CN109917290A - A kind of temperature determining method and device of Vehicular dynamic battery group - Google Patents
A kind of temperature determining method and device of Vehicular dynamic battery group Download PDFInfo
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Abstract
Embodiment of the present invention discloses the temperature determining method and device of a kind of Vehicular dynamic battery group.It include: in the static test process of Vehicular dynamic battery group, measure battery core temperature and tab temperature, using battery core temperature and tab temperature, multiple placement positions of temperature sensor are determined based on the first genetic algorithm, and are respectively arranged temperature sensor in each placement position；In the vehicle operation of Vehicular dynamic battery group, read multiple measured values of the temperature sensor at multiple placement positions, it is dynamically determined out at least one optimum measuring point based on the second genetic algorithm, the temperature of Vehicular dynamic battery group is determined based on the measured value of at least one optimum measuring point.The position that temperature sensor is chosen in static topology, finds optimal selected point again in dynamic reconnaissance, improves thermometric accuracy.Moreover, the objective function assignment when battery pack temperature that this thermometric is obtained is next thermometric, can be improved thermometric accuracy.
Description
Technical field
Embodiment of the present invention is related to electric vehicle engineering field, in particular to a kind of temperature of Vehicular dynamic battery group is true
Determine method and apparatus.
Background technique
Have in national newest standards " term and definition of automobile and trailer type " (GB/T 3730.1-2001) to automobile
Such as give a definition: by power drive, the vehicle of the non-track carrying with 4 or 4 or more wheels is mainly used for: carrying personnel
And (or) cargo；Draw the vehicle of carrying personnel and (or) cargo；Specific use.Energy shortage, oil crisis and environmental pollution
It grows in intensity, brings tremendous influence to people's lives, be directly related to the sustainable development of national economy and society.The world is each
State is all in active development new energy technology.Electric car is as a kind of new energy vapour for reducing consumption of petroleum, low pollution, low noise
Vehicle, it is considered to be solve the important channel of energy crisis and environmental degradation.
At present in the R&D process of electric car, it is (also known as automobile-used dynamic that focus has been concentrated on battery pack by everybody
Power battery pack) temperature control.Battery pack temperature not only influences its service life and performance, it is also possible to cause the danger such as on fire, explosion,
Therefore it needs especially to pay close attention to the temperature under each operating condition when testing battery pack.
In traditional test battery pack temperature, a large amount of temperature sensor is usually arranged inside battery pack, to monitor
Battery pack internal temperature.
However, more blindly, there is no effectively optimized the position of temperature sensor at present.
Summary of the invention
In view of this, the object of the present invention is to provide the temperature determining methods and device of a kind of Vehicular dynamic battery group.
The technical solution of embodiment of the present invention is as follows:
A kind of temperature determining method of Vehicular dynamic battery group, comprising:
In the static test process of Vehicular dynamic battery group, battery core temperature and tab temperature are measured, battery core temperature is utilized
With tab temperature, multiple placement positions of temperature sensor are determined based on the first genetic algorithm, and in each placement position
It is respectively arranged temperature sensor；
In the vehicle operation of the Vehicular dynamic battery group, the multiple of the temperature sensor at multiple placement positions are read
Measured value is dynamically determined out at least one optimum measuring point based on the second genetic algorithm, based at least one optimum measuring point
Measured value determines the temperature of Vehicular dynamic battery group.
In one embodiment, first genetic algorithm includes that the is determined on tab face that tab position is constituted
The step of one population location point；Objective function in first genetic algorithm is F；
Wherein: T1, T2, T3For three with triangle composed by the immediate three tab positions of the first population location point
The temperature value on a vertex；X is abscissa of the first population location point on the tab face；Y is the first population location point in institute
State the ordinate on tab face；Y1, y2, y3 are the ordinate on three vertex of the triangle；x12, x23Respectively described three
Abscissa of the interpolation point on the tab face on angular both sides；A is the battery core temperature.
In one embodiment,
This method further include: calculate the measured temperature of multiple battery cores；By the flat of the measured temperature of the multiple battery core
Mean value is determined as the battery core temperature；And/or
The measured value based at least one optimum measuring point determines that the temperature of Vehicular dynamic battery group includes:
The average value of the measured value of at least one optimum measuring point is determined as to the temperature of Vehicular dynamic battery group；Or
The measured value of optimum measuring point at least one optimum measuring point closest to tab face center is true
It is set to the temperature of Vehicular dynamic battery group.
In one embodiment, second genetic algorithm is included in the population position being made of the first population location point
Set the step of the second population location point is determined on face；The objective function of second genetic algorithm is F '；
Wherein: T1', T2', T3' for three composed by immediate three the first population location points of the second population location point
The temperature value on three angular vertex；X ' is abscissa of the optimum measuring point on population surface of position；Y ' is that optimum measuring point exists
Ordinate on the population surface of position；Y1 ', y2 ', y3 ' are and immediate three the first populations position of the second population location point
Set the ordinate on a little three vertex of composed triangle；x12', x23' it is respectively described closest with the second population location point
Three the first population location points composed by triangle both sides on abscissa of the interpolation point on the population surface of position；
The initial value of a ' is predetermined value, and the value of a ' is updated by the temperature for the Vehicular dynamic battery group that the last time determines.
In one embodiment, the measurement battery core temperature and tab temperature are as follows: in same time period, measure battery core
Temperature and tab temperature.
