CN110162910A - A kind of hill start optimization method based on technology of Internet of things - Google Patents

A kind of hill start optimization method based on technology of Internet of things Download PDF

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CN110162910A
CN110162910A CN201910460366.XA CN201910460366A CN110162910A CN 110162910 A CN110162910 A CN 110162910A CN 201910460366 A CN201910460366 A CN 201910460366A CN 110162910 A CN110162910 A CN 110162910A
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李锐
孙福明
李刚
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Liaoning University of Technology
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Abstract

The invention discloses a kind of hill start optimization method based on technology of Internet of things, comprising: Step 1: the environmental parameter of the position where acquisition automobile, and calculate Environmental Factors；Step 2: master cylinder pressure P, the engine output torque R of acquisition automobileTAnd brake pedal aperture α；Step 3: the parameter of acquisition is input in BP neural network model after normalization, the modulator pressure regulator during car ramp starting is controlled to adjust by BP neural network model training as input layer；Step 4: being input to signal is controlled to adjust in fuzzy controller, the output vector group for indicating to control to adjust classification is obtained, as adjusting answer output.By environmental parameter and driving parameters of the monitoring automobile during uphill starting, the modulator pressure regulator during car ramp starting is controlled to adjust, car slipping phenomenon is prevented, improves uphill starting success rate.

