Hybrid loader hybrid energy storage system 6 sigma robust design
Summary:The composite energy storage hybrid loader, due to its complex system composition and harsh working environment with many uncertainties, does not achieve optimal economic performance.To cope with the complex and variable construction environment and reduce performance degradation caused by system parameter fluctuations, it is necessary to optimize the robustness and reliability of the composite energy storage system.The composite energy storage hybrid loader studied in this paper has a parallel structure.The vehicle' s power sources include the engine system, the battery-motor system, and the hydraulic system.The engine system serves as the primary power source, the battery-motor system mainly provides auxiliary power, and the hydraulic system inputs energy stored in the hydraulic accumulator into the transmission system as another power output system for the loader.The composite energy storage system of the loader mainly consists of these three parts.By robustly optimizing these three subsystems, the composite energy storage system can achieve optimal performance during loader operation.The optimization method used is 6σ robust design.The principle involves combining the robust design of the tolerance model set with the modern optimization design theory of the 6σ quality management standard.This approach not only optimizes the target function but also reduces the impact of some constraints and related variable fluctuations on the optimization target, thereby improving the reliability of the target after optimization and ultimately enhancing robustness.The specific process first involves deterministic analysis for each system, constructing objective functions, parameter variables, and variable constraints.However, the results of deterministic optimization are overly idealistic and do not consider the impact of uncertainties, making them unrealistic for practical engineering applications.Consequently, the optimized parameter values may not be absolutely reliable.Therefore, a 6σ robust design is applied to the corresponding systems, constructing corresponding objective functions, parameter variables, and variable constraints.Using Isight software, a multi-objective optimization algorithm and Monte Carlo simulation robust optimization method are selected to optimize the models.The optimized data is then input into the established vehicle simulation model for validation under various working conditions.Our results show a significant reduction in vehicle fuel consumption, with average fuel savings rates of 11.14％, 10.30％, 10.40％, and 11.38％ across four working conditions.Finally, a hardware-in-the-loop experiment is conducted using dSPACE experimental equipment, and a comparative analysis of experimental and simulation data is performed.Our results demonstrate the effectiveness and feasibility of the 6σ robust design optimization for the loader system.In addition, the application of 6σ robust design in the optimization process not only improves the economic efficiency of loaders, but also enhances their overall performance stability in various operating scenarios.This comprehensive approach ensures that loaders can better adapt to the harsh and unpredictable conditions of construction sites, thereby achieving more reliable and efficient operations.The successful implementation of this robust optimization method demonstrates its potential for wider application in similar engineering fields, where system reliability and performance are equally important in construction scenarios.Through continuous improvement and verification, this method can significantly improve the stability and safety of composite energy storage engineering machinery such as composite energy storage loaders.
six sigma theoryrobust designdynamic conditionscomposite energy storage
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