CN108446520A - The parameter matching control system and optimization method of semi-active suspension system and mechanical elastic vehicle wheel - Google Patents
The parameter matching control system and optimization method of semi-active suspension system and mechanical elastic vehicle wheel Download PDFInfo
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Abstract
The invention discloses the parameter matching control systems and optimization method of a kind of semi-active suspension system and mechanical elastic vehicle wheel,The parameter matching and optimization jointly controlled based on CARSIM and MATLAB,Input has already passed through the mechanical elastic vehicle wheel of processing and the mathematical model of semi-active suspension in CARSIM,To establish the kinetic model of mechanical elastic vehicle wheel and semi-active suspension,Again whole vehicle model is built according to whole-car parameters in CARSIM,And design semi-active suspension rigidity and damping parameter variable,The relevant control prioritization scheme of joint MATLAB/Simulink designs,CARSIM whole vehicle models are imported into MATLAB and carry out different operating mode associative simulations,Obtain semi-active suspension parametric optimal solution collection or the curve under different operating modes,The method for from which further following that best match parameter.This method provides technological guidance for the determination of subsequent matching scheme and the processing and fabricating of suspension system, reduces time and the cost of exploitation high performance suspension system, has good robustness.
Description
Technical field
The invention belongs to Car design technical fields, and in particular to a kind of semi-active suspension system and mechanical elastic vehicle wheel
Parameter matching control system and optimization method.
Background technology
The suspension structure important as vehicle and functional component influence vehicle overall performance great.Automobile-used suspension presses it
Operation principle can be divided into passive suspension, semi-active suspension and Active suspension.Currently, passive suspension in the process of moving join by its correlation
Number is non-adjustable and cannot meet requirement of the people to ride comfort, although the rigidity and damping characteristic of Active suspension can be according to vapour
Vehicle driving conditions carry out automatic adjusument, but need corresponding energy resource system and cost is higher, i.e., both could not obtain extensively
Application.The semi-active suspension system being made of variable damping damper or variable rate spring can be according to pavement conditions and garage
It sails state and responds, not only compensated for the defect of passive suspension system, but also breach the office of Active suspension in practical applications
Limit has higher cost performance and wide application prospect.And with the development of science and technology various modern times intelligent control technologies
Development afterwards in the quite a while, seeks suitable control strategy with prioritization scheme to improve the ride comfort of automobile and relax
Adaptive will be as developing direction from now on.
Simultaneously with the development of automotive engineering, the increasingly raising of people's living standard, broad masses more note when purchasing vehicle
Weight automotive safety and comfort.And the quality of suspension system is related to control stability, ride comfort and the passability of automobile, indirectly
The safety for influencing running car and comfort.Therefore the parameter to suspension is required in new car exploitation or vehicle improve
Matching optimization is carried out, to realize the promotion of vehicle performance.Suspension parameter matching traditional at present mainly rule of thumb, passes through meter
Calculation-trial-production-experiment.- manufacturing experimently again-method tested again is calculated to modification, repeatedly more wheels, until reaching target call.
Although also part uses computer virtual technology during it, but still time-consuming and laborious, and this and current automobile
The keen competition on boundary is also incompatible.
First, heavy workload, the time is long.Suspension parameter matching traditional at present is mainly according to through passing through calculating-trial-production-examination
It tests.- manufacturing experimently again-method tested again is calculated to modification, repeatedly more wheels.The matching process of apparent entire suspension system needs
Multiple calculating and experiment are undergone, inevitably to consume a large amount of manpower and materials, while according to traditional suspension matching system master
It rule of thumb to be calculated and be tested, the uncertainty of whole process certainly will be increased, that is, there is certain blindness, in turn
Workload is caused to increase, time-consuming.
Development cost is high.According to traditional suspension parameter matching process, empirically, pass through calculating-trial-production-experiment.It arrives
Modification calculates-manufacturing experimently again-method tested again, repeatedly more wheels, until reaching target call.It well imagines, is opened in entire matching
During hair, need constantly to develop associated suspension component progress experimental verification according to calculating, therefore inevitably need to waste
Fall a large amount of material to increase cost.