Description

A kind of hill start optimization method based on technology of Internet of things
Technical field
The present invention relates to a kind of hill start optimization method based on technology of Internet of things, belongs to automotive field.
Background technique
Internet of Things this noun proposed by American professor in 1999 first, and from the radio-frequency technique at initial stage, sensing equipment Etc. the intellectual technology for developing to the more scientific and technological content such as embedded system of today, cloud computing.Internet of Things now relies primarily on Internet and communication network, wireless sensor network etc. mutually overlap, to realize the intelligent control to each production field.Internet of Things Net is that the objects such as object, project are set up the hardware such as input and output based on certain Internet protocol, recycle software systems with The signal of hardware realizes that information exchange achievees the purpose that intelligent control.
Car ramp starting refer to that automobile starts on the ramp of certain angle, due to automobile self performance, environment because Element influence etc. factors, the phenomenon that automobile often will appear car slipping in uphill starting, therefore uphill starting auxiliary system meet the tendency of and It is raw, it prevents automobile from gliding by intervention braking, hydraulic braking auxiliary and gearbox regulation, avoids the feelings of engine misses Condition.However existing auxiliary system is mostly based on ABS system proposition, can not be detached from ABS system exclusive use, and existing Auxiliary system cannot regulate and control the optimization method of uphill starting according to environmental factor to uphill starting, and success rate is low, precision Difference.
Summary of the invention
The present invention has designed and developed a kind of hill start optimization method based on technology of Internet of things, is risen by monitoring automobile in ramp Environmental parameter and driving parameters during step control to adjust the modulator pressure regulator during car ramp starting, Car slipping phenomenon is prevented, uphill starting success rate is improved.
Another goal of the invention of the invention, by BP neural network and fuzzy control to automobile during uphill starting Modulator pressure regulator is adjusted, the output torque of engine is improved, it is ensured that automobile can normally start on ramp, not slip Vehicle.
Another goal of the invention of the invention by calculating Environmental Factors as input layer parameter, while passing through calculating The rate of discharge of modulator pressure regulator improves the precision of Throttle Opening Control and the success rate of uphill starting.
Technical solution provided by the invention are as follows:
A kind of hill start optimization method based on technology of Internet of things, comprising:
Step 1: the environmental parameter of the position where acquisition automobile, and calculate Environmental Factors；
Step 2: master cylinder pressure P, the engine output torque R of acquisition automobileTAnd brake pedal aperture α；
Step 3: the parameter of acquisition is input in BP neural network model after normalization, as input layer, pass through BP neural network model training controls to adjust the modulator pressure regulator during car ramp starting；
Step 4: by control to adjust signal be input in fuzzy controller, obtain indicate control to adjust classification output to Group is measured, is exported as answer is adjusted.
Preferably, the environmental parameter include environment temperature T, ambient humidity RH, hill gradient θ, wind scale W, with And visibility S.
Preferably, the step 3 specifically includes:
Step 1, according to the sampling period, obtain the environment temperature T of automobile position, ambient humidity RH, hill gradient θ, Wind scale W and visibility S, and Environmental Factors μ is calculated by the period；
Step 2, master cylinder pressure P, the engine output torque R for acquiring automobileTAnd brake pedal aperture α, and with it is described Environment influences to be normalized together because of μ, determines that the input layer vector of three layers of BP neural network is x={ x1,x2,x3,x4}； Wherein, x1For master cylinder pressure coefficient, x2Engine output torque coefficient, x3For brake pedal aperture coefficient, x4For environment influence because Subsystem number；
Step 3, the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the middle layer vector y={ y1,y2,…,ym}；During m is Interbed node number；
Step 4 obtains output layer vector o={ o1}；o1For Throttle Opening Control valve opening coefficient.
Preferably, the step 5 specifically includes:
Throttle Opening Control valve opening coefficient and preset accelerator open degree coefficients comparison are obtained into throttle adjustment deviation signal, it will be oily Door deviation signal is input to after amplification by throttle deviation variation rate signal is calculated, by throttle deviation variation rate signal Fuzzy controller output adjusts grade.
Preferably, the adjusting grade is I={ I0,I1,I2,I3, I0To operate normally, without adjusting, I1To need Carry out level-one adjusting, I2For second level adjusting, I3To be operating abnormally, alarm.
Preferably, the empirical equation of the Environmental Factors meets:
Wherein, θmaxTo measure period internal ramp gradient maximum value, θminFor hill gradient minimum value,For hill gradient Standard value,For standard air grade,For ambient temperature level value,Ambient humidity standard value,For visibility standards Value, λ1For first environment related coefficient, λ2For second environment related coefficient.
Preferably, work as I=I0When, λ1=0.25~0.42, λ2=0.38~0.65；
Work as I=I1Or I=I2When, λ1=0.43~0.55, λ2=0.65~0.78.
Preferably, at the control valve valve flow velocity empirical equation are as follows:
Wherein, δ is correction coefficient, ω0For valve basis flow velocity, A1For area of section at valve export, A2It is cut for flowline Face area, k1For pipeline flow resistance coefficient, k2For constriction coefficient, C is volume of fuel tank, and L is oil outlet pipe volume, and P is master cylinder pressure,For master cylinder pressure standard value, α is hill gradient,For hill gradient standard value.
