A method for optimizing energy distribution of an eCVT system based on a high-precision map

Abstract

The application discloses a method for optimizing energy distribution of an eCVT system based on a high-precision map, comprising the following steps: S1: obtaining high-precision map information of a subsequent route of a vehicle navigation route according to the vehicle navigation route and a vehicle position; S2: dividing the vehicle navigation route into an uphill section, a downhill section or a horizontal section according to the high-precision map information; S3: predicting passing data of multiple sections of the vehicle navigation route to generate a passing data map; and S4: generating an energy distribution scheme according to the passing data map and executing the energy distribution scheme. The application can increase the energy distribution processing efficiency, perform long-term energy distribution, and realize switching of active discharging and efficient charging modes.

Description

Technical Field

[0001] The present invention relates to the field of eCVT, and in particular to a method for optimizing energy distribution of an eCVT system based on a high-precision map. Background Art

[0002] The energy management strategy of the eCVT hybrid system is a method that uses advanced control strategies to intelligently control and manage the engine, electric motor and battery based on real-time driving data, road conditions and battery status to ensure efficient operation of the system.

[0003] The main energy management strategies currently are:

[0004] (1) Rule-based energy management strategy

[0005] Based on the torque or power requirements, torque and power are distributed according to deterministic rules or fuzzy logic algorithms. This strategy is relatively computationally intensive and easy to implement, but torque distribution and operating point adjustment rely heavily on the engineer's experience.

[0006] (2) Energy management strategy based on optimization algorithm

[0007] Such strategies include global optimization control strategies (dynamic programming, Pontryagin minimum principle, and convex optimization), instantaneous optimization control strategies (equivalent fuel consumption minimum control and model predictive control), etc. These strategies can obtain global or local optimal solutions, but require accurate subsystem models and a large amount of computing power.

[0008] The existing technical solutions have the following disadvantages:

[0009] First, rule-based energy management strategies rely on engineers' experience to properly allocate torque and adjust working nodes, while energy management strategies based on optimization algorithms require high system model accuracy and computing power, making them difficult to apply.

[0010] Secondly, the various current technical solutions all have a common problem. Energy management strategies are based on information that has already occurred. The existing energy management strategies are not adaptable enough to possible changes in future road conditions. Especially on long uphill and downhill sections, the battery cannot properly manage energy. As a result, when the engine and motor need to work at the same time and need to optimize energy utilization efficiency through torque distribution between each other, there may be insufficient battery energy storage, resulting in a single engine working and not running at the optimal point of external characteristics, resulting in energy loss. Moreover, the energy management of the existing solutions does not have long-term planning for a large time span. Usually, energy management is short-sighted and cannot have a significant effect over a large time span.

[0011] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0012] The purpose of the present invention is to provide a method for optimizing the energy distribution of an eCVT system based on a high-precision map, comprising the following steps: S1: obtaining high-precision map information of a subsequent route of the vehicle navigation path based on a vehicle navigation path and a vehicle position; S2: dividing the vehicle navigation path into uphill sections, downhill sections or horizontal sections based on the high-precision map information; S3: predicting the passing data of multiple sections of the vehicle navigation path and generating a passing data map; S4: generating and executing an energy distribution plan based on the passing data map.

[0013] In one or more embodiments, step S1 includes: S11: obtaining permission to read the vehicle navigation path, vehicle position and high-precision map information; S12: reading the vehicle position and dividing the vehicle navigation path; S13: retaining the path data of the subsequent route in the vehicle navigation path; S14: extracting the corresponding high-precision map information based on the geographic coordinates of the path data of the subsequent route.

[0014] In one or more embodiments, the high-precision map information includes height information and slope information of the corresponding location, and the step S2 includes: S21: dividing the focus points of the high-precision map information; S22: determining the overall slope of the interval between adjacent focus points; S23: based on the overall slope, dividing the vehicle navigation path into uphill sections, downhill sections or horizontal sections.

