IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T12:39:55ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2012-03-06T13:42:41Z2013-09-30T12:26:11Zhttp://eprints.imtlucca.it/id/eprint/1222This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12222012-03-06T13:42:41ZDiagnosis and prognosis of automotive systems: motivations, history and some resultsThis paper presents an overview of diagnostic needs and methodologies in the automotive field. The field of automotive engineering has seen an explosion in the presence of electronic components and systems on-board vehicles since the 1970s. This growth was initially motivated by the introduction of emissions regulations that led to the widespread application of electronic engine controls. A secondary but important consequence of these developments was the adoption of on-board diagnostics regulations aimed at insiring that emission control systems remained functional for a prescribed period of time (or vehicle mileage). In addition, the presence of micro-controllers on-board the vehicle led to a proliferation of functions implemented through electronic systems and related software, related to safety and customer convenience, creating the need for more sophisticated on-board diagnostics. Today, a significant percentage of the software code in an automobile is devoted to diagnostic functions. This paper presents an overview of diagnostic needs and requirements in the automotive industry, illustrates some of the challenges that are associated with satisfying these requirements and proposes some future directions, in particular with respect to prognostics.Giorgio RizzoniSimona OnoriMatteo Rubagotti2012-03-06T13:19:59Z2013-09-30T12:26:33Zhttp://eprints.imtlucca.it/id/eprint/1219This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/12192012-03-06T13:19:59ZAutomotive battery prognostics using dual Extended Kalman FilterThis paper proposes a strategy for estimating the remaining useful life of automotive batteries based on dual Extended Kalman Filter. A nonlinear model of the battery is exploited for the on-line estimation of the State of Charge, and this information is used to evaluate the actual capacity and predict its future evolution, from which an estimate of the remaining useful life is obtained with suitable margins of uncertainty. Simulation results using experimental data from lead-acid batteries show the effectiveness of the approachMatteo RubagottiSimona OnoriGiorgio Rizzoni