Pre-adjustment method for battery of all-terrain robot

Abstract

This application provides a pre-adjustment method for the battery of an all-terrain robot. The method includes: acquiring road images, slope, acceleration, and velocity in the robot's forward direction; determining the target peak power and duration of work done on the abnormal power consumption road scene based on the slope, acceleration, and velocity; if a pre-adjustment strategy is triggered, determining the energy transferred to the supercapacitor for pre-charging based on the target peak power, duration, the proportion of energy consumed by the supercapacitor, energy transfer efficiency, and a dynamic safety factor; the dynamic safety factor is the ratio between the actual energy consumed during hill climbing and obstacle crossing in historical data and the transferred energy; and controlling the battery to charge the supercapacitor. This method proactively transfers a portion of the battery's energy to the supercapacitor in advance to cope with upcoming high-power consumption scenarios, allowing the battery to only handle smooth base load power output while the supercapacitor handles instantaneous peak power demands, thus reducing battery heat loss.

Description

Technical Field

[0001] This application relates to the field of robotics, and more particularly to a method for pre-adjusting the battery of an all-terrain robot. Background Technology

[0002] All-terrain robots (such as military reconnaissance / bomb disposal robots, polar / mining inspection robots, and agricultural / forestry off-road platforms) often face sudden high-power demands when working in complex, unstructured terrain, such as steep slope climbing and obstacle crossing (crossing ditches, steps, and rocks). These conditions can cause the instantaneous power demand of the motor to be several times higher than the rated power.

[0003] Traditional battery management systems (BMS) primarily rely on passive responses (such as current limiting and power reduction protection) or simple rules (such as SOC threshold switching), which cannot anticipate and lead to high-current discharge and a surge in instantaneous heat loss. Long-term accumulation of these issues can accelerate SEI film damage, increase the risk of lithium dendrite formation, and cause rapid SOH degradation.

[0004] In some technologies, peak load reduction and regenerative braking energy recovery are achieved through hybrid energy storage (HESS) using batteries and supercapacitors, commonly found in electric vehicles and drones. However, these solutions are mostly real-time passive allocations (based on frequency decoupling, high-pass / low-pass filtering, fuzzy rules, MPC, etc.), lacking proactive pre-scheduling for the "predictable high-impact loads" unique to robots.

[0005] Therefore, how to pre-adjust the battery power for high-power conditions that will occur during the operation of all-terrain robots in order to reduce battery heat loss is an urgent problem to be solved. Summary of the Invention

[0006] This application provides a pre-adjustment method for the battery of an all-terrain robot to reduce the heat loss of the battery under high-power conditions.

[0007] In a first aspect, this application provides a method for pre-adjusting the battery of an all-terrain robot, the method comprising:

[0008] Acquire road images, slope, acceleration, and velocity in the direction the all-terrain robot is moving;

[0009] Based on the road image and the speed, the type of road the robot will travel on in a future preset time period is determined, and the road type includes normal power consumption roads and abnormal power consumption roads.

[0010] If the road type is an abnormal power consumption road, then the target peak power and duration of work done on the abnormal power consumption road are determined based on the slope, the acceleration, and the speed.

[0011] Based on the target peak power, determine whether to trigger the preset pre-adjustment strategy;

[0012] If the pre-adjustment strategy is triggered, the pre-charged energy transferred to the supercapacitor is determined based on the target peak power, the duration, the preset supercapacitor load ratio, the energy transfer efficiency between the battery and the supercapacitor, and the dynamic safety factor; wherein, the dynamic safety factor is the ratio between the actual energy consumed during hill climbing and obstacle crossing and the transferred energy in historical data.

[0013] Based on the transferred energy, the battery is controlled to charge the supercapacitor.

[0014] Furthermore, if the abnormal power consumption path is an uphill path, the target peak power is calculated using the following formula:

[0015] P 目标 = m×g×v×sin(θ) + m×a×v + k×m×g×v;

[0016] Among them, P 目标 Let m be the target peak power, v be the mass of the all-terrain robot, θ be the slope, a be the acceleration, g be the gravitational acceleration, and k be the empirical constant for additional resistance.

[0017] Furthermore, if the abnormal power consumption path is an obstacle-crossing path, the target peak power is calculated using the following formula:

[0018] P 目标 = P 基线 + ΔP 抬升 + ΔP 动能变化 + ΔP 姿态 ;

[0019] ΔP 抬升 = m×g×Δh / Δt;

[0020] ΔP 动能变化 = m×a×v;

[0021] ΔP 姿态 = k×τ max ×ω max ;

[0022] Among them, P 目标 For the target peak power, P 基线 Let m be the power of the all-terrain robot traveling at the speed mentioned above on a flat road, v be the mass of the all-terrain robot, g be the acceleration due to gravity, Δh be the deviation height of the obstacle crossing, and Δt be the estimated duration of the obstacle crossing. max ω represents the maximum torque required during attitude adjustment. maxdenoted as the predicted maximum angular velocity during attitude adjustment, and k is an empirical coefficient.

[0023] Furthermore, if the abnormal power consumption path is an obstacle-crossing path during an uphill climb, the target peak power is calculated using the following formula:

[0024] P 目标 = P 基线 + ΔP 抬升 + ΔP 动能变化 + ΔP 姿态 ;

[0025] ΔP 抬升 = m×g×Δh / Δt;

[0026] ΔP 动能变化 = m×a×v;

[0027] ΔP 姿态 = k×τ max ×ω max ;

[0028] Among them, P 目标 For the target peak power, P 基线 The power of the all-terrain robot traveling at the stated speed on the ramp is given by m, the mass of the all-terrain robot is given by v, the speed is given by g, the acceleration due to gravity is given by g, Δh is the deviation height of the obstacle crossing, Δt is the estimated duration of the obstacle crossing, and τ is given by τ. max ω represents the maximum torque required during attitude adjustment. max denoted as the predicted maximum angular velocity during attitude adjustment, and k is an empirical coefficient.

[0029] Furthermore, the transferred energy is calculated using the following formula:

[0030] Transferred energy = Target peak power × Duration × Bearing ratio × Energy transfer efficiency × Dynamic safety factor.

[0031] Furthermore, the method also includes:

[0032] In historical data, under the same abnormal power consumption road conditions, the actual energy consumed and transferred during the previous hill climb and obstacle crossing;

[0033] The dynamic safety factor is obtained by dividing the actual energy consumed in the previous climb and obstacle crossing by the energy transferred.

[0034] Furthermore, the method also includes:

[0035] In historical data, under the same abnormal power consumption road conditions, the actual total energy consumed and the total energy transferred when climbing and overcoming obstacles;

[0036] The dynamic safety factor is obtained by dividing the total energy actually consumed in climbing and overcoming obstacles by the total energy transferred.

