explained in the paper and applied to real experimental data for the detection of deviation of five battery cells. Discussions and suggestions are made for a practical application aimed at designing a monitoring system for the detection of deviations. © 2013 Wiley Periodicals, Inc.

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