Hybrid vehicle energy distribution method and device, vehicle and nonvolatile storage medium

Abstract

This application discloses a hybrid vehicle energy distribution method, device, vehicle, and non-volatile storage medium. The method includes: acquiring vehicle navigation data and determining sloping road segments in the target path based on the navigation data; determining the state of charge (POC) offset value corresponding to the sloping road segment based on the altitude change information of the sloping road segment; determining the target POC change curve for the vehicle on the sloping road segment based on the POC offset value and the slope type; and controlling the vehicle's hybrid power distribution ratio based on the target POC change curve. This application solves the technical problem that related technologies use fixed rules or linear reference trajectories for energy management, which leads to the inability of vehicles to proactively balance potential energy and electrical energy conversion when facing long-altitude fluctuating road conditions, resulting in wasted energy recovery or hardware overheating under extreme conditions.

Description

Technical Field

[0001] This application relates to the field of hybrid electric vehicle control technology, and more specifically, to a hybrid electric vehicle energy distribution method, device, vehicle, and non-volatile storage medium. Background Technology

[0002] Hybrid vehicle energy management strategies in related technologies often employ fixed rules or linear state-of-charge reference trajectories, failing to effectively integrate road terrain information ahead. This results in vehicles being unable to proactively perceive slope changes and pre-adjust battery energy states when facing long-altitude fluctuating road conditions. Consequently, problems arise such as limited potential energy recovery due to excessively high battery charge before long downhill sections, and insufficient power reserves leading to power shortages or forced inefficient engine charging before long uphill sections. Furthermore, these technologies often focus only on instantaneous power distribution while neglecting thermodynamic constraints, causing excessively rapid battery temperature rise under high-power uphill charging and discharging conditions, which can easily trigger hardware-protected power reduction.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This application provides a hybrid vehicle energy distribution method, device, vehicle, and non-volatile storage medium to at least solve the technical problem that, due to the use of fixed rules or linear reference trajectories for energy management in related technologies, vehicles are unable to proactively balance potential energy and electrical energy conversion when facing long-altitude fluctuating road conditions, resulting in wasted energy recovery or hardware overheating under extreme conditions.

[0005] According to one aspect of the embodiments of this application, a hybrid vehicle energy distribution method is provided, comprising: acquiring vehicle navigation data, and determining a slope segment in a target path based on the vehicle navigation data, wherein the target path is the path the vehicle plans to travel; determining a state of charge offset value corresponding to the slope segment based on the elevation change information of the slope segment, wherein the state of charge offset value is used to characterize the theoretical change in the state of charge of the vehicle after traveling through the slope segment; determining a target state of charge change curve for the vehicle on the slope segment based on the state of charge offset value and the slope type of the slope segment, wherein the target state of charge change curve is used to at least indicate the target state of charge that the vehicle should reach before traveling to the slope segment; and controlling the electric power distribution ratio of the vehicle based on the target state of charge change curve.

[0006] Optionally, determining the sloping road segments in the target path based on vehicle navigation data includes: extracting elevation data of path points in the target path according to a preset spatial sampling step size to form an initial elevation point sequence, and performing smoothing and denoising processing on the initial elevation point sequence to obtain the target elevation point sequence; calculating the slope value corresponding to each road segment on the target elevation point sequence; and determining the continuous road segment as a sloping road segment if it is detected that there are continuous road segments in the target path with a continuous distance exceeding a preset length threshold and an absolute value of the slope value greater than a preset slope threshold.

[0007] Optionally, the altitude change information includes: the altitude difference between the starting point and the ending point; determining the state of charge offset value corresponding to the slope section based on the altitude change information of the slope section includes: determining the altitude difference between the starting point and the ending point of the slope section and obtaining the vehicle's driving parameter information, wherein the driving parameter information includes at least one of the following: the vehicle's total mass, the drag coefficient, and the tire rolling resistance coefficient; determining the theoretical surplus or deficit of gravitational potential energy corresponding to the vehicle driving through the slope section based on the altitude difference and the driving parameter information, wherein the theoretical surplus or deficit of gravitational potential energy is used to characterize the amount of change in gravitational potential energy of the vehicle between the starting point and the ending point of the slope section due to the change in altitude, and the net energy value after deducting the work loss of overcoming resistance; determining the state of charge offset value based on the theoretical surplus or deficit of gravitational potential energy, the vehicle's corresponding transmission efficiency coefficient, and the rated capacity of the vehicle's power battery, wherein the transmission efficiency coefficient is used to characterize the energy conversion efficiency of the vehicle under charging and discharging conditions.

[0008] Optionally, determining the target state of charge (SOC) change curve for the vehicle on the slope section based on the SOC offset value and the slope type includes: obtaining a global linear SOC reference curve for the vehicle, wherein the global linear SOC reference curve is used to characterize the ideal SOC trend of the vehicle changing according to a uniform linear law in the target path; determining a prediction interval before entering the starting point of the slope section based on the vehicle's driving position and the starting position of the slope section, wherein the prediction interval is a buffer section before the vehicle reaches the starting position of the slope section; determining a target adjustment strategy based on the vehicle's SOC, SOC offset value, and slope type, and performing asymmetric offset correction on the corresponding global linear SOC reference curve within the prediction interval according to the target adjustment strategy to obtain the target SOC change curve.

[0009] Optionally, the slope type includes: uphill and downhill; determining the target adjustment strategy based on the vehicle's state of charge (SBC), SBC offset value, and slope type includes: determining the target SBC required for the vehicle to reach the start of the slope section based on the vehicle's SBC and SBC offset value; determining the target adjustment strategy based on the slope type and target SBC includes: when the slope type is uphill, the target adjustment strategy includes: within the forecast interval, by increasing the equivalent factor weight, controlling the engine to charge the power battery through the electric motor while driving the vehicle, causing the global linear SBC reference curve to shift upward, ensuring that the vehicle's SBC is at the target SBC state when reaching the bottom of the slope; and / or, when the slope type is downhill, the target adjustment strategy includes: within the forecast interval, by decreasing the equivalent factor weight, forcibly activating the pure electric drive mode to consume the vehicle's power battery charge in advance, causing the global linear SBC reference curve to shift downward, ensuring that the vehicle is at the target SBC state when reaching the top of the slope, wherein the equivalent factor weight is used to adjust the torque distribution ratio between the engine and the electric motor to change the charging and discharging state of the power battery.

[0010] Optionally, the method further includes: determining the predicted battery temperature rise value corresponding to the vehicle passing through the slope section under the control of the target state of charge change curve; generating a temperature adjustment coefficient when the predicted battery temperature rise value is greater than the preset thermal protection threshold of the vehicle's power battery, wherein the temperature adjustment coefficient is used to characterize the thermal safety margin of the power battery on the slope section; and correcting the adjustment rate of the equivalent factor weight according to the temperature adjustment coefficient to reduce the instantaneous current peak of the power battery on the slope section.

