Vehicle battery state of charge distribution method and device, medium and vehicle

Abstract

The invention discloses a vehicle battery state-of-charge distribution method and device, a medium and a vehicle, and the scheme comprises the steps: obtaining vehicle path information, and carrying out the segmentation processing based on the energy consumption driving attribute corresponding to the vehicle, and obtaining a plurality of path segments; determining whether to perform segmented state of charge allocation; obtaining a corresponding correction factor based on the length ratio of the path segment corresponding to each energy consumption driving attribute; and obtaining a sub-target charge state corresponding to the path segment obtained based on the total target charge state corresponding to the preset vehicle path information, and correcting the sub-target charge state according to the correction factor. The problem that whole-course SOC planning is inaccurate due to storage path distance limitation in a traditional scheme can be solved. The segmentation processing allows the system to more flexibly adapt to the energy consumption characteristics of an ultra-long path under limited storage capacity, so that the rationality of state-of-charge distribution and the matching degree with the intention of a driver are improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle control, and specifically to a method, device, medium, and vehicle for distributing the state of charge of a vehicle battery. Background Art

[0002] For vehicles, current Predictive Energy Management (PEM) mainly reconstructs predicted road attributes based on navigation path information.

[0003] However, due to computing power limitations and optimization requirements, traditional solutions often only store navigation information for long-distance routes. The backend algorithm performs full-distance SOC planning based on the driver's set destination's State of Charge (SOC). However, since only regular-distance navigation information can be stored, the incomplete road condition information used results in inaccurate SOC planning, making it difficult to meet the driver's intent. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides a vehicle battery state of charge distribution method, device, medium and vehicle that overcome the above problems or at least partially solve the above problems. The technical solutions are as follows:

[0005] A method for allocating state of charge of a vehicle battery, comprising:

[0006] Obtain vehicle path information and segment it based on the energy consumption and driving properties of the vehicle to obtain multiple path segments;

[0007] determining whether to perform segmented state of charge allocation based on the vehicle route information, the route segments, and the stored route distance;

[0008] If executed, the corresponding correction factor is obtained based on the length ratio of the path segments corresponding to each energy consumption driving attribute;

[0009] A sub-target state of charge corresponding to the path segment obtained based on a total target state of charge corresponding to the preset vehicle path information is obtained, and the sub-target state of charge is corrected according to the correction factor.

[0010] By segmenting vehicle route information based on energy-driven attributes and dynamically adjusting sub-target SOCs based on the length ratio of each segment, this approach addresses the inaccurate global SOC planning issues inherent in traditional approaches due to limited storage distances. Segmented processing allows the system to more flexibly adapt to the energy consumption characteristics of extremely long routes within limited storage capacity, thereby improving the rationality of SOC allocation and its alignment with driver intent.

[0011] Optionally, segmentation processing is performed based on the energy consumption driving attributes corresponding to the vehicle to obtain multiple path segments, specifically including:

[0012] Obtaining a predicted vehicle speed corresponding to the vehicle based on the energy consumption driving attribute; and determining a preset road unit length;

[0013] Based on a consistency rule, segmenting the vehicle path information to obtain a plurality of path segments;

[0014] A single path segment includes at least one road unit length, the predicted vehicle speeds within the single path segment belong to the same preset vehicle speed interval, and the predicted vehicle speeds of adjacent path segments belong to different preset vehicle speed intervals.

[0015] By segmenting the route based on predicted speed and road unit length, combined with consistency rules, we ensure that speed ranges within each segment are consistent. This segmentation method more accurately reflects the impact of different speed ranges on energy consumption, improves the physical consistency of the segmentation results, and provides a more reliable basis for energy consumption prediction for subsequent state of charge corrections.

[0016] Optionally, after segmenting the vehicle path information to obtain a plurality of path segments, the method further includes:

[0017] Determining, among the plurality of path segments, a first designated path segment whose road length does not conform to a preset length interval;

[0018] For the first designated path segment, among the static attributes of the energy consumption driving attribute, attributes of at least some dimensions are matched and selected as first designated attributes, and segment division and correction of the first designated path segment are performed based on the first designated attributes;

[0019] Among the dynamic attributes of the energy consumption driving attribute, select attributes of at least some dimensions as second designated attributes, and perform segment division correction on the second designated path segment corresponding to the impact range based on the change state and impact range of the second designated attribute;

[0020] The road segment division and correction includes: segmenting the designated path segment, and / or merging the designated path segment with other path segments.

[0021] Static and dynamic attributes are used to modify (for example, split or merge) route segments that do not meet the preset length, optimizing the rationality of segmentation. Static attribute correction can avoid energy consumption prediction errors caused by segments that are too long or too short, while dynamic attribute correction can adapt to changing road conditions (such as congestion and weather) in real time, further improving the segmentation's adaptability to actual driving scenarios.

[0022] Optionally, determining whether to perform segmented state of charge allocation based on the vehicle path information, the path segments, and the stored path distance specifically includes:

[0023] Based on the preset road unit length, the road unit length offset corresponding to the vehicle's current position and the vehicle's end position is determined, and the relative distance is obtained;

[0024] Obtaining a remaining distance based on the relative distance and a stored path distance corresponding to the vehicle;

[0025] If the relative distance is less than the stored path distance, and the number of segments of the path is less than the preset segment upper limit, the segmented state of charge allocation is not performed;

[0026] If the relative distance is greater than the stored path distance; and / or the number of segments of the path is greater than the preset segment upper limit value, and the remaining distance is greater than the preset distance value corresponding to the storage capacity upper limit; then segmented charge state allocation is performed.

[0027] By dynamically determining the remaining distance, storage path distance, and number of segments, it decides whether to execute segmented SOC allocation. This mechanism not only avoids invalid calculations caused by storage limitations (for example, directly using the global plan when the remaining distance is short), but also triggers segment corrections when necessary, rationally allocating computing resources and improving system efficiency.

[0028] Optionally, based on the length ratio of the path segments corresponding to each energy consumption driving attribute, a corresponding correction factor is obtained, specifically including:

[0029] Obtain the length ratio of the path segments corresponding to each predicted vehicle speed;

[0030] Determining a low-speed length ratio of a path segment below a preset vehicle speed;

[0031] Based on the low-speed length ratio, a corresponding correction factor is obtained; wherein, the higher the low-speed length ratio, the higher the correction amount of the correction factor on the sub-target state of charge.

[0032] A correction factor is generated based on the proportion of low-speed sections to specifically enhance State of Charge compensation in low-speed scenarios. Low-speed sections typically consume more energy, and the correction factor is positively correlated with the proportion of low speeds. This allows for dynamic adjustment of State of Charge allocation weights to avoid underestimation of battery life due to a high proportion of low-speed sections.

