Improved Diesel Engine Load Control for Heavy-Duty Transient Testing Using Gain Scheduling and Feed-forward Algorithms

Heavy-duty (HD) engines for sale in the United States must be demonstrated to emit below allowable criteria and particulate emission limits over the operational load and speed cycle specified by the Federal Test Procedure (FTP) Heavy-Duty certification test. The inherently nonlinear load response of internal combustion engines tends to increase torque variability during the most dynamic portions of the test cycle. This clouds assessment of engine developments intended to improve transient performance and leads to frequent invalidation of certification tests. This work sought to develop and evaluate test torque control strategies that reduce this variability. Several load-control algorithms were evaluated for this purpose using a Cummins ISX15 HD diesel engine loaded with a transient alternating current (AC) dynamometer. The evaluations were performed over an abbreviated version of the FTP test which included the most transient portions of the drive cycle and were based on the regression characteristics specified by the EPA. These algorithms included a basic proportional-integral (PI) feedback algorithm, which was designated as the baseline for this work, an open-loop feed-forward-only (OLFF) algorithm, a gain-scheduled PI (GSPI) algorithm, a combined feed-forward and PI (FFPI) algorithm, and a gain-scheduled feed-forward PI (GSFF) algorithm. Each algorithm uniquely changed the torque control depending on the specific part of the cycle observed and the metric used to quantify performance; however, the FFPI and the GSFF had significantly better overall performance than the other algorithms tested. The FFPI had the greatest performance in regression intercept, coefficient of determination (R²), and standard error of estimate (SEE), with 70.1%, 7.3%, and 56.0% respective improvements relative to the baseline algorithm. The GSFF algorithm demonstrated the greatest regression slope performance with an improvement relative to the baseline of 23.0%. Such improvement not only reduced the number of tests invalidated by exceeding regression limits, but the associated improvement in repeatability could allow more accurately quantifying engine and aftertreatment development impacts on transient performance. Such improved development efforts should help facilitate closing the gap between transient and steady-state performance, leading to efficiency improvements and greenhouse gas and criteria emission reductions.