Zugl.: Aachen, Techn. Hochsch., Diss., 2007.

Universiẗat, Diss.--Erlangen-Nürnberg, 2007.

Zugl.: Erlangen, Nürnberg, Univ., Diss., 2007

Zugl.: Siegen, Univ., Diss., 2007

Brandenburgische Techn. Universiẗat, Diss., 2001--Cottbus.

Zugl.: Bremen, Universiẗat, Diss.

A new primary torque control concept for hydrostatics mobile machines was introduced in 2018 [1]. 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 [2]. 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 bidirectional 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.

The combination of hydrostatics and mechanical gearboxes cannot only improve system efficiency but also the usability of mobile machines. A possible solution could be a synchronized gearbox with two gears. Compared to the pure hydrostatic drivetrain, mobile machines with such a combination can have a larger drive torque with the first gear and higher efficiency at high speed. However, the calibration effort of this traditional drivetrain system is enormous; moreover, with a flow-based control, the degrees of freedom to achieve optimum shift performance in all conditions are limited. By adopting the primary torque control concept, we propose a novel hydrostatic-mechanical drivetrain control algorithm so that the holistic shift performance is better whilst the calibration effort is dramatically reduced by systematically adjusting only one hyper-parameter. Experienced engineers and machine operators validated the advantages mentioned here by a series of field experiments. Besides synchronized gearboxes, the controller proposed in this paper is also generalized to other kinds of mechanical transmissions.