A kind of temperature determining device of Vehicular dynamic battery group, comprising:
Sensor arrangement module, for measuring battery core temperature and pole in the static test process of Vehicular dynamic battery group
Ear temperature determines multiple placement positions of temperature sensor based on the first genetic algorithm using battery core temperature and tab temperature,
And temperature sensor is respectively arranged in each placement position；
Determining module, for reading the temperature at multiple placement positions in the vehicle operation of the Vehicular dynamic battery group
The multiple measured values for spending sensor, are dynamically determined out at least one optimum measuring point based on the second genetic algorithm, are based at least one
The measured value of a optimum measuring point determines the temperature of Vehicular dynamic battery group.
In one embodiment, first genetic algorithm includes that the is determined on tab face that tab position is constituted
The step of one population location point；Objective function in first genetic algorithm is F；
Wherein: T1, T2, T3For three with triangle composed by the immediate three tab positions of the first population location point
The temperature value on a vertex；X is abscissa of the first population location point on the tab face；Y is the first population location point in institute
State the ordinate on tab face；Y1, y2, y3 are the ordinate on three vertex of the triangle；x12, x23Respectively described three
Abscissa of the interpolation point on the tab face on angular both sides；A is the battery core temperature.
In one embodiment, sensor arrangement module, for calculating the measured temperature of multiple battery cores；It will be described more
The average value of the measured temperature of a battery core is determined as the battery core temperature；And/or
Determining module, for the average value of the measured value of at least one optimum measuring point to be determined as Vehicular dynamic battery group
Temperature；Or, by the measured value of the optimum measuring point of tab face center closest at least one optimum measuring point
It is determined as the temperature of Vehicular dynamic battery group.
In one embodiment, the objective function of second genetic algorithm is F '；
Wherein: T1', T2', T3' for three composed by immediate three the first population location points of the second population location point
The temperature value on three angular vertex；X ' is abscissa of the optimum measuring point on population surface of position；Y ' is that optimum measuring point exists
Ordinate on the population surface of position；Y1 ', y2 ', y3 ' are and immediate three the first populations position of the second population location point
Set the ordinate on a little three vertex of composed triangle；x12', x23' it is respectively described closest with the second population location point
Three the first population location points composed by triangle both sides on abscissa of the interpolation point on the population surface of position；
The initial value of a ' is predetermined value, and the value of a ' is updated by the temperature for the Vehicular dynamic battery group that the last time determines.
In one embodiment, sensor arrangement module, for measuring battery core temperature and tab in same time period
Temperature.
It can be seen from the above technical proposal that in embodiments of the present invention, comprising: in the static state of Vehicular dynamic battery group
When test process, battery core temperature and tab temperature are measured, using battery core temperature and tab temperature, is determined based on the first genetic algorithm
Multiple placement positions of temperature sensor out, and temperature sensor is respectively arranged in each placement position；In power train in vehicle application electricity
When the vehicle operation of pond group, multiple measured values of the temperature sensor at multiple placement positions are read, the second genetic algorithm is based on
It is dynamically determined out at least one optimum measuring point, Vehicular dynamic battery group is determined based on the measured value of at least one optimum measuring point
Temperature.The position that temperature sensor is chosen in static topology, finds optimal selected point again in dynamic reconnaissance, mentions
High thermometric accuracy.
Moreover, the objective function assignment when battery pack temperature that this thermometric is obtained is next thermometric, can be improved survey
Warm accuracy.
Detailed description of the invention
Only illustratively description and explain the present invention for the following drawings, not delimit the scope of the invention.
Fig. 1 is the flow chart of the temperature determining method of Vehicular dynamic battery group of the present invention.
Fig. 2A is the exemplary schematic representation of the temperature determining method of Vehicular dynamic battery group of the present invention.
Fig. 2 B is the schematic diagram in tab face of the invention and population surface of position.
Fig. 3 is the genetic algorithm flow chart according to Vehicular dynamic battery group of the present invention.
Fig. 4 is population foundation schematic diagram according to the present invention.
Fig. 5 is fitness calculation flow chart according to the present invention.
The present invention is based on the selection course flow charts of fitness according to Fig. 6.
Fig. 7 is intersection according to the present invention and variation schematic diagram.
Fig. 8 A is the schematic diagram of population X according to the present invention.
Fig. 8 B is the schematic diagram of population Z according to the present invention.
Fig. 8 C is popx1 according to the present invention and popz1, fitness (objvalue) and the fitness after re-scaling
(fitvalue) schematic diagram.
Fig. 8 D is accounting P1 of the individual in population according to the present invention, the schematic diagram of accumulated probability P2 and ms.
Fig. 8 E is the schematic diagram of new population X and new population Z according to the present invention.
Fig. 8 F is the new population X after being intersected according to the present invention and the schematic diagram of the new population Z after intersection.
Fig. 8 G is the schematic diagram according to the new population X after present invention variation and the new population Z after variation.
Fig. 9 is the temperature determining device result figure of Vehicular dynamic battery group according to the present invention.
Specific embodiment
In order to which the technical features, objects and effects of invention are more clearly understood, the Detailed description of the invention present invention is now compareed
Specific embodiment, identical label indicates identical part in the various figures.
