CN108460451A - Optimize the method and device of battery charge state estimation key parameter based on particle cluster algorithm - Google Patents
Optimize the method and device of battery charge state estimation key parameter based on particle cluster algorithm Download PDFInfo
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Abstract
The invention discloses a kind of method and devices optimizing battery charge state estimation key parameter based on particle cluster algorithm, wherein, this method passes through random initializtion population, determine the object function of each battery charge state estimation key parameter, each object function is determined as to the fitness function of each dimension of particle, the iterative process for carrying out population, using the global value of each dimension of population as the desired value of each optimization battery charge state estimation key parameter.This method optimizes battery charge state estimation key parameter by particle cluster algorithm, can optimize each key parameter in the case of less parameter with faster convergence rate, subsequently can relatively accurately estimate battery SOC.
Description
Technical field
The present invention relates to electric vehicle engineering fields, more particularly to a kind of particle cluster algorithm that is based on to optimize battery charge state
The method and device of estimation key parameter.
Background technology
With the development of electric vehicle, battery management system (Battery Management System BMS) obtains
Extensive use.In order to give full play to the power performance of battery system, improves safety that it uses, prevents from over-charging of battery from crossing putting,
Extend the service life of battery, optimization drives and improves the performance of electric vehicle, needs BMS to battery charge state
(State of Charge, SOC) is accurately estimated.Battery SOC refers to the current residual charge amount of battery, battery SOC estimation
Be battery thermal management, balanced management and security reliability management basis；But since lithium ion battery structure is complicated, battery
State-of-charge is influenced by the factors such as operating current, the internal resistance of cell and surrounding environment temperature, self discharge and aging so that electricity
SOC estimations in pond are difficult.
Common battery SOC method of estimation is mainly the following both at home and abroad at present：(1) Ah counting method：Pass through electric current
The SOC of battery is calculated to the integral of time, since there are errors for current measurement, and error can increase with time integral
Greatly, and Ah counting method estimation battery SOC also needs to know its initial value；(2) open circuit voltage method：When passing through battery standing
The SOC of open terminal voltage and SOC relationships estimation battery, open circuit voltage method estimation SOC need battery standing for a period of time, are not suitable for
The needs of batteries of electric automobile real-time estimation；(3) neural network：Estimate battery SOC, god using the neural network model of battery
A large amount of data are needed to be trained through network technique, operand and estimated accuracy are related with training method；(4) mathematical model is estimated
Meter method：Battery SOC mathematical model is gone out by the method summary tested, the method is limited by use condition, and when condition becomes
When change, SOC estimated results need to be corrected.(5) Kalman filtering (KalmanFilter, KF) algorithm：By the state of battery
Spatial model estimates that the SOC of battery, estimated accuracy are affected by model accuracy by the method for recursion iteration.
Therefore, a kind of method of accurate estimation battery SOC becomes technical problem urgently to be resolved hurrily.
Invention content
The purpose of the present invention is intended to solve above-mentioned one of technical problem at least to a certain extent.
For this purpose, first purpose of the present invention is being used based on particle cluster algorithm optimization battery charge state estimation for proposition
The method of key parameter determines the target of each battery charge state estimation key parameter by random initializtion population
Each object function is determined as the fitness function of each dimension of particle, carries out the iterative process of population, by grain by function
Desired value of the global value of each dimension of subgroup as each optimization battery charge state estimation key parameter.This method is logical
Particle cluster algorithm optimization battery charge state estimation key parameter is crossed, it can be in the case of less parameter, with faster
Convergence rate optimizes each key parameter, subsequently can relatively accurately estimate battery SOC.
For this purpose, second object of the present invention is to propose that a kind of particle cluster algorithm optimization battery charge state that is based on is estimated
With the device of the method for key parameter.