[0037] Furthermore, determining the duration of work performed on the abnormal power consumption path based on the slope, the acceleration, and the velocity includes:

[0038] Based on the abnormal power consumption road and the slope, a preset obstacle-crossing strategy is determined:

[0039] The duration of traversing the abnormal power consumption road is calculated based on the obstacle-crossing strategy, the speed, and the acceleration.

[0040] Furthermore, determining whether to trigger a preset pre-adjustment strategy based on the target peak power includes:

[0041] Compare the target peak power with the preset power;

[0042] If the target peak power is greater than the preset power, the pre-adjustment strategy is triggered; otherwise, the pre-adjustment strategy is not triggered.

[0043] Furthermore, determining whether to trigger a preset pre-adjustment strategy based on the target peak power includes:

[0044] The ratio of the target peak power to the preset reference power is taken as the first fraction;

[0045] The second score is determined based on the motor current increase rate and the preset motor current increase rate threshold.

[0046] The third score is determined based on the maximum rise height of the abnormal power consumption path;

[0047] Calculate the sum of the first score, the second score, and the third score to obtain the total score;

[0048] Compare the total score with the preset score;

[0049] If the total score is greater than the preset score, the pre-adjustment strategy is triggered; otherwise, the pre-adjustment strategy is not triggered.

[0050] Furthermore, the method also includes:

[0051] Based on a preset fraction-voltage mapping relationship for graded pre-charging, the charging voltage range of the supercapacitor corresponding to the total fraction is determined.

[0052] If, after charging the transferred energy, the voltage of the supercapacitor is less than the lower limit of the charging voltage range, then charging continues until the charging voltage range is reached.

[0053] Furthermore, the method also includes:

[0054] Real-time acquisition of the supercapacitor's SOC and actual peak power;

[0055] If the actual peak power exceeds a preset percentage of the target peak power, or if the SOC drops to a preset value and the SOC drop rate exceeds a preset drop rate, a capacitor recovery strategy is executed to reduce the power of non-critical loads.

[0056] Furthermore, the method also includes:

[0057] Acquire the average temperature change during historical obstacle crossing processes, the current ambient temperature, and the battery temperature;

[0058] The optimal operating range of the battery cell temperature is determined based on the ambient temperature.

[0059] Based on the average temperature change and the battery temperature, determine the predicted battery temperature after overcoming the obstacle.

[0060] If the predicted temperature exceeds the optimal operating range, increase the liquid cooling flow rate for battery cooling.

[0061] Furthermore, the method also includes:

[0062] From historical climbing data, filter out the number of times the climbing distance is greater than the preset distance and the slope is greater than the preset slope, and the number of times the obstacle clearance height exceeds the preset height;

[0063] The total driving distance, the number of times the climbing distance is greater than a preset distance and the slope is greater than a preset slope, and the number of times the obstacle crossing height exceeds a preset height are input into a preset battery degradation model, and the battery health index is output. The battery degradation model is pre-trained with battery health under different climbing and obstacle crossing times.

[0064] Secondly, this application provides a pre-adjustment device for the battery of an all-terrain robot, the device comprising:

[0065] The acquisition module is used to acquire road images, slope, acceleration, and velocity in the direction the all-terrain robot is moving.

[0066] The road determination module is used to determine the type of road the robot will travel on within a preset time period in the future, based on the road image and the speed. The road type includes normal power consumption roads and abnormal power consumption roads.

[0067] The prediction module is used to determine the target peak power and duration of work done on the abnormal power road based on the slope, the acceleration, and the speed if the road type is an abnormal power road.

[0068] The judgment module is used to determine whether a preset pre-adjustment strategy is triggered based on the target peak power.

[0069] The energy transfer calculation module is used to determine the energy to be pre-charged to the supercapacitor if the pre-adjustment strategy is triggered, based on the target peak power, the duration, the preset supercapacitor load ratio, the energy transfer efficiency between the battery and the supercapacitor, and the dynamic safety factor; wherein, the dynamic safety factor is the ratio between the actual energy consumed during hill climbing and obstacle crossing and the transferred energy in historical data.

[0070] The pre-charge module is used to control the battery to charge the supercapacitor based on the transferred energy.

[0071] Thirdly, this application provides an electronic device, including: a memory and a processor;

[0072] The memory stores computer-executed instructions;

[0073] The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any of the first aspects.

[0074] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any of the first aspects.

[0075] Fifthly, this application provides an all-terrain robot, comprising: an all-terrain robot body, a battery, a supercapacitor for storing energy, and a controller, the controller being configured to perform the method as described in any of the first aspects.

[0076] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0077] In a seventh aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0078] This application provides a pre-adjustment method for the battery of an all-terrain robot. The method includes: acquiring a road image, slope, acceleration, and velocity in the direction of travel of the all-terrain robot; determining the road type to be traveled by the robot in a future preset time period based on the road image and the velocity, wherein the road type includes normal power consumption roads and abnormal power consumption roads; if the road type is an abnormal power consumption road, determining the target peak power and duration of work done on the abnormal power consumption road based on the slope, acceleration, and velocity; determining whether a preset pre-adjustment strategy is triggered based on the target peak power; if the pre-adjustment strategy is triggered, determining the transfer energy to the supercapacitor for pre-charging based on the target peak power, the duration, a preset supercapacitor load ratio, the energy transfer efficiency between the battery and the supercapacitor, and a dynamic safety factor; wherein the dynamic safety factor is the ratio between the actual energy consumed in climbing and overcoming obstacles and the transferred energy in historical data; and controlling the battery to charge the supercapacitor based on the transferred energy. By fusing multi-source environmental sensing data and making forward-looking predictions, a portion of the battery's energy is proactively transferred to the supercapacitor in advance to cope with upcoming high-power scenarios. This allows the battery to only handle smooth base load power output, while the supercapacitor handles instantaneous peak power demands. By using the supercapacitor as a buffer for instantaneous peak power, the battery's heat loss is reduced. Attached Figure Description

[0079] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0080] Figure 1 A schematic diagram of the specific all-terrain robot provided in this application;

[0081] Figure 2 A flowchart illustrating a method for pre-adjusting the battery of an all-terrain robot provided in this application;

[0082] Figure 3 A schematic diagram of the pre-adjustment device for the battery of the all-terrain robot provided in this application;

[0083] Figure 4 A schematic diagram of the structure of the electronic device provided in this application.

[0084] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0085] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0086] All-terrain robots are intelligent mobile platforms with high payload capacity, high mobility, and adaptability to multiple scenarios. They can maintain stable operation in typical complex terrains such as snow, mud, shallow water, and swamps. In most scenarios, their four-wheel drive design provides excellent terrain mobility and operational stability, making them suitable for emergency operations in complex terrains and high-risk areas.

[0087] Figure 1 A schematic diagram of the specific all-terrain robot provided in this application, such as... Figure 1 The structure shown can be adapted to different terrains.