It is succinct and intuitive in order to what is described, hereafter by describing several representative embodiments come to side of the invention
Case is illustrated.A large amount of details is only used for helping to understand the solution of the present invention in embodiment.However, it will be apparent that of the invention
Technical solution can be not limited to these details when realizing.In order to avoid unnecessarily having obscured the solution of the present invention, Yi Xieshi
It applies mode not described meticulously, but only gives frame.Hereinafter, " comprising " refers to " including but not limited to ", " root
According to ... " refer to " according at least to ..., but be not limited to according only to ... ".Due to the speech habits of Chinese, hereinafter without spy
When not pointing out the quantity of an ingredient, it is meant that the ingredient is either one or more, or can be regarded as at least one.
In embodiments of the present invention, firstly, the tab temperature obtained during the static test based on new-energy automobile
With battery core temperature, multiple optimization positions that temperature sensor can be arranged in Vehicular dynamic battery group are determined using genetic algorithm
(i.e. placement position) is respectively arranged temperature sensor at multiple optimization position.Then, real-time detection is more in vehicle operation
The detected value of a temperature sensor determines the Optimal Temperature sensor arrangement point of at least one (i.e. using genetic algorithm again
Optimum measuring point), and by this measured value of optimum measuring point of at least one average value, or closest to tab face center
The measured value of optimum measuring point be determined as the temperature of Vehicular dynamic battery group.Specifically, during static test, it can be with base
The position of sensor is selected in genetic algorithm combination trigonometric interpolation function.Then, when in car running process, further
It is further calculated out from position using genetic algorithm combination trigonometric interpolation function for determining Vehicular dynamic battery group
At least one optimum measuring point of temperature.
Vehicular dynamic battery group usually has the connection type gone here and there first and afterwards, i.e., multiple parallel units are serially connected again as vehicle
Use power battery pack.
Fig. 1 is the flow chart of the temperature determining method of Vehicular dynamic battery group of the present invention.
As shown in Figure 1, this method comprises:
Step 101: in the static test process of Vehicular dynamic battery group, measuring battery core temperature and tab temperature, utilize
Battery core temperature and tab temperature determine multiple placement positions of temperature sensor based on the first genetic algorithm, and in each cloth
Office is respectively arranged temperature sensor in position.
Step 102: in the vehicle operation of Vehicular dynamic battery group, reading the temperature sensor at multiple placement positions
Multiple measured values are dynamically determined out at least one optimum measuring point based on the second genetic algorithm, are based at least one optimum measurement
The measured value of point determines the temperature of Vehicular dynamic battery group.
Specifically, the static test process of Vehicular dynamic battery group, can be Vehicular dynamic battery group being installed to vehicle
Test process before.By step 101 as it can be seen that during the test, measuring battery core temperature and tab temperature, first is then utilized
Genetic algorithm determination can be laid out the dot matrix of temperature sensor, then in the good temperature sensor of the Regional Distribution.
Moreover, the first genetic algorithm mentioned in step 101 can use currently used various Genetic Algorithm Models.It loses
Propagation algorithm (Genetic Algorithm) be simulate Darwinian evolutionism natural selection and genetic mechanisms biology into
The calculation method of change process is a kind of method by simulating natural evolution process searches optimal solution.Genetic algorithm is from representative
What one population (population) of the possible potential disaggregation of problem started, and a population by gene (gene) then by compiling
Individual (individual) composition of the certain amount of code.Each individual is actually that chromosome (chromosome) has feature
Entity.Main carriers of the chromosome as inhereditary material, i.e., the set of multiple genes, internal performance (i.e. genotype) is certain
The kind assortment of genes, it determines the external presentation of the shape of individual, as dark hair is characterized in by controlling this spy in chromosome
What certain assortment of genes of sign determined.Therefore, needing to realize the mapping from phenotype to genotype, i.e. coding work at the beginning
Make.Since the work for copying gene to encode is very complicated, often simplified, such as binary coding.After population primary generates, press
According to the principle of the survival of the fittest and the survival of the fittest, the approximate solution become better and better is produced by generation (generation) evolution, each
Generation, according to fitness (fitness) size selection (selection) individual individual in Problem Areas, and by means of natural heredity
Genetic operator (genetic operators) is combined intersection (crossover) and variation (mutation), produces
Represent the population of new disaggregation.This process will lead to the same rear life of kind of images of a group of characters natural evolution and more adapt to for population than former generation
Optimum individual in environment, last reign of a dynasty population can be used as problem approximate optimal solution by decoding (decoding).
Specifically, the first genetic algorithm mentioned in step 101 can be and be used in computer science artificial intelligence field
The search heuritic approach optimized is solved, is one kind of evolution algorithm.It is this heuristic commonly used to generate useful solution
Scheme optimizes and searches for problem.Evolution algorithm is initially some phenomenons used for reference in evolution biology and grows up,
These phenomenons include heredity, mutation, natural selection and hybridization etc..Genetic algorithm is when fitness function selects improperly
It is possible that converging on local optimum, and global optimum cannot be reached.
In one embodiment, the first genetic algorithm includes to determine the first on tab face that tab position is constituted
The step of group's location point；Objective function in first genetic algorithm is F；
Wherein: T1, T2, T3For three with triangle composed by the immediate three tab positions of the first population location point
The temperature value on a vertex；X is abscissa of the first population location point on the tab face；Y is the first population location point in institute
State the ordinate on tab face；Y1, y2, y3 are the ordinate on three vertex of the triangle；x12, x23Respectively described three
Abscissa of the interpolation point on the tab face on angular both sides；A is the battery core temperature.
Preferably, this method further include: calculate the measured temperature of multiple battery cores；By the measured temperature of multiple battery cores
Average value is determined as battery core temperature.
