Pressure Sensor Data-Driven Optimization of Combustion Phase in a Diesel Engine

In this article, a pressure sensor data-driven optimization by using extremum seeking (ES) technique is applied to optimize the combustion phase in a diesel engine. In particular, a cost function is constructed for the ES optimization with the in-cylinder maximum temperature T̂
_maxn
which is an indication of NO
_x
emission performance, and the thermal efficiency η
_n
as tradeoff considerations. The data-driven nature lies in the fact that T̂
_maxn
is estimated from the in-cylinder pressure measurement within each engine cycle by assuming the in-cylinder process to be a quasi-steady state process, which is, then, compensated with the model-free ES optimization. It is noted that the approach developed in this article does not require the use of real NO
_x
sensor data, which could otherwise jeopardize the ES optimization practically due to the unignorable and hard-to-predict gas transportation delay. Therefore, it is argued that this article actually delivers an alternative soft sensor solution to real NO
_x
sensors for engine optimization purpose. The effectiveness of the proposed method is demonstrated on a single-cylinder diesel engine test bench with the help of steady-state engine tests. The method can be extended to data-driven optimization applications that also utilizes physical sensors with measurement delay.