Development of Wheel Loader Duty Cycle Using Hybrid Markov Chain and
Genetic Algorithm 02-14-04-0034
This also appears in
SAE International Journal of Commercial Vehicles-V131-2EJ
Heavy diesel machinery has a significant contribution to the production of air
emissions in large and industrial cities. However, little attention has been
paid to this issue, and no appropriate action has been taken to address it.
Determining the proper duty cycle can be an effective step in reducing emissions
and fuel consumption of these vehicles. The duty cycle includes the driving
cycle along with the vehicle operating cycle and makes sense for vehicles that
have a specific task. In this article, 80,000 experimental data of the wheel
loader (WL) in diggings site by global positioning system (GPS) is collected.
After filtering, the data is converted to micro-trips and divided into four
clusters using the k-means method, and finally, the Markov matrix is produced.
The genetic algorithm (GA) is performed to identify the best combination of
micro-trips. In this research, two types of cycles, fuel consumption, and
emission were derived using the ADVISOR software. The first cycle is the WL
operation at the excavation site, and the second cycle is the short loading
cycle. Tehran WL duty cycle has higher fuel consumption, nitrogen oxides (NOx),
and hydrocarbon (HC) emission than Environmental Protection Agency (EPA) WL and
European non-road transient cycle. While the amount of carbon monoxide (CO)
produced in the Tehran WL duty cycle is lower than all EPA duty cycles but
higher than the European non-road transient cycle. Tehran WL duty cycle fuel
consumption is 265% more than the European non-road transient cycle and 86.12%
higher than EPA WL typical operation 1 duty cycle. These differences show that
each country’s cycle is only specific to the same country. A derived WL cycle
can be used on vehicles with a similar application such as forklift, aircraft
support, and forestry equipment.