[0015] In one or more embodiments, the high-precision map information is of analog data type, i.e., continuous data, and the step S21 includes: S211: generating a slope change function of the high-precision map information based on the slope information; S212: traversing the slope change function to obtain the geographic coordinates corresponding to the zero point; S213: setting the focus point of the high-precision map information according to the geographic coordinates corresponding to the zero point.

[0016] In one or more embodiments, the passing data includes a hybrid torque distribution plan and a total difference between the battery SOC, and the step S3 includes: S31: obtaining vehicle attributes; S32: querying high-precision map information within the vehicle navigation path, and predicting the hybrid torque distribution plan based on the vehicle attributes;

[0017] S33: According to the hybrid torque distribution scheme, the motor consumption is predicted to obtain a first battery SOC difference; S34: When the vehicle navigation path is a downhill section, the battery recovery amount is predicted to obtain a second battery SOC difference; S35: The first battery SOC difference and the second battery SOC difference are summed, and the result is written into the total battery SOC difference; S36: The vehicle navigation path is traversed, and the total battery SOC difference is matched with multiple sections to obtain a passing data map.

[0018] In one or more embodiments, the vehicle attributes include external characteristics of the engine; the hybrid torque distribution scheme refers to the way in which the generator distributes torque when the engine operates in the external characteristics; the step S32 includes: S321: obtaining the required torque based on the slope information; S322: obtaining the optimal torque of the engine based on the external characteristics of the engine; S323: subtracting the optimal torque of the engine from the required torque to obtain the generator torque, and further obtaining the generator power consumption rate.

[0019] In one or more embodiments, the step S33 includes: S331: integrating the generator power consumption rate within the vehicle navigation path to obtain the motor consumption.

[0020] In one or more embodiments, the step S4 includes: S41: obtaining the battery SOC initial value and the battery SOC power range; S42: according to the passing data map, traversing and calculating the battery SOC remaining value after passing through multiple points of interest, and recording it as the battery SOC natural margin; S43: comparing the battery SOC natural margin with the battery SOC power range to generate a battery SOC adjustment amount; S44: in the passing data map, calculating the superimposed position of the battery SOC adjustment amount in the opposite direction of the driving direction, generating an energy distribution plan and executing it.

[0021] In one or more embodiments, the step S43 includes: S431: when the battery SOC natural margin is higher than the peak value of the battery SOC power range, the battery SOC adjustment amount is calculated as the power that needs to be consumed in advance, and the value is a negative value; S432: when the battery SOC natural margin is lower than the minimum value of the battery SOC power range, the battery SOC adjustment amount is calculated as the power that needs to be charged in advance, and the value is a positive value.

[0022] In one or more embodiments, the step S44 includes: S441: searching each road section in the opposite direction of the driving direction until a road section to which the battery SOC adjustment amount can be adapted is found; S442: superimposing the battery SOC adjustment amount and recording the superimposed position; S443: generating an energy distribution plan based on the battery SOC adjustment amount and the superimposed position.

[0023] Compared with the prior art, the multiple technical solutions and embodiments provided by the present invention have at least the following technical effects or advantages:

[0024] By pre-dividing the road sections, we can provide indexes for subsequent large amounts of data and increase processing efficiency.

[0025] By generating battery SOC adjustment values, long-term energy allocation can be carried out, and switching between active discharge and efficiency charging modes can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and can be considered as illustrative of various combinations of preferred embodiments and do not constitute an undue limitation of the present invention. In the drawings:

[0027] Figure 1 A schematic diagram of the overall process of a method for optimizing energy distribution of an eCVT system based on a high-precision map provided by the present invention;

[0028] Figure 2 A schematic diagram of a preferred detailed flow chart of a method for optimizing energy distribution of an eCVT system based on a high-precision map provided by the present invention. DETAILED DESCRIPTION

[0029] Unless otherwise expressly stated, throughout the specification and claims, the term "comprise" or its variations such as "include" or "comprising", "made of", etc. will be understood to include the stated elements or components but not to exclude other elements or other components.