It is difficult to using modern intelligent control and optimisation technique, poor robustness.Since traditional suspension parameter matches main root
According to through passing through calculating-trial-production-experiment.- manufacturing experimently again-method tested again is calculated to modification, repeatedly more wheels.In entire suspension matching system
Seldom use virtual software technology in the process, i.e., most of is all to change model l-G simulation test again repeatedly by constantly trying to gather, because
This almost can not be in conjunction with some modern intelligent controls and optimum theory, to as carrying out identical suspension and different vehicle performances
Timing does not have and repeats applicability, i.e. poor robustness.
Invention content
Goal of the invention：In order to overcome the deficiencies in the prior art, above-mentioned traditional suspension matching system development time is solved
A kind of parameter match control system of semi-active suspension system and mechanical elastic vehicle wheel is proposed with technical problems, the present invention such as of high cost
System and optimization method.
Technical solution：To achieve the above object, the technical solution adopted by the present invention is：
A kind of semi-active suspension system and the matched control system of mechanical elastic vehicle wheel parameter, including sequentially connected vehicle
Kinetic model system, control optimization system and combined simulation system；
The Full Vehicle Dynamics model system is used for the mathematical model according to semi-active suspension system and mechanical elastic vehicle wheel
And whole-car parameters, the CARSIM kinetic models of vehicle, and the design parameter variable in CARSIM are established based on CARSIM；
The control optimization system be used for CARSIM kinetic models that the Full Vehicle Dynamics model system export with
Parametric variable imports MATLAB, and the parameter matching optimization for jointly controlling semi-active suspension system and mechanical elastic vehicle wheel works；Institute
It states MATLAB and is also associated with optimization algorithm module, carried out using the non-dominated sorted genetic algorithm NSGA-II based on elitism strategy
Multiple-objection optimization；
The combined simulation system is used to, to the associative simulation of different operating modes, export the optimal solution set or song under different operating modes
Line obtains optimal semi-active suspension match parameter.
Further, the whole-car parameters include：Car weight, overall height, wheelspan, wheelbase.
Further, the parametric variable includes the rigidity and damping parameter variable of semi-active suspension.
Further, in the Full Vehicle Dynamics model system, first according to mechanical elastic vehicle wheel and semi-active suspension
Mathematical model establishes the kinetic model of mechanical elastic vehicle wheel and semi-active suspension in CARSIM；Exist further according to whole-car parameters
The CARSIM models of vehicle are built in CARSIM.
Further, described to jointly control, being will by the joint MATLAB/Simulink control modules that CARSIM is carried
The Full Vehicle Dynamics model built up, which imports in MATLAB, to be jointly controlled.
A kind of above-mentioned semi-active suspension system and the matched control optimization method of mechanical elastic vehicle wheel parameter, including it is following
Step：
Step 1. is imitated according to the mathematical model and whole-car parameters of semi-active suspension system and mechanical elastic vehicle wheel based on vehicle
True software CARSIM establishes Full Vehicle Dynamics model and corresponding control prioritization scheme with MATLAB, jointly controls semi-active suspension
It works with the parameter matching optimization of mechanical elastic vehicle wheel；
Step 2. obtains the optimal solution set or curve under different operating modes by the associative simulation of different operating modes, obtains optimal
Semi-active suspension match parameter.
Further, described that Full Vehicle Dynamics model is established based on vehicle simulation software CARSIM and MATLAB in step 1
With relevant control prioritization scheme, specific method is：
(1) according to the mathematical model of mechanical elastic vehicle wheel and semi-active suspension, mechanical elasticity vehicle is established in CARSIM
The kinetic model of wheel and semi-active suspension；
(2) whole vehicle model is built in CARSIM according to the dimensional parameters of vehicle；
(3) rigidity and damping parameter variable of semi-active suspension are designed in CARSIM；
(4) it is designed in MATLAB and establishes corresponding control optimization algorithm program；
(5) CARSIM kinetic models are imported into MATLAB and carries out associative simulation.
Further, the whole-car parameters include：Car weight, overall height, wheelspan, wheelbase.
Further, in step 1, the CARSIM and MATLAB jointly controls, and is the joint carried by CARSIM
MATLAB/Simulink control modules, which import the Full Vehicle Dynamics model built up in MATLAB, to be jointly controlled.