[0033] Optionally, based on the low-speed length ratio, a corresponding correction factor is obtained, specifically including:

[0034] Obtaining a corresponding first correction factor according to a preset attenuation coefficient and the low-speed length ratio; wherein the first correction factor does not exceed a preset lower limit value;

[0035] A low-speed ratio parameter is obtained based on a preset low-speed segment ratio weight coefficient and the low-speed length ratio; a distance compensation parameter is obtained based on a preset absolute distance compensation coefficient and an absolute distance normalization parameter of the path segment below the preset vehicle speed;

[0036] Obtaining a second correction factor according to the low-speed ratio parameter and the distance compensation parameter;

[0037] The first correction factor is used to correct the target state of charge, and the second correction factor is used to compensate for the path segment below the preset vehicle speed.

[0038] Through the dual correction mechanism of the first correction factor based on the attenuation coefficient and the second correction factor based on the low-speed proportion and absolute distance compensation, the overall proportional influence of the low-speed section is taken into account, and the normalized compensation of the absolute distance is introduced, avoiding the limitations of a single correction strategy and making the state of charge correction more precise and adaptable.

[0039] Optionally, the method further includes:

[0040] obtaining a driving mode factor corresponding to the driving mode according to the energy consumption driving attribute, and adjusting the first correction factor according to the driving mode factor;

[0041] According to the energy consumption driving attribute, an environmental impact factor corresponding to the external environment is obtained, and the second correction factor is adjusted according to the environmental impact factor.

[0042] By dynamically adjusting the correction factor based on driving mode factors and environmental influencing factors, the state of charge distribution strategy can adapt to different driving styles (for example, sports mode or energy-saving mode) and external environments (for example, temperature), improving the system's personalized adaptability to complex scenarios and enhancing the robustness of the planning results.

[0043] A vehicle battery state of charge distribution device, comprising:

[0044] The path segmentation module obtains the vehicle path information and performs segmentation processing based on the energy consumption and driving properties corresponding to the vehicle to obtain multiple path segments;

[0045] a state of charge allocation module, which determines whether to perform segmented state of charge allocation based on the vehicle route information, the route segments, and the stored route distance;

[0046] A correction factor generation module, if the determination result of the state of charge allocation module is execution, obtains a corresponding correction factor based on the length ratio of the path segments corresponding to each energy consumption driving attribute;

[0047] The state of charge correction module obtains a sub-target state of charge corresponding to the path segment based on the total target state of charge corresponding to the preset vehicle path information, and corrects the sub-target state of charge according to the correction factor.

[0048] A vehicle comprising:

[0049] at least one processor; and,

[0050] a memory communicatively connected to the at least one processor; wherein,

[0051] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: perform any of the vehicle battery charge state distribution methods described above.

[0052] A computer-readable storage medium stores computer-executable instructions, wherein the computer-executable instructions are configured to implement any of the above-mentioned vehicle battery state of charge distribution methods.

[0053] By leveraging the aforementioned technical solutions, the disclosed method, device, medium, and vehicle for vehicle battery SOC allocation, when applied to vehicles, addresses the inaccurate global SOC planning caused by the limited storage path distances of traditional solutions by segmenting vehicle route information according to energy-driven attributes and dynamically correcting sub-target SOCs based on the length ratios of each segment. This segmented processing allows the system to more flexibly adapt to the energy consumption characteristics of extremely long routes within limited storage capacity, thereby improving the rationality of SOC allocation and its alignment with driver intent.

[0054] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific implementation methods of the present disclosure are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present disclosure. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0056] Figure 1A schematic diagram showing a flow chart of a method for allocating state of charge of a vehicle battery in an embodiment of the present disclosure is shown;

[0057] Figure 2 A schematic diagram showing various distances in the segmented state of charge allocation in an embodiment of the present disclosure is shown;

[0058] Figure 3 A schematic structural diagram of a vehicle battery state of charge distribution device according to an embodiment of the present disclosure is shown;

[0059] Figure 4 A schematic structural diagram of a vehicle in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0060] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] Generally speaking, a vehicle is equipped with an Electronic Horizon Provider (EHP) or similar module. The EHP is a module used in smart cars to proactively perceive the road environment. It acts like a smart telescope for the vehicle and generally has the following functions:

[0062] 1. Road attribute prediction, providing static and dynamic information such as slope, curvature, speed limit, lane type, and traffic signs of the road ahead of the vehicle.

[0063] 2. Dynamic road condition perception, integrating real-time traffic data (e.g., congestion, accidents, weather) to predict future vehicle speeds and driving conditions.

[0064] 3. Energy consumption pre-planning supports providing data to energy management systems (including hybrid systems and pure electric systems) to optimize battery SOC distribution and engine start-stop strategies.

[0065] 4. Autonomous driving decision assistance: predict curves and slopes in advance, and assist the autonomous driving system in adjusting vehicle speed, steering and braking.

[0066] Before setting off, users can set a target SOC. Target SOC refers to the desired remaining state of charge upon arrival at the destination. If the user does not set a target SOC, the electronic horizon provider can use the vehicle's current location to predict the driving characteristics of the destination (e.g., vehicle speed, road grade, etc.) to calculate the target SOC.

[0067] However, due to limited controller computing power, when faced with long-distance vehicle path information, the predicted attribute reconstruction matrix is often only able to reconstruct the energy consumption and driving attributes of the first half. For example, it can usually only reconstruct the energy consumption and driving attributes within 500 kilometers, resulting in inaccurate calculation of the target SOC.

[0068] To this end, the present application provides a method for allocating the state of charge of a vehicle battery, such as Figure 1 As shown, the method includes:

[0069] Step S101: Acquire vehicle path information, and perform segmentation processing based on the energy consumption driving attributes corresponding to the vehicle to obtain multiple path segments.

[0070] Vehicle path information can be obtained through the vehicle's starting point and vehicle's destination through the corresponding path generation algorithm. The vehicle's starting point defaults to the vehicle's current location, which can be obtained through the vehicle's own positioning system, while the vehicle's destination requires manual input by the user.

