Science China. Technological sciences, 2020-05-21, Vol.63 (10), p.2082-2097
Heavy commercial vehicles equipped with a hydraulic hub-motor auxiliary system (HHMAS) often operate under complex road conditions. Selecting appropriate operating mode and realizing reasonable energy management to match unpredictable road conditions are the keys to the driving performance and fuel economy of HHMAS. Therefore, a multi-mode energy management strategy (MM-EMS) based on improved global optimization algorithm is proposed in this study for HHMAS. First, an improved dynamic programming (DP) algorithm for HHMAS is developed. This improved DP algorithm considers the effect of SOC and vehicle speed, thereby preventing the calculation results from falling into local optimization. This algorithm also reduces the dimension of the control variable data grid, and the calculation time is reduced by 35% without affecting the accuracy. Second, a MM-EMS with hierarchical control is proposed. This strategy extracts the optimal control rules from the results of the improved DP algorithm. Then it divides the system’s operating region into two types, namely, single-mode working region and mixedmode working region. In the single-mode working region, mode switching is realized through fixed thresholds. In the mixedmode working region, a linear quadratic regulator (LQR) is adopted to determine a target mode and realize SOC tracking control. Finally, the designed MM-EMS is verified separately in offline simulation and hardware-in-the-loop (HIL) under actual vehicle test cycles. Simulation results show that the results between HIL and offline simulation are largely coincidence. Besides, in comparison with the engine optimal control strategy, the designed MM-EMS can achieve an approximate optimal control, with oil savings of 3.96%.
Hardware-in-the-loop simulation ; Optimization algorithms ; Local optimization ; Global optimization ; Algorithms ; Simulation ; Mathematical analysis ; Traffic speed ; Optimal control ; Fuel economy ; Commercial vehicles ; Strategy ; Dynamic programming ; Tracking control ; Energy management ; Linear quadratic regulator
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