To achieve the goals above, first aspect present invention embodiment based on particle cluster algorithm optimize battery charge state
The method of estimation key parameter, including：
Random initializtion population, wherein each particle in population includes three dimensionality position vector and three dimensionality speed
Vector is spent, the first dimension position vector is cell voltage, and the first dimension velocity vector is the knots modification of cell voltage, the second dimension
Position vector is battery temperature, and the second dimension velocity vector is the knots modification of battery temperature, and third dimension position vector is battery
Resistance value, third dimension velocity vector are the knots modification of cell resistance value, the cell voltage, battery temperature, cell resistance value
It is battery charge state estimation key parameter；
Determine the object function of each battery charge state estimation key parameter；
Each object function is determined as to the fitness function of each dimension of particle；
Carry out the iterative process of population, including (1) to (3)：
(1), each of each particle in current particle group is calculated according to the fitness function of each dimension of the particle
The current fitness of dimension determines working as each dimension of each particle according to the current fitness of each dimension of each particle
The current global optimum of each dimension of preceding individual extreme value and population；
(2), respectively according to the current of each dimension of the current individual extreme value of each dimension of each particle and population
Global optimum updates the velocity vector of each dimension of each particle；Respectively according to each dimensions updating of each particle after
Velocity vector, update the position vector of each dimension of each particle；
(3), judge whether current iteration reaches stopping criterion for iteration, if reaching, export the complete of each dimension of population
Office's optimal value, stops iterative process；If not up to, returning and executing (1)；
Using the global optimum of each dimension of population as each optimization battery charge state estimation key parameter
Desired value.
Method as described above, it is described using the global optimum of each dimension of population as each battery charge state
The desired value of estimation key parameter, including：
Using the global optimum of the first dimension of population as target battery voltage, by the complete of the second dimension of population
Office's optimal value is as target cell temperature, using the global optimum of the third dimension of population as target battery resistance value.
Method as described above further includes：
Optimize battery charge state according to the desired value of each battery charge state estimation key parameter.
The specific implementation of method as described above, (2) step of iterative process is：
According to formula Vd1=ω Vd+C1random(0,1)(Pd-Xd)+C2random(0,1)(Pgd-Xd) more new particle
The velocity vector of d dimensions；
According to formula Xd1=Xd+VdThe position vector of the d dimensions of more new particle；
Wherein, d dimensions are any dimension in the first dimension, the second dimension, third dimension, and ω is known as inertial factor, C1
And C2Referred to as aceleration pulse, random (0,1) indicate the random number on section [0,1], VdFor the current speed of the d dimensions of particle
Spend vector, Vd1For the updated velocity vector of the d dimensions of particle, XdFor the current location vector of the d dimensions of particle, Xd1
For the updated position vector of the d dimensions of particle, PdFor the individual extreme value that the d of particle is tieed up, PgdIt is tieed up for the d of population
Global optimum.
Method as described above, the object function of each battery charge state estimation key parameter of determination, including：
Battery charge state design requirement table is inquired, obtains the corresponding battery charge state of different cell voltages, no respectively
With the corresponding battery charge state of battery temperature, the corresponding battery charge state of different cell resistance values；
It is carried out curve fitting according to different cell voltages and the corresponding battery charge state of different cell voltages, obtains battery
The object function of voltage and battery charge state；
It is carried out curve fitting according to different battery temperatures and the corresponding battery charge state of different battery temperatures, obtains battery
The object function of temperature and battery charge state；
It is carried out curve fitting, is obtained according to different cell resistance values and the corresponding battery charge state of different cell resistance values
The object function of cell resistance value and battery charge state.
To achieve the goals above, second aspect of the present invention embodiment based on particle cluster algorithm optimize battery charge state
The device of estimation key parameter, including：
Initialization module is used for random initializtion population, wherein each particle in population includes three dimensionality position
Vector sum three dimensionality velocity vector, the first dimension position vector are cell voltage, and the first dimension velocity vector is cell voltage
Knots modification, the second dimension position vector are battery temperature, and the second dimension velocity vector is the knots modification of battery temperature, third dimension
Position vector is cell resistance value, and third dimension velocity vector is the knots modification of cell resistance value, the cell voltage, battery temperature
Degree, cell resistance value are battery charge state estimation key parameter；
Object function determining module, the object function for determining each battery charge state estimation key parameter；
Fitness function determining module, the fitness letter of each dimension for each object function to be determined as to particle
Number；
Iteration module, the iterative process for carrying out population, including (1) to (3)：
(1), each of each particle in current particle group is calculated according to the fitness function of each dimension of the particle
The current fitness of dimension determines working as each dimension of each particle according to the current fitness of each dimension of each particle
The current global optimum of each dimension of preceding individual extreme value and population；
(2), respectively according to the current of each dimension of the current individual extreme value of each dimension of each particle and population
Global optimum updates the velocity vector of each dimension of each particle；Respectively according to each dimensions updating of each particle after
Velocity vector, update the position vector of each dimension of each particle；
(3), judge whether current iteration reaches stopping criterion for iteration, if reaching, export the complete of each dimension of population
Office's optimal value, stops iterative process；If not up to, returning and executing (1)；
Key parameter determining module, using the global optimum of each dimension of population as each optimization battery charge shape
The desired value of state estimation key parameter.