[0030] Example 1:

[0031] This embodiment provides a method for optimizing energy distribution of an eCVT system based on a high-precision map, characterized by comprising steps S1, S2, S3, and S4:

[0032] S1: According to the vehicle navigation path and the vehicle position, high-precision map information of the subsequent route of the vehicle navigation path is obtained.

[0033] Existing vehicle energy optimization schemes can only optimize based on current road conditions and are incapable of long-term energy regulation. Step S1, however, reads vehicle navigation data, preferably acquiring high-precision map information within a 20-kilometer radius, and optimizes energy management based on the vehicle's scheduled itinerary. If the vehicle's destination is modified or the route is switched, this implementation enables real-time updates and corrections, achieving full coverage of vehicle energy optimization. Specifically, the system acquires the "Position, Stub, Segment, ProfileShort, ProfileLong," and meta messages sent by the map terminal T-BOX according to the Adamis V2 protocol, parses and retrieves the current vehicle offset position information, obtains the slope value and its position offset, the curvature value and its position offset, the traffic sign speed limit and its position offset, and the offset position information of the sub-forks in the road ahead, and stores them in an array for on-demand access. In particular, this implementation can provide optimal energy savings when the vehicle is unmanned.

[0034] As a preferred implementation of this embodiment, step S1 includes:

[0035] S11: Obtain permission to read vehicle navigation path, vehicle location and high-precision map information;

[0036] S12: Read the vehicle position and divide the vehicle navigation path;

[0037] S13: retaining the path data of the subsequent route in the vehicle navigation path;

[0038] S14: Extracting the corresponding high-precision map information based on the geographic coordinates of the path data of the subsequent route.

[0039] Specifically, steps S12 and S13 screen the routes to be traversed in the vehicle navigation path. This embodiment can optimize energy allocation starting from any node in the entire vehicle navigation path. In particular, in the common case of vehicle route switching, such as switching to a route with fewer traffic lights or switching to a route with more highways, this embodiment can perform real-time updates and corrections from any breakpoint. Because high-precision map information usually has a large amount of data, after the route is updated, the system retains the data of the current route, compares it with the array offset based on the vehicle position, and deletes outdated array data located a certain distance behind the vehicle to release storage space. Step S14 associates the high-precision map information with the driving route to provide more accurate location comparison.

[0040] S2: Divide the vehicle navigation path into an uphill section, a downhill section or a horizontal section according to the high-precision map information.

[0041] Specifically, the high-precision map information is an analog data type, that is, continuous data. In order to provide faster and more efficient indexing in navigation, step S2 converts the high-precision map information into digital information in the form of nodes and intervals, and divides it into uphill sections, downhill sections or horizontal sections, so as to facilitate retrieval during subsequent careful optimization of energy allocation methods.

[0042] As a preferred implementation of this embodiment, the high-precision map information includes altitude information and slope information of the corresponding location, and step S2 includes:

[0043] S21: Divide the focus of high-precision map information;

[0044] S22: determining the overall slope of the interval between adjacent focus points;

[0045] S23: Based on the overall slope, dividing the vehicle navigation path into an uphill section, a downhill section, or a horizontal section.

[0046] As a further preferred implementation of this embodiment, step S21 includes:

[0047] S211: generating a slope change function of the high-precision map information according to the slope information;

[0048] S212: traverse the slope change function to obtain the geographical coordinates corresponding to the zero point;

[0049] S213: Setting the focus point of the high-precision map information according to the geographic coordinates corresponding to the zero point.

[0050] Specifically, step S21 constructs the slope change function using the slope change rate as the dependent variable and the geographic location as the independent variable. The mathematical meaning of the zero point value is the upslope to downslope conversion, and the geographic coordinates of the zero point are used as the node (i.e., the focus point) of the interval to divide the interval into overall upslope or downslope.

[0051] S3: Predicting the passing data of multiple road sections of the vehicle navigation path and generating a passing data map.