Preferably, based on the measured value of at least one optimum measuring point determine Vehicular dynamic battery group temperature include: by
The average value of the measured value of at least one optimum measuring point is determined as the temperature of Vehicular dynamic battery group；Or, most by least one
It is determined as the temperature of Vehicular dynamic battery group in good measurement point closest to the measured value of the optimum measuring point of tab face center.
Similarly, the second genetic algorithm can use currently used various Genetic Algorithm Models, can also be using with the
The identical algorithm of one genetic algorithm.
Wherein, the second genetic algorithm includes and determines second on the population surface of position being made of the first population location point
The step of group's location point；The objective function of second genetic algorithm is F '；
Wherein:
Wherein: T1', T2', T3' for three composed by immediate three the first population location points of the second population location point
The temperature value on three angular vertex；X ' is abscissa of the optimum measuring point on population surface of position；Y ' is that optimum measuring point exists
Ordinate on the population surface of position；Y1 ', y2 ', y3 ' are and immediate three the first populations position of the second population location point
Set the ordinate on a little three vertex of composed triangle；x12', x23' it is respectively described closest with the second population location point
Three the first population location points composed by triangle both sides on abscissa of the interpolation point on the population surface of position；
The initial value of a ' is predetermined value, and the value of a ' is updated by the temperature for the Vehicular dynamic battery group that the last time determines.
Preferably, the measurement battery core temperature and tab temperature in step 101 are as follows: in same time period, measure battery core temperature
Degree and tab temperature.
Fig. 2A is the exemplary schematic representation of the temperature determining method of Vehicular dynamic battery group of the present invention.
From Figure 2 it can be seen that measuring the battery core temperature and tab temperature of battery pack during static test, calculated using heredity
Method chooses sensor arrangement position dot matrix, and is based on the sensor arrangement position dot matrix placement sensor.
Then, during the dynamic reconnaissance of vehicle operation, after electric car operation, constantly using genetic algorithm to electricity
The temperature measuring point in pond is estimated to find optimal selected point (number is at least one), while being moved what this thermometric obtained
Triangle difference functions when the temperature a ' of power battery pack is next thermometric carry out assignment.In this way, in the continuous operational process of automobile
Continuous Dynamic calculation, and then can more accurately obtain the temperature of power battery pack.
For example, the present invention is calculated and handled to test data using genetic algorithm, and pole is found out from test result
The minimum value of ear testing temperature point and battery core average temperature difference, temperature sensor is arranged in point locating for the minimum value can be quickly quasi-
Really understand battery pack internal temperature, can reach the purpose of saving time, cost in this way, and guide subsequent design
And the development of test, while ensure that the estimation precision of automobile temperature in the process of running.
Fig. 2 B is the schematic diagram in tab face of the invention and population surface of position.
Wherein, the installation site of each tab of Vehicular dynamic battery group is usually located in a plane, which is
Tab face.Population surface of position is based on the first genetic algorithm, and identified first population location point is constituted in tab face
Plane.Population surface of position belongs to a part in tab face.It, can also be further in population surface of position based on the second genetic algorithm
Determine multiple optimum measuring points.
Fig. 3 is the genetic algorithm flow chart according to Vehicular dynamic battery group of the present invention.The genetic algorithm can be adapted for Fig. 1
In the first genetic algorithm, the second genetic algorithm being readily applicable in Fig. 1.
Based on shown in Fig. 3, the basic operation process of genetic algorithm is as follows: (a) initializing: setting evolutionary generation counter t
=0, maximum evolutionary generation T is set, and M individual of random generation is used as initial population P (0)；(b) individual evaluation: group P is calculated
(t) fitness of each individual in.(c) selection operator Selecting operation: is acted on into group.The purpose of selection is optimization
Body is genetic directly to the next generation or intersects the individual generated newly by pairing is genetic to the next generation again.Selection operation is built upon group
In body on the basis of individual Fitness analysis.(d) crossover operator crossing operation: is acted on into group.Nuclei of origin in genetic algorithm
Heart effect is exactly crossover operator.(e) mutation operator mutation operator: is acted on into group.It is to the individual string in group
Genic value on certain locus changes.Group P (t) obtains next-generation group P after selection, intersection, mutation operator
(t+1).(f) termination condition judges: if t=T, then obtained using in evolutionary process to have maximum adaptation degree individual as optimal
Solution output, terminates and calculates.
Fig. 4 is population foundation schematic diagram according to the present invention.Fig. 5 is fitness calculation flow chart according to the present invention.Fig. 6
According to the present invention is based on the selection course flow charts of fitness.Fig. 7 is intersection according to the present invention and variation schematic diagram.
In population foundation, it is related to binary coding: i.e. using 0,1 random combine, constitutes " the gene of equal length
Chain ".Citing: generation number of individuals is m1, mrna length n1Population X；Generation number of individuals is m2, mrna length n2Population
Z.Fitness is adaptedness of the individual to environment, and numerical value is bigger, and individual survival rate is higher.Citing: to population X/Z decoding (two into
System turns the decimal system), abscissa x_value is obtained, ordinate z_value brings result in data c into, and obtained result is
Individual adaptation degree.In the individual choice based on fitness, the biggish individual of fitness is easier selected.Citing: it asks first
It is individual by the sum of selection Probability p, p=individual adaptation degree/individual adaptation degree out；Then the random number q for generating one 0~1；Again
Compare p and q size, if p > q, individual survival, otherwise individual is removed.
