Development of a novel engine power model to estimate heavy-duty truck fuel consumption

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Development of a novel engine power model to estimate heavy-duty truck fuel consumption

Kan, Yuheng / Liu, Hao / Lu, Xiaoyun / Chen, Qi

An accurate heavy-duty truck (HDT) fuel consumption model is essential for estimating the truck energy consumption and evaluating energy-saving strategies. However, based on recent truck field tests, we noticed that the estimation discrepancies of several published models were considerable since they were only developed for light-duty vehicles and cannot accurately estimate the HDT engine operation states. This inspired us to develop a generic approach for HDT engine-power estimation with a deep learning approach based on numerous tests. The results show that the proposed approach enables a more accurate estimation of HDT engine power, and when applied as the input to the fuel consumption models (e.g. Virginia-Tech model), the average estimation error is reduced to 13.71% from 28.9%. Besides, once calibrated, the proposed model could be applied to various scenarios without re-calibration. In addition, it can depict the fuel consumption during engine braking, which is largely missing in conventional HDT models.