A chassis domain control parameter layered self-adaptive calibration and multi-working-condition self-converging method

Abstract

The application discloses a chassis domain control parameter layered self-adaptive calibration and multi-working-condition self-converging method, relates to the technical field of intelligent vehicle control, and comprises the following steps: acquiring brake execution parameters, steering execution parameters, suspension execution parameters, vehicle body posture parameters and whole vehicle dynamics parameters in the chassis domain control, and performing layered initial calibration according to an executor layer, a vehicle body posture layer and a whole vehicle dynamics layer, so as to complete calibration of the brake execution parameters, the steering execution parameters, the suspension execution parameters, roll posture parameters, yaw posture parameters and whole vehicle dynamics working-condition threshold parameter in sequence. Through multi-dimensional parameter layered decoupling of the chassis domain control, multi-target automatic optimization, multi-working-condition parameter dynamic matching and vehicle-mounted self-adaptive correction, the application realizes intelligent optimization of chassis parameters, reduces parameter coupling interference and mass production performance deviation, shortens a calibration period, reduces test cost, and simultaneously improves low-speed comfort, high-speed stability and safety control capability under complex working conditions of the vehicle.

Description

Technical Field

[0001] This invention relates to the field of intelligent vehicle control technology, specifically to a method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters. Background Technology

[0002] With the rapid development of new energy vehicles and intelligent vehicle technologies, vehicle chassis control systems are gradually evolving from traditional distributed control to chassis domain control. The six-in-one chassis domain control system for new energy vehicles integrates multiple chassis actuators, including braking, steering, suspension, and rear-wheel steering, requiring coordinated control to achieve vehicle handling stability, ride comfort, and anti-loss-of-control capabilities under extreme conditions. Because the chassis domain control system involves numerous control parameters, complex parameter coupling relationships exist between different actuators and different control levels, and the matching effect of control parameters directly affects the dynamic performance of the entire vehicle. Currently, with the increasing complexity of chassis collaborative control algorithms, traditional manual calibration methods relying on engineers' experience for on-vehicle debugging are no longer sufficient to meet the performance optimization needs under full speed, all road conditions, and multiple driving conditions. The industry urgently needs an intelligent chassis calibration technology that can achieve hierarchical decoupling of parameters, automatic optimization, and adaptive adjustment under various operating conditions.

[0003] Existing technologies suffer from the following shortcomings: Current chassis domain control parameter calibration techniques typically employ a single-layer, global, manual parameter tuning approach. Engineers collect vehicle operating data under different conditions and adjust control parameters such as braking gain, suspension damping, and steering assist based on experience, continuously optimizing parameter matching through multiple rounds of real-vehicle testing. This method usually stores only one set of globally fixed calibration parameters, using the same parameters for control under different vehicle speeds, road surface adhesion conditions, and driving states, lacking a mechanism for adapting parameters to specific operating conditions. Furthermore, existing technologies lack automated multi-objective optimization methods; parameter adjustment heavily relies on engineer experience, resulting in long calibration cycles and the absence of clear parameter convergence criteria. Additionally, due to manufacturing differences in components within mass-produced vehicles, traditional calibration methods cannot self-calibrate parameters based on the actual operating conditions of individual vehicles, leading to inconsistencies in chassis handling performance between different vehicles. Therefore, existing technologies suffer from severe coupling of chassis control parameters, the inability of a single parameter to adapt to multiple operating conditions, low efficiency of manual calibration, inability to achieve automatic convergence optimization, and inability to compensate for individual vehicle differences.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters, comprising the following steps: The system acquires braking execution parameters, steering execution parameters, suspension execution parameters, body attitude parameters, and vehicle dynamics parameters from the chassis domain control, and performs initial calibration in layers according to the actuator layer, body attitude layer, and vehicle dynamics layer. The system then sequentially completes the calibration of braking execution parameters, steering execution parameters, suspension execution parameters, roll attitude parameters, yaw attitude parameters, and vehicle dynamics operating condition threshold parameters. Vehicle operation data and simulation data under different vehicle speeds, road conditions, and driving intensities are collected. The multi-layer parameters after initial stratification are input into a multi-objective cost function for iterative optimization. The parameters of each layer are corrected according to vehicle stability and comfort indicators until each performance indicator reaches the preset convergence threshold. After completing iterative optimization and reaching a convergence state, the multi-layer parameters are classified and stored according to vehicle speed, road conditions and driving intensity, forming a set of chassis domain control parameters corresponding to different operating conditions. During vehicle operation, the current operating status of the vehicle is acquired in real time, the corresponding operating conditions are identified, and the matching target parameters are called from the chassis domain control parameter set to dynamically adjust the braking execution parameters, steering execution parameters and suspension execution parameters. The system continuously collects chassis status data during vehicle operation, and adaptively corrects the dynamically adjusted target parameters based on the collected chassis status data, updating the basic parameters in the chassis domain control parameter set to reduce performance deviations caused by differences in vehicle components.

[0007] Initial calibration is performed in layers: actuator layer, vehicle attitude layer, and vehicle dynamics layer, including: The initial parameters corresponding to different control objects in the chassis domain control are obtained. The braking execution parameters, steering execution parameters, and suspension execution parameters are assigned to the actuator layer, the roll attitude parameters and yaw attitude parameters are assigned to the vehicle attitude layer, and the vehicle dynamics operating condition threshold parameters are assigned to the vehicle dynamics layer. Initial calibration is performed in the order from the bottom actuator parameters to the middle attitude parameters and then to the top vehicle dynamics parameters, so that the parameters of each layer complete parameter matching according to the corresponding control objectives, and establish the correlation between parameters of different levels, providing the initial parameter basis for subsequent multi-level parameter iterative optimization.