[0088] When working in complex, unstructured terrain, all-terrain robots often face sudden high-power demands, such as steep slope climbing (slope > 20°), obstacle crossing (crossing ditches, steps, rocks), rapid acceleration / sharp turning / impact recovery, and sudden load changes. These conditions can cause the instantaneous power demand of the motor to reach 3-8 times the rated power, with a duration typically between 0.3 and 2 seconds.

[0089] Traditional battery management systems (BMS) cannot anticipate high-power demand scenarios, leading to a surge in instantaneous heat loss. Hybrid energy storage systems (HESS) rely on real-time power differentials or filtering, with the capacitor only taking over after the peak occurs. The battery still has to withstand the initial surge current, failing to reduce the surge in instantaneous heat loss and impacting battery life.

[0090] Based on this, this application anticipates high-power intent by constructing a short-term power demand prediction model using multi-source heterogeneous signals (real-time slope, motor current change rate, and terrain prediction). A pre-adjustment window is triggered, actively controlling the DC / DC converter to controllably transfer a portion of the battery's energy to the supercapacitor before the high-power event occurs. When the event arrives, the battery primarily provides smooth base load power, while the supercapacitor handles the majority of the instantaneous peak power, reducing the battery current change rate and decreasing heat loss.

[0091] The following describes the technical solution of this application and how it solves the aforementioned technical problems, using the controller as the execution entity and specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0092] Figure 2 A flowchart illustrating a method for pre-adjusting the battery of an all-terrain robot provided in this application is shown below. Figure 2 As shown, the method includes:

[0093] S101. Obtain road images, slope, acceleration, and velocity in the direction the all-terrain robot is moving.

[0094] In this step, the all-terrain robot is equipped with a front-facing camera, such as a forward-facing monocular / binocular depth camera, which can capture images of the road ahead. The robot's acceleration is acquired via an inertial measurement unit (IMU), and its velocity is acquired via a velocity sensor.

[0095] The slope of the road ahead can be measured by lidar, or identified or calculated from road images.

[0096] S102. Based on the road image and speed, determine the type of road the robot will travel on within a preset time period in the future. The road types include normal power consumption roads and abnormal power consumption roads.

[0097] In this step, the road images captured by the all-terrain robot are generally wide-scene images. For the same image, the robot's position at different times will vary depending on its travel speed. This leads to different predicted power consumption at different times. However, this solution has a limited lead time for pre-charging the supercapacitor to prevent energy from remaining in the supercapacitor indefinitely; for example, it may pre-charge it by 0.5 seconds or 1 second. Therefore, it is necessary to determine the road type within a preset short timeframe during the robot's movement, rather than determining the road type across the entire image.

[0098] Based on road images from depth cameras, image recognition technology can determine the road type on the road surface. Roads with abnormal power consumption can be categorized into uphill roads, obstacle-crossing roads, and other types, such as muddy roads, where power consumption increases. Specifically, combining the current speed, the system determines the road area ahead within a preset time period (e.g., 0.5-1 second). Image recognition, geometric analysis, or depth analysis is then performed on this road area to extract terrain features. These extracted features are compared with preset thresholds to classify the road and determine whether it belongs to a normal or abnormal power consumption road. The depth information in the image can detect abrupt changes in height and road slope (i.e., gradient) features.

[0099] To determine the road type, a road type classification model can be built in advance. The road image, depth map, and speed are input into the classification model, and the road type is output.

[0100] In some embodiments, the navigation planning path and current speed are combined to determine the road area ahead for a preset time period in the future.

[0101] If it is a normal power consumption path, no action is taken. If it is an abnormal power consumption path, step S103 is executed.

[0102] S103. If the road type is an abnormal power consumption road, then determine the target peak power and duration of work done on the abnormal power consumption road based on the slope, acceleration, and speed.

[0103] The target peak power refers to the maximum power output required for the robot to perform work on abnormal power consumption paths.

[0104] For abnormal power consumption paths, there are also climbing paths and obstacle-crossing paths. The power calculation method can be different for different types of paths. Quantitatively predicting the upcoming high power demand peak and its duration provides numerical basis for accurate pre-charging of supercapacitors, avoiding undercharging or overcharging.

[0105] The target peak power and duration can be calculated using a combination of physical models, empirical formulas, or data-driven models. For uphill scenarios, the power can be directly estimated using gravity components, acceleration components, and rolling / air resistance. For obstacle-crossing scenarios, the power can be estimated by combining height changes, attitude adjustment requirements, and baseline power. The duration is estimated by forward integration or table lookup based on the slope length / obstacle size obtained from image segmentation and the current speed.

[0106] S104. Based on the target peak power, determine whether to trigger the preset pre-adjustment strategy.

[0107] In this step, a reasonable trigger threshold is set to avoid pre-charging for every slight fluctuation, thereby minimizing unnecessary energy transfer losses between the battery and supercapacitor while ensuring power supply, and improving the overall system energy efficiency.

[0108] In one implementation, a power can be preset (e.g., 1.2 to 1.8 times the battery's safe continuous output power, or a percentage of the battery's rated power). When the calculated target peak power is greater than the preset power, a pre-adjustment strategy is triggered.

[0109] If the pre-adjustment strategy is not triggered, no action will be taken.

[0110] S105. If the pre-adjustment strategy is triggered, the pre-charged energy transferred to the supercapacitor is determined based on the target peak power, duration, preset supercapacitor load ratio, energy transfer efficiency between the battery and the supercapacitor, and dynamic safety factor. The dynamic safety factor is the ratio between the actual energy consumed during hill climbing and obstacle crossing and the transferred energy in historical data.

[0111] In this step, taking into account peak power demand, supercapacitor sharing ratio, charging and discharging path losses, and historical actual deviations, a pre-charging energy with a slight margin is calculated to achieve intelligent energy management that can meet extreme operating conditions without overcharging.

[0112] The supercapacitor's load-bearing capacity can be preset to 60%~95% (determined based on the matching of supercapacitor capacity and battery characteristics); the energy transfer efficiency η can be obtained through real-time measurement or calibration.

[0113] Dynamic safety factor = Actual energy consumed / Transferred energy. In one implementation, the dynamic safety factor can be calculated using the actual energy consumed and transferred energy under the same abnormal power consumption path in the past. In another implementation, the dynamic safety factor can be calculated using the total actual energy consumed and total transferred energy under the same abnormal power consumption path in historical data. In yet another implementation, a sliding window is used to statistically average the actual energy consumed and the transferred energy over a historical period. The dynamic safety factor can correct the amount of transferred energy calculated based on peak power, improving accuracy.

[0114] In one specific implementation, the transferred energy = target peak power × duration × proportion of responsibility × energy transfer efficiency × dynamic safety factor.

[0115] S106. Based on the transferred energy, control the battery to charge the supercapacitor.

[0116] During the window period before abnormal operating conditions occur, the calculated energy is transferred to the supercapacitor in advance with controllable current / power, so that it can quickly release high power when the peak arrives, protecting the battery from high-rate discharge shock.