[0052] Specifically, the passage data refers to the theoretical energy consumption of the vehicle after passing through multiple road sections (i.e., the uphill sections, downhill sections, or horizontal sections described in step S2) according to the optimal solution. Step S3 records the passage data corresponding to the geographic coordinates on a map to generate a passage data map.

[0053] As a preferred implementation of this embodiment, the passing data includes the hybrid torque distribution scheme and the total difference between the battery SOC, and step S3 includes:

[0054] S31: Get vehicle attributes;

[0055] S32: querying high-precision map information within the vehicle navigation path, and predicting the hybrid torque distribution plan based on the vehicle attributes;

[0056] S33: predicting the electric motor consumption according to the hybrid torque distribution scheme to obtain a first battery SOC difference;

[0057] S34: When the vehicle navigation route is a downhill section, predict the battery recovery amount to obtain a second battery SOC difference;

[0058] S35: Sum the first battery SOC difference and the second battery SOC difference, and write the result into the total battery SOC difference;

[0059] S36: Traverse the vehicle navigation route, match the total battery SOC difference with multiple road sections, and obtain a passing data map.

[0060] Specifically, the eCVT hybrid system can make the engine operate at the highest external characteristic efficiency by running the motor and distributing torque, thereby improving work efficiency and saving fuel energy. The vehicle attributes include the external characteristics of the engine. Step S3 takes into account that when the vehicle goes downhill, the vehicle can recover electrical energy to the battery as the second battery SOC difference; combined with the energy consumption of the vehicle hybrid distribution motor as the first battery SOC difference, the two are summed to obtain the total battery SOC difference, that is, the change in battery power after passing the road section. The total battery SOC difference is positive when the vehicle recovers more electrical energy, that is, there is a situation where the battery energy increases after the vehicle goes downhill.

[0061] As a further preferred implementation of this embodiment, the hybrid torque distribution scheme refers to the way the generator distributes torque when the engine operates in the external characteristic;

[0062] The step S32 includes:

[0063] S321: Obtaining required torque based on slope information;

[0064] S322: Obtaining optimal engine torque based on engine external characteristics;

[0065] S323: Subtracting the optimal engine torque from the required torque to obtain the generator torque, and further obtain the generator power consumption rate;

[0066] The step S33 includes:

[0067] S331: Integrate the generator power consumption rate within the vehicle navigation path to obtain the motor consumption.

[0068] In steps S32 and S33, the engine is adjusted to work in the external characteristics, and the motor torque is adjusted to the torque difference. The power consumption rate of the generator is obtained by looking up the table, and integrated within the vehicle navigation path to calculate the motor consumption.

[0069] S4: Generate and execute an energy allocation plan based on the passing data map.

[0070] As an important technical detail, the battery SOC has a power range limitation. As an example, the battery power needs to work in the range of 25% to 85% of the total capacity. The battery power cannot be overcharged, nor can it be too low. It needs to be kept within the power range to supply power normally. Therefore, this embodiment finds that when the vehicle is on a downhill section, the recovered electric energy is used to reverse charge the battery, and there is electric energy that exceeds the power range. The excess electric energy can only be discarded. This is a technical problem that cannot be solved by the existing technology and will lead to energy waste. Therefore, this embodiment considers a way to increase the power consumption in advance on a relatively straight section of the road to reduce the energy consumption of the engine, and to reserve excess electric energy storage capacity in advance to achieve the utilization of excess electric energy. In essence, the excess electric energy pre-compensates the fuel consumption of the engine to achieve energy optimization. On the other hand, this embodiment takes into account the situation of insufficient electric energy storage in an uphill section. In the middle of the uphill section, the battery power may be lower than the lowest value of the power range, the motor cannot run to distribute the engine torque, and the engine cannot work at the outer characteristic point, resulting in reduced efficiency and increased energy consumption. This embodiment proposes another means of pre-charging to reserve surplus electric energy when the engine operating pressure is low.