[0008] include: Vehicle performance evaluation indices are established based on vehicle operation data and simulation data. Stability and comfort indices are used as the evaluation criteria for the multi-objective cost function. The actuator layer parameters, body attitude layer parameters, and vehicle dynamics layer parameters are comprehensively calculated. Based on the evaluation results output by the multi-objective cost function, the parameters at different levels are iteratively adjusted. Through multiple rounds of parameter correction, the deviation between the vehicle operation state and the target performance indices is reduced, so that the optimized multi-level parameters gradually meet the vehicle dynamic performance requirements.

[0009] The multi-objective cost function includes stability evaluation parameters and comfort evaluation parameters. The parameter optimization results are obtained by comprehensively calculating different evaluation parameters. Among them, stability evaluation parameters are used to evaluate the dynamic control performance of the vehicle during operation, and comfort evaluation parameters are used to evaluate the driving performance of the vehicle during driving. Based on the optimization results corresponding to different evaluation parameters, the braking execution parameters, steering execution parameters, suspension execution parameters, roll attitude parameters, yaw attitude parameters, and vehicle dynamics threshold parameters are adjusted so that the optimized multi-layer parameters simultaneously meet the vehicle stability and comfort requirements.

[0010] After completing iterative optimization and reaching convergence, the multi-layer parameters are categorized and stored according to vehicle speed, road conditions, and driving intensity, including: Based on different vehicle speed ranges, road conditions, and driving intensities during vehicle operation, the actuator layer parameters, body attitude layer parameters, and vehicle dynamics layer parameters after reaching the convergence state are classified and managed. A correspondence between operating conditions and parameter combinations is established, and the optimized parameters corresponding to different operating conditions are stored in the chassis domain control parameter storage area to form a chassis domain control parameter set containing various vehicle operating conditions.

[0011] The chassis domain control parameter set includes parameter combinations corresponding to different vehicle speed conditions, parameter combinations corresponding to different road surface conditions, and parameter combinations corresponding to different driving intensities. Each parameter combination includes optimized and converged braking execution parameters, steering execution parameters, suspension execution parameters, vehicle attitude parameters, and vehicle dynamics parameters. By establishing parameter index relationships for different operating conditions, the vehicle can quickly match the corresponding target parameters according to the current state during operation, thereby improving the adaptability between chassis domain control parameters and the actual operating state of the vehicle.

[0012] Real-time acquisition of the vehicle's current operating status, identification of corresponding operating conditions, including: The system acquires information on the vehicle's current speed, road conditions, and driving intensity. Based on the collected vehicle operating status information, it determines the vehicle's current operating condition and searches for target parameters that match the current operating condition in the chassis domain control parameter set. This allows the vehicle's chassis control parameters to be dynamically adjusted according to actual operating conditions, avoiding a decline in control performance caused by using fixed parameter control methods for a long time.

[0013] The system retrieves matching target parameters from the chassis domain control parameter set and dynamically adjusts the braking, steering, and suspension execution parameters, including: Based on the identified current operating conditions, the corresponding optimized parameter combination is called from the chassis domain control parameter set, and the called target parameters are applied to the braking execution control, steering execution control and suspension execution control processes, so that the chassis actuators can adjust the control state according to different vehicle speeds, different road conditions and different driving intensities, and realize multi-condition parameter adaptive matching during vehicle operation.

[0014] The dynamically adjusted target parameters are adaptively corrected based on the collected chassis status data, including: The system continuously acquires chassis status data during vehicle operation, analyzes the actual vehicle control effect corresponding to the dynamically adjusted target parameters, and makes small-scale adjustments to the basic parameters in the chassis domain control parameter set based on the differences between the vehicle's operating status and the target performance indicators. This makes the corrected basic parameters more consistent with the actual operating characteristics of the current vehicle and reduces the impact of differences in different vehicle components on chassis control performance.

[0015] Update the basic parameters in the chassis domain controller parameter set, including: Based on the chassis status data continuously collected during long-term vehicle operation, the basic parameters under the corresponding operating conditions are updated so that the updated parameter set retains the original multi-condition parameter matching relationship and further adapts to the actual operating conditions of a single vehicle. By providing feedback correction to the operating data of mass-produced vehicles, the vehicle performance deviation caused by the differences in components between braking actuators, steering actuators and suspension actuators is reduced, and the consistency of chassis control performance between different vehicles is improved.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention employs a three-layer hierarchical decoupling approach to manage multi-dimensional control parameters in chassis domain control. It independently divides and optimizes parameters at the actuator, vehicle attitude, and vehicle dynamics levels, reducing coupling interference caused by mutual influence between different control parameters. By establishing a hierarchical parameter system composed of bottom-level actuator parameters, mid-level attitude control parameters, and top-level vehicle dynamics parameters, parameters at different levels can be optimized and adjusted according to corresponding control objectives. This facilitates comprehensive matching of chassis performance across the entire domain, improves control consistency and dynamic response capabilities under different operating conditions, and enhances the overall performance of the chassis control system.

[0017] This invention replaces the traditional manual calibration method that relies on engineers' experience to adjust parameters with a multi-objective automatic optimization approach. It inputs vehicle operating data and simulation data collected under different operating conditions into the multi-objective optimization process, comprehensively evaluating performance indicators such as stability and comfort, and automatically completing iterative parameter corrections. By reducing repeated manual adjustments, it improves the efficiency of chassis parameter calibration, shortening the overall vehicle calibration cycle by 50%. Simultaneously, it reduces reliance on test personnel, test vehicles, and test sites during road testing, effectively reducing labor costs and test resource consumption during the calibration phase, and improving parameter optimization efficiency in the vehicle development process.

[0018] This invention establishes a multi-condition grouped parameter set, classifying and storing optimized and converged chassis control parameters under different vehicle speeds, road conditions, and driving intensities, and dynamically matching corresponding parameters according to the actual vehicle operating state. This approach avoids the problem of traditional single fixed parameters being unable to adapt to complex driving environments, enabling the vehicle to meet comfort requirements in low-speed driving scenarios, maintain stable control performance in high-speed driving scenarios, and meet safety control requirements under low-friction road conditions. Thus, it caters to multiple application scenarios such as low-speed comfort, high-speed stability, and low-friction safety, improving the chassis domain control system's adaptability to complex operating environments.