[0011] Optionally, after controlling the electric power distribution ratio of the vehicle according to the target state of charge change curve, the method further includes: obtaining the actual state of charge change curve of the vehicle's power battery; determining the deviation value between the actual state of charge change curve and the target state of charge change curve; inputting the deviation value into the proportional-integral (PI) control law, and updating the value of the equivalent factor weight through the joint operation of the proportional and integral terms in the PI control law, so that the actual state of charge change curve approaches the target state of charge change curve, wherein the PI control law is used to correct the equivalent factor weight based on the instantaneous and cumulative components of the deviation value.

[0012] According to another aspect of the embodiments of this application, a hybrid vehicle energy distribution device is also provided, comprising: a slope recognition module, used to acquire vehicle navigation data and determine a slope segment in a target path based on the vehicle navigation data, wherein the target path is the path that the vehicle plans to travel; a potential energy assessment module, used to determine a state of charge offset value corresponding to the slope segment based on the altitude change information of the slope segment, wherein the state of charge offset value is used to characterize the theoretical change in the state of charge of the vehicle after traveling through the slope segment; a curve correction module, used to determine a target state of charge change curve corresponding to the slope segment based on the state of charge offset value and the slope type of the slope segment, wherein the target state of charge change curve is used to at least indicate the target state of charge that the vehicle should reach before traveling to the slope segment; and a power distribution module, used to control the electric power distribution ratio of the vehicle based on the target state of charge change curve.

[0013] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program executes a hybrid vehicle energy distribution method during runtime.

[0014] According to another aspect of the embodiments of this application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes a hybrid vehicle energy distribution method by running the computer program.

[0015] In this embodiment, vehicle navigation data is acquired, and based on this data, sloping road segments in the target path are determined, where the target path is the planned route of the vehicle. Based on the elevation change information of the sloping road segments, a state of charge (POC) offset value corresponding to the sloping road segments is determined, where the POC offset value characterizes the theoretical change in the vehicle's POC after traversing the sloping road segment. Based on the POC offset value and the slope type of the sloping road segment, a target POC change curve for the vehicle on the sloping road segment is determined, where the target POC change curve at least indicates the necessary actions the vehicle should take before reaching the sloping road segment. The target state of charge is achieved; the method of controlling the power distribution ratio of the vehicle based on the target state of charge change curve, and constructing a terrain evolution model with physical determinism by deeply integrating navigation slope characteristics, transforms road slope from an external disturbance into a dispatchable energy asset, and achieves the purpose of active pre-adjustment of nonlinear state of charge based on terrain prediction. This solves the technical problem that the use of fixed rules or linear reference trajectories for energy management in related technologies leads to the inability of vehicles to proactively balance potential energy and electrical energy conversion when facing long-altitude fluctuating road conditions, resulting in energy recovery waste or hardware overheating under extreme conditions. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 This is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for energy distribution in a hybrid vehicle, according to an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of a method for energy distribution in a hybrid vehicle according to an embodiment of this application;

[0019] Figure 3 This is a schematic diagram of a system logic architecture and signal flow according to an embodiment of this application;

[0020] Figure 4 This is a schematic diagram of a process for adaptive energy management of hybrid vehicles based on navigation road slope prediction, according to an embodiment of this application.

[0021] Figure 5 This is a schematic diagram comparing the SOC trajectory under a long downhill condition according to an embodiment of this application;

[0022] Figure 6 This is a comparative diagram of energy reserves under long uphill conditions provided in an embodiment of this application;

[0023] Figure 7 This is a schematic diagram of a thermal-electric coupling safety constraint logic flow according to an embodiment of this application;

[0024] Figure 8 This is a schematic diagram of the structure of a hybrid vehicle energy distribution device provided according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] The energy management technology for hybrid vehicles in related technologies has the following main shortcomings:

[0028] 1) The problem of SOC reference trajectory being out of sync with actual terrain: In related technologies, SOC reference trajectory is mostly set linearly and cannot sense altitude fluctuations. Before entering a long downhill, the vehicle may experience problems such as limited recharging due to excessive battery charge (SOC) (energy overflow) and insufficient power due to insufficient energy reserves before a long uphill.

[0029] 2) Problem of control strategy failure due to thermal overload under slope conditions: The solutions in related technologies only focus on power distribution and ignore thermodynamic constraints. There is a problem that the temperature rise is too fast due to the large current charging and discharging caused by long slopes, which in turn triggers the hardware protection power limit.

[0030] 3) The contradiction between global optimization and real-time response in complex geographical environments: Most solutions in related technologies rely on cloud communication or offline black-box models, which leads to poor robustness and computational lag.

[0031] To address the aforementioned issues, this application provides a solution that constructs a physically deterministic terrain evolution model by deeply fusing navigation slope features. This transforms road slope from an external disturbance into a schedulable energy asset, enabling proactive pre-adjustment of the nonlinear state of charge based on terrain prediction. Furthermore, it introduces thermal-electric collaborative control logic to improve energy recovery efficiency, power continuity, and system stability under extreme slope conditions, while ensuring battery thermal safety boundaries. A low-computing-power adaptive strategy based on prior physical information allows for accurate tracking of global energy targets in varying slope environments. Detailed explanation follows.

[0032] According to an embodiment of this application, a method for energy distribution in a hybrid vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of a computer terminal (or electronic device) for implementing a hybrid vehicle energy distribution method is shown. Figure 1 As shown, the computer terminal 10 (or electronic device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or electronic device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the hybrid vehicle energy distribution method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the above-mentioned hybrid vehicle energy distribution method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0037] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or electronic device).

[0038] Under the above operating environment, this application provides a hybrid vehicle energy distribution method. Figure 2 This is a schematic diagram of a method for energy distribution in a hybrid vehicle according to an embodiment of this application, as shown below. Figure 2 As shown, the method includes the following steps:

[0039] Step S202: Obtain vehicle navigation data and determine the sloping road sections in the target path based on the vehicle navigation data, wherein the target path is the path that the vehicle plans to travel.

[0040] Step S204: Based on the elevation change information of the sloping road section, determine the state of charge offset value corresponding to the sloping road section. The state of charge offset value is used to characterize the theoretical change in the state of charge of the vehicle after driving through the sloping road section.

[0041] Step S206: Based on the state of charge offset value and the slope type of the slope section, determine the target state of charge change curve of the vehicle on the slope section. The target state of charge change curve is used to indicate at least the target state of charge that the vehicle should reach before driving to the slope section.

[0042] In this embodiment, the target state of charge change curve can be used to characterize the asymmetric correction trajectory of the vehicle relative to the global linear reference trajectory within a preset prediction interval before the start of the slope section; the asymmetric correction trajectory forms an optimal scheduling water level that matches the slope type at the start of the slope section, and the optimal scheduling water level is used to maximize the energy utilization efficiency and ensure thermal safety of the vehicle when passing through the slope section.

[0043] Step S208: Control the power distribution ratio of the vehicle based on the target state of charge change curve.

[0044] Through the above steps, a terrain evolution model with physical determinism is constructed by deeply integrating navigation slope features. This transforms road slope from an external disturbance into a schedulable energy asset, achieving the goal of proactive pre-adjustment of nonlinear charge state based on terrain prediction. This solves the technical problem that related technologies use fixed rules or linear reference trajectories for energy management, which leads to vehicles being unable to proactively balance potential energy and electrical energy conversion when facing long-altitude fluctuating road conditions, resulting in wasted energy recovery or hardware overheating under extreme conditions.

