Target SOC track generation method and device based on road information dynamic optimization

Abstract

The invention relates to the technical field of hybrid electric vehicle energy management, in particular to a target SOC track generation method and device based on road information dynamic optimization, and the method comprises the steps: carrying out segment processing on path data in a current navigation path of a vehicle, constructing a path information matrix through employing a plurality of obtained segment paths, and obtaining a path information matrix; calculating a dynamic basic SOC value of the power battery according to a target input characteristic matrix determined by the path information matrix, performing target correction processing to obtain an initial target SOC value of the power battery, performing adaptive correction processing, smoothing processing, compensation processing and dynamic limiting processing in sequence to generate a processed target SOC value, and outputting the processed target SOC value. And generating a target SOC track of the power battery by using the processed target SOC value. Therefore, the problem that in the related technology, dynamic adjustment near a fixed target is limited, so that target SOC control lacks global adaptability, and energy consumption optimization requirements under complex road conditions cannot be fully matched is solved.

Description

Technical Field

[0001] This application relates to the field of energy management technology for hybrid electric vehicles, and in particular to a method and apparatus for generating a target SOC (State of Charge) trajectory based on dynamic optimization of road information. Background Technology

[0002] With the increasing number of plug-in hybrid and range-extended hybrid vehicles on the road, their "can be driven by both gasoline and electricity" characteristics effectively balance energy conservation and long driving range requirements. Because they are equipped with both an engine and a battery as dual energy sources, and by using control strategies to complement the characteristics of both, cost reduction and energy saving goals can be achieved. Dynamic control of the battery's state of charge (SOC) has become a focus of research for automakers, needing to adapt to diverse user needs for driving comfort and energy efficiency.

[0003] Currently, the mainstream SOC control methods in the market include pure electric, intelligent, and forced modes, which dynamically adjust the power consumption based on a near-fixed target. In addition, although the recently popular AI energy management mode can learn and plan small-scale targets, it is still limited to dynamic adjustments near fixed targets. This results in a lack of global adaptability in target SOC control, which cannot fully match the energy consumption optimization needs under complex road conditions. This reduces the overall energy efficiency and adaptability of the hybrid system, and urgently needs to be addressed. Summary of the Invention

[0004] This application provides a method and apparatus for generating target SOC trajectory based on dynamic optimization of road information, in order to solve the problem in related technologies that are limited to dynamic adjustment near a fixed target, resulting in a lack of global adaptability of target SOC control, which cannot fully match the energy consumption optimization requirements under complex road conditions, and reduces the overall energy efficiency and adaptability of hybrid systems.

[0005] The first aspect of this application provides a method for generating a target SOC trajectory based on dynamic optimization of road information, comprising the following steps: acquiring target path data in the current navigation path of a vehicle, and segmenting the target path data to obtain multiple segmented paths; constructing a target path information matrix using the multiple segmented paths, and determining a target input feature matrix based on the target path information matrix, so as to calculate the dynamic basic SOC value of the vehicle's power battery based on the target input feature matrix, and performing target correction processing on the dynamic basic SOC value to obtain an initial target SOC value of the power battery; sequentially performing adaptive correction processing, smoothing processing, compensation processing, and dynamic constraint processing on the initial target SOC value to generate a processed target SOC value of the power battery, and using the processed target SOC value to generate the target SOC trajectory of the power battery.

[0006] Optionally, in one embodiment of this application, the step of constructing a target path information matrix using the multiple segmented paths includes: generating a target set matrix using the multiple segmented paths; determining a target attribute matrix based on the target type, target distance, and target environment information of each segmented path; and constructing the target path information matrix based on the target set matrix and the target attribute matrix.

[0007] Optionally, in one embodiment of this application, the step of sequentially performing adaptive correction processing, smoothing processing, compensation processing, and dynamic limiting processing on the initial target SOC value to generate the processed target SOC value of the power battery includes: based on the target attribute information of the multiple segmented paths, performing the adaptive correction processing on the initial target SOC value using an adaptive correction function to obtain a corrected target SOC value; performing the smoothing processing on the corrected target SOC value using a smoothing function to obtain a smoothed target SOC value; performing the compensation processing on the smoothed target SOC value using a self-learning compensation function to obtain a compensated target SOC value; and performing the dynamic limiting processing on the compensated target SOC value using a dynamic limiting function to obtain the processed target SOC value of the power battery.

