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This paper first presents a basic mean value engine plant model implemented in a hardware-in-the-loop (HIL) system. The plant model includes some basic engine parameters such as engine speed, manifold absolute pressure, etc., which are critical to both control algorithm integrity and default actions that result from improper signal performance (e.g., ECU shuts down due to corrupted signal(s)). The model is then improved to develop the HIL bench-based testing capabilities in the areas where a vehicle has traditionally been required. The on-board diagnostic monitor tests covered by SID $06 of SAE J1979 are selected as a case study. Specifically, for OBD exhaust gas sensor monitor testing purposes, the oxygen sensor model is developed to simulate normal or abnormal binary switching signals which might have asymmetric “lean to rich” and “rich to lean” transitions, or largely off maximum/minimum sensor voltages, etc.

A coolant temperature model of an internal combustion engine has been formulated to meet the new On-Board Diagnostics II (OBD II) requirement for coolant temperature rationality. The model utilizes information available within the production Engine Control Module (ECM). The temperature prediction capability has been tested for various “real-world” driving conditions and cycles along with regulated drive cycles. The model can be calibrated to find the appropriate timing for initiation of a diagnostic algorithm for engine cooling system and Coolant Temperature Sensor (CTS) faults. A diagnostic scheme has been developed to detect and isolate various types of cooling system failures using engine soak time information available from a low power timer in the ECM.