US 12,240,331 B1
Global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles
Xiangyang Xu, Beijing (CN); Junwei Zhao, Beijing (CN); Peng Dong, Beijing (CN); Yanfang Liu, Beijing (CN); and Shuhan Wang, Beijing (CN)
Assigned to BEIHANG UNIVERSITY, Beijing (CN)
Filed by BEIHANG UNIVERSITY, Beijing (CN)
Filed on Feb. 26, 2024, as Appl. No. 18/587,347.
Claims priority of application No. 202311255135.8 (CN), filed on Sep. 27, 2023.
Int. Cl. B60L 15/20 (2006.01); G07C 5/08 (2006.01)
CPC B60L 15/2045 (2013.01) [B60L 15/2054 (2013.01); G07C 5/0841 (2013.01); B60L 2240/12 (2013.01); B60L 2240/54 (2013.01)] 6 Claims
OG exemplary drawing
 
1. A global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles, comprising the steps of:
step 1: inputting a plurality of pieces of online original information collected by a plurality of environment sensing devices into an M core of a multi-core heterogeneous controller, and performing information sensing and time sequence synchronization alignment, scene-oriented driving speed performance under different paths analysis and future driving road segment feature recognition, thereby obtaining information data, wherein step 1 further comprising:
step 11: inputting the plurality of pieces of online original information collected by the plurality of environment sensing devices into the M core of the multi-core heterogeneous controller, and performing time sequence synchronization alignment, thereby obtaining a multi-source information time sequence synchronization table;
step 12: extracting a driver's driving speed performance under different paths feature data in the historical travel big data at a vehicle end, and obtaining the driver's scene-oriented driving speed performance matrix;
step 13: integrating the multi-source information time sequence synchronization table and the driver's driving speed performance under different paths feature data, thereby obtaining the information data for future driving road segment feature recognition;
step 2: transmitting the information data obtained in step 1 to the A core of the multi-core heterogeneous controller in real time, thereby obtaining information data in a valid state;
step 3: obtaining a target battery power state of charge trace sequence under a future driving road segment based on the multi-source information time sequence synchronization table and the valid information data obtained in steps 1 and 2, wherein step 3 further comprising: performing task prediction based on the multi-source information time sequence synchronization table and the valid information data obtained in steps 1 and 2, thereby obtaining long-term speed and power demand predicted sequences; inputting the long-term speed and power demand predicted sequences in a future driving scene into a dynamic programming model, and performing reverse traversal and forward search based on an operating mode and a set battery power state of the hybrid electric vehicle, thereby obtaining a target battery power state of charge trace sequence in a future driving scene;
step 4: waking up the target battery power state of charge trace sequence under the latest future driving road segment;
step 5: applying the target battery power state of charge trace sequence under a future driving road segment to the M core by the A core, and determining the battery target SoC state in different position interval states in the future driving road segment based on the driving position, driving speed and driving time at a current position of the hybrid electric vehicle, thereby obtaining a target SoC value in a current position state; subsequently, performing hybrid transmission operating mode decision-making and multi-power-source power allocation in combination with the target SoC value in the current position state, thereby generating a global energy management strategy of the hybrid electric vehicle.