[0071] The path generation algorithm generates corresponding vehicle path information based on the vehicle's starting point, destination, and the road attributes along the path. If multiple vehicle paths are available, the user can select one. For example, the path generation algorithm can use the Dijkstra algorithm to traverse all possible path nodes and calculate the shortest path from the starting point to the destination. In complex road networks, the heuristic search algorithm A* can also be introduced to optimize the search direction by introducing an estimated cost function (for example, straight-line distance), significantly improving efficiency while ensuring path optimality. Real-time traffic data fusion can also be performed, accessing traffic management data, sensor networks, and real-time user feedback to obtain information on congestion, accidents, construction, and other issues. This data is combined with historical traffic patterns, and machine learning models are used to predict future road conditions, dynamically adjusting path weights (for example, time and energy consumption). Alternatively, through the interface API of an external navigation application, the corresponding vehicle path information can be generated and fed back through the navigation application.

[0072] Energy consumption driven attributes refer to attributes that may affect the energy consumption of the vehicle during driving. For electric vehicles, energy consumption mainly refers to the amount of electricity, which can be measured by the battery's state of charge. Among them, the state of charge (SOC) is an important indicator for measuring the remaining capacity of the battery. It represents the ratio of the current remaining capacity of the battery to its fully charged state capacity, usually expressed as a percentage, with a value range of 0 to 1. When SOC = 0, it means that the battery is fully discharged; when SOC = 1, it means that the battery is fully charged.

[0073] Energy consumption driving attributes can be divided into two aspects: vehicle attributes and external attributes. Vehicle attributes can include speed, load, driving mode, etc. External attributes can include road (such as slope, width, straight and curved road ratio, etc.), weather, congestion, etc.

[0074] Energy consumption driving attributes can be obtained from the vehicle itself, for example, driving mode, load, and other data can be directly obtained. They can also be provided through the cloud through big data analysis based on historical data. For example, big data analysis of historical data can be performed to integrate vehicle speed information from current vehicle path information. Related attribute data can also be obtained by connecting with other system interfaces. For example, path slope, lane, and other data can be obtained through road planning and geographic mapping systems, weather data can be obtained through weather system, and congestion data can be obtained through traffic system or user feedback.

[0075] After obtaining the energy-driven attributes, segmentation and storage can be performed based on the rule that the attribute information and attribute information values are consistent. Based on actual needs, some or all of the energy-driven attributes can be selected to divide the path into segments, so that the energy-driven attributes in each path segment are as consistent as possible.

[0076] When the selected energy consumption driving attribute is a single attribute, the single attribute is divided into intervals so that the single attribute in each path segment is in the same interval, so that the path segments can be kept consistent.

[0077] When multiple energy-consuming driving attributes are selected, a corresponding weight can be set for each energy-consuming driving attribute, and the vehicle path information can be divided into multiple segments according to the distance. According to all the energy-consuming driving attributes in each segment, the corresponding score is calculated according to the corresponding rules, and then the weighted sum is performed to obtain the total score of each segment. Then, the segments are combined according to the total score to obtain the final path segment.

[0078] Among them, the rule calculation corresponding to each energy consumption driving attribute needs to consider the relationship between itself and energy consumption, that is, the high or low value of its own attribute and the high or low impact it has on energy consumption, so as to set the corresponding calculation rules for the calculation of the score.

[0079] Step S102 : determining whether to perform segmented state of charge allocation based on the vehicle route information, the route segments, and the stored route distance.

[0080] The stored path distance can be determined by the vehicle's electronic horizon provider. Different electronic horizon providers correspond to different stored path distances. The stored path distance includes the storable path length and the pre-set number of segments. The storable path length refers to the total length of the path that can be stored, for example, 500 kilometers. The pre-set number of segments refers to the number of segments into which the total path length is divided for storage.

[0081] When the total length and number of segments of the path in the stored path distance can meet the requirements of vehicle path information and path segmentation, the traditional solution can be used to calculate the distribution of vehicle battery charge status.

[0082] However, when the total length of the path or the number of segments in the stored path distance cannot meet the requirements of vehicle path information or path segmentation, a segmented state of charge allocation solution can be implemented.

[0083] The segmented SOC allocation scheme refers to setting the sub-target SOC for each route segment based on the total target SOC pre-set by the user, for the obtained route segments, so as to ensure that the vehicle's remaining SOC after the user reaches the destination can meet the total target SOC initially set by the user as much as possible.

[0084] Step S103: If executed, a corresponding correction factor is obtained based on the length ratio of the path segments corresponding to each energy consumption driving attribute.

[0085] When generating the correction factor, you can select some or all of the energy consumption drive attributes based on your needs. The main function of the correction factor is to correct the sub-target SOC of each route segment obtained midway based on the user-set total SOC, so that the remaining SOC of the vehicle at the destination is as close to the total target SOC as possible.

[0086] Since the path segmentation itself is obtained through the energy consumption driving attribute, the energy consumption driving attributes in different path segments are different. Therefore, the approximate status of the energy consumption driving attribute can be obtained according to the length ratio of the path segments, thereby obtaining the corresponding correction factor.

[0087] Step S104: obtaining a sub-target state of charge corresponding to the path segment based on the total target state of charge corresponding to the preset vehicle path information, and correcting the sub-target state of charge according to the correction factor.

[0088] The total target state of charge can be set by the user or obtained by the cloud or vehicle based on the path length in the vehicle path information and the remaining state of charge in historical data.

[0089] The sub-target state of charge of each path segment can be set in stages based on the length of each path segment. The consumption of the corresponding state of charge is determined according to the length of each path segment, and the length and consumption are proportionally positively correlated. The sub-target state of charge of each path segment is obtained by subtracting the consumption of the current path segment from the sub-target state of charge of the previous path segment. For example, the current state of charge of the vehicle is 100%, and the total target state of charge is set to 50%. There are three path segments with corresponding lengths of A, 2A, and 2A. At this time, calculated according to the length ratio, the consumption of these three path segments is 10%, 20%, and 20%, respectively, and the corresponding sub-target states of charge are: 90%, 70%, and 50%, respectively.

[0090] However, the sub-target SOC obtained in this way may not be accurate for long-distance vehicle path information, for example, the final remaining SOC may be only 30%. Therefore, the sub-target SOC corresponding to each path segment is corrected using the correction factor that has been obtained, so that when the vehicle reaches the path segment, it can be controlled according to its corresponding sub-target SOC (for example, for hybrid vehicles, the torque distribution between the engine and the motor can be adjusted, and for hybrid vehicles or pure electric vehicles, the energy recovery strategy can be adjusted, etc.), so that when the vehicle reaches the destination, it meets the total target SOC as much as possible.

[0091] By segmenting vehicle route information based on energy-driven attributes and dynamically adjusting sub-target SOCs based on the length ratio of each segment, this approach addresses the inaccurate global SOC planning issues inherent in traditional approaches due to limited storage distances. Segmented processing allows the system to more flexibly adapt to the energy consumption characteristics of extremely long routes within limited storage capacity, thereby improving the rationality of SOC allocation and its alignment with driver intent.