Device as described above, which is characterized in that the key parameter determining module is specifically used for：
Using the global optimum of the first dimension of population as target battery voltage, by the complete of the second dimension of population
Office's optimal value is as target cell temperature, using the global optimum of the third dimension of population as target battery resistance value.
Device as described above further includes：
Optimization module, for optimizing battery charge shape according to the desired value of each battery charge state estimation key parameter
State.
Device as described above, the object function determining module, is specifically used for：
Battery charge state design requirement table is inquired, obtains the corresponding battery charge state of different cell voltages, no respectively
With the corresponding battery charge state of battery temperature, the corresponding battery charge state of different cell resistance values；
It is carried out curve fitting according to different cell voltages and the corresponding battery charge state of different cell voltages, obtains battery
The object function of voltage and battery charge state；
It is carried out curve fitting according to different battery temperatures and the corresponding battery charge state of different battery temperatures, obtains battery
The object function of temperature and battery charge state；
It is carried out curve fitting, is obtained according to different cell resistance values and the corresponding battery charge state of different cell resistance values
The object function of cell resistance value and battery charge state.
The additional aspect of the present invention and advantage will be set forth in part in the description, and will partly become from the following description
Obviously, or practice through the invention is recognized.
Description of the drawings
Above-mentioned and/or additional aspect and advantage of the invention will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, wherein
Fig. 1 is the side for optimizing battery charge state estimation key parameter based on particle cluster algorithm of one embodiment of the invention
The flow diagram of method；
Fig. 2 is further embodiment of this invention based on particle cluster algorithm optimization battery charge state estimation key parameter
The flow diagram of method；
Fig. 3 is the dress for optimizing battery charge state estimation key parameter based on particle cluster algorithm of one embodiment of the invention
The structural schematic diagram set；
Fig. 4 is further embodiment of this invention based on particle cluster algorithm optimization battery charge state estimation key parameter
The structural schematic diagram of device.
Specific implementation mode
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to for explaining the present invention, and is not considered as limiting the invention.
Below with reference to the accompanying drawings the optimization battery charge state estimation based on particle cluster algorithm for describing the embodiment of the present invention is used
The method and device of key parameter.
Fig. 1 optimizes battery charge state estimation key parameter by what the embodiment of the present invention provided based on particle cluster algorithm
Method flow diagram.The executive agent of this method is to optimize battery charge state estimation key based on particle cluster algorithm
The device of parameter, the device can be made of the combination of software and/or hardware.
As shown in Figure 1, the crucial ginseng of the optimization battery charge state estimation provided in this embodiment based on particle cluster algorithm
Several methods, includes the following steps：
S101, random initializtion population.
Specifically, the particle cluster algorithm that the present embodiment is taken can be fundamental particle group algorithm, coordinate particle cluster algorithm,
Discrete particle cluster algorithm learns particle cluster algorithm comprehensively, and but it is not limited to this.
In the present embodiment, determine that the factor of battery charge state SOC includes cell voltage, battery temperature, cell resistance
Value namely cell voltage, battery temperature, cell resistance value are optimization battery charge state estimation key parameter.
In the present embodiment, the number comprising particle of population is set according to actual demand.To each of population
Particle is configured to the particle of three dimension.Specifically, the first dimension position vector of particle is cell voltage, grain
First dimension velocity vector of son is the knots modification of cell voltage；Second dimension position vector of particle is battery temperature, particle
The second dimension velocity vector be battery temperature knots modification；The third dimension position vector of particle is cell resistance value, particle
Third dimension velocity vector be cell resistance value knots modification.
S102, the object function for determining each battery charge state estimation key parameter.
In the present embodiment, it is thus necessary to determine that object function include：The object function of cell voltage and battery charge state,
The object function of the object function of battery temperature and battery charge state, cell resistance value and battery charge state.
Specifically, in entire vehicle design, the design parameter of battery can be provided.For example, according to entire vehicle design defined
Battery design parameter formed battery charge state design requirement table.In the table, it is corresponding that different cell voltages are saved
Battery charge state, the corresponding battery charge state of different battery temperatures, the corresponding battery charge state of different cell resistance values.
