Optimized vehicle dynamics virtual sensing using metaheuristic optimization and unscented Kalman filter

Manuel Acosta, Stratis Kanarachos

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    2 Citations (Scopus)


    This paper presents an Optimized Unscented Kalman Filter for vehicle dynamics virtual sensing. An automated procedure to optimize the virtual sensor parameters based on metaheuristic algorithms is presented in order to avoid the time-consuming and complex manual tuning task. Specifically, Genetic Algorithm Optimization (GA) and contrast-based Fruit Fly optimization (c-FOA) are applied to minimize the estimation error in steady-state and transient driving maneuvers. The virtual sensor is implemented in a high-fidelity vehicle dynamics simulation software (IPG-CarMaker ®) and results demonstrate the improvement of the estimation accuracy with respect to a preliminary filter tuning carried out using a systematic trial and error approach.

    Original languageEnglish
    Title of host publicationComputational Methods in Applied Sciences
    EditorsEsther Andrés-Pérez, Leo M. González, Jacques Periaux, Nicolas Gauger, Domenico Quagliarella, Kyriakos Giannakoglou
    Number of pages16
    ISBN (Electronic)978-3-319-89890-2
    ISBN (Print)978-3-319-89889-6
    Publication statusPublished - 7 Sept 2018

    Publication series

    NameComputational Methods in Applied Sciences
    ISSN (Print)1871-3033

    ASJC Scopus subject areas

    • Civil and Structural Engineering
    • Modelling and Simulation
    • Biomedical Engineering
    • Computer Science Applications
    • Fluid Flow and Transfer Processes
    • Computational Mathematics
    • Electrical and Electronic Engineering


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