Chassis electric control software parameter dynamic optimization method, device, system and product
By collecting and analyzing driving and road data, and using driving style and operating condition mapping models to optimize chassis electronic control parameters, the problem of chassis electronic control software parameters not being able to be adjusted in real time in existing technologies is solved, thereby improving the vehicle's adaptability and control efficiency.
Patent Information
- Application Number
- CN202511588782.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-31
- Publication Date
- 2026-03-17
AI Technical Summary
The existing method for determining the parameters of chassis electronic control software relies on manual calibration, which cannot be adjusted in real time according to the dynamic scenarios in actual driving and cannot match driving habits, resulting in poor adaptability.
By collecting data on throttle and brake pedal opening, steering angle, and vehicle speed, and using pre-trained driving style and operating condition mapping models, the chassis electronic control parameters, including EPS assist coefficient, braking pressure, CDC damping coefficient, and ESC sensitivity, are dynamically optimized to achieve real-time matching between driving style and road conditions.
Dynamic optimization of the chassis electronic control software has been achieved, which improves the adaptability and efficiency of vehicle chassis control, adapts to different driving styles and road conditions, and reduces manual intervention and downtime.
Smart Images
Figure CN121671636A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of vehicle technology, specifically to a method, device, system, and product for dynamic optimization of chassis electronic control software parameters. Background Technology
[0002] In existing related technical solutions, the parameters of chassis electronic control software are basically determined in advance through calibration. During the development process, software parameters are determined based on various operating conditions (such as smooth roads, bumpy roads, icy roads, and different driving conditions, such as low-speed and high-speed braking / steering). However, this method has a single data perception dimension, cannot be adjusted in real time according to the dynamic scenarios in actual driving, and cannot be matched to driving habits. Overall, its adaptability is poor. Moreover, data updates in traditional vehicles rely on manual labor. To input new data, manual calibration and actual vehicle rewriting are required, which necessitates vehicle shutdown, consuming manpower and resulting in low efficiency. Therefore, how to avoid the problems existing in current related technologies is an urgent research topic in the industry. Summary of the Invention
[0003] This invention provides a method, apparatus, system, and product for dynamic optimization of chassis electronic control software parameters, in order to solve the technical problem of poor adaptability of chassis electronic control software parameters in the prior art, and at least provide a beneficial option or create conditions.
[0004] This invention provides a method for dynamic optimization of chassis electronic control software parameters, including: collecting throttle and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data within a set driving cycle; and transmitting the collected throttle and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data to a pre-trained driving style model. The current driving style is determined by the driving style model and recorded as the target driving style. According to the target driving style, the corresponding chassis electronic control parameters are found from a pre-set mapping table and recorded as the first target chassis electronic control parameters. The mapping table is pre-set and records the correspondence between driving styles and chassis electronic control parameters. Collect road environment data within a set driving cycle; input the obtained road environment data and target driving style into a pre-trained working condition mapping model, and obtain chassis electronic control parameters suitable for the current driving scenario through the working condition mapping model, and record the chassis electronic control parameters as the second target chassis electronic control parameters; The first target chassis electronic control parameters and the second target chassis electronic control parameters are transmitted to the vehicle's chassis electronic control software so that the chassis electronic control software can use the first target chassis electronic control parameters and the second target chassis electronic control parameters as a control reference.
[0005] Furthermore, the road surface environment data includes: road surface adhesion system, road surface bumpiness, road congestion level, and road surface slope.
[0006] Furthermore, the target chassis electronic control parameters include: EPS assist coefficient, braking pressure, CDC damping coefficient, ESC sensitivity, and suspension stiffness.
[0007] Furthermore, the driving style model is an artificial intelligence model that has been pre-trained using machine learning.
[0008] On the other hand, a device for dynamically optimizing chassis electronic control software parameters is provided, comprising: a processor and a memory, wherein the memory is used to store a computer-readable program; when the computer-readable program is executed by the processor, the processor enables the processor to implement the method for dynamically optimizing chassis electronic control software parameters as described in any of the above technical solutions.
