Heavy truck electric drive axle long downhill energy recovery intelligent control system and method

Abstract

The invention relates to the technical field of energy recovery, in particular to a heavy truck electric drive axle long downhill energy recovery intelligent control system and method, and the system comprises a sensing module which is used for sensing road environment parameters in a vehicle downhill scene; the analysis module is used for receiving the road environment parameters sensed by the sensing module and analyzing a vehicle energy recovery recommendation index based on the road environment parameters; the vehicle energy recovery recommendation index is analyzed in real time by sensing the road environment parameters in the downhill scene where the vehicle is located, so that whether the vehicle is controlled to be switched to the energy recovery mode or not is judged and decided based on the analysis result, the protection mode is further set, and it is ensured that the vehicle does not conduct energy recovery in the energy sufficient state; and meanwhile, a vehicle energy recovery message is generated in a visual form, so that a user can read a vehicle energy recovery result conveniently.

Description

Technical Field

[0001] The present invention relates to the technical field of energy recovery, and in particular to an intelligent control system and method for long downhill energy recovery of an electric drive axle of a heavy truck. Background Art

[0002] Kinetic energy recovery in new energy vehicles is an efficient and energy-saving technology. When the vehicle decelerates or brakes, the motor switches to generator mode, converting part of the vehicle's kinetic energy into electrical energy and storing it. This not only reduces wear on the brake system, but also improves energy utilization and increases driving range, which is one of the important advantages of new energy vehicles over traditional fuel vehicles.

[0003] The invention patent application with application number 201711440703.6 discloses a control method for vehicle braking and sliding energy recovery, comprising the following steps: completely releasing the accelerator pedal of the vehicle; detecting the distance value between the vehicle and the vehicle in front through a detection system; sending the distance value between the vehicle and the vehicle in front detected by the detection system to the vehicle controller through a CAN bus, and calculating the minimum vehicle speed value for braking and sliding energy recovery at the current distance between the vehicle and the vehicle in front through the vehicle controller; judging through the vehicle controller whether the real-time speed of the vehicle is greater than the minimum vehicle speed value for braking and sliding energy recovery of the vehicle; if the real-time speed of the vehicle is greater than the minimum vehicle speed value for braking and sliding energy recovery of the vehicle, performing braking and sliding energy recovery of the vehicle until the speed of the vehicle is less than the minimum vehicle speed value for braking and sliding energy recovery of the vehicle.

[0004] This application aims to solve the problem that "the current method of braking and coasting energy recovery based on the driver's intention is very blind and is greatly affected by the driver's driving habits. For example, when encountering a traffic light, if the distance to the vehicle in front is far, the driver releases the accelerator pedal and coasts, and the whole vehicle coasts and recovers energy. This may cause the vehicle's kinetic energy to be insufficient to maintain the vehicle in front and it will stop. At this time, the driver needs to step on the accelerator pedal again to refuel. Considering the conversion efficiency of oil and electricity, energy waste has been caused at this time."

[0005] However, for heavy trucks in long downhill scenarios, there are actually relatively good energy recovery conditions, but there is currently no intelligent control system for energy recovery in such scenarios.

[0006] In order to fill this technical gap, an intelligent control system and method for long downhill energy recovery of heavy truck electric drive axle is proposed. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides an intelligent control system and method for long downhill energy recovery of an electric drive axle of a heavy truck, which solves the technical problems raised in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, an intelligent control system for long downhill energy recovery of a heavy truck electric drive axle includes: A perception module is used to perceive the road environment parameters in the vehicle downhill scenario; The perception module runs in real time in the system, synchronously obtains the vehicle navigation road information, and determines whether the vehicle is in a downhill scene based on the vehicle navigation road information; ; Where: The judgment value of whether the vehicle is in a downhill scene; is the total number of sampling points on the road section where the vehicle is located; is the elevation of the jth and j+1th sampling points; Among them, the formula (1) obtains When the equation (2) holds, the road section where the vehicle is located is determined to be in a downhill scenario. The number of sampling points in the road section where the vehicle is located is customized by the system end user. The adjacent spacing of each sampling point in the road section where the vehicle is located is equal. The road section where the vehicle is located is divided into several equal sub-sections based on the sampling points. The total number of sampling points in the road section where the vehicle is located is Contains the starting point and end point of the road section where the vehicle is located; An analysis module is used to receive the road environment parameters sensed by the perception module, and analyze the vehicle energy recovery recommendation index based on the road environment parameters; a determination module is used to set a determination threshold for whether the vehicle switches to the energy recovery mode, receives the analysis result of the vehicle energy recovery recommendation index in the analysis module, compares the analysis result with the determination threshold, and determines whether the analysis result is not less than the determination threshold. If the determination result is yes, the vehicle switches to the energy recovery mode, otherwise, the vehicle does not switch to the energy recovery mode; a protection module is used to set a vehicle battery power health determination threshold, and decides on the opening and closing of the vehicle energy recovery mode based on the comparison of the current battery power of the vehicle with the health determination threshold in real time; a visualization message module is used to generate an energy recovery visualization message for the vehicle energy recovery mode; an output module is used to receive and store the energy recovery visualization message generated in the visualization message module, and output the energy recovery visualization message; The perception module is interactively connected to the analysis module and the determination module through a wireless network. The determination module is interactively connected to the monitoring unit and the refresh unit through a wireless network. The refresh unit is interactively connected to the analysis module through a wireless network. The determination module is interactively connected to the protection module, the visualization message module and the output module through a wireless network.