[0019] This invention utilizes onboard dynamic adaptive matching and mass-production individual self-calibration to continuously collect chassis status data during vehicle operation. Based on actual vehicle operation feedback, it makes small-scale adjustments to basic parameters, allowing chassis control parameters to gradually adapt to the actual characteristics of each vehicle. This method reduces vehicle performance discrepancies caused by differences in component manufacturing and assembly errors, improves the consistency of chassis control performance among mass-produced vehicles, reduces after-sales complaints due to differences in vehicle handling and chassis feel, and enhances the stability of the driving experience during long-term vehicle use.

[0020] This invention establishes clear parameter convergence criteria to assess performance index changes during parameter optimization. Optimization is considered complete once stability, comfort, and related parameter changes reach preset thresholds, avoiding repetitive testing and ineffective adjustments common in traditional manual calibration due to a lack of clear criteria. By quantifying convergence conditions to control the parameter optimization process, unnecessary road tests can be reduced, minimizing wear and tear from long-term testing of prototype vehicles. This also saves testing time and development costs, improving the controllability and engineering application value of chassis domain control parameter optimization. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0022] Figure 1 This is a flowchart of a method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to the present invention. Detailed Implementation

[0023] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0024] This invention provides, for example Figure 1 The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters, as shown, includes the following steps: The system acquires braking execution parameters, steering execution parameters, suspension execution parameters, body attitude parameters, and vehicle dynamics parameters from the chassis domain control, and performs initial calibration in layers according to the actuator layer, body attitude layer, and vehicle dynamics layer. The system then sequentially completes the calibration of braking execution parameters, steering execution parameters, suspension execution parameters, roll attitude parameters, yaw attitude parameters, and vehicle dynamics operating condition threshold parameters. The system acquires braking, steering, suspension, vehicle attitude, and vehicle dynamics parameters from the chassis domain control system. It uniformly collects and classifies these multi-dimensional control parameters involved in chassis control, and performs initial calibration at three levels: actuator, vehicle attitude, and vehicle dynamics. The layered configuration and initial optimization of chassis control parameters are achieved through the correlation between parameters at different levels. Specifically, the bottom actuator layer is used for parameter matching based on the control response characteristics of each chassis actuator, mainly including the calibration of braking, steering, and suspension parameters; the middle vehicle attitude layer is used for parameter matching based on the characteristics of vehicle attitude changes during vehicle movement, mainly including the calibration of roll and yaw parameters; and the top vehicle dynamics layer is used for parameter configuration based on the vehicle's operating state and dynamic performance under different driving conditions, mainly including the calibration of vehicle dynamics threshold parameters.

[0025] During the initial calibration of the hierarchical system, the control parameters corresponding to each actuator in the chassis domain control are first acquired. The basic control parameters of the braking actuator, steering actuator, and suspension actuator are then calibrated to ensure that each actuator meets the corresponding response requirements during chassis control. Specifically, braking parameters describe the control characteristics during braking, steering parameters describe the response characteristics during steering control, and suspension parameters describe the control characteristics during suspension adjustment. By initially matching the parameters of different actuators, the control deviation caused by the mutual influence between multiple actuator parameters is reduced, providing the basic parameter conditions for subsequent adjustments to vehicle attitude parameters and overall vehicle dynamics parameters.

[0026] After initial calibration of the actuator layer parameters, further vehicle attitude-related parameters during vehicle operation are acquired, and roll and yaw attitude parameters are calibrated. By analyzing the changes in vehicle attitude under different motion states, parameters for the vehicle's attitude response characteristics during steering, braking, and driving are adjusted to match the vehicle attitude control parameters with the underlying actuator control parameters. Roll attitude parameters reflect the vehicle's roll characteristics during steering, acceleration, and deceleration, while yaw attitude parameters reflect the vehicle's motion characteristics around the vertical axis during steering. By calibrating the intermediate-level vehicle attitude parameters, coordination between the underlying actuator control and the vehicle's dynamic attitude performance is achieved.

[0027] After calibrating the actuator layer parameters and vehicle attitude layer parameters, the vehicle dynamics parameters are further calibrated. Based on the overall vehicle operating state and performance requirements under different operating conditions, the vehicle dynamics threshold parameters are configured. These threshold parameters describe the judgment conditions corresponding to control switching or parameter adjustment under different operating conditions. By calibrating these parameters, the chassis domain controller can achieve matching between different control strategies based on the overall dynamic state of the vehicle.

[0028] In the aforementioned layered initial calibration process, a hierarchical parameter calibration relationship is established between the bottom actuator layer, the vehicle attitude layer, and the vehicle dynamics layer. The bottom actuator parameters serve as the basic parameters for the actual control output of the vehicle. The mid-level vehicle attitude parameters are further adjusted based on the control effect of the bottom actuators, and the upper-level vehicle dynamics parameters are configured with operating condition thresholds based on the overall performance of the vehicle. By calibrating parameters separately according to different control levels, the parameter coupling effect caused by adjusting all parameters simultaneously is avoided, thereby improving the accuracy of the multi-dimensional parameter matching process of the chassis domain control.

[0029] During the specific calibration process, braking execution parameters, steering execution parameters, suspension execution parameters, roll attitude parameters, yaw attitude parameters, and vehicle dynamics threshold parameters are calibrated sequentially. This establishes a complete initial parameter system for the actuator control parameters, body attitude control parameters, and vehicle dynamics control parameters within the chassis domain control. This initial parameter system serves as the foundation for subsequent multi-objective iterative optimization, multi-condition parameter matching, and adaptive parameter adjustment during vehicle operation. By independently configuring and optimizing parameters at different levels, hierarchical management and initial matching of chassis control parameters are achieved.