[0009] On the other hand, a dynamic optimization system for chassis electronic control software parameters is provided, including: a first acquisition module, a first determination module, a search module, a second acquisition module, a second determination module, and a transmission module; The first acquisition module is used to: acquire accelerator and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data within a set driving cycle; and transmit the acquired accelerator and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data to a pre-trained driving style model. The first determining module is used to: determine the current driving style through the driving style model, and record the driving style as the target driving style; The search module is used to: find the corresponding chassis electronic control parameters from a pre-set mapping table according to the target driving style, and record the chassis electronic control parameters as the first target chassis electronic control parameters; wherein, the mapping table is pre-set and records the correspondence between driving style and chassis electronic control parameters; The second acquisition module is used to: acquire road environment data within a set driving cycle; The second determining module is used to: input the obtained road environment data and target driving style into a pre-trained working condition mapping model, obtain chassis electronic control parameters suitable for the current driving scenario through the working condition mapping model, and record the chassis electronic control parameters as the second target chassis electronic control parameters; The transmission module is used to transmit the first target chassis electronic control parameters and the second target chassis electronic control parameters to the vehicle's chassis electronic control software, so that the chassis electronic control software can use the first target chassis electronic control parameters and the second target chassis electronic control parameters as a control reference.
[0010] Furthermore, the road surface environment data includes: road surface adhesion system, road surface bumpiness, road congestion level, and road surface slope.
[0011] Furthermore, the target chassis electronic control parameters include: EPS assist coefficient, braking pressure, CDC damping coefficient, ESC sensitivity, and suspension stiffness.
[0012] Furthermore, the driving style model is an artificial intelligence model that has been pre-trained using machine learning.
[0013] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the dynamic optimization method for chassis electronic control software parameters as described in any of the above technical solutions.
[0014] This invention has at least the following beneficial effects: The method of this invention, by fully considering the current driving style and driving scenario, obtains the most suitable chassis electronic control parameters through dynamic optimization. This enables the chassis electronic control software to achieve optimal chassis control. Simultaneously, this invention also provides corresponding devices, systems, and products, the beneficial effects of which are similar to the method, and will not be repeated here. This invention is primarily applicable to the field of vehicle technology. Attached Figure Description
[0015] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.
[0016] Figure 1 This is a flowchart illustrating the steps of the dynamic optimization method for chassis electronic control software parameters. Figure 2 This is a schematic diagram of the chassis electronic control software parameter dynamic optimization device; Figure 3 This is the hardware structure of a chassis electronic control software parameter dynamic optimization device according to another embodiment; Figure 4 This is a schematic diagram of the system structure of the chassis electronic control software parameter dynamic optimization system. Detailed Implementation
[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0018] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0019] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.
[0020] EPS assist factor (sometimes also called assist mapping or assist curve) refers to the rules or algorithms by which an electric power steering system determines how much assistance to provide based on vehicle conditions (mainly vehicle speed and steering wheel torque).
[0021] Braking pressure refers to the pressure transmitted by the brake fluid in the brake hydraulic system.
[0022] The CDC damping coefficient refers to the rules or algorithms by which a continuous damping control suspension system adjusts the internal damping force of the shock absorber in real time according to vehicle conditions (such as vehicle movement, road bumps, etc.).
[0023] ESC sensitivity refers to the degree and timing of intervention by the electronic stability system. It determines the extent to which the system allows the vehicle to slip or lose control, and when to correct it.
[0024] Suspension stiffness refers to the degree of stiffness of the springs in the suspension system, that is, the force required per unit deformation.
[0025] The road surface adhesion system is a physical quantity that describes the level of grip between the tire and the road surface. It is a dimensionless coefficient that represents the ratio of the maximum static friction force exerted by the ground on the tire to the vertical load on the vehicle.
