Comprehensive Research on Energy Saving Synergy Optimization and Optimal Real-time Control of Hybrid Electric Mining Trucks - Li Hongliang; Zhang Guojing; Fan Ping (Jixie chuandong)
The fuel economy of hybrid electric vehicles largely depends on the transmission system parameters and energy management strategy. However, due to the interaction between the transmission system parameters and energy management strategies on the optimization of fuel economy, and the difficulty in balancing the real-time and global optimality of energy management strategies, there is still a lack of an effective implementation method. This study takes the hybrid electric mining truck as a research object, establishes the mathematical model of the whole vehicle, takes reducing fuel consumption as the goal, establishes a collaborative optimization model that comprehensively considers the interaction between transmission system parameters and energy management strategies, and combines particle swarm optimization (PSO) algorithm and dynamic programming (DP) algorithm to build a two-layer interactive optimization algorithm to eliminate the interaction between transmission system parameters and energy management strategies. The optimal energy distribution can be achieved while optimizing the transmission parameters. On this basis, in order to solve the problem that the DP algorithm is difficult to realize online control, the optimal solution of DP energy management corresponding to the optimal transmission parameters is integrated, an effective method to extract the optimal control rules of DP is designed, and an energy management strategy based on the optimal control rules is established to realize online control of hybrid electric mining trucks. Thus, the integrated realization method of the coordinated optimization of transmission system parameters and energy management strategy, as well as the optimal energy management real-time control strategy, is established. The obtained optimal transmission system parameters and corresponding optimal energy management real-time control strategy are simulated and verified on the Stateflow-AMESim joint simulation platform. The results show that the fuel consumption results obtained by this method are better than those obtained by DP optimization alone, and they are closer to the results obtained by PSO-DP optimization, indicating that the optimization method is effective and feasible.