It describes to intersect again below and make a variation.Citing:
Intersect: (1) generating one 0~1 number mc, at random；(2), it generates one at random within the scope of mrna length and is greater than 0
Integer, be set as crosspoint；(3), compare the size of mc Yu crossover probability pc, when mc≤pc, monomer is intersected.
Variation: (1) one 0~1 number mm is generated, at random；(2) it generates one at random within the scope of mrna length and is greater than 0
Integer, be set as change point.(3) compare the size of mc Yu crossover probability pm, when mm≤pm, monomer makes a variation.By selecting,
Intersect, variation obtains new population, and replaces initial population.After the iteration of certain number, new population is screened, is selected
Optimal solution required for us.This solution is not necessarily global optimum, it may be possible to near-optimization or local optimum.It is to be determined optimal
Solution, i.e., temperature sensor is arranged in this closest to the point of battery core mean temperature in tab testing temperature point, then can be by the point at
Temperature value rapidly and accurately determines battery pack internal temperature.
A representative instance based on foregoing description, then the present invention is described in detail embodiment.In the example it is assumed that electric
Chi Bao includes 40 battery cores and 8 tabs.
Fig. 8 A is the schematic diagram of population X according to the present invention.Fig. 8 B is the schematic diagram of population Z according to the present invention.Fig. 8 C
Show for popx1 according to the present invention and popz1, fitness (objvalue) with fitness (fitvalue's) after re-calibration
It is intended to.Fig. 8 D is accounting P1 of the individual in population according to the present invention, the schematic diagram of accumulated probability P2 and ms.According to Fig. 8 E
The schematic diagram of new population X and new population Z of the invention.Fig. 8 F is new according to the new population X after present invention intersection and after intersecting
The schematic diagram of population Z.Fig. 8 G is the schematic diagram according to the new population X after present invention variation and the new population Z after variation.
It is specific:
(1) in the static test process of Vehicular dynamic battery group:
Firstly, measuring the temperature of 40 battery cores and the temperature of 8 tabs, and calculate 40 electricity in same time period
The mean temperature of core.Tab areal coordinate system is assumed to X-Z coordinate system.Moreover, using rand function creation population X, x=rand
(10,9) 0 to the 1 random number matrix x that 10 rows (Population Size) 9 arranges (code length) is generated, roundn function, X=are used
Roundn (x) rounds up matrix x element, switchs to the only matrix comprising 0 and 1, i.e. population X.Use rand function creation kind
Group's Z, z=rand (10,7) generation 10 rows (Population Size) 7 arranges 0 to the 1 random number matrix z of (code length), uses roundn
Function, Z=roundn (x) round up matrix x element, switch to the only matrix comprising 0 and 1, i.e. population Z.Moreover, by population
2 scale codings switch to the decimal system, formula are as follows:N is code length, and x is current location numerical value.
Such as:
Individual 1 is 010100011；
Y=0*29-1+1*28-1+0*27-1+1*26-1+0*25-1+0*24-1+0*23-1+1*22-1+1*21-1=162.
After all individual decodings, decimal data popx1 and decimal data popz1 are obtained, using triangle interior
Linear interpolation method to the position (popx1, popz1) carry out linear interpolation, triangular apex choose target point (popx1,
Popz1), the closest three tab temperature spots that may make up triangle.The triangle is immediate with population location point
Triangle composed by three tab positions.
Triangle interior interpolating function are as follows:
Wherein, T1, T2, T3For Atria vertex temperature value；X, y are coordinate of ground point value, y1, y2, y3For triangle
The ordinate on three vertex, x12, x23The abscissa of interpolation point respectively on triangle side, a are battery core mean temperature.
After calculating target function value, fitness objvalue is re-scaled using linear calibration's method, maximum value is solved, makes
It is demarcated with f1=f (x)-fmin+ ξ, wherein fmin is fitness minimum value, and ξ is constant, takes 0.1 here.It solves minimum
Value, using f1=fmax-f (x)+ξ, wherein fmax is fitness maximum value, and ξ is constant, takes 0.1 here.To solve maximum value
For, fitness is fitvalue1 after calibration.
Then, the sum of fitness, total=sum (fitvalue), result total=are found out using sum function
4.49664, accounting of the individual in population is found out, p1=fitvalue/total finds out accumulated probability using cumsum function
p2.10 0 to 1 random numbers are generated at random using rand function and its ascending order is arranged using sort function, ms=sort
(rand (10,1)), statistics ms fall in the number in each section p2, such as: 1 in [0,0.100508] section,
1 in [0.100508,0.159291] section, 2 in [0.340994,0.465135] section, [0.743473,0.872809]
4 in section, 2 in [0.832809,1] section, i.e., individual 1 is selected 1 time, and individual 2 is selected 1 time, and individual 5 is by selection 2
Secondary, individual 9 is selected 4 times, and individual 10 is selected 2 times, and the above individual selected constitutes new population NewX and NewZ.