[0026] Road surface roughness is an indicator that describes the smoothness of the road surface and the level of vibration excitation generated by the vehicle.
[0027] Road congestion level is a traffic status indicator that describes road traffic efficiency and vehicle density.
[0028] Road surface slope is a geometric parameter that describes the degree of inclination of a road; it refers to the angle between the inclined surface of the road and the horizontal plane.
[0029] In vehicle-related technical solutions, a common problem arises during chassis control when the electronic control software obtains inadequate parameters, preventing it from effectively controlling the chassis and achieving optimal chassis control.
[0030] refer to Figure 1 , Figure 1 This is a flowchart illustrating the steps involved in the dynamic optimization method for chassis electronic control software parameters.
[0031] To address the technical problems existing in the prior art, this application discloses a method for dynamic optimization of chassis electronic control software parameters. This method can be executed by a software program. When the software program executes the method, the steps it implements include: Step 1: Collect accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data within the set driving cycle; transmit the collected accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data to the pre-trained driving style model.
[0032] The software program uses the CAN bus to acquire data from the vehicle within a set driving cycle, and uses this data to determine the current driving style. This data includes: accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data.
[0033] To better determine driving style, the software program inputs collected accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data into the driving style model. This driving style model is a pre-trained artificial intelligence model. During training, the model constructs a raw database using a large amount of accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data. After feature extraction, it categorizes driving styles according to actual needs. Driving styles can be divided into: mild, stable, sporty, and aggressive (the specific number of levels depends on the actual vehicle positioning and requirements).
[0034] In actual operation, the software program inputs the currently collected accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data into the driving style model to obtain the corresponding driving style.
[0035] Step 2: Determine the current driving style through the driving style model, and record the driving style as the target driving style.
[0036] The software program determines the current driving style by acquiring the output of the driving style model. For ease of description, this driving style is referred to as the target driving style.
[0037] Step 3: Based on the target driving style, find the corresponding chassis electronic control parameters from the pre-set mapping table, and record these chassis electronic control parameters as the first target chassis electronic control parameters. The mapping table is pre-set and records the correspondence between driving styles and chassis electronic control parameters.
[0038] After determining the target driving style, the software program can retrieve a mapping table from the vehicle's storage unit. This mapping table pre-records the correspondence between driving styles and chassis electronic control parameters. Driving styles can be categorized as: mild, stable, sporty, and aggressive (the specific number of levels depends on the actual vehicle positioning and requirements). Each driving style corresponds to a set of chassis electronic control parameters, including: EPS assist coefficient, braking pressure, CDC damping coefficient, ESC sensitivity, and suspension stiffness. The software program can then look up the corresponding chassis electronic control parameters through the mapping table. For ease of description, the retrieved chassis electronic control parameters are designated as the first target chassis electronic control parameters.
[0039] Step 4: Collect road environment data within the set driving cycle.
[0040] To address the specific road surface conditions, the software program will also acquire road surface environment data. This data includes: road surface adhesion system, road surface roughness, road congestion level, and road surface slope.
[0041] Step 5: Input the obtained road environment data and target driving style into the pre-trained working condition mapping model, and obtain the chassis electronic control parameters suitable for the current driving scenario through the working condition mapping model. Record the chassis electronic control parameters as the second target chassis electronic control parameters.
[0042] After obtaining road environment data, the software program can determine the current driving scenario's environmental conditions based on this data. To comprehensively consider both road environment and driving style, a condition mapping model is pre-set. This model reflects the relationship between the current driving scenario and the condition. During pre-training, multiple sets of conditions are collected using vehicle sensors and cameras, combined with real-vehicle testing and simulation. Based on the previously calibrated driving style, the optimal parameters for each condition are determined, such as road adhesion coefficient, driving style (sport), bumpy road surface (bumpiness can be categorized based on road smoothness), slope, and congested streets, corresponding to EPS assist coefficient, braking pressure, CDC damping coefficient, ESC sensitivity, suspension stiffness, etc. The condition mapping model trained using big data is stored in the vehicle's onboard storage unit.