In one possible implementation, the specific implementation of step S102 is：Inquire battery charge state design
Demand schedule, obtain respectively the corresponding battery charge state of different cell voltages, the corresponding battery charge state of different battery temperatures,
The corresponding battery charge state of different cell resistance values；According to different cell voltages and the corresponding battery charge of different cell voltages
State carries out curve fitting, and obtains the object function of cell voltage and battery charge state；According to different battery temperatures and difference
The corresponding battery charge state of battery temperature carries out curve fitting, and obtains the object function of battery temperature and battery charge state；
It is carried out curve fitting according to different cell resistance values and the corresponding battery charge state of different cell resistance values, obtains cell resistance
The object function of value and battery charge state.
In the present embodiment, each object function is determined by battery charge state design requirement table, it is ensured that follow-up
Obtained each battery charge state estimation key parameter more meets design requirement.
S103, each object function is determined as particle each dimension fitness function.
Specifically, using the object function of cell voltage and battery charge state as the fitness letter of the first dimension of particle
Number, using the object function of battery temperature and battery charge state as the fitness function of the second dimension of particle, by battery electricity
Fitness function of the object function of resistance value and battery charge state as the third dimension of particle.
S104, the iterative process for carrying out population.
In the present embodiment, the iterative process of population, including (1) to (3) are carried out：
(1), each of each particle in current particle group is calculated according to the fitness function of each dimension of the particle
The current fitness of dimension determines working as each dimension of each particle according to the current fitness of each dimension of each particle
The current global optimum of each dimension of preceding individual extreme value and population.
By taking the first dimension of some particle as an example, determine that itself history desired positions of the first dimension of the particle are corresponding
Fitness F0, if the current fitness F1 of the first dimension of the particle is more than the best position of itself history of the first dimension of the particle
Corresponding fitness F0 is set, then the current location vector of the first dimension of the particle is determined as to working as the first dimension of the particle
Preceding individual extreme value；Conversely, itself history desired positions of the first dimension of the particle are determined as to the first dimension of the particle
Current individual extreme value.
By taking the first dimension of some particle as an example, determine that the global history desired positions of the first dimension of population are corresponding
Fitness F2, if the current fitness F1 of the first dimension of the particle is more than the best position of global history of the first dimension of population
Corresponding fitness F2 is set, then the current location vector of the first dimension of the particle is determined as to working as the first dimension of population
Preceding global optimum；Conversely, the global history desired positions of the first dimension of population to be determined as to the first dimension of population
Current global optimum.
(2), respectively according to the current of each dimension of the current individual extreme value of each dimension of each particle and population
Global optimum updates the velocity vector of each dimension of each particle；Respectively according to each dimensions updating of each particle after
Velocity vector, update the position vector of each dimension of each particle.
In the present embodiment, according to formula：
Vd1=ω Vd+C1random(0,1)(Pd-Xd)+C2random(0,1)(Pgd-Xd) (1)
The velocity vector of the d dimensions of more new particle.
In the present embodiment, according to formula：
Xd1=Xd+Vd (2)
The position vector of the d dimensions of more new particle.
Wherein, d dimensions are any dimension in the first dimension, the second dimension, third dimension；ω is known as inertial factor, root
According to actual demand value；C1And C2Referred to as aceleration pulse, according to actual demand value；Random (0,1) is indicated on section [0,1]
Random number；VdFor the current velocity vector of the d dimensions of particle, Vd1For the updated velocity vector of the d dimensions of particle；
XdFor the current location vector of the d dimensions of particle, Xd1For the updated position vector of the d dimensions of particle；PdFor particle
D dimension current individual extreme value, PgdThe current global optimum tieed up for the d of population.
(3), judge whether current iteration reaches stopping criterion for iteration, if reaching, export the complete of each dimension of population
Office's optimal value, stops iterative process；If not up to, returning and executing (1).
Specifically, stopping criterion for iteration can be iteration total degree, and but it is not limited to this.
S105, using the global optimum of each dimension of population as each optimization battery charge state estimation key
The desired value of parameter.
Specifically, using the global optimum of the first dimension of population as target battery voltage, by the second of population
The global optimum of dimension is as target cell temperature, using the global optimum of the third dimension of population as target battery electricity
Resistance value.
