CN112578283B - Battery system parameter determination method and device and vehicle - Google Patents
Battery system parameter determination method and device and vehicle Download PDFInfo
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Abstract
The invention provides a battery system parameter determination method, a battery system parameter determination device and a vehicle, wherein the battery system parameter determination method comprises the following steps: establishing a battery system model based on a reliability function and a cost function, and acquiring a target battery construction cost value, a target charging speed and a target battery attribute value, wherein the target battery attribute value is a related attribute parameter in the battery charging process; and determining battery system parameters of the target vehicle through a self-adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value. The method considers the target charging speed, the target battery construction cost value and the target battery attribute value, avoids the problem of low reliability caused by only considering the target charging speed or the battery construction cost, namely, the method ensures the target charging speed while reducing the cost, improves the reliability of battery parameter determination, and further can improve the working efficiency of operation and maintenance personnel.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to a battery system parameter determining method and device and a vehicle.
Background
With the development of the vehicle field, automobiles become one of indispensable tools for people to go out in life, and electric automobiles gradually become an important branch of the vehicle field due to the characteristics of low emission, low pollution, low noise, energy conservation and the like.
At present, a battery system parameter determining method is designed by increasing a target charging speed in charging of an electric automobile, and the battery system parameter determining method is designed only aiming at increasing the target charging speed, so that technicians cannot comprehensively know the actual charging condition of the electric automobile and cannot accurately evaluate the safety and the service life of a battery in the charging process.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for determining parameters of a battery system, and a vehicle, so as to solve the problems that the method for determining parameters of a battery system is single, and the safety and the service life of a battery during a charging process cannot be accurately evaluated.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
in a first aspect, an embodiment of the present invention provides a method for determining a battery system parameter, where the method includes:
establishing a battery system model based on a reliability function and a cost function, wherein the reliability function is used for expressing a battery of the electric vehicle and a reliability constraint condition of the electric vehicle; the cost function is used for expressing constraint conditions of construction cost of the battery;
acquiring a target battery construction cost value, a target charging speed and a target battery attribute value, wherein the target battery attribute value is a related attribute parameter in the battery charging process;
determining battery system parameters of a target vehicle through a self-adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value; the adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator; the battery system parameter is a parameter necessary for reflecting the battery of the target vehicle at the time of design.
Optionally, the reliability function is established based on a battery reliability parameter, an electric vehicle reliability parameter, a battery reliability weight coefficient and an electric vehicle reliability weight coefficient, wherein the battery reliability parameter is a parameter representing the service life of a battery, and the electric vehicle reliability parameter is a parameter representing that the electric vehicle successfully completes a power failure process after charging is completed;
establishing the cost function based on the price of the battery construction material and the coefficient of the battery construction material;
determining an optimization variable based on the reliability function, the reliability function weight coefficient, the cost function weight coefficient and the magnitude difference constant, wherein the optimization variable is used for expressing a constraint condition of a difference relation between the reliability function and the cost function;
and forming an objective function based on the optimization variables and the cost function to complete the establishment of the battery system model, wherein the objective function is used for expressing the constraint conditions of the quality inspection of the optimization variables and the cost function.
Optionally, the determining, by an adaptive immune algorithm, a battery system parameter of a target vehicle based on the battery system model, the target battery construction cost value, the target charging speed, and the target battery attribute value includes:
determining, by the adaptive immune algorithm, a first objective function value of the target vehicle that satisfies the target battery construction cost value and the target charging speed based on the target function, the target battery construction cost value, the target charging speed, and the target battery attribute value;
determining a target optimization variable value meeting the parameter condition of a preset battery system through a minimum maximum algorithm based on the first target function value; the target optimization variable value is used for expressing the first target function value and a constraint condition corresponding to the battery system parameter;
determining the battery system parameter based on the target optimization variable value.
