A load sensing (LS) supply in combination with control valves is one of the most common solutions for the actuation of implements on heavy-duty mobile machines (HDMMs). A major drawback of this approach is its relatively low energy efficiency due to metering losses—especially for multi-actuator operation and load braking. Several novel, more efficient concepts have already been proposed but involve high component costs for each actuator, which is not acceptable for HDMMs with many actuators that have a medium to low energy turnover. Therefore, this work proposes a novel system design which is based on a conventional LS system—for cheap operation of a high number of low-energy-consuming actuators—but allows to avoid metering losses for single high-energy-consuming actuators by replacing their metering valves with electric-generator-hydraulic-motor (EGHM) units that work similar to pump-controlled concepts. The benefits of the novel concept are explained in detail by looking at the three main throttling functions of an actuator in a typical valve-controlled LS systems, which the novel concept avoids by applying pressure in the actuator return lines and recuperating energy electrically instead of dissipating it by throttling. Furthermore, advantages and challenges for the novel concept are analyzed, and ways to address the latter are presented. Before the novel concept is simulated, the required control algorithms are presented. The simulation study in Amesim and Simulink focuses on a telehandler that utilizes the novel concept for the boom, extension and tilt actuators. Simulation results show that the novel system can decrease the required input energy for typical duty cycles by up to 34% compared to a conventional LS system. At the same time, simulations show that, from an economic perspective, it seems most reasonable to utilize the novel EGHM units only for the boom and extension actuators of the studied telehandler.

No abstract

No abstract

The CRDNN is a combined neural network that can increase the holistic efficiency of torque based mobile working machines by about 9% by means of accurately detecting the truck loading cycles. On the one hand, it is a robust but offline learning algorithm so that it is more accurate and much quicker than the previous methods. However, on the other hand, its accuracy can not always be guaranteed because of the diversity of the mobile machines industry and the nature of the offline method. To address the problem, we utilize the transfer learning algorithm and the Internet of Things (IoT) technology. Concretely, the CRDNN is first trained by computer and then saved in the on-board ECU. In case that the pre-trained CRDNN is not suitable for the new machine, the operator can label some new data by our App connected to the on-board ECU of that machine through Bluetooth. With the newly labeled data, we can directly further train the pretrained CRDNN on the ECU without overloading since transfer learning requires less computation effort than training the networks from scratch. In our paper, we prove this idea and show that CRDNN is always competent, with the help of transfer learning and IoT technology by field experiment, even the new machine may have a different distribution. Also, we compared the performance of other SOTA multivariate time series algorithms on predicting the working state of the mobile machines, which denotes that the CRDNNs are still the most suitable solution. As a by-product, we build up a human-machine communication system to label the dataset, which can be operated by engineers without knowledge about Artificial Intelligence (AI).

No abstract