[0092] In one embodiment, when segmenting the route, the nonlinear relationship between vehicle energy consumption and speed is taken into account, with different speed ranges corresponding to completely different energy consumption patterns. For example, the faster the vehicle speed, the higher the air resistance, which naturally leads to higher vehicle energy consumption. Furthermore, considering the characteristics of the motor efficiency curve and the speed dependence of energy recovery capability, it can be assumed that vehicle speed has a significant impact on energy consumption.

[0093] Based on this, the predicted speed corresponding to the vehicle is obtained according to the energy consumption driving properties; and the pre-set road unit length is determined.

[0094] The predicted vehicle speed can be obtained by using big data in the cloud to obtain historical vehicle speed data for the vehicle's route information, thereby predicting the vehicle speed at each location in the vehicle's route information. For example, for each location in the vehicle's route information, the historical data at the same time as the current moment can be used to average the vehicle speeds to determine the predicted vehicle speed at each location at the current moment.

[0095] The road unit length (offset) can be defined according to the relevant communication protocol, for example, 128 meters. The road unit length refers to the smallest unit used to segment vehicle path information. In other words, the length of each segment is an integer multiple of the road unit length, and a single path segment contains at least one road unit length.

[0096] Based on the consistency rule, the vehicle path information is segmented to obtain multiple path segments. The consistency rule means that within a single path segment, its single energy consumption driving attribute or multiple energy consumption driving attributes can be kept consistent, while the energy consumption driving attributes of adjacent path segments are inconsistent. Consistency and inconsistency mean that the difference between the quantized values of the energy consumption driving attributes is lower than the corresponding threshold. For example, the preset speed interval to which the predicted speed in a single path segment belongs is consistent, while the preset speed interval to which the predicted speed of adjacent path segments belongs is inconsistent. For example, the speed is taken as a speed interval of every 10 km / h, and the average predicted speed corresponding to each unit length of road is taken as its corresponding predicted speed. If the predicted speeds of adjacent unit lengths of road belong to the same speed interval, they are combined into the same path segment.

[0097] At this point, the path segment can be called S brief After adding up all the path segments, the stored path distance corresponding to the vehicle can be obtained. The representation of the stored path distance is as described in Formula 1:

[0098] S storage =S brief1 +S brief2 +S brief3 +……+S briefN Formula 1;

[0099] Where N is the maximum number of storage segments in the predefined path storage matrix, and its value can be set accordingly based on demand and computing power space.

[0100] Furthermore, after setting the path segments, if the segments are only divided according to the predicted vehicle speed, not only will the obtained path segments be inaccurate, but it will also be difficult to dynamically match the occurrence of various situations.

[0101] Therefore, among the multiple path segments, a first designated path segment is identified whose road length does not fit within a preset length range. This preset length range is set based on historical path segmentation results, expert experience, and computing power. Path segments that fit within this preset length range typically have a road length that is reasonably long or short. Path segments that do not fit within this preset length range are likely too long or too short and are therefore referred to as first designated path segments.

[0102] For the first designated path segment, in the static attributes of the energy consumption driving attributes, attributes of at least some dimensions are matched and selected as the first designated attributes, and the first designated path segment is modified by the first designated attributes.

[0103] Static attributes refer to those properties of the vehicle's route information that normally remain unchanged during travel, such as road slope and lane type. While these static attributes may not have as significant an impact on energy consumption as the predicted vehicle speed, they can still influence the state of charge to a certain extent. Therefore, based on requirements, at least some of these static attributes can be selected as first designated attributes, which can be used to correct first designated route segments whose lengths do not meet requirements.

[0104] The method of modifying the segment division includes: segmenting the designated path segment, and / or merging the designated path segment with other path segments.

[0105] The basis for the correction can be to calculate the score of each road unit length by weighted summation and normalization according to the selected first specified attribute. For a first specified path segment that is too short, it is compared with other adjacent path segments (not necessarily the first specified path segment) if the difference between the predicted vehicle speeds between the two is lower than a certain threshold and the difference between the scores is small (obtained by averaging the scores of the road unit lengths in each of them, and a smaller difference indicates a higher similarity between the two in the first specified attribute), then the two can be merged into one path segment. For a first specified path segment that is too long, the score of each road unit length is calculated. For adjacent road unit lengths with a large difference in scores, they can be used as split points to split the road unit lengths on both sides to obtain a new path segment.

[0106] At this point, the static attribute supplementation and correction of the path segment is completed. However, during the actual driving process, there may still be some real-time dynamic influences that make the obtained path segment no longer applicable.

[0107] Therefore, among the dynamic attributes of the energy consumption driving attributes, attributes of at least some dimensions are selected as the second designated attributes, and the second designated path segments corresponding to the affected range are modified based on the change state and affected range of the second designated attributes.

[0108] In contrast to static attributes, dynamic attributes are undetermined and may change during the course of the vehicle's route. For example, these could be weather conditions, traffic accidents, etc. Based on the needs, at least some of these dynamic attributes are selected as the second designated attributes.

[0109] For the second specified attribute, its change state can be set to at least two types: normal state and abnormal state. Different second specified attributes have different definitions of abnormal states. For example, for the weather attribute, its abnormal state may include: strong wind, fog, rain and snow, etc., and for the traffic accident attribute, its abnormal state may include: traffic accident.

[0110] The impact range can be obtained in real time. For example, for a traffic accident, the impact range can be determined based on the length of the currently congested road section. For weather, the impact range can also be determined based on the range of the abnormal weather.

[0111] At this point, the designated path segment within the influence range is referred to as the second designated path segment, and the segment division and correction method is similar to that of the first designated path segment, including: segmenting the designated path segment and / or merging the designated path segment with other path segments. For example, all second designated path segments within the influence range are merged to form a new, separate path segment. Alternatively, the second designated path segment at the edge of the influence range is further segmented according to the influence range to obtain two new path segments.

[0112] Of course, this dynamic segmentation method is usually temporary. When the abnormal state is eliminated, the previous path segmentation method can be restored, so that a more accurate segmentation can be made based on the static correction and in combination with the real-time situation of the road in a dynamic way.

[0113] In one embodiment, whether to perform segmented state of charge allocation may be determined by calculating the corresponding length and number of segments.

