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Construction equipment machines today benefits from hydraulic system due to high power density. And, the development of an excavator using “open-center system with spool valves”, in general, requires iterative hardware design tuning activities for optimized performance and fuel economy while matching operator’s commands. Instead of traditional hydraulic and multi-body dynamic simulation with an operator simulation model, this paper focuses on the methodology development of real-time simulation model for an excavator, including the hydraulic system of an excavator’s boom, arm, bucket, and travel as well as multi-body dynamic system. The real-time capability is realized by reducing unnecessary compressible units and achieving numerical stability at sudden pressure changes when valves open and close. The real-time simulation model has been verified later with an actual Volvo CE excavator machine, and the correlation was quite satisfactory.

Out of necessity, emission and fuel consumption test cycles are a simplified representation of the real-life use of a vehicle or component that is assumed to be most common. In reality, variations are introduced by both the driver and the environment - and to a lesser degree also by the vehicle itself through performance deviations because of tolerances in the components' characteristics. However, since such simplified test cycles exist and are accepted (or even required by law), OEMs tend to use them also in product development to benchmark their products against the competition, and to make decisions on how to optimize design. While this approach might give acceptable results for on-road vehicles, it fails to capture reality in the case of versatile working machines. Here, the variety of possible applications cannot be covered by one common application but rather demands a mix of several cycles.

A neural network-based computer vision system is developed to estimate position of an excavator manipulator in real time. A camera is used to capture images of a manipulator, and the images are down-sampled and used to train a neural network. Then, the trained neural network can estimate the position of the excavator manipulator in real time. To study the feasibility of the proposed system, a webcam is used to capture images of an excavator simulation model and the captured images are used to train a neural network. The simulation results show that the developed neural network-based computer vision system can estimate the position of the excavator manipulator with an acceptable accuracy.

Due to the high demand of fuel efficient construction equipment, significant research effort has been dedicated to improving excavator efficiency. Among various possibilities, methods to recuperate energy during cab swing motion have been widely examined. Electric and hydraulic hybrids designs have shown to greatly improve fuel efficiency but require drastic design changes. The redesigned systems thus require many hours of operation to offset the manufacturing costs with fuel savings. In this research, a relatively simple swing energy recuperation system is presented using an additional accumulator, fixed displacement hydraulic motor, and control valves. With the system, hydraulic fluid is stored in an accumulator, and a simple controller opens a valve to allow the stored energy to assist the engine in running the main pumps.