电液并联轮式装载机行驶速度-能量管理协同优化方法

Co-optimization of Driving Speed and Energy Management for Parallel Electro-hydraulic Hybrid Wheel Loaders

摘要:

在“双碳”战略背景下,农业领域对高效、低排放的散料搬运装备需求不断增加。电动轮式装载机凭借其环保、节能等优势,逐渐在农业生产中得到推广应用。本文针对电液并联混合动力装载机,以降低电池老化和能量消耗为综合目标,提出行驶速度-能量管理策略协同优化方法。首先建立混合动力系统数学模型和电池使用寿命评估模型;然后构建行驶速度和能量管理策略协同优化数学模型,并使用动态规划算法实现协同优化问题的离线数值求解。根据离线结果表现的结构特征与规律,设计了基于规则的在线协同优化方法,实时规划装载机行驶速度和电液功率分配方式。仿真结果表明,提出的在线协同优化算法在电池寿命与整机能耗方面均接近离线最优结果。

Abstract:

Under the “dual-carbon” strategy, the demand for efficient and low-emission bulk material handling equipment in the agricultural sector has been steadily increasing. Electric wheel loaders, owing to their environmental friendliness and energy efficiency, are gradually adopted in agricultural operations for material handling and field logistics. As the core energy-storage component of electric wheel loaders, the battery is subjected to large charge-discharge currents and frequent cycling under high-intensity and high-duty working conditions. These operating characteristics lead to accelerated battery degradation, reduced driving range, and increased operating costs. Targeting parallel electro-hydraulic hybrid (PEHH) wheel loaders, a coordinated optimization method for travel-speed planning and energy-management control was proposed, aiming to simultaneously mitigate battery aging and reduce overall energy consumption. Firstly, a mathematical model of the hybrid powertrain system and a battery lifetime prediction model were established. Subsequently, a coordinated optimization framework for travel speed and energy-management strategy was formulated, and the offline optimal solution was obtained by using a dynamic programming (DP) algorithm. Based on the structural characteristics and regularities observed in the offline optimal results, a rule-based online coordinated optimization strategy was developed to realize real-time travel-speed planning and electro-hydraulic power-split control. Simulation results demonstrated that the proposed online coordinated optimization algorithm achieved performance in battery life extension and energy consumption reduction that was close to the offline optimal benchmark.