Preferably, the normalization formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter: P, RT,α,μ；XjmaxAnd XjminRespectively Maximum value and minimum value in corresponding measurement parameter.
It is of the present invention to join the utility model has the advantages that passing through environmental parameter and traveling of the monitoring automobile during uphill starting Number controls to adjust the modulator pressure regulator during car ramp starting, prevents car slipping phenomenon, improve uphill starting at Power.By BP neural network and fuzzy control to automobile modulator pressure regulator is adjusted during uphill starting, mention The output torque of high engine, it is ensured that automobile can normally start on ramp, not car slipping.It is done by calculating Environmental Factors To input layer parameter, while by the rate of discharge of calculation of throttle control valve, the precision and uphill starting of Throttle Opening Control are improved Success rate.
Specific embodiment
The present invention is described in further detail below, to enable those skilled in the art's refer to the instruction text being capable of evidence To implement.
The present invention provides a kind of hill start optimization method based on Internet of Things, by BP neural network and fuzzy control to automobile Modulator pressure regulator being adjusted during uphill starting, improves the output torque of engine, it is ensured that automobile can be on slope Dao Shangzhen often starts, not car slipping；Wherein, it is monitored by sensors towards ambient information, passes through brake pedal jaw opening sensor Brake pedal aperture is monitored, by controlling control valve valve opening, each sensor and control valve and electronic control unit Electrical connection by controlling the oil inlet quantity of engine, and then controls the output torque of engine, automobile is made to overcome resistance, completes slope Start to walk not car slipping in road.
Hill start optimization method based on Internet of Things specifically comprises the following steps:
Step 1: the environmental parameter of the position where acquisition automobile, and calculate Environmental Factors
Wherein, environmental parameter includes environment temperature T, ambient humidity RH, hill gradient θ, wind scale W and visibility S；
Step 2: master cylinder pressure P, the engine output torque R of acquisition automobileTAnd brake pedal aperture α；
Step 3: the parameter of acquisition is input in BP neural network model after normalization, as input layer, pass through BP neural network model training controls to adjust the modulator pressure regulator during car ramp starting；
Step 5: by control to adjust signal be input in fuzzy controller, obtain indicate control to adjust classification output to Group is measured, is exported as answer is adjusted.
In another embodiment, the empirical equation of Environmental Factors meets:
Wherein, the θmaxTo measure period internal ramp gradient maximum value, θminFor hill gradient minimum value,For ramp slope The standard value of degree,For standard air grade,For ambient temperature level value,Ambient humidity standard value,For visibility mark Quasi- value, λ1For first environment related coefficient, λ2For second environment related coefficient.
In another embodiment, at control valve valve flow velocity empirical equation are as follows:
Wherein, δ is correction coefficient, ω0For valve basis flow velocity, A1For area of section at valve export, A2It is cut for flowline Face area, k1For pipeline flow resistance coefficient, k2For constriction coefficient, C is volume of fuel tank, and L is oil outlet pipe volume, and P is master cylinder pressure,For master cylinder pressure standard value, α is hill gradient,For hill gradient standard value.
In step 3, by BP neural network to network model training, to the throttle during car ramp starting Control valve is controlled to adjust, and is specifically comprised the following steps:
Step 1 establishes BP neural network model；
Totally interconnected connection is formed on BP model between the neuron of each level, is not connected between the neuron in each level It connects, the output of input layer is identical as input, i.e. oi=xi.The operating characteristic of the neuron of intermediate hidden layer and output layer For
opj=fj(netpj)
Wherein, p indicates current input sample, ωjiFor from neuron i to the connection weight of neuron j, opiFor nerve The current input of first j, opjIt is exported for it；fjFor it is non-linear can micro- non-decreasing function, be taken as S type function, i.e. fj(x)=1/ (1+e-x)。
For the BP network architecture that the present invention uses by up of three-layer, first layer is input layer, total n node, corresponding Indicate that n detection signal of equipment working state, these signal parameters are provided by data preprocessing module.The second layer is hidden layer, Total m node is determined in an adaptive way by the training process of network.Third layer is output layer, total p node, by system Actual needs output in response to determining that.
The mathematical model of the network are as follows:
Input layer vector: x=(x1,x2,…,xn)T
Middle layer vector: y=(y1,y2,…,ym)T
Output layer vector: z=(z1,z2,…,zp)T
In the present invention, input layer number is n=4, and output layer number of nodes is p=1.Hidden layer number of nodes m is estimated by following formula It obtains:
Four parameters of input signal respectively indicate are as follows: x1For master cylinder pressure coefficient, x2Engine output torque coefficient, x3 For brake pedal aperture coefficient, x4For Environmental Factors coefficient；
Since the data that sensor obtains belong to different physical quantitys, dimension is different.Therefore, people is inputted in data Before artificial neural networks, need to turn to data requirement into the numerical value between 0-1.
Normalize formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter: P, RT,α,μ；XjmaxAnd XjminRespectively Maximum value and minimum value in corresponding measurement parameter.
Specifically, after being normalized, obtaining master cylinder pressure coefficient x for master cylinder pressure P1:
Wherein, PminWith and PmaxThe minimum value and maximum value of master cylinder pressure respectively；
Likewise, for engine output torque RT, after being normalized, obtain engine output torque coefficient x2:
Wherein, RTminWith and RTmaxThe respectively minimum value and maximum value of engine output torque；