Further, the control prioritization scheme uses the quick non-dominated sorted genetic algorithm based on elitism strategy
NSGA-II carries out multiple-objection optimization, is as follows：
(1) optimization object function is determined：To characterize automobile ride, control stability and the vehicle body of driving safety respectively
The dynamic relatively dynamic displacement v between stroke f and wheel and road surface of acceleration root-mean-square value a, suspension system is object function；
(2) optimized variable is determined：Using semi-active suspension stiffness K and damping C as optimized variable；
(3) constraints is determined：Select the damping ratio of the dynamic deflection of suspension, the dynamic loading of tire and suspension system for constraint
Condition；
(4) design optimization iterative parameter：Including Population Size N, crossover probability pc, mutation probability pm, restart judgement algebraically
M and external Population Size Ne=200；
(5) associative simulation for carrying out different operating modes, obtains the optimal solution set or curve under different operating modes.
Further, in step (4), each Optimized Iterative parameter is：Population Size N=10, crossover probability pc=0.65,
Mutation probability pm=0.05, it restarts and judges algebraically M=3, external Population Size Ne=200.
Further, the multiple-objection optimization is as follows：
(1) according to the initial parameter of optimization aim, i.e. suspension rate K and damped coefficient C generates initial kind that scale is N
Group P and scale are the non-dominant collection D in outside of M, and set iteration coefficient gen=0；
(2) emulated under different operating modes, calculate in population individual target function value L=min (a, f, v) with about
Beam violation value, carries out quick non-dominated ranking；
(3) individual in each non-dominant layerings of selected population P and prize is participated in together with external non-dominant collection D in proportion
Match selection；
(4) intersection and mutation operation that population at individual is carried out with the probability of setting, obtain sub- population Q；
(5) merge parent, progeny population, generate new population R=P ∪ Q；
(6) quick non-dominated ranking is carried out to new population R, empties and dominates individual and comparatively dense individual in D.
(7) it presses adaptive value size to sort, top n excellent individual is as new population P in selection group R；
(8) iteration coefficient gen is judged, if gen<Genmax, then gen=gen+1, return to step (2)；Otherwise algorithm is whole
Only, pareto optimal solution sets are exported.
Further, the optimal semi-active suspension match parameter of acquisition, for being the determination of subsequent matching scheme and hanging
The processing and fabricating of frame system provides technological guidance, specially：According to the Optimal Parameters or curve obtained, trial-production has corresponding ginseng
Several semi-active suspensions carries out real steering vectors with mechanical elastic vehicle wheel and is verified with subjective assessment, so that it is determined that matching scheme.
Advantageous effect：The parameter match control system of a kind of semi-active suspension system provided by the invention and mechanical elastic vehicle wheel
System and optimization method have the advantage that compared with prior art：
1. by establishing the kinetic model of semi-active suspension and mechanical elastic vehicle wheel, and related whole-car parameters is combined to exist
Full Vehicle Dynamics model is built in CARSIM, imports MATLAB and carries out the associative simulation experiment under different operating modes, Ke Yiji
The earth is reduced due to the consuming on the material that specific test tape comes, to reduce research cost.
2. by MATLAB design use different control optimisation strategies, can be well adapted for semi-active suspension with
Mechanical elastic vehicle wheel parameter matches the application in different automobile types, that is, has good robustness.
3. can actually be asked to avoid project initiation difficulty etc. caused by place or fund using virtual software modeling experiment
Topic.
Description of the drawings
Fig. 1 is the system structure diagram of the present invention；
Fig. 2 is the control optimized flow chart of the present invention；
Fig. 3 is semi-active suspension and the matched specific control prioritization scheme flow chart of vehicle elastic wheel parameter.
Specific implementation mode
The invention discloses the parameter matching control systems and optimization of a kind of semi-active suspension system and mechanical elastic vehicle wheel
Method, i.e., the parameter matching and optimization jointly controlled based on CARSIM and MATLAB are inputted in CARSIM and have already passed through processing
The mathematical model of mechanical elastic vehicle wheel and semi-active suspension, to establish the kinetic simulation of mechanical elastic vehicle wheel and semi-active suspension
Type, then whole vehicle model is built according to whole-car parameters in CARSIM, and semi-active suspension rigidity and damping parameter variable are designed, join
CARSIM whole vehicle models are imported MATLAB and carry out different works by the relevant control prioritization scheme for closing MATLAB/Simulink designs
Condition associative simulation obtains semi-active suspension parametric optimal solution collection or curve under different operating modes, from which further follows that best matching
A kind of method of parameter.It is this to carry out the matched method of suspension relevant parameter using virtual emulation software, it is subsequent match party
The determination of case and the processing and fabricating of suspension system provide technological guidance.Heavy workload not only can be matched to avoid conventional suspension systems
The problem of, and it can be significantly reduced time and the cost of exploitation high performance suspension system, also have for different parameter models
There is good robustness.