[0009] Furthermore, the road environment parameters sensed by the perception module include: position information of surrounding vehicles relative to the system service vehicle, and the speed of surrounding vehicles. The analysis logic of the vehicle energy recovery recommendation index in the analysis module is expressed as: ; Where: Recommended index for energy recovery of target vehicle; is the total number of vehicles around the target vehicle; is the distance between the i-th surrounding vehicle and the target vehicle; is the speed of the vehicle in front of the target vehicle; is the target vehicle’s own speed; is the slope of the road section where the target vehicle is located; is the adjustment factor; The current remaining power of the vehicle; Among them, the target vehicle is the system service vehicle, and the target vehicle energy recovery recommendation index is The higher it is, the more suitable it is for the vehicle to start the energy recovery mode. Conversely, the lower it is, the less suitable it is for the vehicle to start the energy recovery mode.

[0010] Furthermore, the adjustment factor The value is subject to: ; Where: The total amount of downhill sections in the vehicle navigation route; is the theoretical kinetic energy recovery amount of the vth downhill section; is the normalization factor; Among them, the normalization factor <1, and is set according to the current congestion of the vehicle's navigation route. The more congested the vehicle's current navigation route is, the higher the normalization factor The smaller the value, the smaller the normalization factor. The larger the value.

[0011] Furthermore, the calculation formula of the theoretical kinetic energy recovery amount in the downhill section is: ; Where: The efficiency of the vehicle's kinetic energy recovery system; is the downhill distance; is the total mass of the vehicle; is the acceleration due to gravity; is the downhill slope; is the vehicle rolling resistance coefficient; is the air density; Set speed limits for road sections; is the air resistance coefficient; It is the projection area of ​​the front end of the vehicle perpendicular to the direction of travel.

[0012] Furthermore, the determination module is provided with submodules at a lower level, including: A monitoring unit, used to monitor the position information of the downhill section where the vehicle is located in real time in a downhill vehicle scenario; A refresh unit, used to receive the downhill section position information of the vehicle monitored by the monitoring unit, and decide whether to refresh the analysis module based on the downhill section position information of the vehicle; Among them, after receiving the position information of the downhill section where the vehicle is located, the refresh unit identifies the travel progress of the vehicle's current section based on the position information of the downhill section where the vehicle is located, and expresses it in the form of a percentage. When the travel progress of the vehicle's current section exceeds 60%, it ends. When the travel progress of the vehicle's current section does not exceed 60%, it jumps to the analysis module operation stage. When the judgment module judges as no, it triggers the monitoring unit to run. The monitoring unit runs continuously based on the specified operating frequency before the travel progress of the vehicle's current section does not exceed 60%.

[0013] Furthermore, the vehicle battery power health determination threshold in the protection module is customized by the system end user. When the current power of the vehicle battery is not less than the health determination threshold, the energy recovery mode is in a normally closed state. Otherwise, the energy recovery is switched on and off based on the determination result of the determination module. Among them, the protection module has a higher control priority over the vehicle energy recovery mode than the determination module.

[0014] Furthermore, the energy recovery visualization message of the vehicle energy recovery mode generated in the visualization message module is represented in the form of a bar graph, the horizontal axis of the bar graph represents the start time and end time of each energy recovery in the vehicle navigation path, the vertical axis represents the cumulative recovery energy value within the corresponding time threshold, and the bars representing the recovery energy values ​​in the bar graph are sorted based on a chronological order from early to late.