Execute crossover probability setting process again: pc=0.4*cos ((i/gen) * (pi/2))+0.5, wherein i is to work as former generation
Number, gen are iteration total degree, and pi is pi.By taking the crossover probability of first time iteration as an example, crossover probability pc=0.9.Its
In: (1), using rand function generate the random number between a 0-1, random number 0.33,0.33 < 0.9, so handing over
Fork.(2), the random number that mc1=round (rand*9) generates one 0 to 9 is then assigned a value of 1, random number 6 if 0.(3),
Individual 1 is intersected with individual 2 at the 6th in NewX, constitutes new individual.Example: 010100011 and 001001001 at the 6th
Intersect, new individual is 010100001 and 001001011.(4), mc2=round (rand*7) generates one 0 to 7 random number,
If 0, then it is assigned a value of 1, random number 4.(5), two individuals are randomly selected in NewZ to be intersected at the 4th, constitute new
Body.(6), it repeats the above process, until completing ten individual crossover operations in each population.
Then, pm=0.05*sin ((i/gen) * (pi/2))+0.01 is calculated, wherein i is current algebra, and gen is iteration
Total degree, pi are pi.By taking the mutation probability of first time iteration as an example, crossover probability pm=0.01.Wherein: (1), using
Rand function generates the random number of a 0-1, random number 0.0026,0.0026 < 0.01, so morphing.(2), mm1=
The random number that round (rand*9) generates one 0 to 9 is then assigned a value of 1, random number 5 if 0.(3), individual 1 exists in NewX
5th is morphed, and becomes 010110001 by 010100001.(4), mm2=round (rand*7) generates one 0 to 7
Random number is then assigned a value of 1, random number 1 if 0.(5), individual 1 morphs at first in NewZ, is become by 1011110
At 0011110.(6), it repeats the above process, until completing the mutation operation of ten individuals in each population.
Followed by Population Regeneration, X=NewX, Z=NewZ carry out next iteration, if iteration is complete if iteration does not complete
At jumping out circulation, carry out next step optimizing operation.
Then, searching process is carried out after the completion of iteration: being found out the fitness of new population, and is re-scaled fitness, looks for
The maximum individual of fitness out, this individual are exactly the optimum individual this time found, this individual is decoded, decimal system band is switched to
Enter into data coordinate system, obtain the optimal solution of this optimizing, and record the position of this optimal solution in a coordinate system, using as cloth
Office position.
(2) in the vehicle operation of Vehicular dynamic battery group:
Firstly, read multiple measured values of the temperature sensor at multiple placement positions, then it is similar in power train in vehicle application
Genetic algorithm when the static test process of battery pack calculates step, determines at least one optimum measuring point, then based at least
The measured value of one optimum measuring point determines the temperature of Vehicular dynamic battery group.For example, by the survey of at least one optimum measuring point
The average value of magnitude is determined as the temperature of Vehicular dynamic battery group；Or, by least one optimum measuring point closest to tab face
The measured value of the optimum measuring point of center is determined as the temperature of Vehicular dynamic battery group.
Wherein, genetic algorithm used by running in the vehicle of Vehicular dynamic battery group is included in by Vehicular dynamic battery group
Static test during on the population surface of position that is constituted of population location point (referred to as the first population location point) that determines again
The step of determining population location point (referred to as the second population location point)；Wherein objective function is F '；
Wherein: T1', T2', T3' for three composed by immediate three the first population location points of the second population location point
The temperature value on three angular vertex；X ' is abscissa of the optimum measuring point on population surface of position；Y ' is that optimum measuring point exists
Ordinate on the population surface of position；Y1 ', y2 ', y3 ' are and immediate three the first populations position of the second population location point
Set the ordinate on a little three vertex of composed triangle；x12', x23' it is respectively described closest with the second population location point
Three the first population location points composed by triangle both sides on abscissa of the interpolation point on the population surface of position；
The initial value of a ' is predetermined value, and the value of a ' is updated by the temperature for the Vehicular dynamic battery group that the last time determines.
Based on foregoing description, embodiment of the present invention also proposed the temperature determining device of Vehicular dynamic battery group.Fig. 9 is
The structure chart of the temperature determining device of Vehicular dynamic battery group according to the present invention.
As shown in figure 9, the temperature determining device of Vehicular dynamic battery group, comprising:
Sensor arrangement module 901, in the static test process of Vehicular dynamic battery group, measurement battery core temperature and
Tab temperature determines multiple layout positions of temperature sensor based on the first genetic algorithm using battery core temperature and tab temperature
It sets, and is respectively arranged temperature sensor in each placement position；
Determining module 902, for reading at multiple placement positions in the vehicle operation of the Vehicular dynamic battery group
Multiple measured values of temperature sensor are dynamically determined out at least one optimum measuring point based on the second genetic algorithm, based at least
The measured value of one optimum measuring point determines the temperature of Vehicular dynamic battery group.
In one embodiment, the first genetic algorithm is included on tab face the step of determining population location point；It is described
Objective function in first genetic algorithm is F；
Wherein: T1, T2, T3For three with triangle composed by the immediate three tab positions of the first population location point
The temperature value on a vertex；X is abscissa of the first population location point on the tab face；Y is the first population location point in institute
State the ordinate on tab face；Y1, y2, y3 are the ordinate on three vertex of the triangle；x12, x23Respectively described three
Abscissa of the interpolation point on the tab face on angular both sides；A is the battery core temperature.
In one embodiment, sensor arrangement module 901 calculates the measured temperature of multiple battery cores；It will be described more
The average value of the measured temperature of a battery core is determined as the battery core temperature.