[0043] The software program inputs the obtained road environment data and target driving style into the working condition mapping model, which can quickly determine the corresponding chassis electronic control parameters. For ease of description, these chassis electronic control parameters are referred to as the second target chassis electronic control parameters.
[0044] Step 6: Transmit the first target chassis electronic control parameters and the second target chassis electronic control parameters to the vehicle's chassis electronic control software so that the chassis electronic control software can use the first target chassis electronic control parameters and the second target chassis electronic control parameters as a control reference.
[0045] After obtaining the first target chassis electronic control parameters and the second target chassis electronic control parameters, the software program can transmit them to the chassis electronic control software. The chassis electronic control software can then use these parameters as a reference to perform corresponding control.
[0046] This invention, by fully considering current driving styles and scenarios, obtains the most suitable chassis electronic control parameters through dynamic optimization. This enables the chassis electronic control software to achieve optimal chassis control.
[0047] refer to Figure 2 , Figure 2 This is a schematic diagram of the chassis electronic control software parameter dynamic optimization device.
[0048] On the other hand, a device for dynamically optimizing chassis electronic control software parameters is provided, comprising: a processor and a memory, wherein the memory is used to store a computer-readable program. When the computer-readable program is executed by the processor, the processor causes the processor to implement the method for dynamically optimizing chassis electronic control software parameters as described in any of the above specific embodiments.
[0049] It will be understood by those skilled in the art that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. As is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.
[0050] Please see Figure 3 , Figure 3 This is another embodiment of the hardware structure of the chassis electronic control software parameter dynamic optimization device. The chassis electronic control software parameter dynamic optimization device includes: a processor 901, a memory 902, an input / output interface 903, a communication interface 904, and a bus 905.
[0051] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the dynamic optimization method for chassis electronic control software parameters provided in the embodiments of this application.
[0052] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the methods described in the embodiments of this application.
[0053] The input / output interface 903 is used to implement information input and output.
[0054] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).
[0055] Bus 905 transmits information between various components of the device, such as processor 901, memory 902, input / output interface 903, and communication interface 904.
[0056] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.
[0057] refer to Figure 4 , Figure 4 This is a schematic diagram of the system structure of the chassis electronic control software parameter dynamic optimization system.
[0058] A dynamic optimization system for chassis electronic control software parameters is provided, characterized in that it includes: a first acquisition module, a first determination module, a search module, a second acquisition module, a second determination module, and a transmission module.
[0059] The first acquisition module is used to: acquire accelerator and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data within a set driving cycle; and transmit the acquired accelerator and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data to a pre-trained driving style model.
[0060] The first data acquisition module uses the CAN bus to obtain data from the vehicle within a set driving cycle, and uses this data to determine the current driving style. This data includes: accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data.
[0061] To better determine driving style, the first data acquisition module inputs the collected accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data into the driving style model. This driving style model is a pre-trained artificial intelligence model. During training, the driving style model constructs a raw database using a large amount of accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data. After feature extraction, it classifies driving styles according to actual needs. Driving styles can be categorized as: mild, stable, sporty, and aggressive (the specific number of levels depends on the actual vehicle positioning and requirements).
[0062] In actual operation, the software program inputs the currently collected accelerator and brake pedal opening data, brake pressure data, steering angle data, and vehicle speed data into the driving style model to obtain the corresponding driving style.
[0063] The first determining module is used to: determine the current driving style through the driving style model, and record the driving style as the target driving style.
[0064] The first determining module determines the current driving style by acquiring the output of the driving style model. For ease of description, this driving style is referred to as the target driving style.
