Motivation

„The cheapest energy is what you don’t use.“ (Arthur H. Rosenfeld, USA) Efficiency increases in a system are suitable for reducing the running costs of a mobile machine. In addition, efficiency increases in combustion engine-driven systems have a direct positive effect on CO2 emissions and the climate. Against this background, the potential of the previous research project KonZwi - Constant Pressure System with Intermediate Pressure Line - is being utilised and usefully expanded through the use of a 4-surface cylinder.

Project objective

The KonZwi2 joint project aims to increase the energy efficiency of the working system of a mobile machine by combining two successfully tested research approaches. This involves modifying the familiar KonZwi system so that a 4-surface cylinder can be optimally utilised. System-related throttling losses that occur when cylinders are activated can be reduced. On the one hand, this is achieved by adapting the required pressure level by combining the four cylinder surfaces to the three available pressure potentials of high pressure, intermediate pressure and tank pressure. Secondly, by regenerating and recuperating potential / kinetic energy with the aid of a pressure accumulator. The control technology to be developed should also ensure the controllability of the overall system. As an example, the potential and performance are illustrated with the aid of simulation using the Y-cycle of a wheel loader and the 90° working cycle of an excavator.

Solution approach

Firstly, the components are adapted to the load cases of the wheel loader or excavator. As a result, the optimum accumulator size, the preload pressure and the cylinder surfaces are determined. In the same way, a suitable valve topology is developed, which enables the necessary switching states. Due to the large number of states, this is a multi-criteria optimisation problem. This can be solved using Pareto optimisation, for example. This optimisation is used to develop an optimum control system based on quasi-static points of the load cycles, which shows the maximum possible efficiency potential of the new system. By creating adapted simulation models for wheel loaders and excavators, a self-optimising control strategy is developed, which then represents the potential increase. Self-optimising control strategies can be, for example, predictive algorithms and machine learning methods. Finally, the results of the simulation are compared with the maximum efficiency potential and evaluated.