CN101367324B - Pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor - Google Patents
Pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor Download PDFInfo
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- CN101367324B CN101367324B CN2008101557869A CN200810155786A CN101367324B CN 101367324 B CN101367324 B CN 101367324B CN 2008101557869 A CN2008101557869 A CN 2008101557869A CN 200810155786 A CN200810155786 A CN 200810155786A CN 101367324 B CN101367324 B CN 101367324B
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Abstract
The invention relates to a road level prediction method based on a vehicle height sensor of an electric control air suspension. The invention aims to provide the road level prediction method which has low cost, is easy to realize, measures and provides road level information for vehicles which adopt the electric control air suspension. The technical proposal to realize the aim of the road level prediction method is as follows: the road level prediction method based on the vehicle height sensor of the electric control air suspension is to acquire suspension travel signals of a front axle height sensor within an adequate time span, perform real-time low-pass filtering on the acquired suspension travel signals, filter signals below an equilibrium position, obtain an average value of suspension travel signals in the forward direction, input the average value of the suspension travel signals in the forward direction and speed signals as BP neural networks, and nonlinearly predict the road level.
Description
Technical field
The present invention relates to the pavement grade determination methods, particularly a kind of pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor.
Background technology
The electron steering air suspension is compared with common air suspension, have function more, use more convenient characteristics such as flexibly, therefore, the application on large-scale high-grade automobile is more and more widely.
Adopt the vehicle of electron steering air suspension to have good riding comfort (travelling comfort), reduce driving system parts and the suffered impact load in road surface, have the ability of regulating air bellow height and resistance of shock absorber within the specific limits.At present, the principal element that restriction electron steering air suspension vehicle further improves vehicle performance is to lack enough information of road surface, therefore, and how effectively, the pavement grade that the judges rightly particular importance that seems.
Surface evenness is one of principal element that causes Vibration of Vehicle Suspensions, and existing electron steering air suspension vehicle does not have the ability of initiatively discerning pavement grade, therefore, after vehicle is changed road, the objective reference index of neither one is weighed the respective performances parameter that whether needs to adjust suspension, can only come suspension parameter (air bellow height, resistance of shock absorber) is regulated by the subjective judgement of chaufeur, be unfavorable for that like this air suspension given play to its performance best.
For addressing this problem domestic aspect, the Wan Gang of Shanghai Tongji University, Zhao Zhiguo, Yu Zhuoping, Sun Zechang, proposition speed of a motor vehicle road inductive automobile semi-active suspension skyhook damping control algorithm (application number: 200510030563.6).External aspect, Japan scholar Kakizaki.S, Taniguchi.M, Kanai.F, they propose to detect pavement grade (Patentnumber:5487006) by monitoring vehicle body lateral acceleration amplitude at appointed time or the interior frequency that surpasses predetermined critical of distance.Above-mentioned two technical schemes or algorithm complexity maybe need to install additional acceleration pick-up, and cost is higher.
Summary of the invention
Purpose of the present invention provides a kind of pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor, and the low and easy realization of this method cost is for the vehicle measuring and calculating of adopting the electron steering air suspension and pavement grade information is provided.
The technical scheme that realizes the object of the invention is: a kind of pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor, this method comprises the following steps:
1. gather from the moving stroke signal of the suspension of propons height sensor;
2. the moving stroke signal of the suspension of gathering is carried out real-time LPF, the signal that the filtering balance position is following is tried to achieve the aviation value that the forward suspension moves stroke signal;
3. import the nonlinear prediction pavement grade as the BP neural network with the aviation value and the speed per hour signal of the moving stroke signal of forward suspension.
Described nonlinear prediction pavement grade may further include, with road roughness mean effective value RMS is the output of neural network target, by all weights and threshold value are determined in the study of emulated data, finish from the nonlinear approximation that is input to output and shine upon, thereby realize specific pavement grade deterministic process.
Under the signal excitation of the road surface of different brackets, the amplitude of the moving stroke of suspension is different, surface evenness is more little, the moving stroke of suspension is corresponding also more little, therefore, adopt the aviation value and the speed per hour signal of the moving stroke signal of forward suspension under a certain operating mode, it is feasible coming the nonlinear prediction pavement grade by the BP neural network.
During running car, the ground-surface out-of-flat can evoke the vibration of automobile, when this vibration acquires a certain degree, the passenger will be felt under the weather, or make the goods that is delivered impaired, shorten service life and the automobile and the ground-surface adhesion effect of vehicle component, the dynamic load road pavement of tire road pavement also can produce destructive effect simultaneously.Beneficial effect of the present invention is, judges by pavement grade, suitably regulates the suspension vibration that decays of the air spring rigidity of electron steering air suspension and resistance of shock absorber, thereby weakens above-mentioned harmful effect.Therefore, analyze the relation of automobile vibration and ground-surface out-of-flat, judge that pavement grade is significant.