Optimization battery charge state estimation key parameter method provided in this embodiment based on particle cluster algorithm, passes through
Random initializtion population determines the object function of each battery charge state estimation key parameter, by each object function
The fitness function for determining each dimension of particle, carries out the iterative process of population, by the overall situation of each dimension of population
It is worth the desired value as each optimization battery charge state estimation key parameter.This method optimizes battery by particle cluster algorithm
State-of-charge estimation key parameter can optimize each key in the case of less parameter with faster convergence rate
Parameter subsequently can relatively accurately estimate battery SOC.
Fig. 2 is further embodiment of this invention based on particle cluster algorithm optimization battery charge state estimation key parameter
The flow diagram of method.The executive agent of this method is to optimize the crucial ginseng of battery charge state estimation based on particle cluster algorithm
Counting apparatus, the device can be made of the combination of software and/or hardware.
As shown in Fig. 2, the crucial ginseng of the optimization battery charge state estimation provided in this embodiment based on particle cluster algorithm
Several methods, includes the following steps：
S201, random initializtion population.
S202, the object function for determining each battery charge state estimation key parameter.
S203, each object function is determined as particle each dimension fitness function.
S204, the iterative process for carrying out population.
S205, using the global optimum of each dimension of population as each optimization battery charge state estimation key
The desired value of parameter.
It should be noted that in the specific implementation and above-described embodiment of step S201 to S205 in the present embodiment
Step S101 to S105 is identical, and details are not described herein.
S206, battery charge state is optimized according to the desired value of each battery charge state estimation key parameter.
Specifically, battery charge state is optimized according to target battery voltage, target cell temperature, target battery resistance value.
For example, according to formula：
C1=C+ λ1U+λ2T+λ3R (3)
Calculate present battery state-of-charge.
Wherein, C1 is present battery state-of-charge, and C is last moment battery charge state, and U is current battery level, and T is
Current battery temperature, R are present battery resistance value, λ1、λ2、λ3It is coefficient, λ1、λ2、λ3It is set all in accordance with design requirement.
According to front to the introduction of particle cluster algorithm it is found that the cell voltage of any moment, battery temperature, cell resistance value
It can be obtained according to the position vector of corresponding particle, therefore, because the battery charge state of initial time can be by detecting
It arrives, therefore the battery charge state of any moment can be obtained according to formula (3).
When terminating the iterative process of population, target battery voltage, target cell temperature, target battery resistance will be obtained
Value, at this moment, obtained according to formula (3) is target battery state-of-charge.
Optimization battery charge state estimation key parameter method provided in this embodiment based on particle cluster algorithm, passes through
Random initializtion population determines the object function of each battery charge state estimation key parameter, by each object function
The fitness function for determining each dimension of particle, carries out the iterative process of population, by the overall situation of each dimension of population
It is worth the desired value as each optimization battery charge state estimation key parameter.This method optimizes battery by particle cluster algorithm
State-of-charge estimation key parameter can optimize each key in the case of less parameter with faster convergence rate
Parameter, and then realize and relatively accurately estimate battery SOC.
Fig. 3 is the dress for optimizing battery charge state estimation key parameter based on particle cluster algorithm of one embodiment of the invention
The structural schematic diagram set.The device is to optimize battery charge state estimation pass based on particle cluster algorithm described in above-described embodiment
The executive agent of the method for bond parameter, the device can be made of the combination of software and/or hardware.
As shown in figure 3, the crucial ginseng of the optimization battery charge state estimation provided in this embodiment based on particle cluster algorithm
Several devices, including：
Initialization module 01 is used for random initializtion population, wherein each particle in population includes three dimensionality position
Vector sum three dimensionality velocity vector is set, the first dimension position vector is cell voltage, and the first dimension velocity vector is cell voltage
Knots modification, the second dimension position vector be battery temperature, the second dimension velocity vector be battery temperature knots modification, the third dimension
Degree position vector is cell resistance value, and third dimension velocity vector is the knots modification of cell resistance value, the cell voltage, battery
Temperature, cell resistance value are battery charge state estimation key parameter；
Object function determining module 02, the object function for determining each battery charge state estimation key parameter；
Fitness function determining module 03, the fitness letter of each dimension for each object function to be determined as to particle
Number；
Iteration module 04, the iterative process for carrying out population, including (1) to (3)：
(1), each of each particle in current particle group is calculated according to the fitness function of each dimension of the particle
The current fitness of dimension determines working as each dimension of each particle according to the current fitness of each dimension of each particle
The current global optimum of each dimension of preceding individual extreme value and population；
(2), respectively according to the current of each dimension of the current individual extreme value of each dimension of each particle and population
Global optimum updates the velocity vector of each dimension of each particle；Respectively according to each dimensions updating of each particle after
Velocity vector, update the position vector of each dimension of each particle；
(3), judge whether current iteration reaches stopping criterion for iteration, if reaching, export the complete of each dimension of population
Office's optimal value, stops iterative process；If not up to, returning and executing (1).