[0065] The search module is used to: find the corresponding chassis electronic control parameters from a pre-set mapping table according to the target driving style, and record the chassis electronic control parameters as the first target chassis electronic control parameters. The mapping table is pre-set and records the correspondence between driving styles and chassis electronic control parameters.
[0066] After determining the target driving style, the search module retrieves a mapping table from the vehicle's storage unit. This mapping table pre-records the correspondence between driving styles and chassis electronic control parameters. Driving styles can be categorized as: mild, stable, sporty, and aggressive (the specific number of levels depends on the vehicle's positioning and requirements). Each driving style corresponds to a set of chassis electronic control parameters, including: EPS assist coefficient, braking pressure, CDC damping coefficient, ESC sensitivity, and suspension stiffness. The software program can then look up the corresponding chassis electronic control parameters using the mapping table. For ease of description, the retrieved chassis electronic control parameters are designated as the first target chassis electronic control parameters.
[0067] The second acquisition module is used to acquire road environment data within a set driving cycle.
[0068] To address the specific road surface conditions, the second data acquisition module will also acquire road surface environment data. This data includes: road surface adhesion system, road surface roughness, road congestion level, and road surface slope.
[0069] The second determining module is used to: input the obtained road environment data and target driving style into a pre-trained working condition mapping model, obtain chassis electronic control parameters suitable for the current driving scenario through the working condition mapping model, and record the chassis electronic control parameters as the second target chassis electronic control parameters.
[0070] After obtaining road environment data, the second determination module can determine the environmental conditions of the current driving scenario based on this data. To comprehensively consider both the road environment and driving style, a condition mapping model is pre-set. This model reflects the relationship between the current driving scenario and the condition. During pre-training, multiple sets of conditions are collected using vehicle sensors and cameras, combined with real-vehicle testing and simulation. Based on the previously calibrated driving style, the optimal parameters for each condition are determined, such as the road adhesion coefficient, driving style (sport), bumpy road surface (bumpiness can be categorized based on road smoothness), slope, and congested streets, corresponding to EPS assist coefficient, braking pressure, CDC damping coefficient, ESC sensitivity, suspension stiffness, etc. The condition mapping model trained using big data is stored in the vehicle's onboard storage unit.
[0071] The second determining module inputs the obtained road environment data and target driving style into the operating condition mapping model. The operating condition mapping model can quickly determine the corresponding chassis electronic control parameters. For ease of description, these chassis electronic control parameters are denoted as the second target chassis electronic control parameters.
[0072] The transmission module is used to transmit the first target chassis electronic control parameters and the second target chassis electronic control parameters to the vehicle's chassis electronic control software, so that the chassis electronic control software can use the first target chassis electronic control parameters and the second target chassis electronic control parameters as a control reference.
[0073] After obtaining the first target chassis electronic control parameters and the second target chassis electronic control parameters, the transmission module can transmit them to the chassis electronic control software. The chassis electronic control software can then use these parameters as a reference to perform corresponding control.
[0074] This application also discloses a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. The processor of the computer device reads the computer program or computer instructions from the computer-readable storage medium and executes the computer program or computer instructions, causing the computer device to perform the chassis electronic control software parameter dynamic optimization method as described in any of the preceding embodiments.
[0075] On the other hand, a computer-readable storage medium is provided, wherein a processor-executable program is stored, which, when executed by a processor, is used to implement the dynamic optimization method for chassis electronic control software parameters as described in any of the above specific embodiments.
[0076] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatuses.
[0077] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0078] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and 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, apparatuses, or units, and may be electrical, mechanical, or other forms.
[0079] 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.
[0080] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0081] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0082] Although the description of this application has been quite detailed and particularly focused on several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment. Rather, it should be considered as effectively covering the intended scope of this application by referring to the appended claims and taking into account the prior art, which provides for a broad possible interpretation of these claims. Furthermore, the foregoing description of this application with respect to embodiments foreseeable by the inventors is intended to provide a useful description, and non-substantial modifications to this application that have not yet been foreseen may still represent equivalent modifications.