[0008] Optionally, in one embodiment of this application, the adaptive correction process of the initial target SOC value based on the target attribute information of the multiple segmented paths using an adaptive correction function includes: based on the target attribute information of the multiple segmented paths, before the vehicle travels to the target congested road condition or the target high-speed condition, performing the adaptive correction process of the initial target SOC value using the adaptive correction function. The correction ratio for the adaptive correction process is designed as follows:

[0009]

[0010]

[0011] in, It is a congestion coefficient designed based on congestion intensity, where It is the sensitivity to congestion intensity. These are correction factors set for high-speed operating conditions. The current target SOC, This refers to the length of the expressway section.

[0012] Optionally, in one embodiment of this application, the target set matrix is ​​represented as:

[0013] in, Represents a collection of data matrices. This represents the first data segment, totaling... part.

[0014] The target attribute matrix is ​​represented as follows:

[0015] ∈{Congestion 3, Congestion 2, Congestion 1, Urban Area, Suburbs, Expressway, Highway}

[0016] in, Representing the Segment path, Representative road section The starting position, Represents road segment type, Represents the length of the road segment.

[0017] Optionally, in one embodiment of this application, the final integrated formula for generating the target SOC trajectory is expressed as:

[0018] in, The final target SOC value, The initial target SOC value, For smoothing coefficients, This is the compensation function.

[0019] A second aspect of this application provides a target SOC trajectory generation device based on dynamic optimization of road information, comprising: an acquisition module, configured to acquire target path data in the current navigation path of a vehicle, and segment the target path data to obtain multiple segmented paths; a processing module, configured to construct a target path information matrix using the multiple segmented paths, and determine a target input feature matrix based on the target path information matrix, to calculate the dynamic basic SOC value of the vehicle's power battery based on the target input feature matrix, and to perform target correction processing on the dynamic basic SOC value to obtain an initial target SOC value of the power battery; and a generation module, configured to sequentially perform adaptive correction processing, smoothing processing, compensation processing, and dynamic constraint processing on the initial target SOC value to generate a processed target SOC value of the power battery, and to generate a target SOC trajectory of the power battery using the processed target SOC value.

[0020] Optionally, in one embodiment of this application, the processing module includes: a generation unit, used to generate a target set matrix using the plurality of segmented paths; a determination unit, used to determine a target attribute matrix based on the target type, target distance, and target environment information of each segmented path; and a construction unit, used to construct the target path information matrix based on the target set matrix and the target attribute matrix.

[0021] Optionally, in one embodiment of this application, the generation module includes: a first processing unit, configured to perform adaptive correction processing on the initial target SOC value using an adaptive correction function based on the target attribute information of the plurality of segmented paths, to obtain a corrected target SOC value; a second processing unit, configured to perform smoothing processing on the corrected target SOC value using a smoothing function, to obtain a smoothed target SOC value; a third processing unit, configured to perform compensation processing on the smoothed target SOC value using a self-learning compensation function, to obtain a compensated target SOC value; and a fourth processing unit, configured to perform dynamic constraint processing on the compensated target SOC value using a dynamic constraint function, to obtain the processed target SOC value of the power battery.

[0022] Optionally, in one embodiment of this application, the first processing unit includes: a processing subunit, configured to perform the adaptive correction processing on the initial target SOC value using the adaptive correction function before the vehicle travels to the target congested road condition or the target high-speed condition, based on the target attribute information of the multiple segmented paths; The correction ratio for the adaptive correction process is designed as follows:

[0023]

[0024]

[0025] in, It is a congestion coefficient designed based on congestion intensity, where It is the sensitivity to congestion intensity. These are correction factors set for high-speed operating conditions. The current target SOC, This refers to the length of the expressway section.

[0026] Optionally, in one embodiment of this application, the target set matrix is ​​represented as: R = {R1, R2, R3, R4, ... R} n} Where R represents the collection of data matrices, R1 represents the first data segment, and there are a total of n segments.