[0117] Specifically, a bidirectional DC-DC converter can be used to connect the battery and the supercapacitor bank; the target charging current or power can be calculated based on the required energy transfer and the voltage difference between the current supercapacitor and the battery; a constant current-constant voltage or constant power charging strategy can be adopted to complete the charging within the estimated lead time window; at the same time, the supercapacitor voltage can be monitored to ensure that it does not exceed its rated voltage, and overcurrent and overtemperature protection can be set.

[0118] In this embodiment, the passive response problem of traditional battery management systems in high-power demand scenarios such as steep slope climbing and obstacle crossing is solved by fusing multi-source environmental perception data and making forward-looking predictions. Specifically, the target peak power is quantified through road image recognition and physical modeling formulas. After triggering the pre-adjustment strategy, the system controllably transfers part of the battery's energy to the supercapacitor through a DC / DC converter, so that the battery only undertakes smooth base load power output, while the supercapacitor undertakes instantaneous peak power demand. This method significantly reduces the frequency and intensity of high-current discharge of the battery, thereby suppressing SEI film damage and lithium dendrite growth, and extending battery life. In addition, the introduction of a dynamic safety factor avoids overcharging or insufficient energy problems caused by fixed thresholds, ensuring that energy transfer dynamically matches actual demand. By using the supercapacitor as a buffer carrier for instantaneous peak power, the battery's heat loss is significantly reduced, and the overall energy efficiency and reliability of the hybrid energy storage system are improved.

[0119] In some embodiments, if the abnormal power consumption path is an uphill path, the target peak power is calculated using the following formula:

[0120] P 目标 = m×g×v×sin(θ) + m×a×v + k×m×g×v;

[0121] Among them, P 目标 Let m be the target peak power, v be the mass of the all-terrain robot, θ be the slope, a be the acceleration, g be the gravitational acceleration, and k be the empirical constant for additional resistance.

[0122] In this formula, the first product term is the power required for the robot to overcome gravity along the ramp, the second product term is the portion of the power contributed by the rate of change of kinetic energy, and the third product term is the power required to overcome the resistance term.

[0123] In some embodiments, if the abnormal power consumption path is an obstacle-crossing path, the target peak power is calculated using the following formula:

[0124] P 目标 = P 基线+ ΔP 抬升 + ΔP 动能变化 + ΔP 姿态 ;

[0125] ΔP 抬升 = m×g×Δh / Δt;

[0126] ΔP 动能变化 = m×a×v;

[0127] ΔP 姿态 = k×τ max ×ω max ;

[0128] Among them, P 目标 Let m be the target peak power, v be the mass of the all-terrain robot, g be the acceleration due to gravity, Δh be the deviation height of the obstacle crossing path, Δt be the estimated duration of obstacle crossing, and τ be the velocity. max ω represents the maximum torque required during attitude adjustment. max denoted as the predicted maximum angular velocity during attitude adjustment, and k is an empirical coefficient.

[0129] P 基线 ΔP represents the power required for an all-terrain robot to travel at a certain speed on a flat road. 抬升 This represents the power required for the robot to increase its height during obstacle crossing. ΔP 动能变化 ΔP represents the power required for the robot's velocity change during obstacle crossing. 姿态 The power required to adjust the robot's posture (roll, pitch, lateral balance).

[0130] In some embodiments, a scenario may arise where obstacle crossing occurs during the hill-climbing process, i.e., both a slope and an obstacle are detected. In this case, the target peak power can be calculated using a combination of the two methods described above. The power P of the all-terrain robot traveling at a certain speed on a flat road in an obstacle-crossing scenario is considered. 基线 This is replaced by the power of the all-terrain robot traveling at that speed on a slope. The power of the all-terrain robot traveling at that speed on a slope can be obtained using the power calculation formula for slopes. The formula is:

[0131] P 目标 = m×g×v×sin(θ) + m×a×v + k×m×g×v + ΔP 抬升 + ΔP 动能变化 + ΔP 姿态

[0132] ΔP 抬升 ΔP 动能变化 ΔP 姿态The calculation method is the same as described above, and will not be repeated here.

[0133] In some embodiments, the robot employs different passage strategies when facing different abnormal power consumption road types or different obstacles. Therefore, the time required to overcome obstacles needs to be calculated specifically. An embodiment is described in detail below.

[0134] Based on the slope of the abnormal power consumption path, the height of the obstacle to be overcome, and the robot's current state (e.g., the robot's power system and battery status), a suitable obstacle-crossing strategy is selected from the preset strategies. For example, if the slope is steep, a higher power output is selected to ensure the robot can overcome gravity. If the slope is gentle, a normal driving mode can be used to avoid unnecessary energy consumption. When overcoming obstacles of a certain height, special execution steps may be required, such as reducing the robot's speed from the current speed to the target speed before overcoming the obstacle and increasing the power output. After determining the obstacle-crossing strategy, the overcoming duration is calculated according to the predetermined control strategy within the obstacle-crossing strategy.

[0135] In some embodiments, to avoid misjudgments caused by calculating the target peak power from image data, multiple key factors such as the target peak power, motor current increase rate, and abnormal power consumption road height are quantitatively scored, and a pre-adjustment strategy is determined based on the comprehensive scoring results, thereby improving the accuracy and stability of system decision-making.

[0136] Specifically, determining whether a pre-adjustment strategy needs to be triggered includes the following steps:

[0137] Step 1: Calculate the ratio of the target peak power to the system's preset reference power to obtain a first score. This first score reflects the robot's power demand when traversing the abnormal power consumption path. When the target peak power approaches or exceeds the preset power, the first score increases accordingly, indicating a higher power demand from the robot and a higher probability of triggering a pre-adjustment strategy.

[0138] Step 2: Obtain the motor current increase rate (i.e., the rate of change of motor current per unit time) and compare it with a preset motor current increase rate threshold. Determine the second score based on the comparison result. If the motor current increase rate is large, it indicates that the load on the robot drive system is changing rapidly, and there may be a sudden increase in instantaneous power demand. In this case, the second score will be increased accordingly to reflect the risk level of the system's power load change.

[0139] Step 3: Determine the third score based on the maximum ascent height of the abnormal power consumption path (e.g., ramp height or obstacle height). Generally, the greater the ascent height, the higher the gravitational potential energy the robot needs to overcome, and the greater the demand on the power system. Therefore, the third score will increase with the increase of the maximum ascent height.

[0140] Step 4: After obtaining the first, second, and third scores, the three scores are weighted or directly summed to obtain the total score. This total score is used to comprehensively assess the robot's dynamic risk level when traversing abnormally power-consuming roads under the current working conditions.

[0141] Step 5: Compare the total score with the preset score.

[0142] When the total score is greater than the preset score, it indicates that the combined risk of the current road environment and the robot's power requirements is high. At this time, the system determines that the power system needs to be adjusted in advance, thus triggering the pre-adjustment strategy.