[0015] In a second aspect, a method for intelligently controlling energy recovery of a heavy truck electric drive axle during a long downhill slope comprises the following steps: Perceive the road environment parameters in the vehicle downhill scenario, and analyze the vehicle energy recovery recommendation index based on the road environment parameters; Set a threshold for determining whether the vehicle should switch to the energy recovery mode, compare the vehicle energy recovery recommendation index obtained through application analysis with the threshold, and decide whether the vehicle should switch to the energy recovery mode; Set the vehicle battery health determination threshold, and decide whether to turn the vehicle energy recovery mode on or off based on the comparison between the current vehicle remaining power and the health determination threshold; Set the vehicle energy recovery mode control priority; The kinetic energy recovery amount and mode execution time of the vehicle kinetic energy recovery mode during the vehicle driving process are recorded, and an energy recovery visualization message is generated, which is fed back to the user.

[0016] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects: The present invention provides an intelligent control system and method for long downhill energy recovery of an electric drive axle of a heavy truck. During the execution of the system and method, the recommended index for energy recovery of the vehicle is analyzed in real time by sensing the road environment parameters in a downhill scene where the vehicle is located, so as to determine whether to control the vehicle to switch to an energy recovery mode based on the analysis result, and further set a protection mode to ensure that the vehicle does not recover energy when the energy is sufficient, so as to maintain the optimal performance state of the vehicle. At the same time, a vehicle energy recovery message is generated in a visual form, so that users can read the vehicle energy recovery results. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a structural schematic diagram of an intelligent control system for energy recovery of a heavy-duty truck electric drive axle on a long downhill slope; Figure 2 The figure is a flow chart of an intelligent control method for energy recovery of a heavy truck electric drive axle during a long downhill slope. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The present invention will be further described below in conjunction with the embodiments.

[0021] Embodiment 1: The embodiment of the invention is a heavy truck electric drive axle long downhill energy recovery intelligent control system, such as Figure 1 As shown, including: A perception module is used to perceive the road environment parameters in the vehicle downhill scenario; The perception module runs in real time in the system, synchronously obtains the on-board navigation road information, and determines whether the vehicle is in a downhill scene based on the on-board navigation road information; ; Where: The judgment value of whether the vehicle is in a downhill scene; is the total number of sampling points on the road section where the vehicle is located; is the elevation of the jth and j+1th sampling points; Among them, formula (1) obtains When the equation (2) holds, the road section where the vehicle is located is determined to be in a downhill scenario. The number of sampling points in the road section where the vehicle is located is customized by the system end user. The adjacent spacing of each sampling point in the road section where the vehicle is located is equal. The road section where the vehicle is located is divided into several equal sub-sections based on the sampling points. The total number of sampling points in the road section where the vehicle is located is Contains the starting point and end point of the road section where the vehicle is located; Through the above logic formula calculation, it is determined whether the vehicle is in a downhill scenario; The road environment parameters perceived by the perception module include: the position information of surrounding vehicles relative to the system service vehicle, and the speed of surrounding vehicles. The analysis logic of the vehicle energy recovery recommendation index in the analysis module is expressed as follows: ; Where: Recommended index for energy recovery of target vehicle; is the total number of vehicles around the target vehicle; is the distance between the i-th surrounding vehicle and the target vehicle; is the speed of the vehicle in front of the target vehicle; is the target vehicle’s own speed; is the slope of the road section where the target vehicle is located; is the adjustment factor; The current remaining power of the vehicle; Among them, the target vehicle is the system service vehicle, and the target vehicle energy recovery recommendation index is The higher the value, the more suitable it is for the vehicle to start the energy recovery mode. Conversely, the lower the value, the less suitable it is for the vehicle to start the energy recovery mode. Adjustment Factor The value is subject to: ; Where: The total amount of downhill sections in the vehicle navigation route; is the theoretical kinetic energy recovery amount of the vth downhill section; is the normalization factor; Among them, the normalization factor <1, and is set according to the current congestion of the vehicle's navigation route. The more congested the vehicle's current navigation route is, the higher the normalization factor The smaller the value, the smaller the normalization factor. The larger the value; The calculation formula for the theoretical kinetic energy recovery on a downhill section is: ; Where: The efficiency of the vehicle's kinetic energy recovery system; is the downhill distance; is the total mass of the vehicle; is the acceleration due to gravity; is the downhill slope; is the vehicle rolling resistance coefficient; is the air density; Set speed limits for road sections; is the air resistance coefficient; It is the projection area of ​​the front end of the vehicle perpendicular to the direction of travel; The target vehicle energy recovery recommendation index is calculated and obtained through the above logic formula, which provides necessary operation data support for the operation of the determination module of the system in this embodiment; An analysis module, configured to receive the road environment parameters sensed by the perception module, and analyze the vehicle energy recovery recommendation index based on the road environment parameters; a determination module, used to set a determination threshold for whether the vehicle switches to the energy recovery mode, receive the analysis result of the vehicle energy recovery recommendation index in the analysis module, compare the analysis result with the determination threshold, and determine whether the analysis result is not less than the determination threshold. If the determination result is yes, the vehicle switches to the energy recovery mode, otherwise, the vehicle does not switch to the energy recovery mode; The judgment module is provided with submodules at the lower level, including: A monitoring unit, used to monitor the position information of the downhill section where the vehicle is located in real time in a downhill vehicle scenario; A refresh unit, used to receive the downhill section position information of the vehicle monitored by the monitoring unit, and decide whether to refresh the analysis module based on the downhill section position information of the vehicle; Among them, after receiving the position information of the downhill section where the vehicle is located, the refresh unit identifies the travel progress of the vehicle's current section based on the position information of the downhill section where the vehicle is located, and expresses it in the form of a percentage. When the travel progress of the vehicle's current section exceeds 60%, it ends. When the travel progress of the vehicle's current section does not exceed 60%, it jumps to the analysis module operation stage. When the determination module determines that it is no, it triggers the monitoring unit to run. The monitoring unit runs continuously based on the specified operation frequency before the travel progress of the vehicle's current section does not exceed 60%; The protection module is used to set the vehicle battery power health judgment threshold, and make decisions on whether to turn on or off the vehicle energy recovery mode based on the comparison between the vehicle battery current power and the health judgment threshold in real time; The vehicle battery power health judgment threshold in the protection module is customized by the system end user. When the current battery power of the vehicle is not less than the health judgment threshold, the energy recovery mode is in a normally closed state. Otherwise, the energy recovery is switched on and off based on the judgment result of the judgment module. Among them, the protection module has a higher priority than the determination module in controlling the vehicle energy recovery mode; A visualization message module, used to generate an energy recovery visualization message of a vehicle energy recovery mode; An output module, used for receiving and storing the energy recovery visualization message generated in the visualization message module, and outputting the energy recovery visualization message; The perception module is interactively connected to the analysis module and the determination module through a wireless network. The determination module is interactively connected to the monitoring unit and the refresh unit through a wireless network. The refresh unit is interactively connected to the analysis module through a wireless network. The determination module is interactively connected to the protection module, the visualization message module and the output module through a wireless network.