Embodiment
It further illustrates below in conjunction with the accompanying drawings.
As shown in Figure 1, for the system structure diagram of the present invention.
As shown in Fig. 2, the working method of the present invention is：
1. obtaining whole-car parameters, such as car weight, overall height, wheelspan, wheelbase according to required matched vehicle, its parameter is inputted
To building whole vehicle model in vehicle simulation software CARSIM.
2. according to the semi-active suspension damping/rigidity and mechanical elastic vehicle wheel vertical stiffness that control optimization needed for matching,
Semi-active suspension damping/rigidity variable is set in CARSIM.
3. the CARSIM whole vehicle models built up are imported in MATLAB, and relevant control and optimization mould are designed in MATLAB
Block builds vehicle Simulink models.
4. carrying out the emulation experiment of different operating modes to the target call of ride comfort and control stability according to the vehicle, design
Corresponding optimization object function, operation program obtain corresponding control and Optimal Parameters or curve.It is as follows：
(1) optimization object function is determined：To promote the Performance Evaluating Indexes that suspension system performance synthesis considers suspension system
Influence with spring rate and resistance of shock absorber to suspension performance, choose with characterize respectively automobile ride, control stability with
The dynamic relatively dynamic displacement v between stroke f and wheel and road surface of the vehicle body acceleration root-mean-square value a of driving safety, suspension system
For object function, i.e. L=min (a, f, v).
(2) optimized variable is determined：Optimized variable is the parameter for needing to optimize in design process, chooses suspension stiffness K
With damping value C variables as an optimization, i.e. X=[K C].
(3) constraints is determined：The state variable of optimized variable and system must satisfy certain constraint in optimization process
Condition selects the damping ratio of the dynamic deflection of suspension, the dynamic loading of tire and suspension system for constraints by considering.
(4) design optimization iterative parameter：Population Size N=10, crossover probability pc=0.65, mutation probability pm=0.05, weight
Startup judges algebraically M=3, external Population Size Ne=200.
(5) associative simulation for carrying out different operating modes, obtains the optimal solution set or curve under different operating modes.
5. according to the Optimal Parameters or curve that are obtained, manufactures experimently corresponding semi-active suspension and carried out in fact with mechanical elastic vehicle wheel
Vehicle is tested to be verified with subjective assessment, so that it is determined that matching scheme.
As shown in figure 3, the control optimisation strategy of the present invention is using the quick non-dominated ranking heredity based on elitism strategy
Algorithm NSGA-II carries out multiple-objection optimization, to characterize automobile ride, control stability and the vehicle body of driving safety respectively
The dynamic relatively dynamic displacement v between stroke f and wheel and road surface of acceleration root-mean-square value a, suspension system is object function；With suspension
Spring rate K and damping value C variable as an optimization；With the dynamic loading of the dynamic deflection of suspension, tire, the damping ratio of suspension system is
Constraints.It is as follows：
(1) according to the initial parameter of optimization aim, i.e. suspension rate K and damped coefficient C generates initial kind that scale is N
Group P and scale are the non-dominant collection D in outside of M, and set iteration coefficient gen=0；
(2) emulated under different operating modes, calculate in population individual target function value L=min (a, f, v) with about
Beam violation value, carries out quick non-dominated ranking；
(3) individual in each non-dominant layerings of selected population P according to a certain percentage and with external non-dominant collection D mono- from the very first
According to prize selection mechanism；Described refers to the generation gap value in genetic algorithm according to a certain percentage, generally takes 0.9, i.e., 90% population
Participate in algorithm of tournament selection；
(4) intersection and mutation operation for carrying out population at individual, obtain sub- population Q；
(5) merge parent, progeny population, generate new population R=P ∪ Q；
(6) quick non-dominated ranking is carried out to new population R, empties and dominates individual and comparatively dense individual in D.
(7) it presses adaptive value size to sort, top n excellent individual is as new population P in selection group R；
(8) iteration coefficient gen is judged, if gen<Genmax, then gen=gen+1, return to step (2)；Otherwise algorithm is whole
Only, pareto optimal solution sets are exported.