[0083] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.
Claims
1. A method for dynamic optimization of software parameters of an engine control unit, characterized in that, The method comprises the following steps: Collecting throttle and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data within a set driving period; and delivering the collected throttle and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data to a pre-trained driving style model; Determining the current driving style through the driving style model, and recording the driving style as a target driving style; finding corresponding chassis electronic control parameters from a pre-set mapping relationship table according to the target driving style, and recording the chassis electronic control parameters as first target chassis electronic control parameters; wherein the mapping relationship table is pre-set and records the corresponding relationship between driving styles and chassis electronic control parameters; Collecting road surface environment data within a set driving period; inputting the obtained road surface environment data and target driving style into a pre-trained working condition mapping model to obtain chassis electronic control parameters suitable for the current driving scene through the working condition mapping model, and recording the chassis electronic control parameters as second target chassis electronic control parameters; Delivering the first target chassis electronic control parameters and the second target chassis electronic control parameters to the chassis electronic control software of the vehicle, so that the chassis electronic control software controls the reference based on the first target chassis electronic control parameters and the second target chassis electronic control parameters.
2. The method of claim 1, wherein the method further comprises: The road surface environment data includes road surface adhesion system, road surface bump degree, road congestion degree and road surface slope.
3. The method of claim 1, wherein the method further comprises: The target chassis electronic control parameters include EPS assistance coefficient, brake pressure, CDC damping coefficient, ESC sensitivity and suspension stiffness.
4. The method of claim 1, wherein the method further comprises: The driving style model is an artificial intelligence model obtained by pre-training through machine learning.
5. A device for dynamic optimization of software parameters of an electrically controlled chassis, characterized in that, The method comprises the following steps: A processor; A memory for storing a computer readable program; When the computer readable program is executed by the processor, the processor implements the chassis electronic control software parameter dynamic optimization method according to any one of claims 1-4.
6. A system for dynamic optimization of software parameters of an engine control unit, characterized in that The method comprises the following steps: A first acquisition module, a first determination module, a finding module, a second acquisition module, a second determination module and a delivery module; The first acquisition module is used for collecting throttle and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data within a set driving period; and delivering the collected throttle and brake pedal opening data, brake pressure data, steering angle data and vehicle speed data to a pre-trained driving style model; The first determination module is used for determining the current driving style through the driving style model, and recording the driving style as a target driving style; The finding module is used for finding corresponding chassis electronic control parameters from a pre-set mapping relationship table according to the target driving style, and recording the chassis electronic control parameters as first target chassis electronic control parameters; wherein the mapping relationship table is pre-set and records the corresponding relationship between driving styles and chassis electronic control parameters; The second acquisition module is used for collecting road surface environment data within a set driving period; The second determining module is configured to input the obtained road surface environment data and the target driving style into a pre-trained working condition mapping model, obtain chassis electric control parameters suitable for the current driving scene through the working condition mapping model, and record the chassis electric control parameters as second target chassis electric control parameters. The transmission module is configured to transmit the first target chassis electric control parameters and the second target chassis electric control parameters to the chassis electric control software of the vehicle, so that the chassis electric control software controls the reference based on the first target chassis electric control parameters and the second target chassis electric control parameters.
7. The system of claim 6, wherein the system is configured to: The road surface environment data includes a road surface adhesion system, a road surface bump degree, a road congestion degree, and a road surface slope.
8. The system of claim 6, wherein the system is configured to: The target chassis electric control parameters include an EPS assistance coefficient, a brake pressure, a CDC damping coefficient, an ESC sensitivity, and a suspension stiffness.
9. The system of claim 6, wherein, The driving style model is an artificial intelligence model obtained by pre-training through machine learning.
10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the chassis electric control software parameter dynamic optimization method of any one of claims 1 to 4.