In one embodiment, the objective function of the second genetic algorithm is F '；
Wherein: T1', T2', T3' for three composed by immediate three the first population location points of the second population location point
The temperature value on three angular vertex；X ' is abscissa of the optimum measuring point on population surface of position；Y ' is that optimum measuring point exists
Ordinate on the population surface of position；Y1 ', y2 ', y3 ' are and immediate three the first populations position of the second population location point
Set the ordinate on a little three vertex of composed triangle；x12', x23' it is respectively described closest with the second population location point
Three the first population location points composed by triangle both sides on abscissa of the interpolation point on the population surface of position；
The initial value of a ' is predetermined value, and the value of a ' is updated by the temperature for the Vehicular dynamic battery group that the last time determines.
In one embodiment, sensor arrangement module 901, in same time period, measurement battery core temperature and
Tab temperature.
Can by embodiment of the present invention proposes the temperature determining method of Vehicular dynamic battery group be applied to various types
Electric car in.For example, can be applied to mixed power electric car (HEV), pure electric automobile (BEV), fuel cell electricity
Electrical automobile (FCEV) and other new energy (such as supercapacitor, flywheel high-efficiency energy storage vehicle) automobiles etc..
In conclusion embodiment of the present invention: in the static test process of Vehicular dynamic battery group, measuring battery core temperature
Multiple layouts of temperature sensor are determined based on the first genetic algorithm using battery core temperature and tab temperature with tab temperature
Position, and temperature sensor is respectively arranged in each placement position；In the vehicle operation of the Vehicular dynamic battery group, read
The multiple measured values for taking the temperature sensor at multiple placement positions are dynamically determined out optimum measurement based on the second genetic algorithm
The measured value of the optimum measuring point is determined as battery core temperature by point.As it can be seen that after using embodiment of the present invention, in static cloth
The position of temperature sensor is chosen in office using genetic algorithm, and is found again using genetic algorithm in dynamic reconnaissance
Optimal selected point, therefore improve battery core temperature accuracy.
Moreover, target letter when embodiment of the present invention can also be next thermometric the battery core temperature that this thermometric obtains
Number assignment, can more accurately obtain battery core temperature by continuous Dynamic iterations.
Hardware module in each embodiment mechanically or can be realized electronically.For example, a hardware module
It may include that the permanent circuit specially designed or logical device (such as application specific processor, such as FPGA or ASIC) are specific for completing
Operation.Hardware module also may include programmable logic device or circuit by software provisional configuration (as included general procedure
Device or other programmable processors) for executing specific operation.Mechanical system is used as specific, or using dedicated permanent
Property circuit, or Lai Shixian hardware module (such as is configured) by software using the circuit of provisional configuration, can according to cost with
Temporal consideration is to determine.
The present invention also provides a kind of machine readable storage medium, storage is for making a machine execute side as described herein
The instruction of method.Specifically, system or device equipped with storage medium can be provided, store in realization on the storage medium
State the software program code of the function of any embodiment in embodiment, and make the system or device computer (or CPU or
MPU the program code being stored in a storage medium) is read and executed.Further, it is also possible to be made by the instruction based on program code
Operating system of hands- operation etc. is calculated to complete partly or completely practical operation.It can also will read from storage medium
The expansion being connected to a computer is write in memory set in the expansion board in insertion computer or write to program code
In the memory being arranged in exhibition unit, then the instruction based on program code makes to be mounted on expansion board or expanding element
CPU etc. comes execution part and whole practical operations, to realize the function of any embodiment in above embodiment.
Storage medium embodiment for providing program code include floppy disk, hard disk, magneto-optic disk, CD (such as CD-ROM,
CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), tape, non-volatile memory card and ROM.Selectively,
It can be by communication network from download program code on server computer or cloud.
It should be noted that step and module not all in above-mentioned each process and each system construction drawing is all necessary
, certain steps or module can be ignored according to the actual needs.Each step execution sequence be not it is fixed, can be according to need
It is adjusted.System structure described in the various embodiments described above can be physical structure, be also possible to logical construction, that is, have
A little modules may be realized by same physical entity, be realized alternatively, some modules may divide by multiple physical entities, alternatively, can be with
It is realized jointly by certain components in multiple autonomous devices.
Herein, " schematic " expression " serving as examplea, instances, or illustrations " should not will be described herein as " showing
Any diagram, the embodiment of meaning property " are construed to technical solution that is a kind of preferred or more having advantages.To make simplified form,
Part related to the present invention is only schematically shown in each figure, and does not represent its practical structures as product.Separately
Outside, so that simplified form is easy to understand, with the component of identical structure or function in some figures, it is only symbolically depicted
In one, or only marked one of those.Herein, "one" is not offered as limiting the quantity of relevant portion of the present invention
It is made as " only this ", and "one" situation for not indicating to exclude the quantity " more than one " of relevant portion of the present invention.At this
Wen Zhong, "upper", "lower", "front", "rear", "left", "right", "inner", "outside" etc. are only used for indicating the opposite position between relevant portion
Set relationship, and the absolute position of these non-limiting relevant portions.
The series of detailed descriptions listed above only for feasible embodiment of the invention specifically
Protection scope that is bright, and being not intended to limit the invention, it is all without departing from equivalent embodiments made by technical spirit of the present invention or
Change, such as the combination, segmentation or repetition of feature, should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of temperature determining method of Vehicular dynamic battery group characterized by comprising
In the static test process of Vehicular dynamic battery group, battery core temperature and tab temperature are measured, battery core temperature and pole are utilized
Ear temperature determines multiple placement positions of temperature sensor based on the first genetic algorithm, and in each placement position respectively
Arrange temperature sensor；
In the vehicle operation of the Vehicular dynamic battery group, multiple measurements of the temperature sensor at multiple placement positions are read
Value, is dynamically determined out at least one optimum measuring point based on the second genetic algorithm, the measurement based at least one optimum measuring point
It is worth the temperature for determining Vehicular dynamic battery group.