[0143] Conversely, if the total score is less than or equal to the preset score, it means that the robot's power system can normally cope with abnormal power consumption paths under the current working conditions, and no additional pre-adjustment operation is required. Therefore, the system does not trigger the pre-adjustment strategy and maintains the current operating state.

[0144] Through the above-mentioned multi-factor comprehensive scoring mechanism, this embodiment can more comprehensively evaluate the robot's power demand in abnormal power consumption road environments. Compared with the method that relies on a single indicator for judgment, it can significantly improve the accuracy of strategy triggering and the stability of system operation, while effectively reducing the risk of power system overload or abnormal energy consumption.

[0145] In some embodiments, to further enhance the robot's dynamic response capability in abnormal power consumption road scenarios, this embodiment also provides a supercapacitor graded pre-charging control method based on a score-voltage mapping relationship, in addition to the above-mentioned method of determining whether to trigger a pre-adjustment strategy based on the total score. By dynamically determining the target charging voltage range of the supercapacitor based on the comprehensive scoring results, the energy storage system can provide appropriate energy reserves under different risk levels, thereby improving the system's energy utilization efficiency and power system stability.

[0146] Specifically, the method includes the following steps:

[0147] First, based on the total score calculated in the aforementioned embodiments and combined with a pre-defined score-voltage mapping relationship, the supercapacitor charging voltage range corresponding to the current total score is determined. The score-voltage mapping relationship is used to implement tiered pre-charge control, that is, matching different target voltage ranges for the supercapacitor according to different risk score levels. For example, when the total score is low, the corresponding charging voltage range is low to reduce unnecessary energy storage; while when the total score is high, a higher charging voltage range is corresponding to store more energy in advance to cope with potential high power demands.

[0148] Subsequently, after the energy transfer from the battery to the supercapacitor is complete, the current voltage of the supercapacitor is compared with the charging voltage range. If, after the energy transfer is complete, the voltage of the supercapacitor is less than the lower limit of the charging voltage range, it indicates that the current energy storage level of the supercapacitor is insufficient to meet the energy requirements corresponding to that rating level.

[0149] In this situation, the control battery will continue to perform the charging operation, further charging the supercapacitor until its voltage reaches or exceeds the lower limit of the charging voltage range, thereby ensuring that the supercapacitor has sufficient energy reserves to meet the high power output demand that may occur later.

[0150] Conversely, if the supercapacitor voltage is already within the charging voltage range or above the lower limit after energy transfer is complete, there is no need to continue charging to avoid overcharging or energy waste.

[0151] Using the above method, this embodiment can dynamically adjust the energy storage level of the supercapacitor based on the road condition risk score, achieving tiered pre-charge control. Compared to a fixed voltage charging strategy, this method can reduce energy loss while ensuring power response capability, thereby improving the intelligence level and operational efficiency of the energy management system.

[0152] In some embodiments, after each successful obstacle crossing / climb, the actual peak energy, duration, power curve, and speed change are recorded, along with corresponding road parameters, climb height, obstacle dimensions, and road friction coefficient. After accumulating a large amount of data, when encountering a similar scenario (slope ±2°, obstacle height ±2cm, same speed range), the required pre-charge energy can be directly retrieved via a query. This technique can be implemented in a table for querying or as a Gaussian mixture model of the scenario; there are no specific limitations.

[0153] In some embodiments, to ensure the energy safety of the supercapacitor and the power stability of the robot, this embodiment, based on the above embodiments, also includes a capacitor recovery strategy under real-time SOC and peak power monitoring. This method achieves intelligent control of non-critical loads by dynamically monitoring the supercapacitor status and load power, thereby preventing capacitor over-discharge or abnormal system power.

[0154] The specific steps are as follows:

[0155] 1. Real-time acquisition of the state charge (SOC) of the supercapacitor and the current actual peak power of the system.

[0156] Among them, SOC reflects the energy storage level of the capacitor, while the actual peak power reflects the instantaneous power demand of the current load on the supercapacitor.

[0157] 2. Determine whether the capacitor recovery strategy is triggered.

[0158] When the actual peak power exceeds the preset proportion of the target peak power, it indicates that the load demand may put pressure on the supercapacitor; or when the SOC drops to the preset low threshold and the SOC drop rate exceeds the preset drop rate, it indicates that the supercapacitor's energy is being consumed rapidly, and there is a risk of over-discharge.

[0159] When any of the above conditions are met, the system triggers the capacitor recovery strategy.

[0160] 3. Implement a capacitor recovery strategy.

[0161] Adjust or limit the power of non-critical loads, such as comfort equipment and auxiliary systems, to prioritize the energy supply of critical loads; by reducing the power of non-critical loads, slow down the rate of SOC decline, reduce the risk of capacitor over-discharge or overload, and stabilize the overall peak power of the system.

[0162] Through the above steps, this embodiment implements an active protection mechanism based on real-time SOC and peak power, enabling dynamic intervention under high capacitor load or rapid discharge conditions to ensure that the supercapacitor's energy storage level remains within a safe range. Compared to traditional fixed strategies, it can more flexibly respond to instantaneous high power demands or rapid capacitor discharge scenarios, improving system reliability and energy efficiency.

[0163] In some embodiments, the battery typically generates significant heat during all-terrain robot climbing (or any high-load, steep-slope driving), often more severely and rapidly than when driving at a constant speed on flat ground. To further improve the thermal safety and operational stability of the battery system under obstacle-crossing conditions, based on the above method embodiments, the method further includes predicting the battery temperature after obstacle crossing based on historical temperature rise characteristics and current environmental conditions, and dynamically adjusting the liquid cooling flow rate accordingly.

[0164] Specifically, the method includes:

[0165] Step 1: Obtain the average temperature change during historical obstacle crossings, the current ambient temperature, and the current battery temperature.

[0166] The average temperature change during historical obstacle-crossing processes characterizes the typical rise in battery temperature under similar obstacle-crossing scenarios, reflecting the impact of obstacle-crossing behavior on the battery's thermal state. The current ambient temperature reflects external heat dissipation conditions, while the current battery temperature reflects the actual thermal state before or during obstacle crossing.

[0167] Step 2: Determine the optimal operating range of the battery cell temperature based on the ambient temperature.

[0168] Specifically, a correlation between ambient temperature and the optimal operating temperature range of the battery cell can be established in advance. When the ambient temperature is low, the optimal operating range can be lower accordingly; when the ambient temperature is high, the optimal operating range can be adjusted accordingly, so that the battery cell can be in a better working state under different external conditions, thereby taking into account performance output, lifespan maintenance, and safety requirements.

[0169] Step 3: Based on the average temperature change and battery temperature, determine the predicted battery temperature after overcoming the obstacle.

