CN107991103A - A kind of batteries of electric automobile pack arrangement Prediction method for fatigue life based on true road spectrum - Google Patents

A kind of batteries of electric automobile pack arrangement Prediction method for fatigue life based on true road spectrum Download PDF

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CN107991103A
CN107991103A CN201710981393.2A CN201710981393A CN107991103A CN 107991103 A CN107991103 A CN 107991103A CN 201710981393 A CN201710981393 A CN 201710981393A CN 107991103 A CN107991103 A CN 107991103A
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msub
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battery pack
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张勇
张蒙阳
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New Energy Automobile Group Co Ltd
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New Energy Automobile Group Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles

Abstract

The invention discloses a kind of batteries of electric automobile pack arrangement Prediction method for fatigue life based on true road spectrum, mainly include：The formulation of electric vehicle on road load working condition, load spectrum collection and analysis, the multi-body Dynamics Model of vehicle are established, and multi-body Dynamics Model transmission function obtains and loading spectrum iteration, and fatigue life prediction.The present invention obtains the real loading spectrum of electric automobile using the alternative manner of half experiment, half emulation, it is ensured that the accuracy of battery pack structure load, engineering practicability is strong, is put into without high stand arrangement, and cost is low.The battery pack structure service life is predicted with broadband Stochastic Fatigue Life Forecasting Methodology using non-proportional loading is theoretical, calculation amount is small, and precision of prediction is higher.

Description

A kind of batteries of electric automobile pack arrangement Prediction method for fatigue life based on true road spectrum
Technical field：
The present invention relates to a kind of batteries of electric automobile pack arrangement Prediction method for fatigue life based on true road spectrum, it belongs to The safe and reliable property field of power battery structure.
Background technology：
Batteries of electric automobile bag is the energy source of electric automobile.Due to the presence of road roughness, installed in electronic vapour Battery pack on car, can inevitably bear the vibration for coming from road surface.Although by the decay of tire and suspension, effect Vibration in battery pack is still very important.Usually, the excitation from road surface is all random, and this random vibration is usual It is long-time, low stress continuous action, the influence to battery pack structure is also slow.If battery structure design is unreasonable, Since battery fatigue damage is constantly accumulated caused by vibration, battery pack structure fatigue failure is also resulted in, is influencing battery just Often use, hidden danger is brought to vehicle security.For this reason, a series of battery pack vibration laboratory standard has been formulated both at home and abroad.If It can repeat the load that battery is born in real roads completely on vibration rack, this method can obtain the accurately tired longevity really Order result.But cost-effective generally in order to shorten the test period, the battery pack vibration standard usually used in laboratory is typically By strengthening, accelerating.Due to strengthening, accelerated method it is different, at present both at home and abroad mainstream on battery pack vibration laboratory standard There is the standard such as IEC 62660-2, ISO 12405-1, SAE J2380, USABC, ECE R100, UN 38.3.By to domestic and international Knowable to each mainstream standard is analyzed, it is in vibration input type, bandwidth, amplitude, experiment duration, SOC states, temperature conditionss etc. Etc. have larger difference.In other words unified standard there is no to evaluate battery pack structure intensity at present.If battery pack exists Vibration bench test results are unsatisfactory for testing standard, it is also necessary to carry out repeatability design and experiment, battery core is general after the completion of experiment It is difficult to be recycled, therefore the battery pack structure strength test synthesis cost based on vibration rack is still very high.It is tired based on CAE The battery fatigue life method of labor simulation and prediction means can battery pack design initial stage can to battery pack structure intensity into Row anticipation, can effectively shorten product development cycle, reduce development cost.But traditional battery pack Prediction method for fatigue life, Or quasi-static computational methods are used, or directly use input load of the laboratory bench vibration standard as vibrating fatigue. In fact, vibrational excitation of the electric automobile in operation to battery pack is considerably complicated, retrieves and find through the prior art, there is no at present The method inputted using true road spectrum as battery pack Calculation of Fatigue Life with prediction.