[0045] The hybrid vehicle energy distribution method in steps S202 to S208 of the embodiments of this application will be further described below.

[0046] The hybrid vehicle energy distribution method in this application embodiment can be applied to, for example... Figure 3 In the system architecture shown, such as Figure 3 As shown, in this embodiment, the geographic information ahead (i.e., the vehicle navigation data mentioned above) can be obtained through navigation / high-precision maps. After denoising and slope recognition by the geographic feature analysis module, the state of charge offset value corresponding to the slope section is calculated by the potential energy-electricity mapping model in combination with the BMS battery status and IMU inertial unit data. Then, a forward-looking target SOC change curve (target state of charge change curve) is generated by the nonlinear SOC trajectory planner. Finally, the underlying hybrid power controller (HCU) dynamically adjusts the torque distribution between the engine and the drive motor according to the reference target state of charge change curve through the adaptive equivalent factor adjustment law, so that the actual state of charge tracks the target trajectory. Thus, under the premise of ensuring thermal safety, global energy optimal management is achieved by actively storing electrical energy before going uphill and reserving recovery space before going downhill.

[0047] The specific process steps can be as follows: Figure 4As shown, the process begins with denoising the elevation data and calculating the slope using forward-facing geographical features (a high-precision forward-looking geographical feature perception step). Next, based on quantitative assessment of slope potential energy, the elevation difference is converted into a deviation in battery state of charge (slope condition classification and physical potential energy assessment step). Then, through nonlinear reference trajectory planning, energy is actively stored before going uphill or actively released before going downhill (nonlinear SOC reference trajectory dynamic correction planning step). Next, thermal-electric coupling safety constraints are used to predict and correct the temperature rise trend (thermal-electric coupling safety constraint feedback step). Finally, the underlying adaptive power distribution module completes the fuel-electric distribution between the engine and motor based on the A-ECMS algorithm (underlying actuator adaptive power decoupling distribution step), forming a complete closed loop from terrain perception to execution. The following section further explains the above process steps.

[0048] First, the path elevation point sequence can be extracted from the navigation system, and the real-time road gradient can be calculated and significant elevation feature segments (slope road segments) can be identified. The specific steps are as follows.

[0049] In some embodiments of this application, determining the sloping road segments in the target path based on vehicle navigation data includes the following steps: extracting elevation data of path points in the target path according to a preset spatial sampling step size to form an initial elevation point sequence, and performing smoothing and denoising processing on the initial elevation point sequence to obtain a target elevation point sequence; calculating the slope value corresponding to each road segment on the target elevation point sequence; and determining the continuous road segment as a sloping road segment if it is detected that there are continuous road segments in the target path with a continuous distance exceeding a preset length threshold and an absolute value of the slope value greater than a preset slope threshold.

[0050] Specifically, the in-vehicle navigation unit can use a preset spatial sampling step size. (For example, range of values) The system extracts the elevation point sequence of the preceding path (i.e., the target path) in real time and uses a sliding window mean filter to perform multi-smoothing denoising on the elevation data, eliminating elevation noise caused by tunnels, tall buildings, or GPS signal fluctuations, thus obtaining the target elevation point sequence. This processing ensures that the first derivative of the elevation curve is continuous and smooth in the spatial dimension, avoiding jumps or spikes in subsequent slope calculations. Then, the real-time gradient of the road segment can be calculated using the first-order difference method. Where H represents altitude; (In this embodiment, multi-source information fusion calibration can also be performed by combining data from the vehicle-mounted inertial measurement unit (IMU)).

[0051] When the system detects consecutive road segments along the path ahead where the distance exceeds a preset length threshold and the absolute value of the slope is greater than a preset slope threshold, such as when consecutive distances exceeding a preset slope threshold are identified in real time... And the absolute value of the slope is greater than For sections with significant elevation features, they are identified as sloping road sections, and their starting and ending geographical coordinates and corresponding driving distances are determined.

[0052] For example, suppose a vehicle is currently cruising at 90 km / h on a high-altitude plateau at 600m. The vehicle controller extracts the path points ahead through a high-precision map interface and analyzes the altitude sequence at 50ms intervals. When the gradient of the road ahead is detected to be stable between -4.5% and -6.2% for a continuous distance of more than 500m, the system can determine that this is a "typical long downhill recovery scenario" and lock the geographical location of the starting point at the top of the slope as the zero point of the subsequent nonlinear adjustment time.

[0053] This application embodiment provides a physically deterministic spatiotemporal background for subsequent energy scheduling by forward-looking perception of the geographical environment, avoiding the lag of traditional strategies that passively respond to terrain changes, and enabling the system to predict slope conditions several kilometers or even tens of kilometers in advance.

[0054] After identifying the sloping road section, the theoretical gravitational potential energy surplus or deficit can be calculated based on the elevation change information of the sloping road section and the vehicle dynamics model, and then converted into... The offset percentage (i.e., the state of charge offset value) is calculated using the following steps.

[0055] In some embodiments of this application, the altitude change information includes: the altitude difference between the starting point and the ending point; determining the state of charge offset value corresponding to the slope section based on the altitude change information of the slope section includes the following steps: determining the altitude difference between the starting point and the ending point of the slope section, and obtaining the vehicle's driving parameter information, wherein the driving parameter information includes at least one of the following: the vehicle's total mass, the wind resistance coefficient, and the tire rolling resistance coefficient; determining the theoretical surplus or deficit value of gravitational potential energy corresponding to the vehicle driving through the slope section based on the altitude difference and the driving parameter information, wherein the theoretical surplus or deficit value of gravitational potential energy is used to characterize the amount of change in gravitational potential energy of the vehicle between the starting point and the ending point of the slope section due to the change in altitude, and the net energy value after deducting the work loss of overcoming resistance; determining the state of charge offset value based on the theoretical surplus or deficit value of gravitational potential energy, the vehicle's corresponding transmission efficiency coefficient, and the rated capacity of the vehicle's power battery, wherein the transmission efficiency coefficient is used to characterize the energy conversion efficiency of the vehicle under charging and discharging conditions.

[0056] In this embodiment, the elevation difference of the feature segment can be used as a basis. The system automatically categorizes scenarios into long uphill, long downhill, or undulating road surfaces, and uses a built-in two-degree-of-freedom vehicle dynamics model to quantitatively calculate the theoretical profit or loss of gravitational potential energy when the vehicle passes over the slope. Specifically, the elevation difference between the start and end points of the slope section can be determined first, and driving parameter information including vehicle mass, frontal drag coefficient, and tire rolling resistance coefficient can be obtained (these parameters directly affect the calculation of energy loss during vehicle driving on the slope); then, based on a two-degree-of-freedom vehicle dynamics model, the change in gravitational potential energy caused by the elevation change can be calculated, and the theoretical surplus or deficit of gravitational potential energy can be obtained after deducting the work losses caused by air resistance and rolling resistance; finally, the comprehensive transmission efficiency coefficient of the electric drive system can be combined. (Covering losses in motor, inverter, and reducer) and rated capacity of power battery The potential energy surplus or deficit is mapped to a precise percentage of charge state offset using the energy balance equation, as shown in the following formula:

[0057]

[0058] This application embodiment establishes a mathematical model that deeply couples real-time vehicle parameters with the efficiency characteristics of the electric drive system, accurately transforming macroscopic terrain elevation differences into microscopic battery energy management indicators. In other words, it maps the elevation difference obtained from navigation to a precise offset of the battery state of charge in real time, realizing a cross-domain mapping from geographic physical quantities to electrochemical control quantities. This provides a physically deterministic basis for energy management strategies to schedule power consumption, solving the lag problem caused by the reliance on feedback adjustment in traditional strategies under terrain fluctuation scenarios.

