Collaborative optimization and energy management of hydraulic hybrid mining trucks

• DOI: 10.1177/09544070211058569
• Publication Date: 2021-11-13T07:33:06Z
• Authors: Hongliang Li; Denglin Zhu; Lihua Shang; Ping Fan
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 7. Clean energy

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.