A comparative analysis of moving average filter and Kalman filter for large diesel engine test cell back-pressure control

Diesel engine combustion releases many harmful components, thus there are continuous efforts into improving the efficiency of these engines and reducing the harmful gasses and particulates to meet the emission authorities targets. To develop and sell new engine-related products, these engines are required to run and to be audited in diesel engine test cells. A critical measurement for benchmark testing is the exhaust back-pressure, which is the resultant exhaust flow from the engine and a product of the air and fuel consumed. The back-pressure is controlled by restricting the flow of the exhaust using a butterfly valve and this pressure must be set to the defined limits to ensure engine compliance. Setting this limit takes time and consumes large volumes of fuel, which causes additional emissions. Therefore, a feedback control solution to regulate this back-pressure is desirable. In current practice, a moving average filter is used on two commercial standard engine softwares – SGS CyFlex® and AVL Puma 2® Data Acquisition and Control Systems to provide a useful signal for feedback control. Considering the presence of erratic noise associated with the back-pressure measurement, a Kalman Filter with tunable measurement uncertainty and process noise gains is also considered. By modifying the script in SGS CyFlex® and AVL PUMA 2®, a Kalman Filter is implemented for the first time on diesel engine test cells and a comparative analysis between the performance of the two filters is provided. Both filters effectively reduce the noise of the system, with the Kalman Filter showing a closer tracking to the desired system response. This demonstrates the potential of applying the Kalman Filter to provide the feedback signal for improved back-pressure control that could reduce the fuel consumption during testing, thereby makes testing process more economical and environment friendly. The script and results presented in this work will open up the opportunities of applying Kalman filtering method’s in various engine testing functions, which will have broader impact in the current industrial practice.