[0022] In this embodiment, the perception module operates to perceive the road environment parameters in the vehicle downhill scenario, the analysis module is post-operated to receive the road environment parameters perceived by the perception module, and the vehicle energy recovery recommendation index is analyzed based on the road environment parameters. The determination module further sets a determination threshold for whether the vehicle switches to the energy recovery mode, receives the vehicle energy recovery recommendation index analysis result in the analysis module, compares the analysis result with the determination threshold, and determines whether the analysis result is not less than the determination threshold. If the determination result is yes, the vehicle switches to the energy recovery mode, otherwise, the vehicle does not switch to the energy recovery mode. The monitoring unit synchronously monitors the vehicle in real time in the vehicle downhill scenario. The refresh unit receives the downhill section position information of the vehicle monitored by the monitoring unit in real time, and decides whether to refresh the analysis module based on the downhill section position information of the vehicle. The protection module sets the vehicle battery health judgment threshold, and compares the current battery power of the vehicle with the health judgment threshold in real time to decide on the opening and closing of the vehicle energy recovery mode, and generates an energy recovery visualization message of the vehicle energy recovery mode through the visualization message module. Finally, the output module is used to receive and store the energy recovery visualization message generated in the visualization message module, and output the energy recovery visualization message.

[0023] Through the operation of the system in the above embodiment, an intelligent control effect is provided for the kinetic energy recovery mode of heavy truck type vehicles in downhill scenarios, ensuring that the kinetic energy recovery mode can operate more intelligently to achieve more stable and efficient kinetic energy recovery.

[0024] like Figure 1 As shown, the energy recovery visualization message of the vehicle energy recovery mode generated in the visualization message module is represented in the form of a bar graph, the horizontal axis of the bar graph represents the start time and end time of each energy recovery in the vehicle navigation path, the vertical axis represents the cumulative recovery energy value within the corresponding time threshold, and the bars representing the recovery energy values ​​in the bar graph are sorted based on the chronological order from early to late.