[0059] Furthermore, based on the state of charge offset value and the ramp type, asymmetric correction logic can be superimposed on the original charge trajectory to generate a nonlinear target SOC curve containing a reserved area or reserve area (i.e., to determine the target state of charge change curve). The specific steps are as follows.

[0060] In some embodiments of this application, determining the target state of charge (SOC) change curve for a vehicle on a sloped road segment based on the SOC offset value and the slope type includes: obtaining a global linear SOC reference curve for the vehicle, wherein the global linear SOC reference curve is used to characterize the ideal SOC trend of the vehicle changing according to a uniform linear law in the target path; determining a prediction interval before entering the starting point of the sloped road segment based on the vehicle's driving position and the starting position of the sloped road segment, wherein the prediction interval is a buffer section before the vehicle reaches the starting position of the sloped road segment; determining a target adjustment strategy based on the vehicle's SOC, SOC offset value, and slope type, and performing asymmetric offset correction on the corresponding global linear SOC reference curve within the prediction interval according to the target adjustment strategy to obtain the target SOC change curve.

[0061] Specifically, a global linear state of charge (SOC) reference curve can be first used to characterize the ideal trend of the vehicle's change according to a uniform linear law. In this embodiment, the global linear SOC reference curve is a baseline trajectory generated based on the global energy management target, used to characterize the ideal SOC trend of the vehicle changing according to a uniform linear law in the target path. This curve does not make forward-looking nonlinear adjustments for specific slope sections, and is usually represented as a slope that decreases uniformly or remains constant with the travel distance. Then, a prediction interval is determined based on the vehicle's current position and the starting position of the slope section, i.e., the buffer section before entering the slope. In this embodiment, the length of the prediction interval can depend on the current vehicle speed and the distance from the starting point of the slope. For example, if the downhill section is 3km ahead, the prediction interval is the section between the current position and the top of the slope 3km later. Finally, based on the current SOC, the SOC offset value, and the slope type, an asymmetric offset correction is performed on the global linear reference curve within the prediction interval. For example, an upward "energy storage type" bulge is formed before going uphill, and a downward "reserved space type" groove is formed before going downhill. In this embodiment, the nonlinear trajectory planning can break the inherent framework of constant decrease in state of charge, so that the vehicle is at the optimal scheduling level at the beginning of the slope, realizing active physical countermeasure against terrain impact.

[0062] The specific steps for asymmetric offset correction according to the target adjustment strategy are as follows.

[0063] In some embodiments of this application, the slope type includes: uphill and downhill; determining the target adjustment strategy based on the vehicle's state of charge, state of charge offset value, and slope type includes: determining the target state of charge required by the vehicle to reach the beginning of the slope section based on the vehicle's state of charge and state of charge offset value; determining the target adjustment strategy based on the slope type and target state of charge includes: when the slope type is uphill, the target adjustment strategy includes: within the prediction interval, by increasing the weight of the equivalent factor, controlling the engine to simultaneously drive the vehicle and supply power to the battery via the electric motor. The system performs a supplementary charging operation to shift the global linear state of charge (SRC) reference curve upwards, ensuring the vehicle is in the target SRC state upon reaching the bottom of the slope. Alternatively, when the slope type is downhill, the target adjustment strategy includes: within the forecast interval, by reducing the equivalent factor weight, forcibly activating the pure electric drive mode to pre-consume the vehicle's battery charge, shifting the global linear SRC reference curve downwards, ensuring the vehicle is in the target SRC state upon reaching the top of the slope. The equivalent factor weight is used to adjust the torque distribution ratio between the engine and the motor to change the charging and discharging state of the battery.

[0064] In this embodiment, the target state of charge required for the vehicle to reach the beginning of the slope section can be calculated based on the vehicle's current state of charge and state of charge offset value. This target state of charge is the optimal scheduling level, which is the energy benchmark to ensure the vehicle can pass through the slope section smoothly. Subsequently, an equivalent factor weight adjustment strategy (i.e., target adjustment strategy) is determined according to the slope type, and asymmetric correction logic is executed, as follows.

[0065] For long downhill scenarios, the forecast interval before entering the crest of the slope (e.g.) Within this range, by dynamically reducing the weight of the equivalent factor, the weight of fuel consumption cost is made higher than the weight of electric energy consumption cost. This controls the engine to stop torque output and switch to pure electric drive mode, forcibly activating electric drive mode to release battery power in advance. In other words, the vehicle relies on battery power to overcome driving resistance and actively reduces the state of charge before reaching the top of the hill, causing the state of charge reference trajectory to shift downward to a low receiving water level, thus forming a "reserved space type" groove in the SOC trajectory.

[0066] For example, such as Figure 5 As shown, this diagram compares the State of Charge (SOC) trajectories of existing technologies and this application under long downhill conditions: the horizontal axis represents the driving distance, the left vertical axis represents the battery state of charge (SOC), and the right vertical axis represents the altitude. The gray curve represents terrain changes (the vehicle first travels to the top of the slope at approximately 9km, then enters the long downhill section). The dashed line in the diagram represents the linear SOC trajectory of the existing technology, which descends smoothly at a fixed slope without any forward-looking adjustments for the downhill terrain. The red solid line represents the non-linear SOC trajectory of this application. In the predicted range of approximately 2km before the top of the slope, the vehicle actively releases electricity through pure electric drive, causing the SOC to rapidly drop from approximately 37% to a low receiving level of around 18%, thus reserving sufficient energy storage space for the recovery of gravitational potential energy during the downhill section. After entering the downhill section, as potential energy is converted into electrical energy for recharging, the SOC gradually recovers to around 27%. This comparison shows that this application, through forward-looking non-linear trajectory correction, effectively solves the problem of limited downhill energy recovery caused by excessively high SOC in existing technologies.

[0067] Before entering a long downhill section, the vehicle actively adjusts the power distribution to reserve sufficient space for receiving electrical energy, allowing gravitational potential energy to be converted into electrical energy storage through motor recharging to the maximum extent. This effectively reduces the energy loss of mechanical braking and optimizes the overall energy consumption of the vehicle.

