This article discusses the fuel economy optimization of a parallel hydraulic hybrid mining truck (HHMT). Considering the influences of various coupled factors, such as the transmission system, energy management strategy, and driving conditions, on the optimization goal, this article proposes the use of a double-layer optimization strategy with a collaborative optimization algorithm that combines particle swarm optimization (PSO) and a dynamic programming algorithm (DP) to eliminate the mutual effects of these coupled factors. A two-layer optimization model is developed, with powertrain parameters and energy management parameters as the optimization variables and the average fuel consumption under various driving conditions as the target. This model combines a variety of driving conditions to perform global optimization of the transmission system parameters while calculating the optimal energy distribution and analyzing the influences of various factors on the optimization goal. To achieve real-time and reliable control of energy management, the optimal energy management strategy rules obtained under various driving conditions are integrated and extracted, and an improved extraction method compared with the traditional extraction method is proposed. Finally, a rule-based energy management strategy is established. The strategy and optimized transmission system parameters are simulated and verified using a MATLAB and AMESIM joint simulation platform, and the effect of the rule strategy is evaluated. The obtained fuel consumption results are close to the results obtained by PSO-DP optimization, and the strategy is robust. The experiment verifies the effectiveness, feasibility and reliability of the optimization scheme, and extraction rule control strategy.

本文讨论了并联液压混合矿用卡车 (HHMT) 的燃油经济性优化。考虑到传动系统、能量管理策略、行驶工况等各种耦合因素对优化目标的影响，本文提出采用双层优化策略和结合粒子群优化的协同优化算法（ PSO) 和动态规划算法 (DP) 来消除这些耦合因素的相互影响。建立了以动力总成参数和能源管理参数为优化变量，以各种行驶工况下的平均油耗为目标的两层优化模型。该模型结合多种行驶工况对传动系统参数进行全局优化，同时计算最优能量分配，分析各因素对优化目标的影响。为实现能源管理的实时可靠控制，将各种行驶工况下得到的最优能源管理策略规则进行整合提取，并提出一种与传统提取方法相比改进的提取方法。最后，建立了基于规则的能源管理策略。使用MATLAB和AMESIM联合仿真平台对策略和优化后的传输系统参数进行仿真验证，并对规则策略的效果进行评估。得到的油耗结果与PSO-DP优化得到的结果接近，策略鲁棒。实验验证了优化方案和提取规则控制策略的有效性、可行性和可靠性。