No abstract

The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system. Although bench test data are convenient to obtain in model parameter estimation, there is a need for the load data to conform to the actual working conditions. Although the operating data include the actual load information, it is not easy to collect the control valve operating data. This paper proposes a model parameter estimation method based on bench test and operating data fusion to solve the above problems. The proposed method is based on Bayesian theory, and its core is a pool fusion of prior information from bench test and operating data. First, a system model is established, and the parameters in the model are analysed. Then, the bench test and operating data of the system are collected, and the model parameters and weight coefficients are estimated using the data fusion method. Finally, the estimated effects of the data fusion method, Bayesian method, and PSO algorithm on system model parameters are compared. The research shows that the parameter estimation result based on the data fusion method is accurate. The weight coefficient represents the contribution of different prior information to the parameter estimation result. The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm. The more complex the load is, the worse the model's accuracy, which verifies the influence of the load on the valve-controlled cylinder system model and proves that the data fusion method plays an essential role in parameter estimation studies.

Accurate energy flow results are the premise of excavator energy-saving control research. Only through an accurate energy flow analysis based on operating data can a practical excavator energy-saving control scheme be proposed. In order to obtain the excavator’s accurate energy flow, the excavator components’ performance and operating data requirements are obtained, and the experimental schemes are designed to collect it under typical working conditions. The typical working condition load is reconstructed based on wavelet decomposition, harmonic function, and theoretical weighting methods. This paper analyzes the excavator system’s energy flow under the typical working condition load. In operation conditions, the output energy of the engine only accounts for 50.21% of the engine’s fuel energy, and the actuation and the swing system account for 9.33% and 4%, respectively. In transportation conditions, the output energy of the engine only accounts for 49.80% of the engine’s fuel energy, and the torque converter efficiency loss and excavator driving energy account for 15.09% and 17.98%, respectively. The research results show that the energy flow analysis method based on typical working condition load can accurately obtain each excavator component’s energy margin, which provides a basis for designing energy-saving schemes and control strategies.