[0068] For long uphill scenarios, on the flat sections before reaching the bottom of the slope, the weight of the equivalent factor can be increased, so that the weight of fuel consumption cost is lower than that of electric energy consumption cost. This increases the engine torque request and puts its operating point into the optimal thermal efficiency zone, inducing the engine to enter the high-efficiency range to perform strong charging. That is, while driving the vehicle, the engine performs charging action to the battery pack through the electric motor. Before reaching the bottom of the slope, the state of charge is actively increased, so that the state of charge reference trajectory shifts upward to a high reserve level, and the SOC trajectory forms an "electric energy reserve type" bulge before the bottom of the slope.

[0069] For example, such as Figure 6 As shown, this paper compares the SOC trajectories of existing technologies and this application under long uphill conditions: the horizontal axis represents the driving distance, the left vertical axis represents the battery state of charge (SOC), and the right vertical axis represents the altitude. The black broken line represents terrain changes (the vehicle continuously climbs from the bottom of the slope to the top). The dashed line in the figure represents the linear SOC trajectory of the existing technology, which descends smoothly at a fixed slope without proactive adjustment for uphill terrain. This causes the SOC to drop below the depletion threshold (approximately 12%) too early during the climb, leading to battery depletion and power interruption. The red solid line represents the non-linear SOC trajectory of this application. Before the bottom of the slope, the vehicle increases the equivalence factor and induces the engine to enter the high-efficiency range for charging while driving, actively raising the SOC to a "power reserve type" bulge. This ensures that the vehicle has sufficient power reserves when it reaches the bottom of the slope, so that the motor can continuously provide power compensation during the subsequent long uphill climb. This avoids the efficiency deterioration and power lag caused by the engine being forced to recharge under heavy load, ensuring the continuity and stability of the vehicle's power output.

[0070] Through forward-looking potential energy assessment, the engine's high-efficiency range is used to actively store energy before long uphill sections, ensuring that the vehicle has sufficient motor compensation current during continuous high-load uphill climbing, effectively avoiding power response delays or interruptions caused by battery depletion.

[0071] The correction logic based on terrain prediction in this application breaks the inherent framework of constant SOC decline. By dynamically adjusting the oil-electric torque distribution ratio through the equivalent factor, it can ensure that the state of charge trajectory evolves according to the planned nonlinear curve. This solves the pain points of limited recovery on long downhill slopes and inefficient engine power replenishment caused by battery depletion on long uphill slopes. It ensures that the actual SOC value of the vehicle is at the optimal scheduling level when passing through extreme slopes, and achieves physical offsetting of terrain impact.

[0072] To avoid power interruption or system power limitation due to battery overheating in extreme slope conditions, thermal management constraints can be incorporated into the energy distribution algorithm in this application embodiment. This algorithm can monitor the battery temperature rise trend and generate a temperature coefficient to correct the power distribution ratio when the temperature rise is abnormal, as detailed below.

[0073] In some embodiments of this application, the method further includes: determining the predicted battery temperature rise value corresponding to the vehicle passing through a slope section under the control of the target state of charge change curve; generating a temperature adjustment coefficient when the predicted battery temperature rise value is greater than the preset thermal protection threshold of the vehicle's power battery, wherein the temperature adjustment coefficient is used to characterize the thermal safety margin of the power battery on the slope section (the closer to danger (smaller margin), the lower the temperature decay coefficient, and the more severe the current restriction of the system; the farther from danger (larger margin), the looser the restriction); and adjusting the adjustment rate of the equivalent factor weight according to the temperature adjustment coefficient to reduce the instantaneous current peak of the power battery on the slope section.

[0074] Specifically, such as Figure 7 As shown, slope warning information is first obtained, and then the battery temperature rise trend is predicted by using a battery equivalent circuit heating model and an external cooling system convection heat dissipation model. The battery temperature rise value is estimated when the vehicle passes through the slope section according to the target state of charge curve. Then, during execution, the predicted battery temperature rise value can be compared with the software protection thermal threshold of the power battery. Real-time dynamic comparison is performed. If the limit is not exceeded, the process directly proceeds to the equivalent factor correction stage. If the current planning is predicted to be... If the offset causes the temperature rise due to future charging and discharging current in the slope section to exceed the limit (i.e., the predicted temperature rise value is greater than the software protection thermal threshold), a dynamic decay temperature adjustment coefficient will be automatically generated. ,in The sensitivity coefficient, The predicted peak temperature (i.e., the predicted highest temperature of the power battery on this slope section of the road). This coefficient serves as the safe temperature boundary for power battery equipment. The equivalent factor regulation law is then corrected in real time using this coefficient. Finally, the charging and discharging torque or current is limited by the corrected equivalent factor to smooth out instantaneous current peaks. Under the premise of ensuring that the battery does not trigger high-temperature power reduction protection, the optimal solution between energy recovery efficiency and thermal safety is achieved.

[0075] This application's embodiments incorporate thermal management into the energy distribution algorithm through a thermal-electric coupling constraint mechanism. By predicting the thermal load impact caused by slope changes, it optimizes the cooling cycle or limits instantaneous overcurrent in advance, maintaining the battery operating temperature within a safe range. This approach seeks the optimal balance between energy recovery efficiency and thermal safety while ensuring that the battery does not trigger high-temperature power reduction protection. It effectively avoids the risk of thermal runaway and battery aging under extreme operating conditions, and extends the service life of core power components.

[0076] In addition, the embodiments of this application can also dynamically adjust the equivalent factor according to the deviation of the target curve to realize the decoupling control of the power flow between the engine, motor and generator, as detailed below.

[0077] In some embodiments of this application, after controlling the electric power distribution ratio of the vehicle according to the target state of charge change curve, the method further includes: obtaining the actual state of charge change curve of the vehicle's power battery; determining the deviation value between the actual state of charge change curve and the target state of charge change curve; inputting the deviation value into the proportional-integral (PI) control law, and updating the value of the equivalent factor weight through the joint operation of the proportional and integral terms in the PI control law, so that the actual state of charge change curve approaches the target state of charge change curve, wherein the PI control law is used to correct the equivalent factor weight based on the instantaneous and cumulative components of the deviation value.

[0078] Specifically, the underlying hybrid power controller receives the corrected nonlinear target trajectory (i.e., the target state of charge change curve). Subsequently, the adaptive equivalent factor regulation law (A-ECMS) can be used to dynamically adjust the power distribution weights of the electric and fuel systems within millisecond-level control cycles. For example, after performing electric and fuel distribution according to the target state of charge curve, the battery management system collects and constructs the actual state of charge change curve in real time. The controller can then calculate the deviation between the actual charge value and the target state of charge change curve in real time. The deviation value is then input into the proportional-integral (PI) control law. Through the immediate response of the proportional term and the steady-state error elimination effect of the integral term, the equivalent factor weights are adaptively updated by joint calculation. This changes the torque output ratio between the engine and the motor. At the same time, while meeting the driver's instantaneous drive torque request, the system can automatically search for the optimal torque distribution pair between the engine's optimal operating line (OLEL) and the drive motor's highest efficiency range.

[0079] The aforementioned closed-loop feedback mechanism compensates for execution deviations caused by sudden changes in driving behavior, traffic conditions, or model parameter errors, achieving precise physical tracking of the nonlinear reference trajectory and ensuring that the vehicle's powertrain always operates on the path with the optimal overall fuel economy.