Likewise, after being normalized, obtaining hill gradient coefficient x for hill gradient α3:
Wherein, αminAnd αmaxThe respectively minimum value and maximum value of hill gradient；
Likewise, after being normalized, obtaining Environmental Factors coefficient x for Environmental Factors4:
Wherein, μminAnd μminThe respectively minimum value and maximum value of Environmental Factors；
Output layer vector o={ o1}；o1For Throttle Opening Control valve opening coefficient.
Step 2 carries out BP neural network training
The sample of training, and the connection between given input node i and hidden layer node j are obtained according to historical empirical data Weight Wij, hidden node j and output node layer k between connection weight Wjk, the threshold θ of hidden node jj, output node layer k's Threshold θk、Wij、Wjk、θj、θkIt is the random number between -1 to 1.
In the training process, W is constantly correctedij、WjkValue, until systematic error be less than or equal to anticipation error when, complete mind Training process through network.
(1) training method
Each subnet is using individually trained method；When training, first have to provide one group of training sample, each of these sample This, to forming, when all reality outputs of network and its consistent ideal output, is shown to train by input sample and ideal output Terminate；Otherwise, by correcting weight, keep the ideal output of network consistent with reality output；
(2) training algorithm
BP network is trained using error back propagation (Backward Propagation) algorithm, and step can be concluded It is as follows:
Step 1: a selected structurally reasonable network, is arranged the initial value of all Node B thresholds and connection weight.
Step 2: making following calculate to each input sample:
(a) forward calculation: to l layers of j unit
In formula,L layers of j unit information weighted sum when being calculated for n-th,For l layers of j units with it is previous Connection weight between the unit i of layer (i.e. l-1 layers),For preceding layer (i.e. l-1 layers, number of nodes nl-1) unit i send Working signal；When i=0, enable For the threshold value of l layers of j unit.
If the activation primitive of unit j is sigmoid function,
And
If neuron j belongs to the first hidden layer (l=1), have
If neuron j belongs to output layer (l=L), have
And ej(n)=xj(n)-oj(n)；
(b) retrospectively calculate error:
For output unit
To hidden unit
(c) weight is corrected:
η is learning rate.
Step 3: new sample or a new periodic samples are inputted, and until network convergence, the sample in each period in training Input sequence is again randomly ordered.
BP algorithm seeks nonlinear function extreme value using gradient descent method, exists and falls into local minimum and convergence rate is slow etc. Problem.A kind of more efficiently algorithm is Levenberg-Marquardt optimization algorithm, it makes the e-learning time shorter, Network can be effectively inhibited and sink into local minimum.Its weighed value adjusting rate is selected as
Δ ω=(JTJ+μI)-1JTe；
Wherein, J is error to Jacobi (Jacobian) matrix of weight differential, and I is input vector, and e is error vector, Variable μ is the scalar adaptively adjusted, for determining that study is completed according to Newton method or gradient method.
In system design, system model is one merely through the network being initialized, and weight needs basis using The data sample obtained in journey carries out study adjustment, devises the self-learning function of system thus.Specify learning sample and In the case where quantity, system can carry out self study, to constantly improve network performance；
As shown in table 1, given the value of each node in one group of training sample and training process
Obtained output layer vector is input in fuzzy controller by step 3, obtains the vector for indicating optimizing regulation classification Group, specific as follows:
By Throttle Opening Control valve opening coefficient o1With with preset accelerator open degree coefficientCompare to obtain Throttle Opening Control Valve opening adjusting deviation signal e1, Throttle Opening Control valve opening deviation signal is become by Throttle Opening Control valve opening deviation is calculated Rate signal ec1, Throttle Opening Control valve opening deviation variation rate signal is input to fuzzy controller output after amplification and is adjusted Grade I={ I0,I1,I2,I3, I0To operate normally, without adjusting, I1To need to carry out level-one adjusting, I2For second level adjusting, I3 To be operating abnormally, alarm.
Wherein, e1Actual change range be [- 1,1], discrete domain be { -5, -4, -3, -2, -1,0,1,2,3,4,5 }, The discrete domain of I is { 0,1,2,3 }, then quantizing factor k1=5/1；
Throttle Opening Control valve opening change rate signal is divided into 7 fringes: PB by ambiguity in definition oneself and subordinating degree function (honest), PM (center), PS (just small), ZR (zero), NS (are born small), and NM (in negative), NB (negative big) obtain air-conditioning tune in conjunction with experience Save change rate signal e1Subordinating degree function table, as shown in table 2:
2 Throttle Opening Control valve opening change rate signal e of table1Subordinating degree function table
Fuzzy reasoning process has to carry out complicated matrix operation, and calculation amount is very big, and on-line implement reasoning is difficult to meet The requirement of control system real-time, the present invention carry out fuzzy reasoning operation using look-up table, and Fuzzy inferential decision is using three inputs The mode singly exported can sum up the preliminary control rule of fuzzy controller by experience, and fuzzy controller is according to the mould obtained Paste value carries out defuzzification to output signal, obtains valve opening and adjusts grade I, fuzzy polling list is sought, since domain is It is discrete, fuzzy control rule and it can be expressed as a fuzzy matrix, using single-point fuzzification, show that I control rule is shown in Table 3。
Table 3
By BP neural network and fuzzy control to automobile modulator pressure regulator is adjusted during uphill starting, Improve the output torque of engine, it is ensured that automobile can normally start on ramp, not car slipping.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited In specific details and embodiment shown and described herein.