The content of the invention：
The present invention is to provide a kind of electronic vapour based on true road spectrum to solve the above-mentioned problems of the prior art Car battery pack structure Prediction method for fatigue life, solves the problems, such as can not obtaining for battery pack structure real load spectrum, utilizes Broad-band random vibration and non-proportional loading life approach, improve battery pack structure fatigue life prediction precision.
The present invention adopts the following technical scheme that：A kind of batteries of electric automobile pack arrangement fatigue life based on true road spectrum is pre- Survey method, step are as follows：
(1), the formulation of electric vehicle on road load working condition, investigates according to electric automobile user vehicle and obtains, or directly Using the reliability test standard at some test site as foundation；
(2), load spectrum collection and analysis, according to test request, install sensor, at spindle nose on electric automobile Acceleration signal is installed, displacement sensor is installed at spring, on battery pack structure and corresponding vehicle body installation side arrangement is some Acceleration transducer, GPS sensor, contrasts the multigroup signal collected in time domain, frequency domain, amplitude, and to signal Carry out repeated verification, correlation analysis；
(3), the multi-body Dynamics Model of vehicle is established, and vehicle frame or body portion use elastomeric model, and battery pack uses Rigid model, final vehicle is a Rigid-flexible Coupling Dynamics model, and before road test, the tire for remeasuring vehicle carries Lotus, position of centre of gravity and suspension limiter gap parameter, and be adjusted according to measurement result to model, Holonomic Dynamics model it Afterwards, it is necessary to carry out dynamics simulation verification experimental verification, to ensure the correctness of kinetic model；
(4), the acquisition of multi-body Dynamics Model transmission function and loading spectrum iteration, regard whole vehicle PM prototype model as one System, excitation of the road surface at core wheel solve transmission of the input to output as input, the response of each sensor as output Function, then inverts transmission function, and as the relation inputted by output reverse, the vehicle PM prototype model established is one A nonlinear system, and transmission function is linear, it is necessary to correct drive signal repeatedly, response signal is approached measured value, most Accurately excitation input is obtained eventually；
(5), battery pack structure fatigue life prediction, electric automobile under steam, pacifying by the battery pack structure installed thereon The power from 6 directions, including 3 power and three moment bear in the place of decorateeing, and the input power spectrum of mount point plays a leading role, only examines Consider the vibration input of three direction force, according to random vibration theory, the stress response power spectral density matrix of battery pack structure can Write as：
Wherein it is Hσ(f) stress frequency response function matrix, Gσ(f) composed for input load, * is conjugate operation, is utilized Von.Mises equivalent methods, its is equivalent into single shaft equivalent stress
Gσeq(f)=Trace [AGσσ(f)]
Wherein Trace is the mark of matrix, and the expression formula of A is as follows：
Frequency response function matrix between battery pack structure stress and loading spectrum, frequency response function is carried out by finite element analysis software Analysis obtains, and the power spectral density matrix of the stress response of structure is obtained by random vibration analysis, using S-N Curve, Simple stress power spectral density after battery pack is equivalent, and Dirlink broad-band random vibrations method obtain the structure of battery pack Service life.
Further, in step (4), the load of input is the Z-direction displacement of 4 core wheels, is exported as spindle nose acceleration, spring Displacement, and corresponding vehicle body installation side acceleration responsive, the relation responded according to input load, transmission function with output, into The solution of the initial driving load of row：
Wherein,For the Z-direction displacement of input load, as 4 core wheels, Hm×nFor transmission function, Ym×1Responded for output, As spindle nose acceleration, spring displacement, on battery pack structure and corresponding vehicle body installation side acceleration responsive, these responses are For load spectrum, n is input load number, and m is output number of responses, calculates the residual error of load spectrum, and with residual error threshold value δ Contrast,
As residual error is unsatisfactory for requiring, and the 2nd time is carried out to kth time iteration：
Once analogize K times, until error convergence, stops iteration
Drive signal is corresponded at this timeFor final driving source, and as the input of multi-body Dynamics Model, then pass through The Forward simulation of multi-body Dynamics Model, obtains the input load spectrum of battery pack.