[0114] Specifically, if Figure 2 As shown, based on the preset road unit length, the road unit length offset corresponding to the vehicle's current position and the vehicle's destination position is determined, and the relative distance is obtained. The road unit length offset corresponding to the vehicle's destination position is subtracted from the road unit length offset corresponding to the vehicle's current position to obtain the difference offset, which is then multiplied by the road unit length to obtain the relative distance. The formula for calculating the relative distance is shown in Formula 2:

[0115] S Destination =(Posn Offset-Destination -Posn offset-Vehicle )×Offset

[0116] Formula 2;

[0117] Among them, S Destination The distance between the vehicle and the destination is called relative distance. Offset is the preset road unit length, for example, it can be set to 128. Offset-Destination 、Posn Offset-Vehicle These are the road unit length offsets corresponding to the vehicle's current position and the vehicle's final destination. The road unit length offset is the number of road unit lengths between the current position and the vehicle's starting point. The difference between the road unit length offsets between the current and final locations is the road unit length difference, which can be multiplied by the road unit length to determine the relative distance.

[0118] Based on the relative distance and the stored path distance corresponding to the vehicle, the remaining distance is obtained. The difference between the relative distance and the stored path distance is used to obtain the remaining distance. The calculation of the remaining distance is shown in Formula 3:

[0119] S Difference =S Destination -S storage Formula 3;

[0120] Among them, S Difference is the remaining distance, S Destination is the relative distance, S storage The difference between the relative distance and the stored path distance can be used to determine how much distance of the path that the vehicle still needs to move forward is not stored.

[0121] Of course, if a new path is planned or the path deviates and needs to be replanned, the above distances need to be recalculated.

[0122] At this time, whether segmented state of charge allocation needs to be performed is determined based on the obtained distance.

[0123] If the relative distance is less than the stored path distance, and the number of path segments is less than the preset segment upper limit, the segmented state of charge allocation is not performed, as shown in Formula 4 and Formula 5:

[0124] S Destination ≤S Storage Formula 4;

[0125] N<Nmax Formula 5;

[0126] Among them, N is the number of segments of the current path, N max The upper limit of the preset segment.

[0127] If both Formula 4 and Formula 5 are satisfied, it means that the current vehicle storage capacity can meet the vehicle's subsequent driving needs, so there is no need to perform segmented charge state allocation.

[0128] If the relative distance is greater than the stored path distance; and / or the number of path segments is greater than the preset segment upper limit, and the remaining distance is greater than the preset distance value corresponding to the storage capacity upper limit; segmented state of charge allocation is performed, which is shown in Formula 6 and Formula 7:

[0129] S Destination >S storage Formula 6;

[0130] N≥N max &S Difference >C Formula 7;

[0131] Among them, C is the preset distance value, which is a positive value and is obtained according to the upper limit of the storage capacity. When the remaining distance S Difference When the preset distance value is exceeded, it is considered that the remaining distance exceeds the upper limit of the storage capacity and it is difficult to store the entire remaining distance.

[0132] If any of the formulas 6 and 7 are not satisfied, it means that the current vehicle storage capacity is difficult to meet the vehicle's subsequent driving needs, and it is necessary to perform segmented state of charge allocation to reduce the original set total target state of charge SOC targetDriver , divided into sub-target state of charge SOC corresponding to the path segment target1 , SOC target2 ,…,SOC targetn , and is corrected by the correction factor. The sub-target SOC for each path segment can be determined by the consumption of the corresponding SOC based on the length of each path segment as described above. The length and consumption are proportionally positively correlated. The sub-target SOC for each path segment is calculated by subtracting the consumption of the current path segment from the sub-target SOC of the previous path segment.

[0133] Similarly, if a new route is planned or the route deviates and needs to be replanned, it is necessary to re-determine whether to perform segmented state of charge allocation.

[0134] In one embodiment, when obtaining the correction factor, it can be obtained based on the length ratio of each path segment mentioned above.

[0135] Specifically, the length ratio of the path segments corresponding to each predicted vehicle speed is obtained. For example, after the storage path is constructed, the remaining distance S is extracted. Difference At this time, the predicted speed is divided into multiple intervals: sections with predicted speeds below 30km / h, sections with 30km / h≤predicted speed≤60km / h, sections with 60km / h<predicted speed≤90km / h, and sections with 90km / h<predicted speed. The lengths of the path segments corresponding to each interval are accumulated to obtain the following: S lowspd 、S midspdlow 、S midspdhigh 、S highspd , which represent the length of the low-speed section, the length of the medium-low-speed section, the length of the medium-high-speed section, and the length of the high-speed section respectively, and calculate the length ratio P of the path segment corresponding to each interval, and finally get P lowspd 、P nidspdlow 、P midspdhigh 、P high , respectively represent the low-speed length ratio, medium-low-speed length ratio, medium-high-speed length ratio, and high-speed length ratio. The process is shown in Formula 8:

[0136]

[0137] Among them, the way to divide the intervals (including the number of intervals and the upper and lower speed limits corresponding to each interval) can be set based on actual needs.

[0138] In actual driving, low-speed sections often have a greater impact on energy consumption, so obtaining S lowspd The corresponding state of charge consumption ΔSOC can be obtained based on a large model in the cloud, or by statistically analyzing historical data and calculating the average value, or by conducting vehicle experiments.

[0139] Determine the low-speed length ratio of the path segment below the preset speed, for example, only obtain the low-speed length ratio P corresponding to the lowest interval lowspd .

[0140] Based on the low-speed length ratio, a corresponding correction factor is derived. The higher the low-speed length ratio, the greater the correction factor's effect on the sub-target State of Charge. A higher low-speed length ratio indicates a slower overall vehicle speed, which in turn increases the State of Charge consumption. Therefore, a higher correction factor for the sub-target State of Charge allows the battery to retain more charge within each sub-target State of Charge.

[0141] Specifically, a corresponding first correction factor is obtained based on a preset attenuation coefficient and a low-speed length ratio; wherein the first correction factor does not exceed a preset lower limit value.

[0142] For example, by multiplying the SOC attenuation coefficient by the low-speed length ratio, the attenuated ratio of the low-speed length ratio is obtained, which is used as the influencing factor of the SOC. The influencing factor is subtracted from 1 to obtain the first correction factor. Of course, in actual operation, in order to ensure that the influencing factor is not too low, a corresponding lower limit value can be set to ensure that the first correction factor does not fall below the lower limit value. In this case, the first correction factor F1 is set, and its corresponding calculation formula is shown in Formula 9:

[0143] F1=max(α,1-λ*P lowspd ) Formula 9;

[0144] Among them, α∈[0,1] is the preset stability coefficient (also called the lower limit), which is mainly used to prevent F1 from being too small, resulting in excessive correction amplitude and affecting vehicle driving. The default value can be set to 0.5, and λ∈[0,1] is the preset SOC attenuation coefficient, which is used to describe P lowspd The default value of the effect can be set to 0.5.