[0170] By superimposing the current battery temperature with the average temperature change during historical obstacle-crossing processes, the predicted battery temperature can be obtained after the current obstacle crossing ends or in the subsequent stages of the obstacle crossing process. In this way, the potential temperature rise level of the battery can be known in advance before the actual temperature exceeds the limit.

[0171] Step 4: After obtaining the predicted temperature, compare it with the optimal operating range.

[0172] Step 5: If the predicted temperature exceeds the optimal operating range, increase the liquid cooling flow rate for battery cooling.

[0173] Specifically, the heat dissipation capacity of the battery system can be enhanced by increasing the speed of the liquid cooling pump, adjusting the opening of the cooling circuit valve, or increasing the circulation rate of the cooling medium, so as to reduce the temperature rise of the battery and bring the battery temperature back to or keep it within the optimal operating range.

[0174] The above-described solution enables proactive prediction of battery temperature rise during obstacle crossing operations and allows for enhanced liquid cooling measures to be implemented in advance when a potential temperature exceedance is anticipated. Compared to methods that only implement heat dissipation control after the battery temperature has already exceeded a threshold, this embodiment offers greater proactivity and foresight, which helps reduce battery heat accumulation during high-load obstacle crossing scenarios and improves the safety, stability, and reliability of the battery system.

[0175] In some embodiments, when an all-terrain robot encounters abnormal power consumption road types, vibrations and shocks occur, and the abnormal power consumption affects battery life, leading to a deterioration in battery SOC estimation and impacting power output. Therefore, to more accurately assess the impact of the robot on the lifespan of the power battery under complex operating conditions, based on the above method embodiments, the battery degradation can be further estimated by statistically analyzing the robot's historical behavior when encountering abnormal power consumption roads, thereby obtaining battery health indicators.

[0176] Specifically, the method includes:

[0177] Step 1: Analyze and filter historical climbing data from the robot. The historical climbing data is traversed and statistically analyzed to filter climbing behaviors that meet specific conditions. These conditions include climbing events where the climbing distance is greater than a preset distance and the slope is greater than a preset slope.

[0178] By statistically analyzing the climbing behaviors that meet the above conditions, the number of times the climbing distance is greater than a preset distance and the slope is greater than a preset slope was obtained. This type of climbing behavior usually corresponds to the robot being under high load conditions, with high demand on battery output power, which can easily cause battery temperature rise and accelerate battery aging.

[0179] Step 2: Analyze the robot's obstacle-crossing history data, filter out obstacle-crossing events where the obstacle-crossing height exceeds the preset height, and count the number of occurrences. An obstacle-crossing height exceeding the preset height means the robot needs to output a large driving force instantaneously, resulting in a significant power surge to the battery. Therefore, such events can also serve as an indicator of the battery's high-load usage.

[0180] Step 3: Obtain the total distance traveled by the robot.

[0181] The total driving distance can be obtained through a robot odometer or robot operation recording system, and is used to characterize the overall battery usage time and cycle load.

[0182] Step 4: After obtaining the above-mentioned multiple feature parameters, the total driving distance, the number of times the climbing distance is greater than the preset distance and the slope is greater than the preset slope, and the number of times the obstacle crossing height exceeds the preset height are used as input parameters and input into the pre-built battery degradation model to output the corresponding battery health indicators.

[0183] Battery health metrics can be used to characterize the health status of a battery, for example, by expressing the degree to which the current battery capacity is maintained relative to the rated capacity as a percentage.

[0184] The battery degradation model can be trained using historical experimental data or robot operation data. Specifically, the battery health changes can be monitored over a long period under different climbing and obstacle crossing conditions to establish a corresponding dataset, and the battery degradation model can be trained based on this dataset. For example, the battery degradation model can employ regression models, machine learning models, or neural network models to predict the battery health status by learning the relationship between different high-load operating conditions and battery health.

[0185] By employing the above methods, typical high-load operating conditions exhibited by the robot during actual use (such as long-distance, steep incline climbing and high-altitude obstacle crossing) can be incorporated into the battery health assessment system, thereby more accurately reflecting the impact of the robot's operating environment on battery degradation. Compared to methods that only assess based on the number of cycles or mileage, this embodiment can more comprehensively characterize the actual usage intensity of the battery, improve the accuracy of battery health assessment, and provide a basis for subsequent battery management strategy optimization and pre-adjustment decisions.

[0186] Specific examples

[0187] The all-terrain robot collects current operating status information through a front-facing vision camera, inertial measurement unit (IMU), and speed sensor. The parameters and preset parameters are as follows:

[0188] The robot's mass is m = 120 kg;

[0189] The driving speed is v = 1.5 m / s;

[0190] Acceleration a = 0.4 m / s² 2 ;

[0191] The slope θ = 18°;

[0192] The acceleration due to gravity g = 9.8 m / s² 2 ;

[0193] The empirical constant for additional resistance is k = 0.05;

[0194] The supercapacitor accounts for a proportion β = 0.6;

[0195] Energy transfer efficiency η = 0.9;

[0196] The dynamic safety factor λ = 1.2;

[0197] Based on the road image recognition results and the robot's current speed, the driving environment in the next 5 seconds is predicted. The robot detects that the terrain ahead is a continuous uphill slope with a slope greater than 15°, and therefore it is identified as an uphill road in the abnormal power consumption road.

[0198] Calculate the target peak power based on the climbing power model:

[0199] P 目标 = m×g×v×sin(θ) + m×a×v + k×m×g×v = 705W;

[0200] Based on image recognition, the ramp is estimated to be approximately 6 meters long, and the robot's speed is 1.5 m / s. Therefore, the estimated duration is 4 seconds.

[0201] The target peak power of 705W is greater than the battery's safe continuous output power of 400W, triggering the pre-adjustment strategy.

[0202] Calculate the pre-charge energy of the supercapacitor:

[0203] E 转移 = P 目标 ×t×β×η×λ= 1827 J

[0204] The DC / DC energy management module is controlled to transfer electrical energy from the battery to the supercapacitor in advance, thereby raising the supercapacitor voltage to a preset level.

[0205] When the robot enters the ramp, the supercapacitor bears about 60% of the instantaneous power and the battery bears 40% of the power, thereby avoiding instantaneous high current discharge of the battery, reducing heat loss, improving system stability, and reducing battery degradation.

[0206] Figure 3 A schematic diagram of the pre-adjustment device for the battery of the all-terrain robot provided in this application is shown below. Figure 3 As shown, the apparatus provided in this embodiment includes:

[0207] The acquisition module 301 is used to acquire road images, slope, acceleration, and velocity in the forward direction of the all-terrain robot;

[0208] The road determination module 302 is used to determine the type of road the robot will travel on within a preset time period in the future based on the road image and speed. The road types include normal power consumption roads and abnormal power consumption roads.

[0209] The prediction module 303 is used to determine the target peak power and duration of work done on the abnormal power road based on the slope, acceleration and speed if the road type is an abnormal power road.

