In order to solve the problem of high fuel consumption and poor emission performance in high horsepower tractors, a parallel hybrid tractor system was designed using a dual power source of an engine and motor matched with a hydro-mechanical continuously variable transmission (HMCVT). An equivalent fuel consumption minimization strategy (ECMS) was used for power distribution of this hybrid system. To address the problem of poor adaptability of the equivalence factor to different working cycles in the conventional ECMS, a fuzzy adaptive equivalent fuel consumption minimization strategy (FA-ECMS) was proposed. A fuzzy PI controller based on battery SOC (State of Charge) feedback was designed to adjust the equivalence factor in real time, so as to achieve adaptive control of the equivalence factor. The physical model of the system was built by SimulationX, and the model of the control strategy was built using Matlab/Simulink. Two typical cycles of tractor plowing and road transportation were simulated. Under ECMS, the fuel consumption of the hybrid agricultural tractor was 14.3 L and 1.19 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.75% and 60.32%, respectively. Under FA-ECMS, the hybrid farm tractor consumed 13.34 L and 1.13 L in one plowing cycle and one transport cycle, respectively, with final battery SOC values of 60.27% and 60.17%, respectively. The results showed that, with the introduction of a fuzzy PI controller to dynamically adjust the equivalence factor, the overall fuel consumption was reduced by 6.71% and 5.04%, respectively, and the battery power maintenance performance was improved. The designed control strategy could achieve a more reasonable power distribution between the engine and motor while maintaining the balance of the battery SOC.

Real-time grain breakage sensing for rice combine harvesters using machine vision technology

Condition monitoring ensures the safety of freight railroad operations. With the development of machine vision technology, visual inspection has become a p

The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type, the data used in RUL prognostics is accurate, and the RUL prognostics are conducted in the short term. Due to which, a generalizing RUL prognostics method is designed based on fault signal data. Firstly, the fault signal model is designed with the signal in a complex and mutative environment. Then, the generalizing RUL prognostics method is designed based on the fault signal model. Lastly, the simplified solution of the generalizing RUL prognostics method is deduced. The experimental results show that the proposed method gained good accuracies for RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. The proposed method can process inaccurate fault signals with different kinds of noise in the actual working environment, and it can be conducted in the long term. Therefore, the RUL prognostics method has a good generalization.