[0145] A low-speed proportion parameter is obtained based on a preset low-speed segment proportion weight coefficient and a low-speed length ratio; a distance compensation parameter is obtained based on a preset absolute distance compensation coefficient and an absolute distance normalization parameter of a path segment below a preset vehicle speed; and a second correction factor is obtained based on the low-speed proportion parameter and the distance compensation parameter.

[0146] For example, the first part is obtained by multiplying the low-speed length ratio by the corresponding low-speed section power consumption weight coefficient, and the second part is obtained by the absolute distance compensation coefficient and the normalized low-speed section length. The two parts are added together to obtain the second correction factor. The second correction factor F2 is set, and its corresponding calculation formula is shown in Formula 10:

[0147]

[0148] Among them, μ is the low-speed power consumption weight coefficient (also called the low-speed proportional weight coefficient), which is set according to different vehicle models and is used to describe P lowspd The default value can be set to 1.2, which is related to the P lowspd After multiplication, we get the low-speed proportional parameter; γ is the absolute distance compensation coefficient to prevent short-distance low-proportional errors. The default value can be set to 0.3. bade It is a preset reference distance, mainly used for normalization. Its default value can be set to 500km. lowdpd The distance S from the reference base Divide, that is, normalize the length of the low-speed section, then we can It is called the absolute distance normalization parameter, and γ is After multiplication, the distance compensation parameter can be obtained.

[0149] The second correction factor F2 can be obtained by adding the low-speed ratio parameter and the distance compensation parameter.

[0150] Thus, the first correction factor is used to modify the target SOC. F1 is primarily used to adjust for low speeds. The higher the low speed ratio, the lower the user-set SOC retention ratio, leaving more battery capacity. For F1, its effect is on the overall SOC efficiency, primarily used to correct for systemic efficiency losses caused by low-speed sections. The second correction factor is used to compensate for route segments that fall below the preset speed. F2 considers both the ratio and the absolute distance. For example, a congestion ratio of 20% but a total length of 100km would require significant compensation. For F2, its effect is on additional compensation for congested sections, primarily the amount of battery capacity that needs to be reserved for congested sections.

[0151] At this time, F1 is multiplied by the sub-target state of charge before correction to correct the set sub-target state of charge from a global perspective. F2 is multiplied by the consumption of the state of charge to compensate for congestion in long-distance low-speed sections. The two are added together to obtain the corrected sub-target state of charge. The correction factor can be used as shown in Formula 11:

[0152] SOC target =SOC targetDriver *F1+ΔSOC*F2 Formula 11;

[0153] Among them, SOC targetDriver is the sub-target state of charge before correction, SOC target is the corrected sub-target state of charge.

[0154] In the embodiment of the present application, since low-speed sections have a greater impact on SOC, only the sub-target SOC of low-speed sections is corrected. In actual processing, a similar approach can be adopted for medium-speed sections, high-speed sections, etc., by replacing the parameters related to low-speed sections in the above formula (such as low-speed length ratio, low-speed section length, etc.) with the corresponding parameters of medium-speed sections, high-speed sections, etc., and then calculating the corresponding F1 and F2 correction factors to correct the section.

[0155] Furthermore, the correction factor obtained only through the above method may not be able to fully describe the vehicle's conditions, resulting in the correction factor still being inaccurate.

[0156] Based on this, the driving mode factor corresponding to the driving mode is obtained according to the energy consumption driving attribute, and the first correction factor is adjusted based on the driving mode factor. For example, there are three pre-set driving modes: ECO mode, Normal mode, and Sport mode, with corresponding driving mode factors of 0.8, 1.0, and 1.3 respectively.

[0157] Driving Mode is a function in which the vehicle's electronic control system comprehensively adjusts parameters such as power output, transmission logic, steering feel, and suspension firmness to suit different driving scenarios or user preferences. It accesses data such as vehicle speed, throttle position, and steering wheel angle via the vehicle's CAN bus and dynamically adjusts control unit (ECU / TCU) parameters based on pre-set strategies. The corresponding actuator adjustments include: the powertrain adjusts the engine ignition advance angle and motor torque output curve; the transmission system changes the transmission shift logic (for example, Sport mode adopts a more aggressive downshift strategy); the chassis system adjusts the damping force of the active suspension; and the auxiliary system adjusts the intervention thresholds of ESP and ABS (for example, Snow mode allows for slight slip).

[0158] Based on this, different driving modes directly affect the speed at which the battery's state of charge changes by adjusting power output, energy recovery strategies, and auxiliary system energy consumption. For example, ECO mode reduces energy consumption per unit time by reducing motor power, delaying gear shifts (mainly reflected in fuel vehicles), or limiting torque output (mainly reflected in electric vehicles). SPORT mode, on the other hand, increases the motor's power response speed, allowing for greater torque output. Instantaneous power consumption during rapid acceleration in SPORT mode can even reach 1.5 times that of ECO mode.

[0159] Based on this, when the driving mode is energy-saving mode, it means that the user's driving process is relatively energy-saving, and a lower sub-target state of charge can be reserved, which can also meet the user's driving needs. At this time, the driving mode factor is set to 0.8, and after the lower driving mode factor is multiplied by the first correction factor F1, the corrected sub-target state of charge SOC target Also lower.

[0160] When the driving mode is standard mode, no adjustment is required.

[0161] The driving mode is sports mode, which means the user's driving style is more aggressive, and the energy consumption is naturally higher. It is necessary to retain more sub-target state of charge to meet the user's driving needs. At this time, the driving mode factor is set to 1.3. After the higher driving mode factor is multiplied by the first correction factor F1, the corrected sub-target state of charge SOC target Also higher.

[0162] At this time, the calculation formula corresponding to the first correction factor is shown in Formula 12:

[0163] F1=max(α,1-λ*P lowspd *D mode ) Formula 12;

[0164] Among them, D mode is the driving mode factor. Compared with Formula 9, it adds the driving mode factor when considering F1. According to the driving mode factor, the value of F1 in different driving modes is adaptively adjusted.

[0165] According to the energy consumption driving attribute, the environmental impact factor corresponding to the external environment is obtained, and the second correction factor is adjusted by the environmental impact factor. Since different external environments have different effects on battery energy consumption, the temperature can be selected as a representative attribute in the environmental impact factor to further correct the second correction factor. For example, the external temperature is set to multiple intervals, which are the environmental impact factors T amb ≤-10℃, -10℃ <T amb ≤10℃, T amb >10℃, where T amb is the external environment temperature, and a corresponding environmental impact factor T is set for each temperature range comp The values are 1.5, 1.2, and 1.0 respectively.