[0025] Through the above settings, further operation logic support is provided for the visual message module of the system in this embodiment.

[0026] Embodiment 2: In terms of specific implementation, based on Example 1, this example refers to Figure 2 The intelligent control system for long downhill energy recovery of a heavy truck electric drive axle in Example 1 is further described in detail: An intelligent control method for long downhill energy recovery of a heavy truck electric drive axle comprises the following steps: Perceive the road environment parameters in the vehicle downhill scenario, and analyze the vehicle energy recovery recommendation index based on the road environment parameters; Set a threshold for determining whether the vehicle should switch to the energy recovery mode, compare the vehicle energy recovery recommendation index obtained through application analysis with the threshold, and decide whether the vehicle should switch to the energy recovery mode; Set the vehicle battery health determination threshold, and decide whether to turn the vehicle energy recovery mode on or off based on the comparison between the current vehicle remaining power and the health determination threshold; Set the vehicle energy recovery mode control priority; The kinetic energy recovery amount and mode execution time of the vehicle kinetic energy recovery mode during the vehicle driving process are recorded, and an energy recovery visualization message is generated, which is fed back to the user.

[0027] In summary, during the execution of the systems and methods in the above embodiments, the vehicle energy recovery recommendation index is analyzed in real time through the perception of the road environment parameters in the downhill scene where the vehicle is located, and then a decision is made based on the analysis result whether to control the vehicle to switch to the energy recovery mode, and a protection mode is further set to ensure that the vehicle does not recover energy when the energy is sufficient, so as to maintain the vehicle in the best performance state, and at the same time generate a vehicle energy recovery message in a visual form to facilitate users to read the vehicle energy recovery results.

[0028] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent control system for energy recovery of a heavy truck electric drive axle on a long downhill slope, characterized in that: include: A perception module is used to perceive the road environment parameters in the vehicle downhill scenario; An analysis module, configured to receive the road environment parameters sensed by the perception module, and analyze the vehicle energy recovery recommendation index based on the road environment parameters; a determination module, used to set a determination threshold for whether the vehicle switches to the energy recovery mode, receive the analysis result of the vehicle energy recovery recommendation index in the analysis module, compare the analysis result with the determination threshold, and determine whether the analysis result is not less than the determination threshold. If the determination result is yes, the vehicle switches to the energy recovery mode, otherwise, the vehicle does not switch to the energy recovery mode; The protection module is used to set the vehicle battery power health judgment threshold, and make decisions on whether to turn on or off the vehicle energy recovery mode based on the comparison between the vehicle battery current power and the health judgment threshold in real time; A visualization message module, used to generate an energy recovery visualization message of a vehicle energy recovery mode; The output module is used to receive and store the energy recovery visualization message generated in the visualization message module, and output the energy recovery visualization message.

2. According to claim 1, a heavy truck electric drive axle long downhill energy recovery intelligent control system is characterized in that: The perception module runs in real time in the system, synchronously obtains the vehicle navigation road information, and determines whether the vehicle is in a downhill scene based on the vehicle navigation road information; ； Where: The judgment value of whether the vehicle is in a downhill scene; is the total number of sampling points on the road section where the vehicle is located; is the elevation of the jth and j+1th sampling points; Among them, the formula (1) obtains When the equation (2) holds, the road section where the vehicle is located is determined to be in a downhill scenario. The number of sampling points in the road section where the vehicle is located is customized by the system end user. The adjacent spacing of each sampling point in the road section where the vehicle is located is equal. The road section where the vehicle is located is divided into several equal sub-sections based on the sampling points. The total number of sampling points in the road section where the vehicle is located is Contains the start and end points of the road segment the vehicle is on.

3. According to claim 1, a heavy truck electric drive axle long downhill energy recovery intelligent control system is characterized in that: The road environment parameters sensed by the perception module include: the position information of surrounding vehicles relative to the system service vehicle, and the speed of surrounding vehicles. The analysis logic of the vehicle energy recovery recommendation index in the analysis module is expressed as: ； Where: Recommended index for energy recovery of target vehicle; is the total number of vehicles around the target vehicle; is the distance between the i-th surrounding vehicle and the target vehicle; is the speed of the vehicle in front of the target vehicle; is the target vehicle’s own speed; is the slope of the road section where the target vehicle is located; is the adjustment factor; The current remaining power of the vehicle; Among them, the target vehicle is the system service vehicle, and the target vehicle energy recovery recommendation index is The higher it is, the more suitable it is for the vehicle to start the energy recovery mode. Conversely, the lower it is, the less suitable it is for the vehicle to start the energy recovery mode.