[0027] The target attribute matrix is ​​represented as follows: R k =(s k ,s k+1 ,T k ,L k ) T k ∈{Congestion 3, Congestion 2, Congestion 1, Urban Area, Suburbs, Expressway, Highway} L k = s k+1 - s k Among them, R k s represents the k-th path segment. k T represents the starting position of road segment k. k Represents road segment type, L k Represents the length of the road segment.

[0028] Optionally, in one embodiment of this application, the final integrated formula for generating the target SOC trajectory is expressed as:

[0029] in, The final target SOC value, The initial target SOC value, For smoothing coefficients, This is the compensation function.

[0030] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the target SOC trajectory generation method based on dynamic optimization of road information as described in the above embodiments.

[0031] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described target SOC trajectory generation method based on dynamic optimization of road information.

[0032] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described method for generating a target SOC trajectory based on dynamic optimization of road information.

[0033] This application embodiment can segment the target path data in the current navigation path of the vehicle, construct a target path information matrix using multiple segmented paths, thereby determining the target input feature matrix, calculating the dynamic basic SOC value of the power battery, and performing target correction processing on the dynamic basic SOC value. Then, the initial target SOC value obtained after correction is sequentially subjected to adaptive correction processing, smoothing processing, compensation processing, and dynamic constraint processing to generate the processed target SOC value of the power battery. This processed target SOC value is then used to generate the target SOC trajectory of the power battery, effectively improving the overall energy efficiency and adaptability of the hybrid system. Therefore, it solves the problem in related technologies where dynamic adjustment is limited to the vicinity of a fixed target, resulting in a lack of global adaptability in target SOC control and an inability to fully match the energy consumption optimization needs under complex road conditions.

[0034] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0035] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a target SOC trajectory generation method based on dynamic optimization of road information provided in an embodiment of this application; Figure 2 This is a logic diagram of target SOC trajectory generation based on dynamic optimization of road information in a specific embodiment of this application; Figure 3 A schematic diagram of the target SOC trajectory curve calculated for a certain vehicle model according to a specific embodiment of this application; Figure 4 A schematic diagram comparing the simulated SOC curves of two different technologies in a specific embodiment of this application; Figure 5 This is a schematic diagram of the speed curve of a certain vehicle under current operating conditions, according to a specific embodiment of this application. Figure 6 A schematic diagram comparing the engine impact points of two different technologies in a specific embodiment of this application; Figure 7 This is a schematic diagram of a target SOC trajectory generation device based on dynamic optimization of road information according to an embodiment of this application; Figure 8 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0036] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0037] The following describes a method and apparatus for generating a target SOC trajectory based on dynamic optimization of road information, according to embodiments of this application, with reference to the accompanying drawings. Addressing the issue mentioned in the background section that related technologies are limited to dynamic adjustments near a fixed target, resulting in a lack of global adaptability in target SOC control and an inability to fully match energy consumption optimization requirements under complex road conditions, thus reducing the overall energy efficiency and adaptability of hybrid systems, this application provides a method for generating a target SOC trajectory based on dynamic optimization of road information. In this method, target path data in the current navigation path of the vehicle can be segmented, and a target path information matrix can be constructed using the obtained multiple segmented paths to determine the target input feature matrix. This allows for the calculation of the dynamic basic SOC value of the power battery, followed by target correction processing on the dynamic basic SOC value. Then, the initial target SOC value obtained after correction is sequentially subjected to adaptive correction processing, smoothing processing, compensation processing, and dynamic constraint processing to generate the processed target SOC value of the power battery. The processed target SOC value is then used to generate the target SOC trajectory of the power battery, effectively improving the overall energy efficiency and adaptability of the hybrid system. This solves the problem in related technologies that are limited to dynamic adjustment near a fixed target, resulting in a lack of global adaptability of target SOC control, an inability to fully match the energy consumption optimization needs under complex road conditions, and a reduction in the overall energy efficiency and adaptability of the hybrid system.

[0038] Specifically, Figure 1 This is a flowchart illustrating a target SOC trajectory generation method based on dynamic optimization of road information, provided in an embodiment of this application.

[0039] like Figure 1 As shown, the target SOC trajectory generation method based on dynamic optimization of road information includes the following steps: In step S101, target path data in the vehicle's current navigation path is obtained, and the target path data is segmented to obtain multiple segmented paths.