Wherein, the specific implementation of (2) step of iterative process is：
According to formula Vd1=ω Vd+C1random(0,1)(Pd-Xd)+C2random(0,1)(Pgd-Xd) more new particle
The velocity vector of d dimensions；
According to formula Xd1=Xd+VdThe position vector of the d dimensions of more new particle；
Wherein, d dimensions are any dimension in the first dimension, the second dimension, third dimension, and ω is known as inertial factor, C1
And C2Referred to as aceleration pulse, random (0,1) indicate the random number on section [0,1], VdFor the current speed of the d dimensions of particle
Spend vector, Vd1For the updated velocity vector of the d dimensions of particle, XdFor the current location vector of the d dimensions of particle, Xd1
For the updated position vector of the d dimensions of particle, PdFor the individual extreme value that the d of particle is tieed up, PgdIt is tieed up for the d of population
Global optimum.
Key parameter determining module 05, using the global optimum of each dimension of population as each optimization battery charge
The desired value of state estimation key parameter.
Further, the key parameter determining module 05, is specifically used for：
Using the global optimum of the first dimension of population as target battery voltage, by the complete of the second dimension of population
Office's optimal value is as target cell temperature, using the global optimum of the third dimension of population as target battery resistance value.
Further, the object function determining module 02, is specifically used for：
Battery charge state design requirement table is inquired, obtains the corresponding battery charge state of different cell voltages, no respectively
With the corresponding battery charge state of battery temperature, the corresponding battery charge state of different cell resistance values；
It is carried out curve fitting according to different cell voltages and the corresponding battery charge state of different cell voltages, obtains battery
The object function of voltage and battery charge state；
It is carried out curve fitting according to different battery temperatures and the corresponding battery charge state of different battery temperatures, obtains battery
The object function of temperature and battery charge state；
It is carried out curve fitting, is obtained according to different cell resistance values and the corresponding battery charge state of different cell resistance values
The object function of cell resistance value and battery charge state.
Device in this present embodiment is closed, wherein modules execute the concrete mode of operation in related this method
It is described in detail in embodiment, explanation will be not set forth in detail herein.
The device provided in this embodiment for being optimized battery charge state estimation key parameter based on particle cluster algorithm, is passed through
Random initializtion population determines the object function of each battery charge state estimation key parameter, by each object function
The fitness function for determining each dimension of particle, carries out the iterative process of population, by the overall situation of each dimension of population
It is worth the desired value as each optimization battery charge state estimation key parameter.This method optimizes battery by particle cluster algorithm
State-of-charge estimation key parameter can optimize each key in the case of less parameter with faster convergence rate
Parameter subsequently can relatively accurately estimate battery SOC.
Fig. 4 is further embodiment of this invention based on particle cluster algorithm optimization battery charge state estimation key parameter
The structural schematic diagram of device.The device is to optimize battery charge state estimation use based on particle cluster algorithm described in above-described embodiment
The executive agent of the method for key parameter, the device can be made of the combination of software and/or hardware.
As shown in figure 4, the dress shown in Fig. 3 for optimizing battery charge state estimation key parameter based on particle cluster algorithm
On the basis of setting, further include：Optimization module 06, for excellent according to the desired value of each battery charge state estimation key parameter
Electrochemical cell state-of-charge.
Device in this present embodiment is closed, wherein modules execute the concrete mode of operation in related this method
It is described in detail in embodiment, explanation will be not set forth in detail herein.