The present invention has the advantages that：The present invention obtains electric automobile using the alternative manner of half experiment, half emulation Real loading spectrum, it is ensured that the accuracy of battery pack structure load, engineering practicability is strong, is thrown without high stand arrangement Enter, cost is low.The battery pack structure service life is predicted with broadband Stochastic Fatigue Life Forecasting Methodology using non-proportional loading is theoretical, Calculation amount is small, and precision of prediction is higher.
Brief description of the drawings：
Fig. 1 is the structural frames of batteries of electric automobile pack arrangement Prediction method for fatigue life of the present invention based on true road spectrum Figure.
Fig. 2 is actual measurement road original spectrum.
Fig. 3 is actual measurement load spectrum splicing.
Fig. 4 is load iterative process block diagram.
Embodiment：
The present invention is further illustrated below in conjunction with the accompanying drawings.
Battery pack structure failure is mostly due to that structural fatigue causes, i.e. electric automobile in normally travel, hold by battery pack By repeat load, and stress level is low, usually less than the structure yields limit, finally since fatigue damage adds up to cause structure Fatigue failure.Analyzed from driving source, battery pack is primarily subjected to the Random Vibration Load from ground.For this kind of random load Analyze and predict that the fatigue life of battery pack structure is very favorable using vibrating fatigue analysis method, merely with response PSD (power spectral density), avoids the huge calculation amount of transient analysis.This method is utilized to move from input load signal to structure and answered Transmission function between power, is multiplied by the transmission function so as to solve the PSD of acquisition dynamic stress, using dynamic by the PSD of real load The PSD of stress extrapolates the fatigue damage of structure, can be more prone to than obtaining the time-domain signal of stress using dynamic stress PSD.
Vibrating fatigue analysis method is to calculate fatigue life according to a PSD stress signal.It is assumed that Stressing history For Narrow―band random process, then its stress amplitude obeys Rayleigh distributions, i.e.,
Wherein SaFor stress amplitude, σ is stress RMS.G (f) is stress one-sided power spectrum density, defines G (f) the i-th rank square：
For Narrow―band random process, the expectation E [0 of speed is passed through on 0+] with peak value pass through speed it is expected E [P] it is equal, I.e.
It is assumed that Cyclic Stress-life curve：
C=N (Sa)b
Theory, structure fatigue damage are added up according to Miner fatigue damages：
Wherein, niFor stress level SaiCycle-index, NiIt is structure in stress level SaiUnder fatigue life.Time T Inherent strain scope (Sai,Sai+ΔSai) in stress-number of cycles be：
ni=E (P) TPDF (Sai)·ΔSai
Then structure fatigue damage：
Summation symbol is changed into integrating, and considers Cyclic Stress-life curve
For broad-band random vibration, many correction algorithms are developed on the basis of narrowband random, have not repeated.Usually profit Broadband time-domain signal is handled with narrowband random evaluation method, acquisition is than more conservative fatigue life.Dirlink is utilized Monte Carlo technologies give the semiempirical closed solution of the stress amplitude distributed model of wideband random signal：
Wherein,D3=1-D1-D2；
Many scholars propose different rain stream amplitude distribution models to simulate broadband Gaussian random process afterwards. Dirlink methods are with a wide range of applications, and are verified this method later and are always better than all other method.The present invention will adopt Fatigue life prediction is carried out to battery pack structure with Dirlink methods.The invention mainly comprises：Electric vehicle on road load working condition Formulation, load spectrum collection and analysis, the multi-body Dynamics Model of vehicle establish, and multi-body Dynamics Model transmission function obtains Take and loading spectrum iteration, and fatigue life prediction, flow such as attached drawing 1.
The directly measurement of batteries of electric automobile pack arrangement input power spectrum is very difficult, and the present invention combines vehicle many-body dynamics mould Type and actual measurement road spectrum, are obtained by the method for the virtual iteration of load.Meet the road of electric automobile actual condition firstly the need of formulation The operating condition of test at road test site, usually there is two methods, when the method based on user investigation, second, directly according to reliability Testing standard.Test site actual road test operating mode is generally by some different brackets, the road of different length, such as Block Road, cobble Road, Belgian road, washboard road etc..According to electric automobile actual condition, the section of different road conditions is subjected to organic assembling, most end form Into electric vehicle on road load working condition.