[0040] In this embodiment, the vehicle is a plug-in hybrid or range-extended hybrid electric vehicle; the target path data comes from the full-journey path information output by the vehicle navigation system, including the geographical coordinates of the path, road segment type (such as congested road segment, urban road segment, expressway, highway segment, etc.), road segment length, driving environment parameters (such as weather, temperature, altitude, etc.) and basic data related to energy consumption, which can provide a complete data input basis for subsequent segmented processing and SOC trajectory calculation.

[0041] It is understood that the embodiments of this application can obtain target path data in the vehicle's current navigation path, that is, such as Figure 2 As shown, the current navigation path selected by the user in real time is obtained, and the current navigation path is segmented according to the above classification features to obtain multiple segmented paths. This can transform complex full-journey paths into structured, feature-unified segmented paths, thereby improving the adaptability of SOC trajectory generation.

[0042] In step S102, a target path information matrix is ​​constructed using multiple segmented paths, and a target input feature matrix is ​​determined based on the target path information matrix. Based on the target input feature matrix, the dynamic basic SOC value of the vehicle's power battery is calculated, and the dynamic basic SOC value is subjected to target correction processing to obtain the initial target SOC value of the power battery.

[0043] It is understood that embodiments of this application can utilize multiple segmented paths in the following steps to construct a target path information matrix, wherein, as... Figure 2 As shown, the target path information matrix includes a set matrix and an attribute matrix, and the target input feature matrix is ​​determined based on the target path information matrix, i.e.: X k =[ T k , T k+1 ,Eh(k),Tr(k),…] Among them, X k The input feature matrix is ​​represented by Eh(k); Eh(k) represents the cumulative energy consumption of the k-th path segment, with a theoretical range of [0.1, 1000] and a unit of kWh; Tr(k) represents the environmental factors of the k-th path segment, such as weather, temperature, altitude, etc.

[0044] Next, as Figure 2 As shown, this embodiment of the application can calculate the dynamic baseline SOC value of the vehicle's power battery. First, the dynamic baseline target SOC is determined, namely:

[0045] in, The SOC represents the basic target, with a theoretical range of [0.1, 0.8]. This range needs to be determined comprehensively based on factors such as the vehicle's power battery capacity and vehicle type. A multi-parameter hybrid computational model for calculating basic SOC values.

[0046] Secondly, the embodiments of this application can use correction coefficients and transition functions to correct the dynamic basic SOC value to obtain the initial target SOC value, that is:

[0047] in, This represents the target SOC value function; This represents the correction factor, with a theoretical range of [-0.3, 0.3] (which can be adapted to project requirements). This represents the transition function, with a theoretical range of [0,1].

[0048] Therefore, the embodiments of this application can accurately calculate the dynamic basic SOC value of the power battery and complete the target correction, so that the initial target SOC value is adapted to the road conditions and energy consumption characteristics of each segment path, laying an accurate foundation for subsequent dynamic SOC optimization.

[0049] In one embodiment of this application, constructing a target path information matrix using multiple segmented paths includes: generating a target set matrix using multiple segmented paths; determining a target attribute matrix based on the target type, target distance, and target environment information of each segmented path; and constructing a target path information matrix based on the target set matrix and the target attribute matrix.

[0050] As one possible implementation, embodiments of this application can utilize multiple segmented paths to generate a target set matrix, that is, the target set matrix is ​​represented as:

[0051] in, Represents a collection of data matrices. This represents the first data segment, totaling... Segments. Theoretically, the more segments the better, but this will consume more memory and processor computing power. In this application embodiment, the controller hardware performance can be comprehensively considered, and considering the accuracy of data processing and analysis, n≥100 can be set.

[0052] Next, the target attribute matrix can be determined based on the type of each segment path (e.g., congested road segment, urban road segment, expressway, highway segment, etc.), distance, and environmental information (e.g., weather, temperature, altitude, etc.). The target attribute matrix is ​​represented as follows:

[0053] ∈{Congestion 3, Congestion 2, Congestion 1, Urban Area, Suburbs, Expressway, Highway}

[0054] in, Representing the Segment path, Representative road section The starting position, Represents road segment type, Represents the length of the road segment.