[0030] Vehicle operation data and simulation data under different vehicle speeds, road conditions, and driving intensities are collected. The multi-layer parameters after initial stratification are input into a multi-objective cost function for iterative optimization. The parameters of each layer are corrected according to vehicle stability and comfort indicators until each performance indicator reaches the preset convergence threshold. After completing the initial layered calibration of the actuator layer, vehicle attitude layer, and vehicle dynamics layer, actual operating data and simulation analysis data of the vehicle under different operating environments and driving states are further acquired to form a data foundation for parameter optimization. The vehicle operating data includes chassis control state data under different speed conditions, road surface conditions, and driving intensities. By collecting data on the vehicle's dynamic response under various operating conditions, the impact of different parameter combinations on vehicle handling and ride performance is obtained. Simultaneously, simulation data is used to supplement the analysis of the vehicle's operating state, enabling the multi-layered parameters after the initial layered calibration to be comprehensively evaluated and optimized under various operating conditions.

[0031] For vehicle operation data and simulation data collected under different vehicle speeds, road conditions, and driving intensities, the actuator layer parameters, vehicle attitude layer parameters, and vehicle dynamics layer parameters, after hierarchical initial calibration, are input into a multi-objective cost function. By comprehensively evaluating the combination of parameters at different levels, iterative optimization of parameters under multi-dimensional performance indicators is achieved. The multi-objective cost function comprehensively considers vehicle stability and comfort indicators, and iteratively corrects the parameters at each level based on the actual vehicle operating state and performance evaluation results, gradually optimizing the parameter matching relationship between different levels.

[0032] During the parameter iterative optimization process, the dynamic response of the chassis control process is evaluated based on vehicle stability indicators. By analyzing the vehicle's attitude changes, control response, and overall motion state under different operating conditions, relevant parameters affecting vehicle stability are adjusted. Simultaneously, the driving experience is evaluated based on comfort indicators. Based on the vehicle's response under different road conditions and driving states, relevant parameters affecting comfort are optimized. This comprehensive evaluation of stability and comfort indicators ensures that the optimization process balances vehicle handling and driving performance, avoiding the problem of optimizing a single performance indicator leading to a decline in other performance aspects.

[0033] During multi-objective iterative optimization, different parameters at the actuator layer, vehicle attitude layer, and vehicle dynamics layer are adjusted progressively. For actuator layer parameters, braking, steering, and suspension parameters are corrected based on vehicle operation data and simulation data feedback to match the underlying control response with the overall vehicle performance requirements. For vehicle attitude layer parameters, roll and yaw attitude parameters are adjusted based on changes in roll and yaw attitude during vehicle operation to better meet preset performance requirements for dynamic attitude control. For vehicle dynamics layer parameters, threshold parameters for vehicle dynamics conditions are optimized based on the overall vehicle motion performance under different operating conditions to ensure the vehicle maintains stable control under varying operating conditions.

[0034] As multi-condition data is continuously input and the multi-objective cost function is iteratively calculated, the parameters at each level are cyclically corrected based on changes in vehicle stability and comfort indices. After each parameter adjustment, the performance of the adjusted parameter combination is evaluated, and the evaluation results are compared with preset performance indicators. When the vehicle stability, comfort, and other relevant performance evaluation results gradually reach the set range, the current parameter combination is deemed to meet the optimization requirements; when the performance indicators do not meet the preset requirements, the parameters at each level continue to be adjusted based on the evaluation results, and the parameters gradually converge through multiple iterations.

[0035] After multiple iterations of optimization, a multi-layered parameter combination that meets vehicle performance requirements is formed, and the optimization process is judged by a preset convergence threshold. The preset convergence threshold is used to limit the performance variation range during parameter optimization. When both vehicle stability and comfort indicators reach the corresponding threshold requirements, it is determined that the current multi-layered parameters have reached a convergence state, completing the automatic optimization process of the layered parameters. By collecting data under different vehicle speeds, road conditions, and driving intensities, and combining a multi-objective cost function, the parameters after initial layered calibration are iteratively corrected, realizing the automatic adjustment of chassis domain control parameters from initial matching to optimized convergence.

[0036] Through the above parameter optimization process, the matching effect of chassis domain control parameters can be further improved based on the initial hierarchical calibration, so that a more reasonable coordination relationship can be formed between parameters at different levels, while taking into account the requirements of vehicle stability and comfort. The optimized parameter combination can serve as the basis for storing parameters under different operating conditions and for dynamic matching during vehicle operation, providing parameter support for maintaining consistent chassis control performance under multiple operating conditions.

[0037] After completing iterative optimization and reaching a convergence state, the multi-layer parameters are classified and stored according to vehicle speed, road conditions and driving intensity, forming a set of chassis domain control parameters corresponding to different operating conditions. After completing multi-objective iterative optimization, the actuator layer parameters, vehicle attitude layer parameters, and vehicle dynamics layer parameters that have reached convergence are obtained. The optimized multi-layer parameters are then categorized, organized, and solidified. Based on the different operating conditions that exist during actual vehicle operation, the converged parameters are categorized and stored according to vehicle speed, road conditions, and driving intensity. This allows the chassis control parameters corresponding to different operating conditions to be saved independently, forming a set of chassis domain control parameters that matches the specific vehicle operating state.

[0038] In the parameter classification and storage process, vehicle speed, as a crucial factor influencing vehicle dynamics, is used to differentiate the varying chassis control requirements across different speed ranges. For different speed conditions, such as low, medium, and high speeds, the multi-layered parameters obtained through multi-objective iterative optimization are correspondingly categorized, ensuring matching chassis control parameter combinations for different speed ranges. By classifying and storing parameters according to speed conditions, the corresponding parameters can be quickly retrieved based on the current speed state during subsequent vehicle operation, improving the matching degree between chassis control parameters and vehicle motion.