Accurately judge pavement grade, can provide information to suspension control system, suspension control system is regulated the height and the resistance of shock absorber of air bellow according to the information of road surface that obtains, thereby makes vehicle that ride comfort preferably be arranged in the process of travelling.Directly utilize ECAS height sensor signal and speed signal, just increased the calculated amount of ECAS--ECU, not only can save cost (not needing to install acceleration pick-up), and realize than being easier in actual applications.
Description of drawings
Fig. 1 is the embodiment of the invention 1 neural network model;
The specific embodiment
Be described further below in conjunction with accompanying drawing.
Embodiment 1
A kind of pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor, in the vehicle ' process, gather propons height sensor signal, note the moving stroke signal of vehicle suspension, the signal of intercepting appropriate time length, the signal that the filtering balance position is following is tried to achieve the aviation value that the forward suspension moves stroke signal; Aviation value and vehicle speed per hour signal with the moving stroke signal of forward suspension are imported the nonlinear prediction pavement grade as the BP neural network.
Neural network pavement grade judgment models block diagram as shown in Figure 1, its input is two-dimentional, ground floor (hidden layer) has 5 neurons, transfer function is tansig; The second layer (output layer) is single neuron, and transfer function is linear, and the training function is chosen trainlm.The BP neural network is input with aviation value (SWS), the Vehicle Speed (V) of the moving stroke signal of forward suspension, with road roughness mean effective value (RMS) is the output of neural network target, by all weights and threshold value are determined in the study of emulated data, finish from the nonlinear approximation that is input to output and shine upon, thereby realize specific pavement grade deterministic process.
According to neural network output, synopsis 1 just can draw the category of roads that is travelled under the vehicle present case.
8 grades of criteria for classifications of table 1 road roughness
Claims (5)
1. the pavement grade prediction technique of an air suspension chassis-height sensor, it is characterized in that: this method comprises the following steps:
1. gather from the moving stroke signal of the suspension of propons height sensor;
2. the moving stroke signal of the suspension of gathering is carried out real-time LPF, the signal that the filtering balance position is following is tried to achieve the aviation value that the forward suspension moves stroke signal;
3. import the nonlinear prediction pavement grade as neural network with the aviation value and the speed per hour signal of the moving stroke signal of forward suspension.
2. pavement grade prediction technique according to claim 1 is characterized in that, described step 2. forward suspension is moved in the computation of mean value of stroke signal, the moving stroke signal of forward suspension of intercepting appropriate time length.
3. pavement grade prediction technique according to claim 1 is characterized in that, described step is neural network target output with road roughness mean effective value RMS further in 3..
4. pavement grade prediction technique according to claim 1, it is characterized in that, described step further comprises in 3.: by all weights and threshold value are determined in the study of emulated data, finish neural network and shine upon, thereby realize specific pavement grade deterministic process from the nonlinear approximation that is input to output.
5. pavement grade prediction technique according to claim 1 is characterized in that, the pavement grade judgment models input of described neural network is two-dimentional, and ground floor has 5 neurons, and transfer function is tansig; The second layer is single neuron, and transfer function is linear, and the training function is chosen trainlm.
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| Application Number | Priority Date | Filing Date | Title |
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| CN2008101557869A CN101367324B (en) | 2008-10-15 | 2008-10-15 | Pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor |
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| CN2008101557869A CN101367324B (en) | 2008-10-15 | 2008-10-15 | Pavement grade prediction technique based on electronic control air spring vehicle altimetric sensor |
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| CN101367324A CN101367324A (en) | 2009-02-18 |
| CN101367324B true CN101367324B (en) | 2011-01-12 |
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| CN110001335B (en) * | 2019-03-11 | 2021-08-03 | 江苏大学 | Suspension dynamic stroke-based road surface grade identification system and method |
| CN110143108B (en) * | 2019-03-26 | 2020-10-20 | 江西科技学院 | Semi-active control method and system for automobile suspension |
| CN110210339B (en) * | 2019-05-19 | 2021-03-23 | 瑞立集团瑞安汽车零部件有限公司 | Method for identifying road jolt by fusing multiple sensors for ECAS system |
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| JP7314904B2 (en) * | 2020-10-30 | 2023-07-26 | トヨタ自動車株式会社 | Vibration control device |
| CN112597212B (en) * | 2020-12-21 | 2023-02-21 | 黑龙江亿林网络股份有限公司 | Time sequence data prediction device for road surface induction system |
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| CN114261251B (en) * | 2021-12-09 | 2024-04-09 | 科大讯飞股份有限公司 | Target vehicle suspension control method, system, vehicle, device and storage medium |
| CN114537070B (en) * | 2022-02-15 | 2024-04-16 | 辽宁工业大学 | Intelligent sensing method for road surface grade in front of automobile running for suspension control |
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