[0055] Therefore, the embodiments of this application can associate and fuse the target set matrix with the target attribute matrix of each segment path to form a complete target path information matrix, providing standardized and quantifiable core data support for the subsequent determination of the target input feature matrix and the accurate calculation of the dynamic basic SOC value.

[0056] In step S103, the initial target SOC value is sequentially subjected to adaptive correction processing, smoothing processing, compensation processing and dynamic limitation processing to generate the processed target SOC value of the power battery, so as to generate the target SOC trajectory of the power battery using the processed target SOC value.

[0057] It is understood that the embodiments of this application can sequentially perform adaptive correction processing, smoothing processing, compensation processing, and dynamic limiting processing on the initial target SOC value in the following steps to generate a processed target SOC value for the power battery, thereby eliminating deviations in the initial target SOC value, avoiding SOC jumps, compensating for the influence of operating conditions and the environment, and controlling the power capacity within a safe range, such as... Figure 2 As shown, the target SOC trajectory generated in this embodiment of the application, namely the optimal SOC trajectory, can more accurately adapt to the road conditions throughout the entire journey, taking into account energy consumption optimization, battery life and driving comfort.

[0058] In one embodiment of this application, the initial target SOC value is sequentially subjected to adaptive correction processing, smoothing processing, compensation processing, and dynamic constraint processing to generate a processed target SOC value for the power battery. This includes: based on target attribute information from multiple segmented paths, using an adaptive correction function to adaptively correct the initial target SOC value to obtain a corrected target SOC value; using a smoothing function to smooth the corrected target SOC value to obtain a smoothed target SOC value; using a self-learning compensation function to compensate the smoothed target SOC value to obtain a compensated target SOC value; and using a dynamic constraint function to dynamically constrain the compensated target SOC value to obtain a processed target SOC value for the power battery.

[0059] In actual implementation, such as Figure 2As shown, embodiments of this application can adaptively correct the initial target SOC value based on the target attribute information of multiple segmented paths in the following steps using an adaptive correction function. For example, embodiments of this application can adaptively correct the initial target SOC value based on the target attribute information of multiple segmented paths before the vehicle reaches congested road conditions or highway conditions using an adaptive correction function. The correction ratio of the adaptive correction process is designed as follows:

[0060]

[0061]

[0062] in, It is a congestion coefficient designed based on congestion intensity, where It is the sensitivity to congestion intensity. These are correction factors set for high-speed operating conditions. The current target SOC, This refers to the length of the expressway section.

[0063] Therefore, the embodiments of this application can make corrections before congested road conditions, generate electricity in advance in urban areas, suburbs or expressways to improve SOC, and use electricity to drive in congested road conditions to avoid frequent engine start-stop in congested road conditions, thus optimizing comfort and fuel consumption; and make corrections before high-speed conditions to avoid a continuous decrease in power supply or the engine being unable to work in the optimal operating range due to continuous high power generation caused by power demand in high-speed conditions.

[0064] Next, in this embodiment, the obtained modified target SOC value can be smoothed using a smoothing function. That is, to avoid large fluctuations or jumps in the modified target SOC value that would affect comfort and economy, a transition function and a smoothing function can be designed for smoothing, as follows: Design of nonlinear transition functions:

[0065] Where ε is the slope factor, which defaults to 5 and increases to 8 when congestion occurs.

[0066] Smooth function design:

[0067] in:

[0068] in, Represents the smoothing coefficient. It is a piecewise polynomial. The term represents the fitting constant. To avoid distortion caused by excessive smoothing, the following boundary conditions need to be defined:

[0069] in, Represents the endpoint of the path.

[0070] Secondly, the embodiments of this application can use a self-learning compensation function to compensate for the smoothed target SOC value based on the obtained smoothed target SOC value. For example, due to factors such as the user's driving habits, vehicle load and power consumption, and SOC self-correction during actual driving, the actual SOC value may deviate from the target value to a certain extent, and a self-learning compensation function needs to be designed for correction.

[0071] in, This represents the compensation function, with a suggested range of [-0.05, 0.05]. , All of these represent compensation coefficients, with a theoretical range of [0,1]. This represents the short-term error compensation function within the road segment. This represents a long-term learning compensation function based on user habit data statistics.

[0072] in,

[0073]

[0074] in, A multi-parameter hybrid computational model representing long-term learning compensation.

