In this article, requirements for future battery state estimation for HEV traction batteries were derived, in particular with focus on battery state prediction for predictive HEV operation strategies. Furthermore, a promising ansatzes for the solution was presented. Among the rechargeable battery systems presently available, the lithium-ion system provides higher energy and power densities than alternative systems, thus being a very attractive candidate for automotive applications. Hence, the authors focus on lithium-ion systems. The authors introduced the concepts of CPM and PCPM as advanced, generalized substitutes for SOC and SOH, respectively. Figures show exemplary CPM and PCPM maps generated for input profiles specified by constant current discharge. The authors discussed approaches to the derivation of high-performance models on an electrochemical basis, with physically meaningful parameters. The authors also discussed the concurrent reduction of numerical requirements and parametric complexity. The presented model reduction methodology which is based on a deterministic output sensitivity analysis and particularly useful for the development of on-board parameter estimation schemes.


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    Titel :

    Model-based battery state prediction for future hybrid electric vehicles


    Weitere Titelangaben:

    Modellbasierte Vorhersage des Batterieladungszustandes für künftige elektrische Hybridfahrzeuge


    Beteiligte:


    Erscheinungsdatum :

    2008


    Format / Umfang :

    12 Seiten, 6 Bilder, 5 Quellen


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Datenträger


    Sprache :

    Englisch




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