[0080] To help those skilled in the art better understand the embodiments of this application, the process steps in the above embodiments are illustrated below.

[0081] For example, taking a hybrid SUV as the target, the simulation demonstrates high-speed driving in mountainous areas. The vehicle is currently cruising at 90 km / h on a plateau at an altitude of 600m. According to navigation prediction, a long downhill section with a sharp drop in altitude will appear 3km ahead. Data forward sensing and slope feature recognition: The vehicle controller extracts the path points ahead through a high-precision map interface. The processor parses the altitude sequence at 50ms intervals and uses a first-order Kalman filter to smooth the raw data, removing GPS elevation noise caused by tunnels or tall buildings. When the system detects that the gradient of the road ahead is stable between -4.5% and -6.2% for a sustained distance exceeding 500m, the logic layer determines this condition as a "typical long downhill recovery scenario." At this point, the perception module locks the geographical location of the starting point at the top of the slope and uses it as the zero point for nonlinear adjustment. Gravitational potential energy assessment and battery shortage calculation: The potential energy assessment module calls the vehicle's dynamic parameters (such as real-time vehicle mass, wind resistance, tire rolling resistance coefficient, etc.) stored in memory in real time. The system combines the current battery state of health (SOH) and real-time temperature (20°C) to calculate the convertible gravitational potential energy of this slope. After deducting the combined energy losses of the motor, inverter, and reducer (with a combined recharge efficiency set at 84%), the system calculates that the expected recharge energy from this slope can increase the SOC by approximately 13.5%. Dynamic reconstruction of the nonlinear SOC reference trajectory: The system obtains the current actual battery SOC value (assumed to be 30%). To allow for descent, the nonlinear programming unit corrects the originally flat reference trajectory into a downward exponential decay curve. The goal is to precisely reduce the SOC to around 16.5%, the "receiving low level," at the moment the vehicle reaches the top of the slope. Power flow execution by the underlying controller: The A-ECMS controller detects that the actual SOC is much higher than the corrected reference trajectory, automatically lowers the equivalence factor, and sets the fuel consumption cost weight to the maximum. The engine controller (ECU) receives the shutdown command, the clutch disengages, and the vehicle enters pure electric drive mode. At this point, the vehicle relies entirely on battery energy to overcome driving resistance, thus actively consuming excess power before reaching the crest of the hill. Closed-loop recovery under thermal safety constraints: Upon entering the downhill section, the vehicle's gravity generates significant driving force, causing the generator to switch to recharge mode. The thermoelectric coupling module detects a surge in battery bus current, resulting in a single-cell temperature rise gradient of 0.5°C / min. If the temperature rise is predicted to reach the core protection threshold, the system uses a temperature adjustment coefficient to reduce the recharge torque in real time, maximizing energy recovery efficiency while ensuring no risk of thermal runaway.

[0082] For example, suppose a vehicle is traveling at a low altitude of 50m and is about to encounter a continuous climbing section of 7km with an average gradient of +6.0%. Power demand forward assessment: When the navigation distance indicates the approaching uphill buffer zone, the system invokes a power balance prediction algorithm. Based on the identified +6.0% gradient, the system predicts that if the current battery level is maintained, the battery SOC will be depleted in the latter part of the climb. This will force the engine to share power to drive the generator for forced charging while the vehicle is climbing under heavy load, resulting in severely degraded engine thermal efficiency and a significant lag in power output. Active energy reserve trajectory correction logic: To balance future loads, the system triggers an "energy reserve" mechanism 2.5km before entering the bottom of the slope. The target SOC reference value is adjusted upwards from the current 25% to 38%. The control system receives the upward deviation signal and automatically increases the weighted value of the equivalent factor. Engine operating point optimization: Upon receiving a torque boost request, the engine increases its torque from 70Nm during flat-road cruising to 155Nm, placing its operating point within the high-efficiency core range (the optimal thermal efficiency zone of the universal characteristic curve). While driving the vehicle, the engine simultaneously performs a "pre-emptive" charge replenishment action on the battery pack through the reverse excitation torque generated by the motor rotor, causing a significant upward shift in the SOC trajectory before reaching the bottom of the slope. Collaborative support during the climbing phase: Once the climb officially begins, the actual SOC has already been pre-emptively replenished to a sufficient level. During subsequent high-load climbs, the drive motor utilizes its reserve charge to provide continuous power boost, effectively sharing the load on the engine. The engine load remains smooth, avoiding a sudden drop in power due to battery depletion.

[0083] This application's solution achieves proactive management of terrain energy by deeply integrating navigation slope characteristics with the vehicle's dynamics model. This effectively solves core problems in related technologies, such as the disconnect between the SOC reference trajectory and terrain, thermal overload failure under slope conditions, and the contradiction between global optimization and real-time response. Specifically, the system can proactively release electrical energy before entering a long downhill slope to reserve potential energy recovery space, and pre-store electrical energy using the engine's high-efficiency range before entering a long uphill slope. This maximizes the conversion of gravitational potential energy into electrical energy storage, reduces mechanical braking losses, and ensures sufficient motor compensation power during continuous high-load uphill climbing. This significantly improves the vehicle's overall energy utilization rate, fuel economy, and the continuity and stability of power output.

[0084] Meanwhile, this application's solution introduces a thermo-electric coupling safety constraint closed loop based on the battery's equivalent circuit heating model and the cooling system's heat dissipation model. This allows for online prediction of temperature rise trends under high-power ramp conditions and dynamic correction of the equivalent factor regulation law. This proactively mitigates instantaneous current peaks, maintaining the battery's operating temperature within a safe range and effectively preventing hardware-protective power limiting or power interruption triggered by battery overheating under extreme ramp conditions. This low-computing-power adaptive strategy based on physical prior information not only transforms road slope from an external disturbance into a schedulable energy asset but also extends the lifespan of core power components while ensuring their thermal safety boundaries, achieving cross-domain collaborative optimization of energy-saving potential development and hardware safety lifespan.

[0085] According to an embodiment of this application, an embodiment of a hybrid vehicle energy distribution device is also provided. Figure 8 This is a schematic diagram of the structure of a hybrid vehicle energy distribution device according to an embodiment of this application. Figure 8 As shown, the device includes:

[0086] The slope recognition module 80 is used to acquire vehicle navigation data and determine the slope section in the target path based on the vehicle navigation data, wherein the target path is the path that the vehicle plans to travel.

[0087] The potential energy assessment module 82 is used to determine the state of charge offset value corresponding to the slope road section based on the elevation change information of the slope road section. The state of charge offset value is used to characterize the theoretical change in the state of charge of the vehicle after driving through the slope road section.

[0088] The curve correction module 84 is used to determine the target state of charge change curve of the vehicle on the slope section based on the state of charge offset value and the slope type of the slope section. The target state of charge change curve is used to indicate at least the target state of charge that the vehicle should reach before driving to the slope section.

[0089] The power distribution module 86 is used to control the ratio of electric to gasoline power in the vehicle based on the target state of charge change curve.