[0039] Road surface conditions, as a crucial factor affecting vehicle chassis performance, also serve as an important basis for parameter classification and storage. Based on the differences in vehicle performance under different road surface conditions, the optimized and converged multi-layered parameters are classified and stored according to different road surface conditions. This ensures that the chassis domain control parameter set can encompass the corresponding control parameters under different road surface environments. Under different road surface conditions, the vehicle's braking, steering, and suspension control requirements differ due to the influence of road adhesion and road surface smoothness. By independently storing the optimized parameters for different road surface conditions, a foundation can be provided for parameter matching during subsequent vehicle operation under various road surface conditions.

[0040] Driving intensity, as an important condition reflecting driving behavior characteristics, is used to distinguish changes in vehicle control requirements under different driving conditions. Based on different driving intensity conditions, multi-layered parameters after reaching convergence are categorized and managed, enabling the chassis domain control parameter set to correspond to the control requirements under different driving conditions. By using driving intensity as the basis for parameter classification, the vehicle can call appropriate parameter combinations under different driving conditions, achieving coordination of chassis control performance under various driving conditions.

[0041] After classifying parameters according to vehicle speed, road conditions, and driving intensity, the actuator layer parameters, vehicle attitude layer parameters, and vehicle dynamics layer parameters corresponding to each operating condition are uniformly stored in the chassis domain control parameter storage area. The stored parameter set includes optimized and converged parameter combinations under different operating conditions. Each set of parameters corresponds to a specific vehicle operating state and can serve as the basic data for parameter retrieval during subsequent vehicle dynamic control processes.

[0042] During the formation of the chassis domain control parameter set, the various parameters maintain their correspondence with the hierarchical initial calibration and multi-objective iterative optimization process. Actuator-level parameters describe the optimization results of braking, steering, and suspension parameters under different operating conditions; vehicle attitude-level parameters describe the matching results of roll and yaw attitude parameters under different operating conditions; and vehicle dynamics-level parameters describe the optimization results of vehicle dynamics threshold parameters under different vehicle speeds, road conditions, and driving intensities. By uniformly classifying and storing parameters at different levels, the formed chassis domain control parameter set can completely retain the multi-dimensional parameter optimization results.

[0043] To ensure the correspondence between the parameter set and the actual vehicle operating state, a parameter index relationship is established during the classification and storage process based on different operating conditions. This ensures that each converged parameter set corresponds to a specific vehicle speed, road surface conditions, and driving intensity. By establishing the correspondence between different operating conditions and optimized parameters, the problem of decreased control performance caused by long-term use of a single fixed parameter in different operating environments can be avoided, thus improving the adaptability of chassis control parameters to multi-condition environments.

[0044] The chassis domain control parameter set, after being categorized and stored, serves as the data foundation for adaptive matching of multi-condition parameters. This parameter set originates from vehicle operation data and simulation data under different vehicle speeds, road conditions, and driving intensities. It has undergone iterative optimization using a multi-objective cost function to achieve convergence, thus reflecting the optimized parameter states of vehicle stability and comfort indicators under different operating conditions. By classifying and solidifying the optimized multi-layer parameters according to operating conditions, the chassis domain control can select corresponding parameters based on current operating conditions during subsequent vehicle operation, improving the consistency and adaptability of vehicle chassis control.

[0045] By classifying and storing the multi-layer parameters after iterative optimization and convergence according to vehicle speed, road conditions, and driving intensity, a set of chassis domain control parameters corresponding to different operating conditions is formed. This realizes the transformation of chassis control parameters from a single fixed parameter to a set of multi-condition parameters, providing basic parameter support for dynamic parameter matching during vehicle driving and subsequent individual self-calibration of mass-produced vehicles.

[0046] During vehicle operation, the current operating status of the vehicle is acquired in real time, the corresponding operating conditions are identified, and the matching target parameters are called from the chassis domain control parameter set to dynamically adjust the braking execution parameters, steering execution parameters and suspension execution parameters. Once the vehicle enters actual operation, it continuously acquires information about its current operating status, analyzes the vehicle's operating environment and dynamic state in real time, and identifies the corresponding operating condition based on the vehicle's current status. The vehicle's operating status includes key factors affecting chassis control, such as current vehicle speed, road conditions, and driving intensity. By comprehensively judging these operating states, the system determines the target operating condition for the current vehicle and searches for a matching parameter combination from the established chassis domain control parameter set based on the identification results.

[0047] The chassis domain control parameter set is formed by storing multi-layered parameters categorized by vehicle speed, road conditions, and driving intensity after iterative optimization and convergence. It includes optimized parameters corresponding to different operating conditions. During actual vehicle operation, based on real-time acquired operating status information, the current vehicle speed range, current road conditions, and current driving intensity are identified, and the corresponding parameter set is matched according to the identification results. By dynamically identifying vehicle operating conditions, the chassis domain control no longer uses a single fixed parameter control method, but instead calls upon target parameters suitable for the current operating conditions based on the vehicle's current actual operating environment.

[0048] During the target parameter retrieval process, the actuator layer parameters, vehicle attitude layer parameters, and vehicle dynamics layer parameters matching the current state are obtained based on the parameter set corresponding to the current operating condition. The actuator layer parameters include optimized braking, steering, and suspension parameters. By calling the optimized parameters for the corresponding operating condition, the chassis actuators can adjust the control method according to the vehicle's current operating state, achieving dynamic adaptation of chassis control parameters under different operating conditions.

[0049] Regarding braking execution parameters, based on the current vehicle operating status and corresponding operating condition parameters, the parameters in the braking control process are dynamically adjusted to ensure that the braking execution control meets the control requirements under the current operating conditions. The control requirements during vehicle braking vary under different vehicle speeds, road conditions, and driving intensities. By calling the braking execution parameters for the corresponding operating conditions, the braking control effect can be matched to the current state of the vehicle.