The present invention provides the parameter match control optimization methods of a kind of semi-active suspension system and mechanical elastic vehicle wheel.I.e.
Based on the parameter matching and optimization that CARSIM and MATLAB jointly control, by building vehicle mould according to whole-car parameters in CARSIM
Type, and semi-active suspension and damping parameter variable are set, CARSIM whole vehicle models are imported into MATLAB and combine MATLAB/
The relevant control optimization program of Simulink designs carries out related operating mode associative simulation, obtain optimal solution set under different operating modes or
Curve, further to obtain optimal semi-active suspension and mechanical elastic vehicle wheel parameter.For subsequent matching scheme determination and
The processing and fabricating of suspension system provides technological guidance.
The above is only a preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of semi-active suspension system and the matched control system of mechanical elastic vehicle wheel parameter, it is characterised in that：Including successively
Full Vehicle Dynamics model system, control optimization system and the combined simulation system of connection；
The Full Vehicle Dynamics model system is used for according to the mathematical model of semi-active suspension system and mechanical elastic vehicle wheel and whole
Vehicle parameter establishes the CARSIM kinetic models of vehicle, and the design parameter variable in CARSIM based on CARSIM；
The CARSIM kinetic models and parameter that the control optimization system is used to export the Full Vehicle Dynamics model system
Variable import MATLAB, the parameter matching optimization for jointly controlling semi-active suspension system and mechanical elastic vehicle wheel work；It is described
MATLAB is also associated with optimization algorithm module, is carried out using the non-dominated sorted genetic algorithm NSGA-II based on elitism strategy more
Objective optimization；
The combined simulation system is used to, to the associative simulation of different operating modes, export the optimal solution set or curve under different operating modes,
Obtain optimal semi-active suspension match parameter.
2. semi-active suspension system according to claim 1 and the matched control system of mechanical elastic vehicle wheel parameter, special
Sign is：The whole-car parameters include：Car weight, overall height, wheelspan, wheelbase.
3. semi-active suspension system according to claim 1 and the matched control system of mechanical elastic vehicle wheel parameter, special
Sign is：The parametric variable includes the rigidity and damping parameter variable of semi-active suspension.
4. semi-active suspension system according to claim 1 and the matched control system of mechanical elastic vehicle wheel parameter, special
Sign is：In the Full Vehicle Dynamics model system, first according to the mathematical model of mechanical elastic vehicle wheel and semi-active suspension,
The kinetic model of mechanical elastic vehicle wheel and semi-active suspension is established in CARSIM；It is built in CARSIM further according to whole-car parameters
The CARSIM models of vehicle.
5. semi-active suspension system according to claim 1 and the matched control system of mechanical elastic vehicle wheel parameter, special
Sign is：It is described to jointly control, it is the vehicle that the joint MATLAB/Simulink control modules carried by CARSIM will be built up
Kinetic model, which imports in MATLAB, to be jointly controlled.
6. according to any semi-active suspension systems of claim 1-5 and the matched control system of mechanical elastic vehicle wheel parameter
Optimization method, it is characterised in that：Include the following steps：
Step 1. is emulated soft according to the mathematical model and whole-car parameters of semi-active suspension system and mechanical elastic vehicle wheel based on vehicle
Part CARSIM establishes Full Vehicle Dynamics model and corresponding control prioritization scheme with MATLAB, jointly controls semi-active suspension and machine
The parameter matching optimization of tool elastic wheel works；
Step 2. obtains the optimal solution set or curve under different operating modes by the associative simulation of different operating modes, and trial-production has corresponding
The semi-active suspension of parameter carries out real steering vectors with mechanical elastic vehicle wheel and is verified with subjective assessment, so that it is determined that half optimal active
Suspension matching system parameter.
7. the optimization of semi-active suspension system according to claim 6 and the matched control system of mechanical elastic vehicle wheel parameter
Method, it is characterised in that：It is described that Full Vehicle Dynamics model is established based on vehicle simulation software CARSIM and MATLAB in step 1
With relevant control prioritization scheme, specific method is：
(1) according to the mathematical model of mechanical elastic vehicle wheel and semi-active suspension, mechanical elastic vehicle wheel and half are established in CARSIM
The kinetic model of Active suspension；
(2) whole vehicle model is built in CARSIM according to whole-car parameters；
(3) rigidity and damping parameter variable of semi-active suspension are designed in CARSIM；
(4) it is designed in MATLAB and establishes corresponding control optimization algorithm program；
(5) CARSIM kinetic models are imported into MATLAB and carries out associative simulation.