Claims (9)

1. a kind of hill start optimization method based on technology of Internet of things characterized by comprising
Step 1: the environmental parameter of the position where acquisition automobile, and calculate Environmental Factors；
Step 2: master cylinder pressure P, the engine output torque R of acquisition automobileTAnd brake pedal aperture α；
Step 3: the parameter of acquisition is input in BP neural network model after normalization, as input layer, by BP mind Through network model training, the modulator pressure regulator during car ramp starting is controlled to adjust；
Step 4: being input to signal is controlled to adjust in fuzzy controller, the output vector group for indicating to control to adjust classification is obtained, It is exported as answer is adjusted.
2. the hill start optimization method according to claim 1 based on technology of Internet of things, which is characterized in that the environmental parameter Including environment temperature T, ambient humidity RH, hill gradient θ, wind scale W and visibility S.
3. the hill start optimization method according to claim 2 based on technology of Internet of things, which is characterized in that the step 3 tool Body includes:
Step 1, according to the sampling period, obtain environment temperature T, ambient humidity RH, the hill gradient θ, wind-force of automobile position Grade W and visibility S, and Environmental Factors μ is calculated by the period；
Step 2, master cylinder pressure P, the engine output torque R for acquiring automobileTAnd brake pedal aperture α, and with the environment shadow It rings and determines that the input layer vector of three layers of BP neural network is x={ x because μ is normalized together1,x2,x3,x4}；Wherein, x1 For master cylinder pressure coefficient, x2Engine output torque coefficient, x3For brake pedal aperture coefficient, x4For Environmental Factors coefficient；
Step 3, the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the middle layer vector y={ y1,y2,…,ym}；M is middle layer Node number；
Step 4 obtains output layer vector o={ o1}；o1For Throttle Opening Control valve opening coefficient.
4. the hill start optimization method according to claim 3 based on technology of Internet of things, which is characterized in that the step 4 tool Body includes:
Throttle Opening Control valve opening coefficient and preset accelerator open degree coefficients comparison are obtained into throttle adjustment deviation signal, throttle is inclined Difference signal is input to after amplification fuzzy by throttle deviation variation rate signal is calculated, by throttle deviation variation rate signal Controller output adjusts grade.
5. the hill start optimization method according to claim 4 based on technology of Internet of things, which is characterized in that the adjusting grade For I={ I0,I1,I2,I3, I0To operate normally, without adjusting, I1To need to carry out level-one adjusting, I2For second level adjusting, I3For It is operating abnormally, alarms.
6. the hill start optimization method according to claim 5 based on technology of Internet of things, which is characterized in that the environment influences The empirical equation of the factor meets:
Wherein, θmaxTo measure period internal ramp gradient maximum value, θminFor hill gradient minimum value,For the standard of hill gradient Value,For standard air grade,For ambient temperature level value,Ambient humidity standard value,For visibility standards value, λ1 For first environment related coefficient, λ2For second environment related coefficient.
7. the hill start optimization method according to claim 6 based on technology of Internet of things, which is characterized in that
Work as I=I0When, λ1=0.25~0.42, λ2=0.38~0.65；
Work as I=I1Or I=I2When, λ1=0.43~0.55, λ2=0.65~0.78.
8. the hill start optimization method according to claim 7 based on technology of Internet of things, which is characterized in that the control valve valve The empirical equation of flow velocity at door are as follows:
Wherein, δ is correction coefficient, ω0For valve basis flow velocity, A1For area of section at valve export, A2For fuel-displaced tube section face Product, k1For pipeline flow resistance coefficient, k2For constriction coefficient, C is volume of fuel tank, and L is oil outlet pipe volume, and P is master cylinder pressure,For Master cylinder pressure standard value, α are hill gradient,For hill gradient standard value.
9. the hill start optimization method according to claim 8 based on technology of Internet of things, which is characterized in that the normalization is public Formula are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter: P, RT,α,μ；XjmaxAnd XjminIt is respectively corresponding Maximum value and minimum value in measurement parameter.
CN201910460366.XA 2019-05-30 2019-05-30 A kind of hill start optimization method based on technology of Internet of things Withdrawn CN110162910A (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110560948A (en) * 2019-09-06 2019-12-13 中国化学工程第六建设有限公司 High-pressure pipeline welding device and welding method
CN110594187A (en) * 2019-09-06 2019-12-20 中国化学工程第六建设有限公司 Installation and debugging method for large compressor set instrument system
CN110644977A (en) * 2019-09-16 2020-01-03 中海艾普油气测试（天津）有限公司 Control method for receiving and sending underground small signals for testing
CN114397811A (en) * 2022-01-08 2022-04-26 杭州市路桥集团股份有限公司 Fuzzy control method and system for hot melting kettle based on Internet of things

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110560948A (en) * 2019-09-06 2019-12-13 中国化学工程第六建设有限公司 High-pressure pipeline welding device and welding method
CN110594187A (en) * 2019-09-06 2019-12-20 中国化学工程第六建设有限公司 Installation and debugging method for large compressor set instrument system
CN110594187B (en) * 2019-09-06 2020-06-16 中国化学工程第六建设有限公司 Installation and debugging method for large compressor set instrument system
CN110644977A (en) * 2019-09-16 2020-01-03 中海艾普油气测试（天津）有限公司 Control method for receiving and sending underground small signals for testing
CN114397811A (en) * 2022-01-08 2022-04-26 杭州市路桥集团股份有限公司 Fuzzy control method and system for hot melting kettle based on Internet of things
CN114397811B (en) * 2022-01-08 2023-08-29 杭州市路桥集团股份有限公司 Fuzzy control method and control system for hot melting kettle based on Internet of things

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