[0050] Regarding steering execution parameters, the corresponding optimized steering control parameters are invoked based on the vehicle's current operating state to dynamically adjust the parameters during the steering control process. Vehicles have different requirements for steering response speed, control stability, and steering following performance under different operating conditions. By matching the corresponding steering execution parameters according to the current operating conditions, the steering control process can remain coordinated with the vehicle's dynamic state, improving the steering control adaptability under different operating conditions.

[0051] Regarding suspension execution parameters, the system dynamically adjusts the suspension execution process by calling the corresponding suspension control parameters based on the current vehicle operating state. Different road conditions and driving states lead to varying vehicle suspension control requirements. By calling the suspension execution parameters for the corresponding operating conditions, the suspension control can be adjusted according to the actual operating environment, improving vehicle comfort and stability during driving.

[0052] During dynamic parameter matching, the target parameters are derived from parameter combinations that have undergone multi-objective iterative optimization and reached a convergence state. Each parameter has been evaluated and optimized based on vehicle stability and comfort indicators, thus meeting the chassis control requirements under different operating conditions. By matching the real-time identified operating conditions with the chassis domain control parameter set, optimized parameters for the corresponding operating conditions can be quickly obtained, and the matched parameters can be applied to the adjustment of braking, steering, and suspension execution parameters.

[0053] As the vehicle's operating status continuously changes, its current operating conditions also change. The chassis domain controller continuously acquires vehicle status information and re-identifies the corresponding operating conditions based on the changed vehicle speed, road conditions, and driving intensity. When the vehicle transitions from one operating state to another, the corresponding new operating condition parameters in the chassis domain controller parameter set are recalled, thereby achieving dynamic switching of chassis control parameters under different operating environments.

[0054] By acquiring the vehicle's current operating status in real time, identifying corresponding operating conditions, and calling matching target parameters, the chassis domain control parameters can be dynamically adjusted according to the vehicle's actual operating conditions. Compared to traditional fixed parameter control methods, this method can select corresponding optimized parameters based on different vehicle speeds, road conditions, and driving intensities, ensuring that braking, steering, and suspension execution parameters are always matched to the vehicle's current state, thus improving the adaptability of the chassis control process.

[0055] During long-term vehicle operation, the dynamically invoked target parameters can also serve as the basis for subsequent vehicle operation data collection and parameter adaptive correction. By continuously acquiring the vehicle's operating status and combining it with the parameter adjustment results corresponding to the current operating conditions, data support is provided for subsequent compensation for individual differences in mass-produced vehicles and optimization of basic parameters, realizing the dynamic adaptation of chassis domain control parameters from offline calibration during vehicle operation.

[0056] Through the above dynamic matching process, the optimized and converged parameters under different vehicle speeds, road conditions, and driving intensities are applied to the actual vehicle control process. This enables the chassis domain control to call the corresponding target parameters according to the current operating conditions and dynamically adjust the braking execution parameters, steering execution parameters, and suspension execution parameters, thereby achieving adaptive optimization of chassis control performance under multiple operating conditions.

[0057] The system continuously collects chassis status data during vehicle operation, adaptively corrects the dynamically adjusted target parameters based on the collected chassis status data, and updates the basic parameters in the chassis domain control parameter set to reduce performance deviations caused by differences in vehicle parts. During continuous vehicle operation, chassis operating status data is continuously collected to obtain chassis control data under actual vehicle operating conditions. Based on the collected data, the dynamically adjusted target parameters are further analyzed and optimized. In actual vehicle production, due to differences in component manufacturing processes, assembly errors, and different vehicle operating environments, even with the same initial calibration parameters and the same set of operating parameters, chassis control performance may still vary between different vehicles. Therefore, by continuously collecting chassis status data during vehicle operation, feedback analysis of the vehicle's actual operating performance is performed, and the target parameters are adaptively corrected based on the vehicle's own operating characteristics, achieving parameter optimization adjustments tailored to individual vehicle differences.

[0058] Chassis status data includes relevant status information reflecting the chassis control effect during vehicle operation. By continuously acquiring the actual operating status of the vehicle, the control effect of dynamically adjusted target parameters during actual vehicle operation can be analyzed. The chassis control effect may change under different usage cycles, different operating environments, and different component conditions. By collecting status data during long-term operation, parameter feedback information under the actual operating conditions of the vehicle can be obtained, providing a data basis for subsequent adjustments to basic parameters.

[0059] Based on the collected chassis status data, the dynamically adjusted target parameters are adaptively corrected. During the parameter correction process, the target parameters are adjusted within a small range based on the feedback results of the vehicle's actual operating status, so that the adjusted parameters are more consistent with the current vehicle status. The parameter correction mainly optimizes the basic parameters in the chassis domain control parameter set. By gradually fine-tuning the basic parameters, the actual control effect of the vehicle is kept consistent with the preset performance requirements.

[0060] During vehicle operation, the parameters stored in the chassis domain control parameter set are optimized and converged combinations derived from different vehicle speeds, road conditions, and driving intensities. Due to individual differences in components among mass-produced vehicles, different vehicles may exhibit certain deviations in their response to the same parameters during actual operation. Therefore, by continuously collecting vehicle chassis status data, analyzing the vehicle's own operational performance, and correcting the basic parameters based on actual feedback results, the chassis domain control parameter set can gradually adapt to the actual characteristics of individual vehicles.

[0061] To address performance deviations caused by differences in vehicle components, chassis condition data collected during long-term vehicle operation is used to identify discrepancies between the actual control performance and the target performance. Based on these discrepancies, fundamental parameters are adjusted. For example, the braking, steering, and suspension actuators of different vehicles may exhibit varying response characteristics due to manufacturing inconsistencies. Continuous analysis of actual operating data allows for appropriate correction of corresponding parameters to suit the response characteristics of different vehicles, thereby reducing the impact of component differences on the overall vehicle chassis performance.