It is on the road state of cyclic operation of formulation that load spectrum, which is gathered with the main task of analysis, gathers corresponding signal And carry out data analysis.Need that accelerometer is installed in the relevant position of electric automobile according to test request, displacement sensor should Become piece etc..Primary data is handled accordingly after data acquisition, is such as filtered, deburring, removes trend term etc., finally Obtain load spectrum.
Load iteration is carried out using the multi-body Dynamics Model and load spectrum gathered data of vehicle, is accurate simulation, Flexible object modeling is used in the body portion of battery pack installation, battery pack is modeled using rigid body, finally establishes firm, the soft coupling of vehicle Close multi-body Dynamics Model.To ensure the correctness of kinetic model, Verification on Kinetic Model need to be carried out.
The thought of the virtual iteration of load spectrum is to obtain associated components response signal by test site real steering vectors, is passed through External drive at iteration reverse core wheel.Regard vehicle PM prototype model as a system, excitation of the road surface at core wheel is as defeated Enter, the response of each sensor is as output.Inputted firstly the need of target is obtained to the transmission function of target output.To transmitting letter Number is inverted, as the relation inputted by output reverse.Vehicle PM prototype model is a nonlinear system, and transmission function is It is linear, by correcting drive signal (excitation) repeatedly, response signal is approached measured value, finally obtain accurately encourage it is defeated Enter.
Battery pack structure may bear to come from different directions load, to improve precision of prediction, utilize the non-proportional loading service life Theory is predicted battery pack fatigue life.The present invention, should by three axis using Von.Mises equivalent stress processing methods are based on Power is transformed into simple stress.Calculated using the equivalent load of acquisition, based on the battery pack structure finite element model established, calculated Battery pack structure stochastic and dynamic responds.According to S-N Curve, battery pack is predicted based on Dirlink broad-band random vibrations method The fatigue life of structure.
Batteries of electric automobile pack arrangement Prediction method for fatigue life of the present invention based on true road spectrum, specific implementation step is such as Under：
(1), the formulation of electric vehicle on road load working condition.It can be investigated and obtained according to electric automobile user vehicle, or Directly using the reliability test standard at some test site as foundation.The present invention is tried with certain test site for light bus reliability Test, the reinforcing bad road test formulated is tested exemplified by case.By Block Road second, Block Road third distorts road second, pebble path second, washboard road second, The composition battery of tests circulation road condition such as long wave paths, gravel road, the good road of pitch.To ensure that signal is correct, it is necessary to carry out multigroup Repeatability collection, if possible it is also possible to consider different drivers is changed, carries out repetitive test.
(2), load spectrum collection and analysis.According to test request, sensor is installed in the relevant position of electric automobile. Acceleration signal is installed generally at spindle nose, displacement sensor is installed at spring, on battery pack structure, and corresponding vehicle body peace Fill side and arrange some acceleration transducers, GPS sensor etc..To ensure the reliability of data, it is necessary to carry out multigroup repeatability examination Test.The multigroup signal collected is contrasted in time domain, frequency domain, amplitude, and repeated verification, correlation are carried out to signal Analysis etc..Check whether each channel data is abnormal, can carry out such as deburring, the processing such as trend term, filtering being gone, to obtain if needed Obtain the data of high quality.
(3), the multi-body Dynamics Model of vehicle is established.For boundary condition of the accurate simulated battery bag on vehicle body, vehicle frame Or body portion will use elastomeric model, battery pack can use rigid model, and final vehicle should be that a Coupled Rigid-flexible moves Mechanical model.Since the configuration of instruction carriage, loaded-up condition are not necessarily completely the same with the design point at initial stage, so in road drive test Before examination, it should remeasure the parameters such as the tyre load, position of centre of gravity and suspension limiter gap of vehicle, and tied according to measurement Fruit is adjusted model., it is necessary to carry out K＆C experiments or other dynamics simulation verification experimental verifications after Holonomic Dynamics model, Ensure the correctness of kinetic model.