[0166] The lower the temperature, the faster the battery is consumed. Therefore, a higher environmental impact factor needs to be set so that the corrected sub-target state of charge obtained by the second correction factor F2 is also higher, which reserves more power for the user and prevents accidents.

[0167] At this time, the calculation formula corresponding to the second correction factor is shown in Formula 13:

[0168]

[0169] Among them, T comp is the value of the environmental impact factor. Compared with Formula 10, the environmental impact factor is added when considering F2. According to the environmental impact factor, the value of F2 under different external environments is adaptively adjusted.

[0170] Of course, in addition to temperature data, environmental impact factors can also include weather data, slope data, traffic jam probability, etc. When the weather is abnormal, or the slope data has a large number of uphill slopes, or the traffic jam probability is high, the final environmental impact factor value can be obtained by setting corresponding weights for each dimension in the environmental impact factor and taking a weighted sum.

[0171] In addition, if Figure 3 As shown, Figure 3 : is a structural diagram of a vehicle battery state of charge distribution device provided in an embodiment of the present application, the device comprising:

[0172] The path segmentation module 301 obtains vehicle path information and performs segmentation processing based on the energy consumption driving attributes corresponding to the vehicle to obtain multiple path segments;

[0173] The SOC allocation module 302 determines whether to perform segmented SOC allocation based on the vehicle route information, the route segments, and the stored route distance;

[0174] The correction factor generation module 303 obtains corresponding correction factors based on the length ratios of the path segments corresponding to the energy consumption driving attributes if the determination result of the state of charge allocation module is execution;

[0175] The state of charge correction module 304 obtains a sub-target state of charge corresponding to the path segment based on the total target state of charge corresponding to the preset vehicle path information, and corrects the sub-target state of charge according to the correction factor.

[0176] In a specific embodiment, the path segmentation module 301 obtains a predicted speed corresponding to the vehicle according to the energy consumption driving attribute; and determines a preset road unit length;

[0177] Based on a consistency rule, segmenting the vehicle path information to obtain a plurality of path segments;

[0178] A single path segment includes at least one road unit length, the predicted vehicle speeds within the single path segment belong to the same preset vehicle speed interval, and the predicted vehicle speeds of adjacent path segments belong to different preset vehicle speed intervals.

[0179] In a specific embodiment, after segmenting the vehicle path information to obtain a plurality of path segments, the path segmentation module 301 further includes:

[0180] Determining, among the plurality of path segments, a first designated path segment whose road length does not conform to a preset length interval;

[0181] For the first designated path segment, among the static attributes of the energy consumption driving attribute, attributes of at least some dimensions are matched and selected as first designated attributes, and segment division and correction of the first designated path segment are performed based on the first designated attributes;

[0182] Among the dynamic attributes of the energy consumption driving attribute, select attributes of at least some dimensions as second designated attributes, and perform segment division correction on the second designated path segment corresponding to the impact range based on the change state and impact range of the second designated attribute;

[0183] The road segment division and correction includes: segmenting the designated path segment, and / or merging the designated path segment with other path segments.

[0184] In a specific embodiment, the state of charge allocation module 302 determines the road unit length offsets corresponding to the vehicle's current position and the vehicle's destination position, respectively, based on a preset road unit length, and obtains the relative distances;

[0185] Obtaining a remaining distance based on the relative distance and a stored path distance corresponding to the vehicle;

[0186] If the relative distance is less than the stored path distance, and the number of segments of the path is less than the preset segment upper limit, the segmented state of charge allocation is not performed;

[0187] If the relative distance is greater than the stored path distance; and / or the number of segments of the path is greater than the preset segment upper limit value, and the remaining distance is greater than the preset distance value corresponding to the storage capacity upper limit; then segmented charge state allocation is performed.

[0188] In a specific embodiment, the correction factor generation module 303 obtains the length ratio of the path segment corresponding to each predicted vehicle speed;

[0189] Determining a low-speed length ratio of a path segment below a preset vehicle speed;

[0190] Based on the low-speed length ratio, a corresponding correction factor is obtained; wherein, the higher the low-speed length ratio, the higher the correction amount of the correction factor on the sub-target state of charge.

[0191] In a specific embodiment, the correction factor generation module 303 obtains a corresponding first correction factor according to a preset attenuation coefficient and the low-speed length ratio; wherein the first correction factor does not exceed a preset lower limit value;

[0192] A low-speed ratio parameter is obtained based on a preset low-speed segment ratio weight coefficient and the low-speed length ratio; a distance compensation parameter is obtained based on a preset absolute distance compensation coefficient and an absolute distance normalization parameter of the path segment below the preset vehicle speed;

[0193] Obtaining a second correction factor according to the low-speed ratio parameter and the distance compensation parameter;

[0194] The first correction factor is used to correct the target state of charge, and the second correction factor is used to compensate for the path segment below the preset vehicle speed.

[0195] In a specific embodiment, the correction factor generation module 303 further includes:

[0196] obtaining a driving mode factor corresponding to the driving mode according to the energy consumption driving attribute, and adjusting the first correction factor according to the driving mode factor;

[0197] According to the energy consumption driving attribute, an environmental impact factor corresponding to the external environment is obtained, and the second correction factor is adjusted according to the environmental impact factor.

[0198] Figure 4 It is a structural schematic diagram of a vehicle provided in an embodiment of the present application.

[0199] For example, Figure 4 As shown, the vehicle includes: a memory 401 and a processor 402, wherein the memory 401 stores an executable program code 4011, and the processor 402 is used to call and execute the executable program code 4011 to perform a vehicle battery state of charge distribution method.

[0200] This embodiment can divide the vehicle into functional modules based on the above-described method example. For example, each functional module can be mapped to a specific function, or two or more functions can be integrated into a single processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used.

[0201] In the case of dividing each functional module into corresponding functional modules, the vehicle may include:

[0202] The path segmentation module obtains the vehicle path information and performs segmentation processing based on the energy consumption and driving properties corresponding to the vehicle to obtain multiple path segments;

[0203] a state of charge allocation module, which determines whether to perform segmented state of charge allocation based on the vehicle route information, the route segments, and the stored route distance;

[0204] A correction factor generation module, if the determination result of the state of charge allocation module is execution, obtains a corresponding correction factor based on the length ratio of the path segments corresponding to each energy consumption driving attribute;

[0205] The state of charge correction module obtains a sub-target state of charge corresponding to the path segment based on the total target state of charge corresponding to the preset vehicle path information, and corrects the sub-target state of charge according to the correction factor.