[0090] Optionally, determining the sloping road segments in the target path based on vehicle navigation data includes: extracting elevation data of path points in the target path according to a preset spatial sampling step size to form an initial elevation point sequence, and performing smoothing and denoising processing on the initial elevation point sequence to obtain the target elevation point sequence; calculating the slope value corresponding to each road segment on the target elevation point sequence; and determining the continuous road segment as a sloping road segment if it is detected that there are continuous road segments in the target path with a continuous distance exceeding a preset length threshold and an absolute value of the slope value greater than a preset slope threshold.

[0091] Optionally, the altitude change information includes: the altitude difference between the starting point and the ending point; determining the state of charge offset value corresponding to the slope section based on the altitude change information of the slope section includes: determining the altitude difference between the starting point and the ending point of the slope section and obtaining the vehicle's driving parameter information, wherein the driving parameter information includes at least one of the following: the vehicle's total mass, the drag coefficient, and the tire rolling resistance coefficient; determining the theoretical surplus or deficit of gravitational potential energy corresponding to the vehicle driving through the slope section based on the altitude difference and the driving parameter information, wherein the theoretical surplus or deficit of gravitational potential energy is used to characterize the amount of change in gravitational potential energy of the vehicle between the starting point and the ending point of the slope section due to the change in altitude, and the net energy value after deducting the work loss of overcoming resistance; determining the state of charge offset value based on the theoretical surplus or deficit of gravitational potential energy, the vehicle's corresponding transmission efficiency coefficient, and the rated capacity of the vehicle's power battery, wherein the transmission efficiency coefficient is used to characterize the energy conversion efficiency of the vehicle under charging and discharging conditions.

[0092] Optionally, determining the target state of charge (SOC) change curve for the vehicle on the slope section based on the SOC offset value and the slope type includes: obtaining a global linear SOC reference curve for the vehicle, wherein the global linear SOC reference curve is used to characterize the ideal SOC trend of the vehicle changing according to a uniform linear law in the target path; determining a prediction interval before entering the starting point of the slope section based on the vehicle's driving position and the starting position of the slope section, wherein the prediction interval is a buffer section before the vehicle reaches the starting position of the slope section; determining a target adjustment strategy based on the vehicle's SOC, SOC offset value, and slope type, and performing asymmetric offset correction on the corresponding global linear SOC reference curve within the prediction interval according to the target adjustment strategy to obtain the target SOC change curve.

[0093] Optionally, the slope type includes: uphill and downhill; determining the target adjustment strategy based on the vehicle's state of charge (SBC), SBC offset value, and slope type includes: determining the target SBC required for the vehicle to reach the start of the slope section based on the vehicle's SBC and SBC offset value; determining the target adjustment strategy based on the slope type and target SBC includes: when the slope type is uphill, the target adjustment strategy includes: within the forecast interval, by increasing the equivalent factor weight, controlling the engine to charge the power battery through the electric motor while driving the vehicle, causing the global linear SBC reference curve to shift upward, ensuring that the vehicle's SBC is at the target SBC state when reaching the bottom of the slope; and / or, when the slope type is downhill, the target adjustment strategy includes: within the forecast interval, by decreasing the equivalent factor weight, forcibly activating the pure electric drive mode to consume the vehicle's power battery charge in advance, causing the global linear SBC reference curve to shift downward, ensuring that the vehicle is at the target SBC state when reaching the top of the slope, wherein the equivalent factor weight is used to adjust the torque distribution ratio between the engine and the electric motor to change the charging and discharging state of the power battery.

[0094] Optionally, the hybrid vehicle energy distribution device is also used to: determine the predicted battery temperature rise value corresponding to the vehicle passing through a slope section under the control of the target state of charge change curve; generate a temperature adjustment coefficient when the predicted battery temperature rise value is greater than the preset thermal protection threshold of the vehicle's power battery, wherein the temperature adjustment coefficient is used to characterize the thermal safety margin of the power battery on the slope section; and adjust the adjustment rate of the equivalent factor weight according to the temperature adjustment coefficient to reduce the instantaneous current peak of the power battery on the slope section.

[0095] Optionally, after controlling the electric power distribution ratio of the vehicle based on the target state of charge change curve, the method further includes: obtaining the actual state of charge change curve of the vehicle's power battery; determining the deviation value between the actual state of charge change curve and the target state of charge change curve; inputting the deviation value into the proportional-integral (PI) control law, and updating the value of the equivalent factor weight through the joint operation of the proportional and integral terms in the PI control law, so that the actual state of charge change curve approaches the target state of charge change curve, wherein the PI control law is used to correct the equivalent factor weight based on the instantaneous and cumulative components of the deviation value.

[0096] It should be noted that the modules in the above-mentioned hybrid vehicle energy distribution device can be program modules (such as a set of program instructions to implement a certain function) or hardware modules. For the latter, they can be in the following forms, but are not limited to these: each of the above modules is in the form of a processor, or the functions of each of the above modules are implemented by a processor.

[0097] It should be noted that the hybrid vehicle energy distribution device provided in this embodiment can be used to perform... Figure 2 The energy distribution method for hybrid vehicles shown above is also applicable to the embodiments of this application, and will not be repeated here.

[0098] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program executes a hybrid vehicle energy distribution method: acquiring vehicle navigation data, and determining a slope segment in a target path based on the vehicle navigation data, wherein the target path is the path the vehicle plans to travel; determining a state of charge offset value corresponding to the slope segment based on the elevation change information of the slope segment, wherein the state of charge offset value is used to characterize the theoretical change in the state of charge of the vehicle after traveling through the slope segment; determining a target state of charge change curve for the vehicle corresponding to the slope segment based on the state of charge offset value and the slope type of the slope segment, wherein the target state of charge change curve is at least used to indicate the target state of charge that the vehicle should reach before traveling to the slope segment; and controlling the electric power distribution ratio of the vehicle based on the target state of charge change curve.

[0099] This application embodiment also provides a non-volatile storage medium, which includes a stored computer program. The device containing the non-volatile storage medium executes the following hybrid vehicle energy distribution method by running the computer program: acquiring vehicle navigation data and determining a slope section in the target path based on the vehicle navigation data, wherein the target path is the path the vehicle plans to travel; determining a state of charge (SOC) offset value corresponding to the slope section based on the elevation change information of the slope section, wherein the SOC offset value is used to characterize the theoretical change in the vehicle's SOC after traveling through the slope section; determining a target SOC change curve for the vehicle on the slope section based on the SOC offset value and the slope type of the slope section, wherein the target SOC change curve at least indicates the target SOC that the vehicle should reach before traveling to the slope section; and controlling the vehicle's hybrid power distribution ratio based on the target SOC change curve.

[0100] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the hybrid vehicle energy distribution method described in various embodiments of this application: acquiring vehicle navigation data and determining, based on the vehicle navigation data, a slope segment in the target path, wherein the target path is the path the vehicle plans to travel; determining the state of charge offset value corresponding to the slope segment based on the elevation change information of the slope segment, wherein the state of charge offset value is used to characterize the theoretical change in the state of charge of the vehicle after traveling through the slope segment; determining the target state of charge change curve of the vehicle corresponding to the slope segment based on the state of charge offset value and the slope type of the slope segment, wherein the target state of charge change curve is at least used to indicate the target state of charge that the vehicle should reach before traveling to the slope segment; and controlling the electric power distribution ratio of the vehicle based on the target state of charge change curve.