(4), multi-body Dynamics Model transmission function obtains and loading spectrum iteration.Regard whole vehicle PM prototype model as one System, excitation of the road surface at core wheel is as input, and the response of each sensor is as output.Solve transmission of the input to output Function, then inverts transmission function, as the relation inputted by output reverse.The vehicle PM prototype model established is one A nonlinear system, and transmission function is linear, it is necessary to repeatedly correct drive signal (excitation), response signal is approached actual measurement Value, finally obtains accurately excitation input.The load that the present invention inputs is the Z-direction displacement of 4 core wheels, exports and accelerates for spindle nose Degree, spring displacement, and corresponding vehicle body installation side acceleration responsive.According to input load, transmission function and output response Relation, carries out the solution of initial driving load：
Wherein,It is here the Z-direction displacement of 4 core wheels, H for input loadm×nFor transmission function, Ym×1Rung for output Should, it is here spindle nose acceleration, spring displacement, on battery pack structure, and corresponding vehicle body installation side acceleration responsive, these Response can be collectively referred to as load spectrum.N is input load number, and m is output number of responses.The residual error of calculating load spectrum, and with Residual error threshold value δ is contrasted,
As residual error is unsatisfactory for requiring, and the 2nd time is carried out to kth time iteration：
Once analogize K times, until error convergence, stops iteration
Drive signal is corresponded at this timeFor final driving source, and as the input of multi-body Dynamics Model, then by more The Forward simulation of body dynamics model, obtains the input load spectrum of battery pack.
(5), battery pack structure fatigue life prediction.Electric automobile under steam, pacifying by the battery pack structure installed thereon The place of decorateeing will bear the power from 6 directions, and including 3 power and three moment, in other words battery bears multiple spot, multiaxis Vibration input.In general the input power spectrum of mount point plays a leading role, and the present invention only considers the vibration input of three direction force. According to random vibration theory, the stress response power spectral density matrix of battery pack structure can be write as：
Wherein it is Hσ(f) stress frequency response function matrix, Gσ(f) composed for input load, for the sake of simplicity, * is conjugate operation.Profit It is with Von.Mises equivalent methods, its is equivalent into single shaft equivalent stress
Gσeq(f)=Trace [AGσσ(f)]
Wherein Trace is the mark of matrix, and the expression formula of A is as follows：
Frequency response function matrix between battery pack structure stress and loading spectrum, can carry out frequency response by finite element analysis software Functional Analysis obtains.The power spectral density matrix of the stress response of structure is obtained by random vibration analysis.It is bent using material S-N Line, the simple stress power spectral density after battery pack is equivalent, and Dirlink broad-band random vibrations method obtain the knot of battery pack The structure service life.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, some improvement can also be made without departing from the principle of the present invention, these improvement also should be regarded as the present invention's Protection domain.

Claims (2)

  1. A kind of 1. batteries of electric automobile pack arrangement Prediction method for fatigue life based on true road spectrum, it is characterised in that：Step is such as Under：
    (1), the formulation of electric vehicle on road load working condition, investigates according to electric automobile user vehicle and obtains, or directly with certain The reliability test standard at a test site is foundation；
    (2), load spectrum collection and analysis, according to test request, install sensor on electric automobile, are installed at spindle nose Acceleration signal, installs displacement sensor at spring, and on battery pack structure and corresponding vehicle body installation side arranges some acceleration Sensor is spent, GPS sensor, contrasts the multigroup signal collected in time domain, frequency domain, amplitude, and signal is carried out Repeatability verification, correlation analysis；