[0075] Furthermore, in this embodiment, the compensated target SOC value can be dynamically limited using a dynamic limiting function. Specifically, based on the optimal operating range and safe SOC range of the power battery, a dynamic limiting function is designed to restrict the target SOC from exceeding its reasonable operating range, i.e.:

[0076]

[0077] in, The representative limit factor is generally determined based on a comprehensive assessment of battery capacity, vehicle model, and the status of various systems, and can also be determined through road testing; This is the distance already traveled, in km; Total distance traveled, in km; The coefficient is suggested to be 0.1, and the theoretical range is [0,1].

[0078] Thus, through progressive processing based on segmented path attribute information, including adaptive correction, smoothing, self-learning compensation, and dynamic constraints, the processed target SOC value of the power battery can be obtained. This can accurately match road condition characteristics, eliminate SOC jumps, compensate for deviations caused by complex factors, and safeguard the battery safety boundary, providing reliable support for generating the optimal SOC trajectory that is suitable for the entire journey.

[0079] In one embodiment of this application, the final integrated formula for generating the target SOC trajectory is expressed as:

[0080] in, The final target SOC value, The initial target SOC value, For smoothing coefficients, This is the compensation function.

[0081] For example, combined Figure 4 and Figure 5 As shown, compared with the prior art, the embodiments of this application have significant performance improvements in fuel consumption, engine start-stop frequency, and engine operating range. Table 1 is a comparison table of engine start-stop and fuel consumption data, as detailed in Table 1 below: Table 1

[0082] In addition, such as Figure 6 The diagram shows a comparison of engine landing points in vehicles according to embodiments of this application. The green dot represents the engine landing point under the dynamic target SOC scheme of this application, while the red dot represents the engine landing point under other schemes. This demonstrates that embodiments of this application can effectively control the engine landing point within the economic range. Therefore, embodiments of this application can achieve dynamic target SOC management based on route requirements, and cover comprehensive changes in conditions such as complex road conditions, varying operating conditions, and different driving habits of different drivers. Simultaneously, this control method can address complaints about high fuel consumption, poor comfort, frequent mode adjustments by customers, and target SOC issues associated with current control methods, meeting users' needs for dynamic, intelligent, and automated battery management.

[0083] The target SOC trajectory generation method based on dynamic optimization of road information proposed in this application can segment the target path data in the current navigation path of the vehicle, construct a target path information matrix using multiple segmented paths, thereby determining the target input feature matrix, calculating the dynamic basic SOC value of the power battery, and performing target correction processing on the dynamic basic SOC value. Then, the initial target SOC value obtained after correction is sequentially subjected to adaptive correction processing, smoothing processing, compensation processing, and dynamic constraint processing to generate the processed target SOC value of the power battery. This processed target SOC value is then used to generate the target SOC trajectory of the power battery, effectively improving the overall energy efficiency and adaptability of the hybrid system. This solves the problem in related technologies where dynamic adjustment is limited to the vicinity of a fixed target, resulting in a lack of global adaptability in target SOC control, failing to fully match the energy consumption optimization requirements under complex road conditions, and reducing the overall energy efficiency and adaptability of the hybrid system.

[0084] Next, referring to the accompanying drawings, a target SOC trajectory generation device based on dynamic optimization of road information proposed in the embodiments of this application is described.

[0085] Figure 7 This is a block diagram of a target SOC trajectory generation device based on dynamic optimization of road information according to an embodiment of this application.

[0086] like Figure 7 As shown, the target SOC trajectory generation device 10 based on dynamic optimization of road information includes: an acquisition module 100, a processing module 200, and a generation module 300.

[0087] Specifically, the acquisition module 100 is used to acquire target path data in the vehicle's current navigation path and to segment the target path data to obtain multiple segmented paths.

[0088] The processing module 200 is used to construct a target path information matrix using multiple segmented paths, and determine a target input feature matrix based on the target path information matrix. Based on the target input feature matrix, the module calculates the dynamic basic SOC value of the vehicle's power battery, and performs target correction processing on the dynamic basic SOC value to obtain the initial target SOC value of the power battery.