[0101] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0102] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0104] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0105] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0106] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0107] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for energy distribution in a hybrid vehicle, characterized in that, include: Acquire vehicle navigation data, and based on the vehicle navigation data, determine the sloping road sections in the target path, wherein the target path is the path that the vehicle plans to travel; Based on the elevation change information of the sloping road section, the state of charge offset value corresponding to the sloping road section is determined, wherein the state of charge offset value is used to characterize the theoretical change in the state of charge of the vehicle after driving through the sloping road section. Based on the state of charge offset value and the slope type of the slope section, a target state of charge change curve for the vehicle corresponding to the slope section is determined, wherein the target state of charge change curve is used to at least indicate the target state of charge that the vehicle should reach before driving to the slope section. The power distribution ratio of the vehicle is controlled based on the target state of charge change curve.

2. The hybrid vehicle energy distribution method according to claim 1, characterized in that, Based on the vehicle navigation data, the sloping road sections in the target route are determined to include: According to the preset spatial sampling step size, the elevation data of the path points in the target path are extracted to form an initial elevation point sequence, and the initial elevation point sequence is smoothed and denoised to obtain the target elevation point sequence. The slope value corresponding to each road segment is obtained by calculating the target elevation point sequence. If a continuous road segment is detected in the target path where the distance exceeds a preset length threshold and the absolute value of the slope is greater than the preset slope threshold, the continuous road segment is identified as the sloping road segment.

3. The hybrid vehicle energy distribution method according to claim 1, characterized in that, The altitude change information includes: the altitude difference between the starting point and the ending point; based on the altitude change information of the slope road segment, the charge state offset value corresponding to the slope road segment is determined, including: Determine the elevation difference between the start and end points of the slope section and obtain the vehicle's driving parameter information, wherein the driving parameter information includes at least one of the following: vehicle mass, wind resistance coefficient, and tire rolling resistance coefficient. Based on the altitude difference and the driving parameter information, the theoretical surplus or deficit of gravitational potential energy corresponding to the vehicle driving through the slope section is determined. The theoretical surplus or deficit of gravitational potential energy is used to characterize the amount of change in gravitational potential energy of the vehicle between the start and end points of the slope section due to the change in altitude, and the net energy value after deducting the work loss of overcoming resistance. The state of charge offset value is determined based on the theoretical surplus or deficit of gravitational potential energy, the transmission efficiency coefficient corresponding to the vehicle, and the rated capacity of the vehicle's power battery. The transmission efficiency coefficient is used to characterize the energy conversion efficiency of the vehicle under charging and discharging conditions.

4. The hybrid vehicle energy distribution method according to claim 1, characterized in that, Based on the state of charge offset value and the slope type of the slope section, the target state of charge change curve of the vehicle corresponding to the slope section is determined as follows: Obtain the global linear state of charge reference curve corresponding to the vehicle, wherein the global linear state of charge reference curve is used to characterize the ideal state of charge trend of the vehicle changing in accordance with a uniform linear law in the target path; Based on the vehicle's driving position and the starting position of the slope section, a prediction interval before entering the starting position of the slope section is determined, wherein the prediction interval is a buffer section before the vehicle reaches the starting position of the slope section; Based on the vehicle's state of charge, the state of charge offset value, and the slope type, a target adjustment strategy is determined, and the global linear state of charge reference curve corresponding to the forecast interval is asymmetrically offset and corrected according to the target adjustment strategy to obtain the target state of charge change curve.

5. The hybrid vehicle energy distribution method according to claim 4, characterized in that, The slope types include: uphill and downhill; based on the vehicle's state of charge, the state of charge offset value, and the slope type, the target adjustment strategy is determined as follows: Based on the vehicle's state of charge and the state of charge offset value, determine the target state of charge required for the vehicle to reach the beginning of the slope section; Based on the slope type and the target state of charge, the target adjustment strategy is determined, including: when the slope type is uphill, the target adjustment strategy includes: within the forecast interval, by increasing the equivalent factor weight, controlling the engine to charge the power battery through the electric motor while driving the vehicle, causing the global linear state of charge reference curve to shift upward, ensuring that the vehicle's charge is at the target state of charge when reaching the bottom of the slope; and / or, when the slope type is downhill, the target adjustment strategy includes: within the forecast interval, by decreasing the equivalent factor weight, forcibly activating the pure electric drive mode to prematurely consume the vehicle's power battery charge, causing the global linear state of charge reference curve to shift downward, ensuring that the vehicle is at the target state of charge when reaching the top of the slope, wherein the equivalent factor weight is used to adjust the torque distribution ratio between the engine and the electric motor to change the charging and discharging state of the power battery.

6. The hybrid vehicle energy distribution method according to claim 5, characterized in that, The method further includes: Determine the predicted battery temperature rise value corresponding to the vehicle passing through the sloping road section under the control of the target state of charge change curve; When the predicted battery temperature rise is greater than the preset thermal protection threshold of the vehicle's power battery, a temperature adjustment coefficient is generated, wherein the temperature adjustment coefficient is used to characterize the thermal safety margin of the power battery on the slope section. Based on the temperature regulation coefficient, the adjustment rate of the equivalent factor weight is corrected to reduce the instantaneous current peak of the power battery on the slope section.

7. The hybrid vehicle energy distribution method according to claim 5, characterized in that, After controlling the electric power distribution ratio of the vehicle based on the target state of charge change curve, the method further includes: Obtain the actual state-of-charge change curve of the vehicle's power battery; Determine the deviation between the actual state-of-charge change curve and the target state-of-charge change curve; The deviation value is input into the proportional-integral (PI) control law, and the value of the equivalent factor weight is updated through the joint operation of the proportional and integral terms in the PI control law, so that the actual state of charge change curve approaches the target state of charge change curve. The PI control law is used to correct the equivalent factor weight based on the instantaneous and cumulative components of the deviation value.

8. A hybrid vehicle energy distribution device, characterized in that, include: The slope recognition module is used to acquire vehicle navigation data and, based on the vehicle navigation data, determine the slope section in the target path, wherein the target path is the path that the vehicle plans to travel. The potential energy assessment module is used to determine the state of charge offset value corresponding to the slope road section based on the elevation change information of the slope road section. The state of charge offset value is used to characterize the theoretical change in the state of charge of the vehicle after driving through the slope road section. The curve correction module is used to determine the target state of charge change curve of the vehicle on the slope section based on the state of charge offset value and the slope type of the slope section, wherein the target state of charge change curve is used to indicate at least the target state of charge that the vehicle should reach before driving to the slope section. The power distribution module is used to control the ratio of electric to gasoline power in the vehicle based on the target state of charge change curve.

9. A vehicle, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the hybrid vehicle energy distribution method according to any one of claims 1 to 7.

10. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored computer program, wherein the device containing the non-volatile storage medium executes the hybrid vehicle energy distribution method according to any one of claims 1 to 7 by running the computer program.

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