The device provided in this embodiment for being optimized battery charge state estimation key parameter based on particle cluster algorithm, is passed through
Random initializtion population determines the object function of each battery charge state estimation key parameter, by each object function
The fitness function for determining each dimension of particle, carries out the iterative process of population, by the overall situation of each dimension of population
It is worth the desired value as each optimization battery charge state estimation key parameter.This method optimizes battery by particle cluster algorithm
State-of-charge estimation key parameter can optimize each key in the case of less parameter with faster convergence rate
Parameter, and then realize and relatively accurately estimate battery SOC.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiments or example.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
In addition, term " first ", " second " are used for description purposes only, it is not understood to indicate or imply relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include at least one this feature.In the description of the present invention, the meaning of " plurality " is at least two, such as two, three
It is a etc., unless otherwise specifically defined.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing custom logic function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discuss suitable
Sequence, include according to involved function by it is basic simultaneously in the way of or in the opposite order, to execute function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (system of such as computer based system including processor or other can be held from instruction
The instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium " can any can be included, store, communicating, propagating or passing
Defeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium includes following：Electricity with one or more wiring
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other are suitable
Medium, because can be for example by carrying out optical scanner to paper or other media, then into edlin, interpretation or when necessary with it
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the present invention can be realized with hardware, software, firmware or combination thereof.Above-mentioned
In embodiment, software that multiple steps or method can in memory and by suitable instruction execution system be executed with storage
Or firmware is realized.Such as, if realized in another embodiment with hardware, following skill well known in the art can be used
Any one of art or their combination are realized：With for data-signal realize logic function logic gates from
Logic circuit is dissipated, the application-specific integrated circuit with suitable combinational logic gate circuit, programmable gate array (PGA), scene can compile
Journey gate array (FPGA) etc..
Those skilled in the art are appreciated that realize all or part of step that above-described embodiment method carries
Suddenly it is that relevant hardware can be instructed to complete by program, the program can be stored in a kind of computer-readable storage medium
In matter, which includes the steps that one or a combination set of embodiment of the method when being executed.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing module, it can also
That each unit physically exists alone, can also two or more units be integrated in a module.Above-mentioned integrated mould
The form that hardware had both may be used in block is realized, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized in the form of software function module and when sold or used as an independent product, can also be stored in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..Although having been shown and retouching above
The embodiment of the present invention is stated, it is to be understood that above-described embodiment is exemplary, and should not be understood as the limit to the present invention
System, those skilled in the art can be changed above-described embodiment, change, replace and become within the scope of the invention
Type.
Claims (9)
1. a kind of method optimizing battery charge state estimation key parameter based on particle cluster algorithm, which is characterized in that including：
Random initializtion population, wherein each particle in population includes three dimensionality position vector and three dimensionality speed arrow
Amount, the first dimension position vector are cell voltage, and the first dimension velocity vector is the knots modification of cell voltage, the second dimension position
Vector is battery temperature, and the second dimension velocity vector is the knots modification of battery temperature, and third dimension position vector is cell resistance
Value, third dimension velocity vector are the knots modification of cell resistance value, and the cell voltage, battery temperature, cell resistance value are
Battery charge state estimation key parameter；
Determine the object function of each battery charge state estimation key parameter；
Each object function is determined as to the fitness function of each dimension of particle；
Carry out the iterative process of population, including (1) to (3)：
(1), each dimension of each particle in current particle group is calculated according to the fitness function of each dimension of the particle
Current fitness, determined according to the current fitness of each dimension of each particle each dimension of each particle work as the one before
The current global optimum of each dimension of body extreme value and population；
(2), respectively according to the current overall situation of the current individual extreme value of each dimension of each particle and each dimension of population
Optimal value updates the velocity vector of each dimension of each particle；Respectively according to the speed after each dimensions updating of each particle
Vector is spent, the position vector of each dimension of each particle is updated；
(3), judge whether current iteration reaches stopping criterion for iteration, if reaching, export the overall situation of each dimension of population most
The figure of merit stops iterative process；If not up to, returning and executing (1)；
Using the global optimum of each dimension of population as the mesh of each optimization battery charge state estimation key parameter
Scale value.
2. according to the method described in claim 1, it is characterized in that, the global optimum of each dimension by population is made
For the desired value of each battery charge state estimation key parameter, including：
Using the global optimum of the first dimension of population as target battery voltage, most by the overall situation of the second dimension of population
The figure of merit is as target cell temperature, using the global optimum of the third dimension of population as target battery resistance value.
3. according to the method described in claim 1, it is characterized in that, further including：
Optimize battery charge state according to the desired value of each battery charge state estimation key parameter.