    (3), the multi-body Dynamics Model of vehicle is established, and vehicle frame or body portion use elastomeric model, and battery pack uses rigid body Model, final vehicle is a Rigid-flexible Coupling Dynamics model, before road test, remeasure vehicle tyre load, Position of centre of gravity and suspension limiter gap parameter, and model is adjusted according to measurement result, after Holonomic Dynamics model, Need to carry out dynamics simulation verification experimental verification, to ensure the correctness of kinetic model；
    (4), the acquisition of multi-body Dynamics Model transmission function and loading spectrum iteration, regarding whole vehicle PM prototype model as one is System, excitation of the road surface at core wheel solve input to the transmission letter of output as input, the response of each sensor as output Number, then inverts transmission function, as the relation inputted by output reverse, the vehicle PM prototype model established is one Nonlinear system, and transmission function is linear, it is necessary to correct drive signal repeatedly, response signal is approached measured value, finally Obtain accurately excitation input；
    (5), battery pack structure fatigue life prediction, under steam, the battery pack structure installed thereon is in mount point for electric automobile The power from 6 directions, including 3 power and three moment bear in place, and the input power spectrum of mount point plays a leading role, and only considers three The vibration input of a direction force, according to random vibration theory, the stress response power spectral density matrix of battery pack structure can be write as：
    <mrow> <msub> <mi>G</mi> <mrow> <mi>&amp;sigma;</mi> <mi>&amp;sigma;</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>H</mi> <mi>&amp;sigma;</mi> <mo>*</mo> </msubsup> <mrow> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> <msub> <mi>G</mi> <mi>&amp;sigma;</mi> </msub> <mrow> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> <msubsup> <mi>H</mi> <mi>&amp;sigma;</mi> <mi>T</mi> </msubsup> <mrow> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </mrow>
    Wherein it is Hσ(f) stress frequency response function matrix, Gσ(f) composed for input load, * is conjugate operation, utilizes Von.Mises etc. Efficacious prescriptions method, its is equivalent into single shaft equivalent stress
    Gσeq(f)=Trace [AGσσ(f)]
    Wherein Trace is the mark of matrix, and the expression formula of A is as follows：
    <mrow> <mi>A</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>-</mo> <mn>0.5</mn> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mn>0.5</mn> </mrow> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mn>0.5</mn> </mrow> </mtd> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>-</mo> <mn>0.5</mn> </mrow> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <mn>0.5</mn> </mrow> </mtd> <mtd> <mrow> <mo>-</mo> <mn>0.5</mn> </mrow> </mtd> <mtd> <mn>1</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>3</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>3</mn> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>0</mn> </mtd> <mtd> <mn>3</mn> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Frequency response function matrix between battery pack structure stress and loading spectrum, frequency response function analysis is carried out by finite element analysis software Obtain, the power spectral density matrix of the stress response of structure is obtained by random vibration analysis, utilizes S-N Curve, battery Wrap it is equivalent after simple stress power spectral density, and Dirlink broad-band random vibrations method obtain battery pack structural life-time.
  2. 2. the batteries of electric automobile pack arrangement Prediction method for fatigue life as claimed in claim 1 based on true road spectrum, its feature It is：In step (4), the load of input is the Z-direction displacement of 4 core wheels, is exported as spindle nose acceleration, spring displacement, Yi Jixiang The vehicle body installation side acceleration responsive answered, according to input load, transmission function and the relation of output response, carries out initial driving and carries The solution of lotus：
    <mrow> <msubsup> <mi>U</mi> <mrow> <mi>n</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msub> <mi>Y</mi> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> </msub> </mrow>