4. According to claim 3, a heavy truck electric drive axle long downhill energy recovery intelligent control system is characterized in that: The adjustment factor The value is subject to: ； Where: The total amount of downhill sections in the vehicle navigation route; is the theoretical kinetic energy recovery amount of the vth downhill section; is the normalization factor; Among them, the normalization factor <1, and is set according to the current congestion of the vehicle's navigation route. The more congested the vehicle's current navigation route is, the higher the normalization factor The smaller the value, the smaller the normalization factor. The larger the value.

5. According to claim 4, a heavy truck electric drive axle long downhill energy recovery intelligent control system is characterized in that: The calculation formula of the theoretical kinetic energy recovery amount of the downhill section is: ； Where: The efficiency of the vehicle's kinetic energy recovery system; is the downhill distance; is the total mass of the vehicle; is the acceleration due to gravity; is the downhill slope; is the vehicle rolling resistance coefficient; is the air density; Set speed limits for road sections; is the air resistance coefficient; It is the projection area of ​​the front end of the vehicle perpendicular to the direction of travel.

6. The intelligent control system for long downhill energy recovery of heavy truck electric drive axle according to claim 1 is characterized in that: The determination module is provided with submodules at the lower level, including: A monitoring unit, used to monitor the position information of the downhill section where the vehicle is located in real time in a downhill vehicle scenario; A refresh unit, used to receive the downhill section position information of the vehicle monitored by the monitoring unit, and decide whether to refresh the analysis module based on the downhill section position information of the vehicle; Among them, after receiving the position information of the downhill section where the vehicle is located, the refresh unit identifies the travel progress of the vehicle's current section based on the position information of the downhill section where the vehicle is located, and expresses it in the form of a percentage. When the travel progress of the vehicle's current section exceeds 60%, it ends. When the travel progress of the vehicle's current section does not exceed 60%, it jumps to the analysis module operation stage. When the judgment module judges as no, it triggers the monitoring unit to run. The monitoring unit runs continuously based on the specified operating frequency before the travel progress of the vehicle's current section does not exceed 60%.

7. The intelligent control system for long downhill energy recovery of heavy truck electric drive axle according to claim 1 is characterized in that: The vehicle battery power health judgment threshold in the protection module is customized by the system end user. When the current power of the vehicle battery is not less than the health judgment threshold, the energy recovery mode is in a normally closed state. Otherwise, the energy recovery is switched on and off based on the judgment result of the judgment module. Among them, the protection module has a higher control priority over the vehicle energy recovery mode than the determination module.

8. The intelligent control system for long downhill energy recovery of heavy truck electric drive axle according to claim 1 is characterized in that: The energy recovery visualization message of the vehicle energy recovery mode generated in the visualization message module is represented in the form of a bar graph, the horizontal axis of the bar graph represents the start time and end time of each energy recovery in the vehicle navigation path, the vertical axis represents the cumulative recovery energy value within the corresponding time threshold, and the bars representing the recovery energy values ​​in the bar graph are sorted based on a chronological order from early to late.

9. The intelligent control system for long downhill energy recovery of heavy truck electric drive axle according to claim 1 is characterized in that: The perception module is interactively connected to the analysis module and the determination module through a wireless network. The determination module is interactively connected to the monitoring unit and the refresh unit through a wireless network. The refresh unit is interactively connected to the analysis module through a wireless network. The determination module is interactively connected to the protection module, the visualization message module and the output module through a wireless network.

10. A method for intelligently controlling the long downhill energy recovery of a heavy truck electric drive axle, the method being an implementation method of the intelligent control system for long downhill energy recovery of a heavy truck electric drive axle as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Perceive the road environment parameters in the vehicle downhill scenario, and analyze the vehicle energy recovery recommendation index based on the road environment parameters; Set a threshold for determining whether the vehicle should switch to the energy recovery mode, compare the vehicle energy recovery recommendation index obtained through application analysis with the threshold, and decide whether the vehicle should switch to the energy recovery mode; Set the vehicle battery health determination threshold, and decide whether to turn the vehicle energy recovery mode on or off based on the comparison between the current vehicle remaining power and the health determination threshold; Set the vehicle energy recovery mode control priority; The kinetic energy recovery amount and mode execution time of the vehicle kinetic energy recovery mode during the vehicle driving process are recorded, and an energy recovery visualization message is generated, which is fed back to the user.

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