Vehicle Engine Torque Estimation via Unknown Input Observer and Adaptive Parameter Estimation

Jing Na, Anthony Siming Chen, Guido Herrmann, Richard Burke, Chris Brace

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This paper presents two torque estimation methods for vehicle engines: unknown input observer (UIO) and adaptive parameter estimation.We first propose a novel yet simple unknown input observer based on the crankshaft rotation dynamics only. For this purpose, an invariant manifold is derived by defining auxiliary variables in terms of first-order low-pass filters, where only one constant (filter coefficient) needs to be tuned. These filtered variables are used to calculate the estimated torque. Robustness of this UIO against sensor noise is studied and compared to two other estimators. On the other hand, since the engine torque dynamics can be formulated as a parameterized form with unknown time-varying parameters, we further present several adaptive laws for time-varying parameter estimation. The parameter estimation errors are derived to drive these adaptive laws and time-varying adaptive gains are introduced. The two proposed estimators only use the measured air mass flow rate and engine speed, and thus allow for improved computational efficiency. Both estimators are verified via a dynamic engine simulator built in a commercial software GT-Power, and also practically tested via experimental data collected in a dynamometer test-rig. Both simulations and practical tests show very encouraging results with small estimation errors even in the presence of sensor noise.
    Original languageEnglish
    Pages (from-to)409-422
    Number of pages14
    JournalIEEE Transactions on Vehicular Technology
    Volume67
    Issue number1
    Early online date14 Aug 2017
    DOIs
    Publication statusPublished - 1 Jan 2018

    Keywords

    • Engine torque estimation
    • Mean value engine model
    • Time-varying parameter estimation
    • Unknown input observer

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