[0206] It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module and will not be repeated here.

[0207] The vehicle provided in this embodiment is used to execute the above-mentioned vehicle battery state of charge distribution method, and thus can achieve the same effect as the above-mentioned implementation method.

[0208] In the case of an integrated unit, the vehicle may include a processing module and a storage module. The processing module may be used to control and manage the vehicle's movements, while the storage module may be used to support the vehicle's execution of program codes and data.

[0209] The processing module may be a processor or controller that implements or executes various exemplary logic blocks, modules, and circuits disclosed herein. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processing (DSP) and a microprocessor, and the storage module may be a memory.

[0210] This embodiment also provides a computer-readable storage medium, which stores computer program code (including but not limited to disk storage, CD-ROM, optical storage, etc.). When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement the vehicle battery charge state distribution method provided in the above embodiment.

[0211] This embodiment also provides a computer program product. When the computer program product is run on a computer, it enables the computer to execute the above-mentioned related steps to implement the vehicle battery state of charge distribution method provided in the above embodiment.

[0212] Among them, the beneficial effects of the above embodiments can refer to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0213] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0214] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0215] In the description of the present disclosure, it should be understood that if the terms "up", "down", "front", "back", "left" and "right" are used to indicate directions or positional relationships, they are based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the positions or elements referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, they should not be understood as limitations of the present disclosure.

[0216] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. It should also be noted that the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, commodity, or device comprising the element.

[0217] The above are merely examples of the present disclosure and are not intended to limit the present disclosure. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure are intended to be included within the scope of the claims of the present disclosure.

Claims

1. A method for allocating state of charge of a vehicle battery, characterized in that: include: Obtain vehicle path information and segment it based on the energy consumption and driving properties of the vehicle to obtain multiple path segments; determining whether to perform segmented state of charge allocation based on the vehicle route information, the route segments, and the stored route distance; If executed, the corresponding correction factor is obtained based on the length ratio of the path segments corresponding to each energy consumption driving attribute; A sub-target state of charge corresponding to the path segment obtained based on a total target state of charge corresponding to the preset vehicle path information is obtained, and the sub-target state of charge is corrected according to the correction factor.

2. The method according to claim 1, characterized in that Segment processing is performed based on the energy consumption driving attributes corresponding to the vehicle to obtain multiple path segments, including: Obtaining a predicted vehicle speed corresponding to the vehicle based on the energy consumption driving attribute; and determining a preset road unit length; Based on a consistency rule, segmenting the vehicle path information to obtain a plurality of path segments; A single path segment includes at least one road unit length, the predicted vehicle speeds within the single path segment belong to the same preset vehicle speed interval, and the predicted vehicle speeds of adjacent path segments belong to different preset vehicle speed intervals.

3. The method according to claim 2, characterized in that After segmenting the vehicle path information to obtain a plurality of path segments, the method further includes: Determining, among the plurality of path segments, a first designated path segment whose road length does not conform to a preset length interval; For the first designated path segment, among the static attributes of the energy consumption driving attribute, attributes of at least some dimensions are matched and selected as first designated attributes, and segment division and correction of the first designated path segment are performed based on the first designated attributes; Among the dynamic attributes of the energy consumption driving attribute, select attributes of at least some dimensions as second designated attributes, and perform segment division correction on the second designated path segment corresponding to the impact range based on the change state and impact range of the second designated attribute; The road segment division and correction includes: segmenting the designated path segment, and / or merging the designated path segment with other path segments.

4. The method according to claim 1, wherein Determining whether to perform segmented state of charge allocation based on the vehicle route information, the route segments, and the stored route distance specifically includes: Based on the preset road unit length, the road unit length offset corresponding to the vehicle's current position and the vehicle's end position is determined, and the relative distance is obtained; Obtaining a remaining distance based on the relative distance and a stored path distance corresponding to the vehicle; If the relative distance is less than the stored path distance, and the number of segments of the path is less than the preset segment upper limit, the segmented state of charge allocation is not performed; If the relative distance is greater than the stored path distance; and / or the number of segments of the path is greater than the preset segment upper limit value, and the remaining distance is greater than the preset distance value corresponding to the storage capacity upper limit; then segmented charge state allocation is performed.

5. The method according to claim 2, characterized in that Based on the length ratio of the path segments corresponding to each energy consumption driving attribute, the corresponding correction factor is obtained, including: Obtain the length ratio of the path segments corresponding to each predicted vehicle speed; Determining a low-speed length ratio of a path segment below a preset vehicle speed; Based on the low-speed length ratio, a corresponding correction factor is obtained; wherein, the higher the low-speed length ratio, the higher the correction amount of the correction factor on the sub-target state of charge.

6. The method according to claim 5, characterized in that Based on the low-speed length ratio, a corresponding correction factor is obtained, specifically including: Obtaining a corresponding first correction factor according to a preset attenuation coefficient and the low-speed length ratio; wherein the first correction factor does not exceed a preset lower limit value; A low-speed ratio parameter is obtained based on a preset low-speed segment ratio weight coefficient and the low-speed length ratio; a distance compensation parameter is obtained based on a preset absolute distance compensation coefficient and an absolute distance normalization parameter of the path segment below the preset vehicle speed; Obtaining a second correction factor according to the low-speed ratio parameter and the distance compensation parameter; The first correction factor is used to correct the target state of charge, and the second correction factor is used to compensate for the path segment below the preset vehicle speed.

7. The method according to claim 6, characterized in that The method further comprises: obtaining a driving mode factor corresponding to the driving mode according to the energy consumption driving attribute, and adjusting the first correction factor according to the driving mode factor; According to the energy consumption driving attribute, an environmental impact factor corresponding to the external environment is obtained, and the second correction factor is adjusted according to the environmental impact factor.

8. A vehicle battery state of charge distribution device, characterized in that: include: The path segmentation module obtains the vehicle path information and performs segmentation processing based on the energy consumption and driving properties corresponding to the vehicle to obtain multiple path segments; a state of charge allocation module, which determines whether to perform segmented state of charge allocation based on the vehicle route information, the route segments, and the stored route distance; A correction factor generation module, if the determination result of the state of charge allocation module is execution, obtains a corresponding correction factor based on the length ratio of the path segments corresponding to each energy consumption driving attribute; The state of charge correction module obtains a sub-target state of charge corresponding to the path segment based on the total target state of charge corresponding to the preset vehicle path information, and corrects the sub-target state of charge according to the correction factor.

9. A vehicle, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: the vehicle battery charge state distribution method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that: The computer executable instructions are configured as: the vehicle battery state of charge distribution method according to any one of claims 1 to 7.

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