8. any semi-active suspension system of according to claim 6 or 7 is with the matched control of mechanical elastic vehicle wheel parameter
The optimization method of system, it is characterised in that：The control prioritization scheme is using the quick non-dominated ranking heredity based on elitism strategy
Algorithm NSGA-II carries out multiple-objection optimization, is as follows：
(1) optimization object function is determined：Vehicle body to characterize automobile ride, control stability and driving safety respectively accelerates
It is object function to spend the dynamic relatively dynamic displacement v between stroke f and wheel and road surface of root-mean-square value a, suspension system；
(2) optimized variable is determined：Using semi-active suspension stiffness K and damping C as optimized variable；
(3) constraints is determined：The damping ratio of the dynamic deflection of suspension, the dynamic loading of tire and suspension system is selected to constrain item
Part；
(4) design optimization iterative parameter：Including Population Size N, crossover probability pc, mutation probability pm, restart judge algebraically M and
External Population Size Ne=200；
(5) associative simulation for carrying out different operating modes, obtains the optimal solution set or curve under different operating modes.
9. the optimization of semi-active suspension system according to claim 8 and the matched control system of mechanical elastic vehicle wheel parameter
Method, it is characterised in that：In step (4), each Optimized Iterative parameter is：Population Size N=10, crossover probability pc=0.65,
Mutation probability pm=0.05, it restarts and judges algebraically M=3, external Population Size Ne=200.
10. semi-active suspension system according to claim 8, and the matched control system of mechanical elastic vehicle wheel parameter
Optimization method, it is characterised in that：The multiple-objection optimization is as follows：
(1) according to the initial parameter of optimization aim, i.e. suspension rate K and damped coefficient C, generate initial population P that scale is N and
Scale is the non-dominant collection D in outside of M, and sets iteration coefficient gen=0；
(2) it is emulated under different operating modes, calculates target function value L=min (a, f, v) individual in population and disobeyed with constraint
Converse value carries out quick non-dominated ranking；
(3) it the individual in each non-dominant layerings of selected population P and participates in championship in proportion together with external non-dominant collection D and selects
It selects；
(4) intersection and mutation operation that population at individual is carried out with the probability of setting, obtain sub- population Q；
(5) merge parent, progeny population, generate new population R=P ∪ Q；
(6) quick non-dominated ranking is carried out to new population R, empties and dominates individual and comparatively dense individual in D.
(7) it presses adaptive value size to sort, top n excellent individual is as new population P in selection group R；
(8) iteration coefficient gen is judged, if gen<Genmax, then gen=gen+1, return to step (2)；Otherwise algorithm terminates, defeated
Go out pareto optimal solution sets.
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| CN109657393A (en) * | 2018-12-28 | 2019-04-19 | 上汽通用五菱汽车股份有限公司 | For tire and the matched emulation platform of chassis electric-control system and match emulation mode |
| CN111444623A (en) * | 2020-03-31 | 2020-07-24 | 桂林电子科技大学 | Collaborative optimization method and system for damping nonlinear commercial vehicle suspension dynamics |
| CN111506979A (en) * | 2020-06-10 | 2020-08-07 | 南京航空航天大学 | Complete vibration isolation design method for automobile suspension system |
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| CN111444623B (en) * | 2020-03-31 | 2024-01-26 | 桂林电子科技大学 | Collaborative optimization method and system for damping nonlinear commercial vehicle suspension dynamics |
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| CN113158326A (en) * | 2021-01-29 | 2021-07-23 | 西藏宁算科技集团有限公司 | Automobile suspension active control joint simulation method and device and electronic equipment |
| CN113536468A (en) * | 2021-07-30 | 2021-10-22 | 宜宾凯翼汽车有限公司 | Optimization method for K & C characteristics of suspension under multiple working conditions and multiple targets |
| CN116361919B (en) * | 2023-04-03 | 2023-11-21 | 小米汽车科技有限公司 | Subframe data processing method and device, storage medium and electronic equipment |
| CN116361919A (en) * | 2023-04-03 | 2023-06-30 | 小米汽车科技有限公司 | Subframe data processing method and device, storage medium and electronic equipment |
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