  A new primary torque control concept for hydrostatics mobile machines was
introduced in 2018. The mentioned concept controls the pressure in a closed
circuit by changing the angle of the hydraulic pump to achieve the desired
pressure based on a feedback system. Thanks to this concept, a series of
advantages are expected. However, while working in a Y cycle, the primary
torque-controlled wheel loader has worse performance in efficiency compared to
secondary controlled earthmover due to lack of recuperation ability.
Alternatively, we use deep learning algorithms to improve machines'
regeneration performance. In this paper, we firstly make a potential analysis
to show the benefit by utilizing the regeneration process, followed by
proposing a series of CRDNNs, which combine CNN, RNN, and DNN, to precisely
detect Y cycles. Compared to existing algorithms, the CRDNN with bi-directional
LSTMs has the best accuracy, and the CRDNN with LSTMs has a comparable
performance but much fewer training parameters. Based on our dataset including
119 truck loading cycles, our best neural network shows a 98.2% test accuracy.
Therefore, even with a simple regeneration process, our algorithm can improve
the holistic efficiency of mobile machines up to 9% during Y cycle processes if
primary torque concept is used.