4. according to the method described in claim 1, it is characterized in that, the specific implementation of (2) step of iterative process is：
According to formula Vd1=ω Vd+C1random(0,1)(Pd-Xd)+C2random(0,1)(Pgd-Xd) more new particle d dimensions
Velocity vector；
According to formula Xd1=Xd+VdThe position vector of the d dimensions of more new particle；
Wherein, d dimensions are any dimension in the first dimension, the second dimension, third dimension, and ω is known as inertial factor, C1And C2Claim
For aceleration pulse, random (0,1) indicates the random number on section [0,1], VdFor the present speed arrow of the d dimensions of particle
Amount, Vd1For the updated velocity vector of the d dimensions of particle, XdFor the current location vector of the d dimensions of particle, Xd1For grain
The updated position vector of the d dimensions of son, PdFor the individual extreme value that the d of particle is tieed up, PgdFor population d tie up it is complete
Office's optimal value.
5. according to the method described in claim 1, it is characterized in that, the crucial ginseng of each battery charge state estimation of the determination
Several object functions, including：
Battery charge state design requirement table is inquired, obtains the corresponding battery charge state of different cell voltages, different electricity respectively
The corresponding battery charge state of pond temperature, the corresponding battery charge state of different cell resistance values；
It is carried out curve fitting according to different cell voltages and the corresponding battery charge state of different cell voltages, obtains cell voltage
With the object function of battery charge state；
It is carried out curve fitting according to different battery temperatures and the corresponding battery charge state of different battery temperatures, obtains battery temperature
With the object function of battery charge state；
It is carried out curve fitting according to different cell resistance values and the corresponding battery charge state of different cell resistance values, obtains battery
The object function of resistance value and battery charge state.
6. one kind is based on particle cluster algorithm optimization battery charge state estimation key parameter device, which is characterized in that including：
Initialization module is used for random initializtion population, wherein each particle in population includes three dimensionality position vector
With three dimensionality velocity vector, the first dimension position vector is cell voltage, and the first dimension velocity vector is the change of cell voltage
Amount, the second dimension position vector are battery temperature, and the second dimension velocity vector is the knots modification of battery temperature, third dimension position
Vector be cell resistance value, third dimension velocity vector be cell resistance value knots modification, the cell voltage, battery temperature,
Cell resistance value is battery charge state estimation key parameter；
Object function determining module, the object function for determining each battery charge state estimation key parameter；
Fitness function determining module, the fitness function of each dimension for each object function to be determined as to particle；
Iteration module, the iterative process for carrying out population, including (1) to (3)：
(1), each dimension of each particle in current particle group is calculated according to the fitness function of each dimension of the particle
Current fitness, determined according to the current fitness of each dimension of each particle each dimension of each particle work as the one before
The current global optimum of each dimension of body extreme value and population；
(2), respectively according to the current overall situation of the current individual extreme value of each dimension of each particle and each dimension of population
Optimal value updates the velocity vector of each dimension of each particle；Respectively according to the speed after each dimensions updating of each particle
Vector is spent, the position vector of each dimension of each particle is updated；
(3), judge whether current iteration reaches stopping criterion for iteration, if reaching, export the overall situation of each dimension of population most
The figure of merit stops iterative process；If not up to, returning and executing (1)；
Key parameter determining module is estimated the global optimum of each dimension of population as each optimization battery charge state
The desired value of calculation key parameter.
7. device according to claim 6, which is characterized in that the key parameter determining module is specifically used for：
Using the global optimum of the first dimension of population as target battery voltage, most by the overall situation of the second dimension of population
The figure of merit is as target cell temperature, using the global optimum of the third dimension of population as target battery resistance value.
8. device according to claim 6, which is characterized in that further include：
Optimization module, for optimizing battery charge state according to the desired value of each battery charge state estimation key parameter.
9. device according to claim 6, which is characterized in that the object function determining module is specifically used for：
Battery charge state design requirement table is inquired, obtains the corresponding battery charge state of different cell voltages, different electricity respectively
The corresponding battery charge state of pond temperature, the corresponding battery charge state of different cell resistance values；
It is carried out curve fitting according to different cell voltages and the corresponding battery charge state of different cell voltages, obtains cell voltage
With the object function of battery charge state；
It is carried out curve fitting according to different battery temperatures and the corresponding battery charge state of different battery temperatures, obtains battery temperature
With the object function of battery charge state；
It is carried out curve fitting according to different cell resistance values and the corresponding battery charge state of different cell resistance values, obtains battery
The object function of resistance value and battery charge state.
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