2. the temperature determining method of Vehicular dynamic battery group according to claim 1, which is characterized in that first heredity
Algorithm includes the step of determining the first population location point on tab face that tab position is constituted；In first genetic algorithm
Objective function be F；
Wherein: T1, T2, T3To be pushed up with three of triangle composed by the immediate three tab positions of the first population location point
The temperature value of point；X is abscissa of the first population location point on the tab face；Y is the first population location point in the pole
Ordinate on ear face；Y1, y2, y3 are the ordinate on three vertex of the triangle；x12, x23The respectively described triangle
Both sides on abscissa of the interpolation point on the tab face；A is the battery core temperature.
3. the temperature determining method of Vehicular dynamic battery group according to claim 2, which is characterized in that
This method further include: calculate the measured temperature of multiple battery cores；By the average value of the measured temperature of the multiple battery core
It is determined as the battery core temperature；And/or
The measured value based at least one optimum measuring point determines that the temperature of Vehicular dynamic battery group includes:
The average value of the measured value of at least one optimum measuring point is determined as to the temperature of Vehicular dynamic battery group；Or
The measured value of optimum measuring point at least one optimum measuring point closest to tab face center is determined as
The temperature of Vehicular dynamic battery group.
4. the temperature determining method of Vehicular dynamic battery group according to claim 2, which is characterized in that second heredity
Algorithm includes the step of determining the second population location point on the population surface of position being made of the first population location point；Described
The objective function of two genetic algorithms is F '；
Wherein: T1', T2', T3' be and triangle composed by immediate three the first population location points of the second population location point
Three vertex temperature value；X ' is abscissa of the optimum measuring point on population surface of position；Y ' is optimum measuring point described
Ordinate on population surface of position；Y1 ', y2 ', y3 ' are and immediate three the first population location points of the second population location point
The ordinate on three vertex of composed triangle；x12', x23' it is respectively described and the second population location point immediate three
Abscissa of the interpolation point on the population surface of position on the both sides of triangle composed by a first population location point；A's '
Initial value is predetermined value, and the value of a ' is updated by the temperature for the Vehicular dynamic battery group that the last time determines.
5. the temperature determining method of Vehicular dynamic battery group according to claim 1, which is characterized in that the measurement battery core
Temperature and tab temperature are as follows: in same time period, measure battery core temperature and tab temperature.
6. a kind of temperature determining device of Vehicular dynamic battery group characterized by comprising
Sensor arrangement module, for measuring battery core temperature and tab temperature in the static test process of Vehicular dynamic battery group
Degree determines multiple placement positions of temperature sensor based on the first genetic algorithm using battery core temperature and tab temperature, and
Temperature sensor is respectively arranged in each placement position；
Determining module, for reading the temperature at multiple placement positions and passing in the vehicle operation of the Vehicular dynamic battery group
Multiple measured values of sensor are dynamically determined out at least one optimum measuring point based on the second genetic algorithm, most based at least one
The measured value of good measurement point determines the temperature of Vehicular dynamic battery group.
7. the temperature determining device of Vehicular dynamic battery group according to claim 6, which is characterized in that
First genetic algorithm includes the step of determining the first population location point on tab face that tab position is constituted；Institute
Stating the objective function in the first genetic algorithm is F；
Wherein: T1, T2, T3To be pushed up with three of triangle composed by the immediate three tab positions of the first population location point
The temperature value of point；X is abscissa of the first population location point on the tab face；Y is the first population location point in the pole
Ordinate on ear face；Y1, y2, y3 are the ordinate on three vertex of the triangle；x12, x23The respectively described triangle
Both sides on abscissa of the interpolation point on the tab face；A is the battery core temperature.
8. the temperature determining device of Vehicular dynamic battery group according to claim 6, which is characterized in that
Sensor arrangement module, for calculating the measured temperature of multiple battery cores；By the measured temperature of the multiple battery core
Average value is determined as the battery core temperature；And/or
Determining module, for the average value of the measured value of at least one optimum measuring point to be determined as to the temperature of Vehicular dynamic battery group
Degree；Or, the measured value of the optimum measuring point at least one optimum measuring point closest to tab face center is determined
For the temperature of Vehicular dynamic battery group.
9. the temperature determining device of Vehicular dynamic battery group according to claim 7, which is characterized in that
The objective function of second genetic algorithm is F '；
Wherein: T1', T2', T3' be and triangle composed by immediate three the first population location points of the second population location point
Three vertex temperature value；X ' is abscissa of the optimum measuring point on population surface of position；Y ' is optimum measuring point described
Ordinate on population surface of position；Y1 ', y2 ', y3 ' are and immediate three the first population location points of the second population location point
The ordinate on three vertex of composed triangle；x12', x23' it is respectively described and the second population location point immediate three
Abscissa of the interpolation point on the population surface of position on the both sides of triangle composed by a first population location point；A's '
Initial value is predetermined value, and the value of a ' is updated by the temperature for the Vehicular dynamic battery group that the last time determines.
10. the temperature determining device of Vehicular dynamic battery group according to claim 6, which is characterized in that
Sensor arrangement module, for measuring battery core temperature and tab temperature in same time period.
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