[0210] The judgment module 304 is used to determine whether to trigger a preset pre-adjustment strategy based on the target peak power.

[0211] The energy transfer calculation module 305 is used to determine the energy to be pre-charged to the supercapacitor if the pre-adjustment strategy is triggered, based on the target peak power, duration, preset supercapacitor load ratio, energy transfer efficiency between the battery and the supercapacitor, and dynamic safety factor; wherein, the dynamic safety factor is the ratio between the actual energy consumed during hill climbing and obstacle crossing and the transferred energy in historical data.

[0212] The pre-charge module 306 is used to control the battery to charge the supercapacitor based on the transferred energy.

[0213] Optionally, if the abnormal power consumption path is an uphill path, the prediction module 303 calculates the target peak power using the following formula:

[0214] P 目标 = m×g×v×sin(θ) + m×a×v + k×m×g×v;

[0215] Among them, P 目标 Let m be the target peak power, v be the mass of the all-terrain robot, θ be the slope, a be the acceleration, g be the gravitational acceleration, and k be the empirical constant for additional resistance.

[0216] Optionally, if the abnormal power consumption path is an obstacle-crossing path, the prediction module 303 calculates the target peak power using the following formula:

[0217] P 目标 = P 基线 + ΔP 抬升 + ΔP 动能变化 + ΔP 姿态 ;

[0218] ΔP 抬升 = m×g×Δh / Δt;

[0219] ΔP 动能变化 = m×a×v;

[0220] ΔP 姿态 = k×τ max ×ω max ;

[0221] Among them, P 目标 For the target peak power, P 基线 Let τ be the power of the all-terrain robot traveling at a speed of Δh on a flat road, m be the mass of the all-terrain robot, v be the velocity, g be the acceleration due to gravity, Δh be the deviation height of the obstacle crossing, and Δt be the estimated duration of the obstacle crossing. max ω represents the maximum torque required during attitude adjustment. max denoted as the predicted maximum angular velocity during attitude adjustment, and k is an empirical coefficient.

[0222] Optionally, if the abnormal power consumption path is an obstacle-crossing path during an uphill climb, the prediction module 303 calculates the target peak power using the following formula:

[0223] P 目标 = P 基线 + ΔP 抬升 + ΔP 动能变化 + ΔP 姿态 ;

[0224] ΔP 抬升 = m×g×Δh / Δt;

[0225] ΔP 动能变化 = m×a×v;

[0226] ΔP 姿态 = k×τ max ×ω max ;

[0227] Among them, P 目标 For the target peak power, P 基线 Let τ be the power of the all-terrain robot traveling at a speed on a slope, m be the mass of the all-terrain robot, v be the velocity, g be the acceleration due to gravity, Δh be the deviation height of the obstacle crossing, and Δt be the estimated duration of the obstacle crossing. max ω represents the maximum torque required during attitude adjustment. max denoted as the predicted maximum angular velocity during attitude adjustment, and k is an empirical coefficient.

[0228] Optionally, the energy transfer calculation module calculates the transferred energy using the following formula:

[0229] Energy transferred = Target peak power × Duration × Proportion of responsibility × Energy transfer efficiency × Dynamic safety factor.

[0230] Optionally, the device may also include a parameter determination module for:

[0231] In historical data, under the same abnormal power consumption road conditions, the actual energy consumed and transferred during the previous hill climb and obstacle crossing;

[0232] The dynamic safety factor is obtained by dividing the actual energy consumed in the previous climb and obstacle crossing by the transferred energy.

[0233] Optionally, the parameter determination module is also used for:

[0234] In historical data, under the same abnormal power consumption road conditions, the actual total energy consumed and the total energy transferred when climbing and overcoming obstacles;

[0235] The dynamic safety factor is obtained by dividing the total energy actually consumed in climbing and overcoming obstacles by the total energy transferred.

[0236] Optionally, the prediction module 303 is also used for:

[0237] Based on the abnormal power consumption path and slope, determine the preset obstacle-crossing strategy:

[0238] Calculate the duration of traversing an abnormal power consumption path based on the obstacle-crossing strategy, velocity, and acceleration.

[0239] Optionally, the judgment module is specifically used for:

[0240] Compare the target peak power with the preset power;

[0241] If the target peak power is greater than the preset power, the pre-adjustment strategy is triggered; otherwise, the pre-adjustment strategy is not triggered.

[0242] Optionally, the judgment module is specifically used for:

[0243] The ratio of the target peak power to the preset reference power is used as the first score;

[0244] The second score is determined based on the motor current increase rate and the preset motor current increase rate threshold.

[0245] The third score is determined based on the maximum rise height of the abnormal power consumption path;

[0246] Calculate the sum of the first, second, and third fractions to get the total score;

[0247] Compare the total score with the preset score;

[0248] If the total score is greater than the preset score, the pre-adjustment strategy is triggered; otherwise, the pre-adjustment strategy is not triggered.

[0249] Optionally, the device also includes a hierarchical verification module for:

[0250] Based on the preset fraction-voltage mapping relationship used for graded pre-charging, the charging voltage range of the supercapacitor corresponding to the total fraction is determined.

[0251] If the voltage of the supercapacitor is lower than the lower limit of the charging voltage range after energy transfer during charging, then charging will continue until the charging voltage range is reached.

[0252] Optionally, the device also includes a remediation module for:

[0253] Real-time acquisition of the supercapacitor's SOC and actual peak power;

[0254] If the actual peak power exceeds a preset percentage of the target peak power, or if the SOC drops to a preset value and the SOC drop rate exceeds a preset drop rate, a capacitor recovery strategy will be implemented to reduce the power of non-critical loads.

[0255] Optionally, the device also includes a temperature control module for:

[0256] Acquire the average temperature change during historical obstacle crossing processes, the current ambient temperature, and the battery temperature;

[0257] Determine the optimal operating range of the battery cell temperature based on the ambient temperature.

[0258] Based on the average temperature change and the battery temperature, determine the predicted battery temperature after overcoming the obstacle.

[0259] If the predicted temperature exceeds the optimal operating range, increase the liquid cooling flow rate for battery cooling.

[0260] Optionally, the device also includes a health estimation module for:

[0261] From historical climbing data, filter out the number of times the climbing distance is greater than the preset distance and the slope is greater than the preset slope, and the number of times the obstacle clearance height exceeds the preset height;

[0262] The total driving distance, the number of times the climbing distance is greater than the preset distance and the slope is greater than the preset slope, and the number of times the obstacle crossing height exceeds the preset height are input into the preset battery degradation model, and the battery health index is output. The battery degradation model is pre-trained with battery health under different climbing and obstacle crossing times.

[0263] The apparatus provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0264] This application also provides an all-terrain robot, comprising: an all-terrain robot body, a battery, a supercapacitor for storing energy, and a controller, the controller being used to perform the method as described in any of the above method embodiments.