[0071] As a preferred implementation of this embodiment, step S4 includes:

[0072] S41: Obtaining the battery SOC initial value and the battery SOC power range;

[0073] S42: traversing and calculating the battery SOC remaining value after passing through the plurality of the focus points according to the passing data map, and recording the value as the battery SOC natural margin;

[0074] S43: Compare the battery SOC natural margin with the battery SOC power range to generate a battery SOC adjustment value;

[0075] S44: In the passing data map, calculate the superimposed position of the battery SOC adjustment amount in the opposite direction of the driving direction, generate an energy distribution plan and execute it.

[0076] Specifically, the battery SOC adjustment amount refers to the surplus or missing electricity when the electric energy is insufficient or excess electric energy occurs. After calculating the specific battery SOC adjustment amount for the entire journey, this embodiment traverses the entire driving distance to seek a section that can supplement the battery SOC adjustment amount for pre-energy distribution.

[0077] As a further preferred implementation of this embodiment, step S43 includes:

[0078] S431: When the battery SOC natural margin is higher than the peak value of the battery SOC power range, the battery SOC adjustment amount is calculated as the power that needs to be consumed in advance, and the value is a negative value;

[0079] S432: When the battery SOC natural margin is lower than the minimum value of the battery SOC power range, the battery SOC adjustment amount is calculated as the power required to be charged in advance, and the value is a positive value.

[0080] As a further preferred implementation of this embodiment, step S44 includes:

[0081] S441: Searching for each road section in the opposite direction of the driving direction until a road section where the battery SOC adjustment value can be adapted is found;

[0082] S442: superimposing the battery SOC adjustment amount and recording the superimposed position;

[0083] S443: Generate an energy distribution plan according to the battery SOC adjustment amount and the superposition position.

[0084] Specifically, after determining the battery SOC adjustment, the present application searches for road sections in the opposite direction of travel that can pre-allocate energy. After one or more searches, the most suitable road section is determined, and an energy allocation plan is set for traversing that section to compensate for the battery SOC adjustment. For example, if the energy recovery during a downhill drive exceeds the power range after traversing multiple road sections, the system adjusts the hybrid motor to consume more energy and the engine to consume less energy during the current straight section. Alternatively, if the system predicts that a long and steep uphill section after traversing multiple road sections will cause the battery's energy to be insufficient to reach the end of the section, the system controls the engine to output maximum power to charge the battery pack during the current straight section. In other words, the present application enables pre-storage or consumption of energy before multiple road sections, enabling long-distance and long-duration energy allocation and switching between active discharge and efficiency charging modes. This is expected to save approximately 3% fuel compared to the original vehicle's fuel consumption.

[0085] Example 2:

[0086] This embodiment provides an energy distribution control system. Based on the same concept, this embodiment is used to implement the method for optimizing energy distribution of an eCVT system based on a high-precision map as described in Example 1. The energy distribution control system includes:

[0087] An acquisition module, configured to acquire high-precision map information of a subsequent route of the vehicle navigation route based on the vehicle navigation route and the vehicle position;

[0088] a division module, configured to divide the vehicle navigation path into an uphill section, a downhill section or a horizontal section according to the high-precision map information;

[0089] A first generating module is used to predict the passing data of multiple road sections of the vehicle navigation path and generate a passing data map;

[0090] The second generating module is used to generate and execute an energy allocation plan according to the passing data map.

[0091] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is apparent that many variations and modifications are possible in light of the foregoing teachings. The exemplary embodiments have been selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and modifications. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. A method for optimizing energy distribution of an eCVT system based on a high-precision map, characterized in that: Including steps: S1: Acquire high-precision map information of a subsequent route of the vehicle navigation path according to the vehicle navigation path and the vehicle position; S2: Dividing the vehicle navigation path into an uphill section, a downhill section, or a horizontal section according to the high-precision map information; S3: predicting the passing data of multiple road sections of the vehicle navigation path and generating a passing data map; S4: generating and executing an energy allocation plan based on the passing data map; Wherein, the passing data includes the hybrid torque distribution scheme and the total difference between the battery SOC, and the step S3 includes: S31: Get vehicle attributes; S32: querying high-precision map information within the vehicle navigation path, and predicting the hybrid torque distribution plan based on the vehicle attributes; S33: predicting the electric motor consumption according to the hybrid torque distribution scheme to obtain a first battery SOC difference; S34: When the vehicle navigation route is a downhill section, predict the battery recovery amount to obtain a second battery SOC difference; S35: Sum the first battery SOC difference and the second battery SOC difference, and write the result into the total battery SOC difference; S36: Traverse the vehicle navigation route, match the total battery SOC difference with multiple road sections, and obtain a passing data map.

