Heuristic information-rich evolutionary modelling for engine soft sensors of hybrid electric vehicles

Under the explosive demand of the electrified powertrain market, modelling schemes with strong robustness, low cost, and fast implementation are urgently required for hybrid vehicle engine development. This paper presents a data-driven holistic solution integrated with heuristic information-rich feature selection for engine soft sensors, i.e., fuel consumption, thermal efficiency, and volumetric efficiency, namely heuristic information-rich warm-start evolutionary modelling framework. Five filter methods are developed as heaters, and their selected features are converted to warm up the initialisation process in the evolutionary modelling, alleviating the inefficient exploration and local optimal problems caused by the pseudo-random initialisation of a single wrapper during the optimisation process. Meanwhile, a new factor of heuristic information richness is introduced to determine and adjust the proportion of the filter particles, further accelerate evolutionary convergence through the filter information guidance and avoid local optimality through free exploration of the particles without filter information, achieving a balance between computational efficiency and global search capability. Validated by the testing bench of a BYD 1.5 L naturally aspirated engine specially made for a hybrid powertrain, the Lasso method is the best heater and helps the proposed framework to reduce up to 54.9% of mean squared error compared to that of the cold-start one. Compared to industry-used modelling frameworks, the proposed one achieves the equivalent prediction performance while reducing the database size by up to 85%. • A soft sensor integrated with heuristic information-rich feature selection is proposed. • A warm-start initialization strategy is designed to accelerate the feature selection. • A comparative analysis is carried out, considering the tradeoff between complexity and accuracy.