[0265] Figure 4 A schematic diagram of the structure of the electronic device provided in this application. Figure 4 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0266] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0267] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0268] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0269] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0270] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0271] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0272] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0273] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0274] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0275] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0276] 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 network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0277] In addition, the functional units in the various embodiments of the present invention 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.

[0278] If a function 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 invention, or the part that contributes to the prior art, or a 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 of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0279] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0280] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for pre-adjusting the battery of an all-terrain robot, characterized in that, The method includes: Acquire road images, slope, acceleration, and velocity in the direction the all-terrain robot is moving; Based on the road image and the speed, the type of road the robot will travel on in a future preset time period is determined, and the road type includes normal power consumption roads and abnormal power consumption roads. If the road type is an abnormal power consumption road, then the target peak power and duration of work done on the abnormal power consumption road are determined based on the slope, the acceleration, and the speed. Based on the target peak power, determine whether to trigger the preset pre-adjustment strategy; If the pre-adjustment strategy is triggered, the pre-charged energy transferred to the supercapacitor is determined based on the target peak power, the duration, the preset supercapacitor load ratio, the energy transfer efficiency between the battery and the supercapacitor, and the dynamic safety factor; wherein, the dynamic safety factor is the ratio between the actual energy consumed during hill climbing and obstacle crossing and the transferred energy in historical data. Based on the transferred energy, the battery is controlled to charge the supercapacitor.

2. The method according to claim 1, characterized in that, If the abnormal power consumption path is an uphill path, the target peak power is calculated using the following formula: P 目标 = m×g×v×sin(θ) + m×a×v + k×m×g×v; Among them, P 目标 Let m be the target peak power, v be the mass of the all-terrain robot, θ be the slope, a be the acceleration, g be the gravitational acceleration, and k be the empirical constant for additional resistance.

3. The method according to claim 1, characterized in that, If the abnormal power consumption path is an obstacle-crossing path, the target peak power is calculated using the following formula: P 目标 = P 基线 + ΔP 抬升 + ΔP 动能变化 + ΔP 姿态 ; ΔP 抬升 = m×g×Δh / Δt; ΔP 动能变化 = m×a×v; ΔP 姿态 = k×τ max ×ω max ; Among them, P 目标 For the target peak power, P 基线 Let m be the power of the all-terrain robot traveling at the speed mentioned above on a flat road, v be the mass of the all-terrain robot, g be the acceleration due to gravity, Δh be the deviation height of the obstacle crossing, and Δt be the estimated duration of the obstacle crossing. max ω represents the maximum torque required during attitude adjustment. max denoted as the predicted maximum angular velocity during attitude adjustment, and k is an empirical coefficient.

4. The method according to claim 1, characterized in that, If the abnormal power consumption path is an obstacle-crossing path during an uphill climb, the target peak power is calculated using the following formula: P 目标 = P 基线 + ΔP 抬升 + ΔP 动能变化 + ΔP 姿态 ; ΔP 抬升 = m×g×Δh / Δt; ΔP 动能变化 = m×a×v; ΔP 姿态 = k×τ max ×ω max ; Among them, P 目标 For the target peak power, P 基线 The power of the all-terrain robot traveling at the stated speed on the ramp is given by m, the mass of the all-terrain robot is given by v, the speed is given by g, the acceleration due to gravity is given by g, Δh is the deviation height of the obstacle crossing, Δt is the estimated duration of the obstacle crossing, and τ is given by τ. max ω represents the maximum torque required during attitude adjustment. max denoted as the predicted maximum angular velocity during attitude adjustment, and k is an empirical coefficient.

5. The method according to any one of claims 1-4, characterized in that, The transferred energy is calculated using the following formula: Transferred energy = Target peak power × Duration × Bearing ratio × Energy transfer efficiency × Dynamic safety factor.

6. The method according to claim 5, characterized in that, The method further includes: In historical data, under the same abnormal power consumption road conditions, the actual energy consumed and transferred during the previous hill climb and obstacle crossing; The dynamic safety factor is obtained by dividing the actual energy consumed in the previous hill climb and obstacle crossing by the transferred energy. or, In historical data, under the same abnormal power consumption road conditions, the actual total energy consumed and the total energy transferred when climbing and overcoming obstacles; The dynamic safety factor is obtained by dividing the total energy actually consumed in climbing and overcoming obstacles by the total energy transferred.

7. The method according to any one of claims 1-4, characterized in that, Determining the duration of work performed on the abnormal power consumption path based on the slope, acceleration, and velocity includes: Based on the abnormal power consumption road and the slope, a preset obstacle-crossing strategy is determined: The duration of traversing the abnormal power consumption road is calculated based on the obstacle-crossing strategy, the speed, and the acceleration.

8. The method according to claim 3, characterized in that, The step of determining whether to trigger a preset pre-adjustment strategy based on the target peak power includes: The ratio of the target peak power to the preset reference power is taken as the first fraction; The second score is determined based on the motor current increase rate and the preset motor current increase rate threshold. The third score is determined based on the maximum rise height of the abnormal power consumption path; Calculate the sum of the first score, the second score, and the third score to obtain the total score; Compare the total score with the preset score; If the total score is greater than the preset score, the pre-adjustment strategy is triggered; otherwise, the pre-adjustment strategy is not triggered.

9. The method according to claim 8, characterized in that, The method further includes: Based on a preset fraction-voltage mapping relationship for graded pre-charging, the charging voltage range of the supercapacitor corresponding to the total fraction is determined. If, after charging the transferred energy, the voltage of the supercapacitor is less than the lower limit of the charging voltage range, then charging continues until the charging voltage range is reached.

10. The method according to any one of claims 1-4, characterized in that, The method further includes: Real-time acquisition of the supercapacitor's SOC and actual peak power; If the actual peak power exceeds a preset percentage of the target peak power, or if the SOC drops to a preset value and the SOC drop rate exceeds a preset drop rate, a capacitor recovery strategy is executed to reduce the power of non-critical loads.

11. The method according to any one of claims 1-4, characterized in that, The method further includes: Acquire the average temperature change during historical obstacle crossing processes, the current ambient temperature, and the battery temperature; The optimal operating range of the battery cell temperature is determined based on the ambient temperature. Based on the average temperature change and the battery temperature, determine the predicted battery temperature after overcoming the obstacle. If the predicted temperature exceeds the optimal operating range, increase the liquid cooling flow rate for battery cooling.

12. The method according to any one of claims 1-4, characterized in that, The method further includes: From historical climbing data, filter out the number of times the climbing distance is greater than the preset distance and the slope is greater than the preset slope, and the number of times the obstacle clearance height exceeds the preset height; The total driving distance, the number of times the climbing distance is greater than a preset distance and the slope is greater than a preset slope, and the number of times the obstacle crossing height exceeds a preset height are input into a preset battery degradation model, and the battery health index is output. The battery degradation model is pre-trained with battery health under different climbing and obstacle crossing times.

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