2. The method for optimizing energy distribution of an eCVT system based on a high-precision map according to claim 1, characterized in that: The step S1 comprises: S11: Obtain permission to read vehicle navigation path, vehicle location and high-precision map information; S12: Read the vehicle position and divide the vehicle navigation path; S13: retaining the path data of the subsequent route in the vehicle navigation path; S14: Extracting the corresponding high-precision map information based on the geographic coordinates of the path data of the subsequent route.

3. The method for optimizing energy distribution of an eCVT system based on a high-precision map according to claim 1, characterized in that: The high-precision map information includes height information and slope information of the corresponding location, and step S2 includes: S21: Divide the focus of high-precision map information; S22: determining the overall slope of the interval between adjacent focus points; S23: Based on the overall slope, dividing the vehicle navigation path into an uphill section, a downhill section, or a horizontal section.

4. The method for optimizing energy distribution of an eCVT system based on a high-precision map according to claim 3, characterized in that: The step S21 includes: S211: generating a slope change function of the high-precision map information according to the slope information; S212: traverse the slope change function to obtain the geographical coordinates corresponding to the zero point; S213: Setting the focus point of the high-precision map information according to the geographic coordinates corresponding to the zero point.

5. The method for optimizing energy distribution of an eCVT system based on a high-precision map according to claim 4, characterized in that: The vehicle attributes include the external characteristics of the engine; the hybrid torque distribution scheme refers to the way the generator distributes torque when the engine operates in the external characteristics; The step S32 includes: S321: Obtaining required torque based on slope information; S322: Obtaining optimal engine torque based on engine external characteristics; S323: Subtract the optimal torque of the engine from the required torque to obtain the generator torque, and further obtain the generator power consumption rate.

6. The method for optimizing energy distribution of an eCVT system based on a high-precision map according to claim 5, characterized in that: The step S33 includes: S331: Integrate the generator power consumption rate within the vehicle navigation path to obtain the motor consumption.

7. The method for optimizing energy distribution of an eCVT system based on a high-precision map according to any one of claims 5 and 6, characterized in that: The step S4 comprises: S41: Obtaining the battery SOC initial value and the battery SOC power range; S42: traversing and calculating the battery SOC remaining value after passing through the plurality of the focus points according to the passing data map, and recording the value as the battery SOC natural margin; S43: Compare the battery SOC natural margin with the battery SOC power range to generate a battery SOC adjustment value; S44: In the passing data map, calculate the superimposed position of the battery SOC adjustment amount in the opposite direction of the driving direction, generate an energy distribution plan and execute it.

8. The method for optimizing energy distribution of an eCVT system based on a high-precision map according to claim 7, characterized in that: The step S43 includes: S431: When the battery SOC natural margin is higher than the peak value of the battery SOC power range, the battery SOC adjustment amount is calculated as the power that needs to be consumed in advance, and the value is a negative value; S432: When the battery SOC natural margin is lower than the minimum value of the battery SOC power range, the battery SOC adjustment amount is calculated as the power required to be charged in advance, and the value is a positive value.

9. The method for optimizing energy distribution of an eCVT system based on a high-precision map according to claim 7, characterized in that: The step S44 includes: S441: Searching for each road section in the opposite direction of the driving direction until a road section where the battery SOC adjustment value can be adapted is found; S442: superimposing the battery SOC adjustment amount and recording the superimposed position; S443: Generate an energy distribution plan according to the battery SOC adjustment amount and the superposition position.

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