Combined fuel consumption and emission optimization model for heavy construction equipment

In this paper, a novel optimization model is developed for heavy construction equipment. This approach investigates a complete range of engine operating points, considering fuel consumption and emission maps to accurately model fuel efficiency and level of emissions in different working conditions. As a case study, a tracked bulldozer is investigated on different terrains. The target of the optimization problem is defined as a specific digging or grading depth with minimum fuel consumption and emissions (unburned hydrocarbons, carbon monoxide, and nitrogen oxides) based on EU non-road diesel engine emission standard values. Interaction between the terrain and the bulldozer track is modeled using a semi-empirical method. Dry sand, clayey soil, and snow terrain are considered. The studied bulldozer is a Caterpillar D8T with 233 kW engine power and 8A type blade. The design variables are engine speed, transmission gear number, and throttle position. Genetic algorithm, a famous optimization method, is employed. In order to reduce computational costs, integer programming genetic algorithm is utilized. Due to the complexity of the problem, a constrained nonlinear optimization problem with combined-objectives is developed. Results show that the general trend of fuel consumption and emissions rise as the digging depth increases, as expected. However, this study indicates that the bulldozer traction and digging control can be effectively manipulated by controlling the engine operating point, characterized by engine speed and gear number, to obtain significant improvements in fuel consumption and reduction of exhaust emissions. The results also indicate that re-performing the optimizing problem for different terrain types leads to optimized fuel and emissions targets of up to 77%.