Modeling of the thermal behavior and the diagnostic systems of a SI engine

Hoppe, Matthias; Heßeler, Frank-Josef; Bruckner, Daniel; Missy, Stephan; Abel, Dirk

doi:10.3182/20130904-4-jp-2042.00095

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…The signal based methods have been used for thermostat fault detection as it is detailed in [12] and others reviewed in [13]. In case of overall cooling system fault detection, the model based approach is preferred as it is done in [14,15] where a model is developed that is driven by the measured engine signals such as engine speed, fuel/air combustion ratio and so on. By comparing the predicted and the measured engine‐radiator coolant temperatures, the faulty component is detected.…”

Section: Introductionmentioning

confidence: 99%

Fault severity estimation in a vehicle cooling system

Nemati

Seyedtabaii

2019

Asian Journal of Control

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On-time water pump malfunction, thermostat sticking, fan defect, and radiator clogging severity estimation in a vehicle cooling system prevents system breakdown and saves repair costs and service time. In this respect, to minimize the required number of engine variable measurements, the engine body temperature, T b is introduced as a single driving force of the thermal system and a modified adaptive second order sliding mode (SOSM) fault observer is utilized to estimate the (single case) faults despite the system nonlinear uncertainty. The faults are applied and the estimates are obtained within the defined temperature ranges. The results of extensive simulations indicate that the algorithm works well in assessing the occurred damages to the uncertain system. KEYWORDS adaptive second order sliding mode, automobile cooling system, fault detection, observer 2 | AUTOMOBILE COOLING SYSTEM MODEL The engine heat, not released through the exhaust, is dissipated by the cooling system. Since the engine body is massive with high thermal conductivity, it is approximately modeled by a variable temperature source, T b. This possibility simplifies the model and lets a simpler estimation algorithm to be built.

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Section: Introductionmentioning

confidence: 99%