[0089] The generation module 300 is used to perform adaptive correction processing, smoothing processing, compensation processing and dynamic limitation processing on the initial target SOC value in sequence to generate the processed target SOC value of the power battery, so as to generate the target SOC trajectory of the power battery using the processed target SOC value.

[0090] Optionally, in one embodiment of this application, the processing module 200 includes: a generation unit, a determination unit, and a construction unit.

[0091] The generating unit is used to generate a target set matrix using multiple segmented paths.

[0092] The determination unit is used to determine the target attribute matrix based on the target type, target distance, and target environment information of each segment path.

[0093] The building unit is used to construct the target path information matrix based on the target set matrix and the target attribute matrix.

[0094] Optionally, in one embodiment of this application, the generation module 300 includes: a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit.

[0095] The first processing unit is used to adaptively correct the initial target SOC value based on the target attribute information of multiple segmented paths using an adaptive correction function, so as to obtain the corrected target SOC value.

[0096] The second processing unit is used to smooth the corrected target SOC value using a smoothing function to obtain a smoothed target SOC value.

[0097] The third processing unit is used to compensate the smoothed target SOC value using a self-learning compensation function to obtain the compensated target SOC value.

[0098] The fourth processing unit is used to dynamically limit the compensated target SOC value using a dynamic limiting function to obtain the processed target SOC value of the power battery.

[0099] Optionally, in one embodiment of this application, the first processing unit includes a processing subunit.

[0100] The processing subunit is used to adaptively correct the initial target SOC value based on the target attribute information of multiple segmented paths, before the vehicle travels to the target congested road condition or the target high-speed condition. The correction ratio for the adaptive correction process is designed as follows:

[0101]

[0102]

[0103] in, It is a congestion coefficient designed based on congestion intensity, where It is the sensitivity to congestion intensity. These are correction factors set for high-speed operating conditions. The current target SOC, This refers to the length of the expressway section.

[0104] Optionally, in one embodiment of this application, the target set matrix is ​​represented as: R = {R1, R2, R3, R4, ... R} n} Where R represents the collection of data matrices, R1 represents the first data segment, and there are a total of n segments.

[0105] The target attribute matrix is ​​represented as follows: R k =(s k ,s k+1 ,T k ,L k ) T k ∈{Congestion 3, Congestion 2, Congestion 1, Urban Area, Suburbs, Expressway, Highway} L k = s k+1 - s k Among them, R k s represents the k-th path segment. k T represents the starting position of road segment k. k Represents road segment type, L k Represents the length of the road segment.

[0106] Optionally, in one embodiment of this application, the final integrated formula for generating the target SOC trajectory is expressed as:

[0107] in, The final target SOC value, The initial target SOC value, For smoothing coefficients, This is the compensation function.

[0108] It should be noted that the foregoing explanation of the target SOC trajectory generation method based on dynamic optimization of road information also applies to the target SOC trajectory generation device based on dynamic optimization of road information in this embodiment, and will not be repeated here.

[0109] The target SOC trajectory generation device based on dynamic optimization of road information proposed in this application can segment the target path data in the current navigation path of the vehicle, construct a target path information matrix using multiple segmented paths, thereby determining the target input feature matrix, calculating the dynamic basic SOC value of the power battery, and performing target correction processing on the dynamic basic SOC value. Then, the initial target SOC value obtained after correction is sequentially subjected to adaptive correction processing, smoothing processing, compensation processing, and dynamic constraint processing to generate the processed target SOC value of the power battery. This processed target SOC value is then used to generate the target SOC trajectory of the power battery, effectively improving the overall energy efficiency and adaptability of the hybrid system. This solves the problem in related technologies where dynamic adjustment is limited to the vicinity of a fixed target, resulting in a lack of global adaptability in target SOC control, failing to fully match the energy consumption optimization requirements under complex road conditions, and reducing the overall energy efficiency and adaptability of the hybrid system.

[0110] Figure 8 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0111] When the processor 802 executes the program, it implements the target SOC trajectory generation method based on dynamic optimization of road information provided in the above embodiments.

[0112] Furthermore, the vehicle also includes: Communication interface 803 is used for communication between memory 801 and processor 802.

[0113] The memory 801 is used to store computer programs that can run on the processor 802.

[0114] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0115] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0116] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0117] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0118] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described target SOC trajectory generation method based on dynamic optimization of road information.