    Wherein,For the Z-direction displacement of input load, as 4 core wheels, Hm×nFor transmission function, Ym×1Responded for output, be Spindle nose acceleration, spring displacement, on battery pack structure and corresponding vehicle body installation side acceleration responsive, these responses are road Road loading spectrum, n are input load number, and m is output number of responses, calculate the residual error of load spectrum, and with δ pairs of residual error threshold value Than,
    <mrow> <mo>|</mo> <mo>|</mo> <mi>&amp;epsiv;</mi> <mo>|</mo> <msup> <mo>|</mo> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> </msup> <mo>=</mo> <mo>|</mo> <mo>|</mo> <msub> <mi>Y</mi> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mi>n</mi> </mrow> </msub> <msubsup> <mi>U</mi> <mrow> <mi>n</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> </msubsup> <mo>|</mo> <mo>|</mo> </mrow>
    As residual error is unsatisfactory for requiring, and the 2nd time is carried out to kth time iteration：
    <mrow> <msubsup> <mi>U</mi> <mrow> <mi>n</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>U</mi> <mrow> <mi>n</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mi>&amp;epsiv;</mi> <mrow> <mo>(</mo> <mn>0</mn> <mo>)</mo> </mrow> </msup> </mrow>
    <mrow> <msubsup> <mi>U</mi> <mrow> <mi>n</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <msubsup> <mi>U</mi> <mi>n</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <msup> <mi>&amp;epsiv;</mi> <mrow> <mo>(</mo> <mi>k</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </msup> </mrow>
    Once analogize K times, until error convergence, stops iteration
    <mrow> <mo>|</mo> <mo>|</mo> <mi>&amp;epsiv;</mi> <mo>|</mo> <msup> <mo>|</mo> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msup> <mo>=</mo> <mo>|</mo> <mo>|</mo> <msub> <mi>Y</mi> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> </msub> <mo>-</mo> <msub> <mi>H</mi> <mrow> <mi>m</mi> <mo>&amp;times;</mo> <mi>n</mi> </mrow> </msub> <msubsup> <mi>U</mi> <mrow> <mi>n</mi> <mo>&amp;times;</mo> <mn>1</mn> </mrow> <mrow> <mo>(</mo> <mi>k</mi> <mo>)</mo> </mrow> </msubsup> <mo>|</mo> <mo>|</mo> <mo>&lt;</mo> <mi>&amp;delta;</mi> </mrow>
    Drive signal is corresponded at this timeFor final driving source, and moved as the input of multi-body Dynamics Model, then by more bodies The Forward simulation of mechanical model, obtains the input load spectrum of battery pack.
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Cited By (35)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108646200A (en) * 2018-08-23 2018-10-12 重庆雅讯电源技术有限公司 Cell health state appraisal procedure and system
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SU1273756A1 (en) * 1985-03-28 1986-11-30 Северо-Кавказский Ордена Дружбы Народов Горно-Металлургический Институт Method of measuring load on direct current electric drive shaft
CN101393079A (en) * 2008-11-06 2009-03-25 上海交通大学 Automobile vehicle body structure fatigue life predicting system
CN101667221A (en) * 2009-09-29 2010-03-10 同济大学 Method for determining dual-layer top cover of motor coach with top-positioned gas cylinder
CN102945307A (en) * 2012-11-27 2013-02-27 北京汽车股份有限公司 Automobile chassis key structural member structure optimization design method
CN104239734A (en) * 2014-09-24 2014-12-24 重庆长安汽车股份有限公司 Load analysis method for four-wheel six-component road spectrum of finished automobile
CN105092261A (en) * 2015-06-03 2015-11-25 北京汽车股份有限公司 Road load test method and system
CN107219079A (en) * 2017-04-24 2017-09-29 中国第汽车股份有限公司 A kind of commercial car bracket assembly for battery Multi-axial Loading test-bed and endurance test method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SU1273756A1 (en) * 1985-03-28 1986-11-30 Северо-Кавказский Ордена Дружбы Народов Горно-Металлургический Институт Method of measuring load on direct current electric drive shaft
CN101393079A (en) * 2008-11-06 2009-03-25 上海交通大学 Automobile vehicle body structure fatigue life predicting system
CN101667221A (en) * 2009-09-29 2010-03-10 同济大学 Method for determining dual-layer top cover of motor coach with top-positioned gas cylinder
CN102945307A (en) * 2012-11-27 2013-02-27 北京汽车股份有限公司 Automobile chassis key structural member structure optimization design method
CN104239734A (en) * 2014-09-24 2014-12-24 重庆长安汽车股份有限公司 Load analysis method for four-wheel six-component road spectrum of finished automobile
CN105092261A (en) * 2015-06-03 2015-11-25 北京汽车股份有限公司 Road load test method and system
CN107219079A (en) * 2017-04-24 2017-09-29 中国第汽车股份有限公司 A kind of commercial car bracket assembly for battery Multi-axial Loading test-bed and endurance test method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
谷理想: "电动汽车电池包疲劳寿命预测关键技术研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 *
贺光宗等: "多轴向与单轴向随机振动疲劳试验对比研究", 《振动工程学报》 *

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