Quiet design of hydraulic systems employing quantum annealing

Noise emissions from construction and agricultural vehicles are a critical concern. As electrification progresses globally, noise from essential hydraulic systems, particularly pressure ripple in hydraulic-mechanical transmissions (HMT), becomes relatively dominant. Optimization for quiet designs of hydraulic systems is becoming more challenging due to the increased degree of freedom associated with the spread of model-based design (MBD), which often leads to NP-hard problems. Conventional methods - including those utilizing model approximation, gradient information, or probabilistic search - often fail to secure global optimality or require prohibitive computational costs. This study therefore proposes quantum annealing (QA), leveraging recent progress in quantum computing and the quantum mechanical tunneling effect for efficient global exploration, making it ideal for this class of NP-hard problems. This study verifies the feasibility of QA for reducing HMT pressure ripple. Using MBD data, the objective (pressure ripple) and constraint (efficiency) have been formulated. Continuous variables have been binary-encoded, and then the cost function suitable for the QUBO formulation required by QA has been defined via the penalty method. The QA-derived optimal solution has successfully located the Pareto front for the pressure ripple and the transmission efficiency, confirming the effectiveness of the method in exploring the optimal solution space. Future work will focus on enhancing mathematical model reliability and improving search efficiency through strategic SQA parameter adjustment based on engineering insights.