[0119] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described method for generating target SOC trajectories based on dynamic optimization of road information.

[0120] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0121] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0122] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0124] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0125] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0126] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0127] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for generating a target SOC trajectory based on dynamic optimization of road information, characterized in that, The method comprises the following steps: obtaining target path data in a current navigation path of a vehicle, and segmenting the target path data to obtain a plurality of segmented paths; constructing a target path information matrix by using the plurality of segmented paths, and determining a target input feature matrix according to the target path information matrix, so as to calculate a dynamic basic state of charge (SOC) value of a power battery of the vehicle based on the target input feature matrix, and to perform target correction processing on the dynamic basic SOC value to obtain an initial target SOC value of the power battery; sequentially performing adaptive correction processing, smoothing processing, compensation processing and dynamic limit processing on the initial target SOC value to generate a processed target SOC value of the power battery, and generating a target SOC trajectory of the power battery by using the processed target SOC value.

2. The method of claim 1, wherein, The construction of the target path information matrix by using the plurality of segmented paths comprises: generating a target set matrix by using the plurality of segmented paths; determining a target attribute matrix according to a target type, a target distance and target environment information of each segmented path; constructing the target path information matrix based on the target set matrix and the target attribute matrix.

3. The method of claim 1, wherein, The generation of the processed target SOC value of the power battery by sequentially performing adaptive correction processing, smoothing processing, compensation processing and dynamic limit processing on the initial target SOC value comprises: performing the adaptive correction processing on the initial target SOC value by using an adaptive correction function based on target attribute information of the plurality of segmented paths to obtain a corrected target SOC value; performing the smoothing processing on the corrected target SOC value by using a smoothing function to obtain a smoothed target SOC value; performing the compensation processing on the smoothed target SOC value by using a self-learning compensation function to obtain a compensated target SOC value; performing the dynamic limit processing on the compensated target SOC value by using a dynamic limit function to obtain the processed target SOC value of the power battery.

4. The method of claim 3, wherein, The performance of the adaptive correction processing on the initial target SOC value by using an adaptive correction function based on target attribute information of the plurality of segmented paths comprises: performing the adaptive correction processing on the initial target SOC value by using the adaptive correction function before the vehicle travels to a target congested road condition or a target high-speed working condition based on the target attribute information of the plurality of segmented paths; wherein a correction ratio of the adaptive correction processing is designed as: wherein, is a congestion coefficient designed based on congestion intensity, wherein is a congestion intensity sensitivity, is a correction coefficient set for high-speed working conditions, is a current target SOC, is a length of the highway section part.

5. The method of claim 2, wherein, the target set matrix is represented as: wherein, representing a data matrix collection, representing a first segment of data segment, in total segments; the target attribute matrix is represented as: e {congestion 3, congestion 2, congestion 1, city, suburb, fast, highway} wherein, represents a first segment path, represents a road segment start position, represents a road segment type, represents a road segment length.

6. The method of claim 1, wherein, a final integration formula of the target SOC trajectory generation is represented as: wherein, is the final target SOC value, is the initial target SOC value, is a smoothing coefficient, is a compensation function.

7. An apparatus for generating a target SOC trajectory based on dynamic optimization of road information, characterized by, comprises: an obtaining module, configured to obtain target path data in a current navigation path of a vehicle, and segment the target path data to obtain a plurality of segmented paths; The processing module is configured to construct a target path information matrix by using the plurality of segmented paths, and determine a target input feature matrix according to the target path information matrix, calculate a dynamic basic state of charge (SOC) value of the power battery of the vehicle based on the target input feature matrix, and perform target correction processing on the dynamic basic SOC value to obtain an initial target SOC value of the power battery. The generating module is configured to sequentially perform adaptive correction processing, smoothing processing, compensation processing and dynamic limitation processing on the initial target SOC value, generate a processed target SOC value of the power battery, and generate a target SOC trajectory of the power battery by using the processed target SOC value.

8. A vehicle characterized by comprising: Comprise: A memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to implement the target SOC trajectory generation method based on road information dynamic optimization according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the target SOC trajectory generation method based on road information dynamic optimization according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the target SOC trajectory generation method based on road information dynamic optimization according to any one of claims 1-6.

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