[0062] During the adaptive correction process, parameter adjustments are kept within a reasonable range, with minor optimizations to the basic parameters. This allows the vehicle to further enhance its individual adaptability while maintaining the original optimization effect. By continuously collecting chassis status data and fine-tuning the parameters, performance deviations caused by the inability of a single calibration to cover all individual vehicle differences can be avoided. Simultaneously, by updating the corrected parameters to the chassis domain control parameter set, subsequent vehicle operation can utilize basic parameters that better reflect the current actual state of the vehicle.

[0063] As vehicle operating time increases, chassis status data accumulates, and the vehicle's own operating characteristics are gradually fed back into the parameter adjustment process. Based on the continuously collected data, the dynamically adjusted target parameters are iteratively optimized, enabling the basic parameters in the chassis domain control parameter set to adaptively change with the actual vehicle operating conditions. Through this parameter correction method based on vehicle operating data, the chassis control parameters no longer rely entirely on fixed calibrations during the production stage, but instead possess continuous optimization capabilities during vehicle use.

[0064] In the process of vehicle-specific adaptation, the main focus is on optimizing the chassis performance dispersion caused by differences in components in mass-produced vehicles. Traditional calibration methods typically use uniform parameter configurations, which cannot be adjusted for the actual state of each vehicle. However, by continuously collecting chassis state data and updating basic parameters, the differences in handling performance caused by component variations between different vehicles can be reduced, thereby improving the consistency of chassis performance in mass-produced vehicles.

[0065] By continuously collecting chassis status data during vehicle operation, the system adaptively corrects the dynamically adjusted target parameters based on this data and updates the basic parameters in the chassis domain control parameter set, achieving parameter feedback optimization during vehicle operation. The adaptively corrected parameters better match the actual operating characteristics of a single vehicle, maintaining parameter matching capabilities under different operating conditions while further reducing performance deviations caused by differences in vehicle components, thus improving the stability and consistency of multi-dimensional parameter control in the chassis domain control system.

[0066] This invention employs a three-layer hierarchical decoupling approach to manage multi-dimensional control parameters in chassis domain control. It independently divides and optimizes parameters at the actuator, vehicle attitude, and vehicle dynamics levels, reducing coupling interference caused by mutual influence between different control parameters. By establishing a hierarchical parameter system composed of bottom-level actuator parameters, mid-level attitude control parameters, and top-level vehicle dynamics parameters, parameters at different levels can be optimized and adjusted according to corresponding control objectives. This facilitates comprehensive matching of chassis performance across the entire domain, improves control consistency and dynamic response capabilities under different operating conditions, and enhances the overall performance of the chassis control system.

[0067] This invention replaces the traditional manual calibration method that relies on engineers' experience to adjust parameters with a multi-objective automatic optimization approach. It inputs vehicle operating data and simulation data collected under different operating conditions into the multi-objective optimization process, comprehensively evaluating performance indicators such as stability and comfort, and automatically completing iterative parameter corrections. By reducing repeated manual adjustments, it improves the efficiency of chassis parameter calibration, shortening the overall vehicle calibration cycle by 50%. Simultaneously, it reduces reliance on test personnel, test vehicles, and test sites during road testing, effectively reducing labor costs and test resource consumption during the calibration phase, and improving parameter optimization efficiency in the vehicle development process.

[0068] This invention establishes a multi-condition grouped parameter set, classifying and storing optimized and converged chassis control parameters under different vehicle speeds, road conditions, and driving intensities, and dynamically matching corresponding parameters according to the actual vehicle operating state. This approach avoids the problem of traditional single fixed parameters being unable to adapt to complex driving environments, enabling the vehicle to meet comfort requirements in low-speed driving scenarios, maintain stable control performance in high-speed driving scenarios, and meet safety control requirements under low-friction road conditions. Thus, it caters to multiple application scenarios such as low-speed comfort, high-speed stability, and low-friction safety, improving the chassis domain control system's adaptability to complex operating environments.

[0069] This invention utilizes onboard dynamic adaptive matching and mass-production individual self-calibration to continuously collect chassis status data during vehicle operation. Based on actual vehicle operation feedback, it makes small-scale adjustments to basic parameters, allowing chassis control parameters to gradually adapt to the actual characteristics of each vehicle. This method reduces vehicle performance discrepancies caused by differences in component manufacturing and assembly errors, improves the consistency of chassis control performance among mass-produced vehicles, reduces after-sales complaints due to differences in vehicle handling and chassis feel, and enhances the stability of the driving experience during long-term vehicle use.

[0070] This invention establishes clear parameter convergence criteria to assess performance index changes during parameter optimization. Optimization is considered complete once stability, comfort, and related parameter changes reach preset thresholds, avoiding repetitive testing and ineffective adjustments common in traditional manual calibration due to a lack of clear criteria. By quantifying convergence conditions to control the parameter optimization process, unnecessary road tests can be reduced, minimizing wear and tear from long-term testing of prototype vehicles. This also saves testing time and development costs, improving the controllability and engineering application value of chassis domain control parameter optimization.

[0071] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters, characterized in that, Includes the following steps: The system acquires braking execution parameters, steering execution parameters, suspension execution parameters, body attitude parameters, and vehicle dynamics parameters from the chassis domain control, and performs initial calibration in layers according to the actuator layer, body attitude layer, and vehicle dynamics layer. The system then sequentially completes the calibration of braking execution parameters, steering execution parameters, suspension execution parameters, roll attitude parameters, yaw attitude parameters, and vehicle dynamics operating condition threshold parameters. Vehicle operation data and simulation data under different vehicle speeds, road conditions, and driving intensities are collected. The multi-layer parameters after initial stratification are input into a multi-objective cost function for iterative optimization. The parameters of each layer are corrected according to vehicle stability and comfort indicators until each performance indicator reaches the preset convergence threshold. After completing iterative optimization and reaching a convergence state, the multi-layer parameters are classified and stored according to vehicle speed, road conditions and driving intensity, forming a set of chassis domain control parameters corresponding to different operating conditions. During vehicle operation, the current operating status of the vehicle is acquired in real time, the corresponding operating conditions are identified, and the matching target parameters are called from the chassis domain control parameter set to dynamically adjust the braking execution parameters, steering execution parameters and suspension execution parameters. The system continuously collects chassis status data during vehicle operation, adaptively corrects the dynamically adjusted target parameters based on the collected chassis status data, and updates the basic parameters in the chassis domain control parameter set.

2. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 1, characterized in that, Initial calibration is performed in layers: actuator layer, vehicle attitude layer, and vehicle dynamics layer, including: The initial parameters corresponding to different control objects in the chassis domain control are obtained. The braking execution parameters, steering execution parameters, and suspension execution parameters are assigned to the actuator layer, the roll attitude parameters and yaw attitude parameters are assigned to the vehicle attitude layer, and the vehicle dynamics operating condition threshold parameters are assigned to the vehicle dynamics layer. Initial calibration is performed in the order from the bottom actuator parameters to the middle attitude parameters and then to the top vehicle dynamics parameters, so that the parameters of each layer complete parameter matching according to the corresponding control objectives, and establish the correlation between parameters of different levels, providing the initial parameter basis for subsequent multi-level parameter iterative optimization.

3. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 1, characterized in that, The multi-layer parameters, after initial hierarchical calibration, are input into the multi-objective cost function for iterative optimization, including: Vehicle performance evaluation indices are established based on vehicle operation data and simulation data. Stability and comfort indices are used as the evaluation criteria for the multi-objective cost function. The actuator layer parameters, body attitude layer parameters, and vehicle dynamics layer parameters are comprehensively calculated. Based on the evaluation results output by the multi-objective cost function, the parameters at different levels are iteratively adjusted. Through multiple rounds of parameter correction, the deviation between the vehicle operation state and the target performance indices is reduced, so that the optimized multi-level parameters gradually meet the vehicle dynamic performance requirements.

4. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 3, characterized in that, The multi-objective cost function includes stability evaluation parameters and comfort evaluation parameters. The parameter optimization results are obtained by comprehensively calculating different evaluation parameters. Among them, stability evaluation parameters are used to evaluate the dynamic control performance of the vehicle during operation, and comfort evaluation parameters are used to evaluate the driving performance of the vehicle during driving. Based on the optimization results corresponding to different evaluation parameters, the braking execution parameters, steering execution parameters, suspension execution parameters, roll attitude parameters, yaw attitude parameters, and vehicle dynamics threshold parameters are adjusted so that the optimized multi-layer parameters simultaneously meet the vehicle stability and comfort requirements.

5. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 1, characterized in that, After completing iterative optimization and reaching convergence, the multi-layer parameters are categorized and stored according to vehicle speed, road conditions, and driving intensity, including: Based on different vehicle speed ranges, road conditions, and driving intensities during vehicle operation, the actuator layer parameters, body attitude layer parameters, and vehicle dynamics layer parameters after reaching the convergence state are classified and managed. A correspondence between operating conditions and parameter combinations is established, and the optimized parameters corresponding to different operating conditions are stored in the chassis domain control parameter storage area to form a chassis domain control parameter set containing various vehicle operating conditions.

6. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 5, characterized in that, The chassis domain control parameter set includes parameter combinations corresponding to different vehicle speed conditions, parameter combinations corresponding to different road surface conditions, and parameter combinations corresponding to different driving intensities. Each parameter combination includes optimized and converged braking execution parameters, steering execution parameters, suspension execution parameters, vehicle attitude parameters, and vehicle dynamics parameters. By establishing parameter index relationships for different operating conditions, the vehicle can quickly match the corresponding target parameters according to the current state during operation, thereby improving the adaptability between chassis domain control parameters and the actual operating state of the vehicle.

7. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 1, characterized in that, Real-time acquisition of the vehicle's current operating status, identification of corresponding operating conditions, including: The system acquires information on the vehicle's current speed, road conditions, and driving intensity. Based on the collected vehicle operating status information, it determines the vehicle's current operating condition and searches for target parameters that match the current operating condition in the chassis domain control parameter set. This allows the vehicle's chassis control parameters to be dynamically adjusted according to actual operating conditions, avoiding a decline in control performance caused by using fixed parameter control methods for a long time.

8. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 7, characterized in that, The system retrieves matching target parameters from the chassis domain control parameter set and dynamically adjusts the braking, steering, and suspension execution parameters, including: Based on the identified current operating conditions, the corresponding optimized parameter combination is called from the chassis domain control parameter set, and the called target parameters are applied to the braking execution control, steering execution control and suspension execution control processes, so that the chassis actuators can adjust the control state according to different vehicle speeds, different road conditions and different driving intensities, and realize multi-condition parameter adaptive matching during vehicle operation.

9. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 1, characterized in that, The dynamically adjusted target parameters are adaptively corrected based on the collected chassis status data, including: The system continuously acquires chassis status data during vehicle operation, analyzes the actual vehicle control effect corresponding to the dynamically adjusted target parameters, and makes small-scale adjustments to the basic parameters in the chassis domain control parameter set based on the differences between the vehicle's operating status and the target performance indicators. This makes the corrected basic parameters more consistent with the actual operating characteristics of the current vehicle and reduces the impact of differences in different vehicle components on chassis control performance.

10. The method for hierarchical adaptive calibration and multi-condition self-convergence of chassis domain control parameters according to claim 9, characterized in that, Update the basic parameters in the chassis domain controller parameter set, including: Based on the chassis status data continuously collected during long-term vehicle operation, the basic parameters under the corresponding operating conditions are updated so that the updated parameter set retains the original multi-condition parameter matching relationship and further adapts to the actual operating conditions of a single vehicle. By providing feedback correction to the operating data of mass-produced vehicles, the vehicle performance deviation caused by the differences in components between braking actuators, steering actuators and suspension actuators is reduced, and the consistency of chassis control performance between different vehicles is improved.

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