Global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles
A global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles, belonging to the technical field of hybrid electric vehicle energy management, which is capable of solving the problem that conventional hybrid electric vehicles cannot realize global energy management at a vehicle end; through adopting the present invention, the problems relating to the failure of timely achieving global energy management for hybrid electric vehicles, poor vehicle speed performance and driving safety caused by the high difficulty of effectively connecting and deploying different computing tasks of global energy management are solved; a multi-core heterogeneous controller is used to perform multi-task deployment, which solves the problems caused by one type of tasks requiring high computing power but low real-time response while another type of tasks requiring low computing power but high real-time response, ensures the efficient utilization of control chips of the hybrid transmission.
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.
2. The global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles of claim 1 , wherein in step 2, obtaining the information data in a valid state further comprising: detecting a plurality of information status flags in the information data by the A core, and determining the validity of the received information data by using the plurality of information status flags, thereby obtaining the information data in a valid state.
3. The global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles of claim 2 , wherein in step 2, detecting the plurality of information status flags by the A core, and determining the validity of the received information data by using the plurality of information status flags further comprising: determining that the corresponding information status flag is 1 and the transmitted information data is valid when there is a normal value exists in the received information data.
4. The global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles of claim 3 , wherein the method further comprises step: determining an energy management strategy according to the initiating condition.
5. The global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles of claim 2 , wherein the method further comprises step: determining an energy management strategy according to the initiating condition.
6. The global energy management optimization method in multi-task cross-core deployment for hybrid electric vehicles of claim 1 , wherein the method further comprises step: determining an energy management strategy according to the initiating condition.