Optionally, the establishing the reliability function based on the battery reliability parameter, the electric vehicle reliability parameter, the battery reliability weight coefficient, and the electric vehicle reliability weight coefficient includes:
r ═ α × X + β × Y; wherein, R represents the reliability function, a represents the battery reliability weighting factor, X represents the battery reliability parameter, Y represents the electric vehicle reliability parameter, and β represents the electric vehicle reliability weighting factor.
Optionally, the establishing the cost function based on the battery construction material price and the battery construction material coefficient includes:
Optionally, the determining an optimization variable based on the reliability function, the reliability function weight coefficient, the cost function weight coefficient, and the magnitude difference constant includes:
F＝W1×R-W2x θ × C; wherein F represents the optimization variable; w1Representing the reliability function weight coefficients; w2Representing the cost function weight coefficients; θ represents the order of magnitude difference constant; r represents the reliability function; c represents the cost function.
Optionally, the constructing an objective function based on the optimization variables and the cost function to complete the building of the battery system model includes:
S(F,C)＝[(S1(F,C1)),(S1(F,C2)),...,(S1(F,Cn))]T(ii) a S (F, C) represents the objective function; f represents the optimization variable; c represents the cost function; t denotes the sub-target for a number of different cost function valuesAnd the function is transposed and solved.
Optionally, the determining, by a minimum maximum algorithm, a target optimization variable value that satisfies a preset battery system parameter condition based on the first objective function value includes:
φ(F*)＝min·max(S1(F,Ci)),(i＝1,2,3,...,n)；S1(F,Ci) Representing the first objective function value; phi (F) represents the target optimization variable value, and min max represents the infinitesimal maximum algorithm.
Optionally, after determining the battery system parameters of the target vehicle through an adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed, and the target battery attribute value, the method further includes:
designing a battery of the target vehicle based on battery system parameters.
In a second aspect, an embodiment of the present invention provides a battery system parameter determining apparatus, where the apparatus includes:
the system comprises a battery system model establishing module, a cost function establishing module and a control module, wherein the battery system model establishing module is used for establishing a battery system model based on a reliability function and a cost function, and the reliability function is used for expressing a battery of an electric vehicle and a reliability constraint condition of the electric vehicle; the cost function is used for expressing constraint conditions of construction cost of the battery;
the battery attribute value acquisition module is used for acquiring a target battery construction cost value, a target charging speed and a target battery attribute value, wherein the target battery attribute value is a related attribute parameter in the battery charging process;
the battery system parameter determining module is used for determining battery system parameters of a target vehicle through a self-adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value;
the adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator; the battery system parameter is a parameter necessary for reflecting the battery of the target vehicle at the time of design.
Optionally, the battery system modeling module includes:
the reliability function establishing submodule is used for establishing the reliability function based on a battery reliability parameter, an electric vehicle reliability parameter, a battery reliability weight coefficient and an electric vehicle reliability weight coefficient, wherein the battery reliability parameter is a parameter for representing the service life of a battery, and the electric vehicle reliability parameter is a parameter for representing that the electric vehicle successfully completes the power-off process after charging is completed;
the cost function establishing submodule is used for establishing the cost function based on the price of the battery construction material and the coefficient of the battery construction material;
an optimized variable determining submodule, configured to determine an optimized variable based on the reliability function, the reliability function weight coefficient, the cost function weight coefficient, and the magnitude difference constant, where the optimized variable is used to express a constraint condition of a difference relationship between the reliability function and the cost function;
and the objective function construction submodule is used for constructing an objective function based on the optimization variables and the cost function so as to complete the establishment of the battery system model.
Optionally, the battery system parameter determination module includes:
a first objective function value determination submodule configured to determine, based on the objective function, the target battery construction cost value, the target charging speed, and the target battery attribute value, a first objective function value that satisfies the target battery construction cost value and the target charging speed by an adaptive immune algorithm;
a target optimization variable value determining submodule, configured to determine, based on the first target function value, a target optimization variable value that satisfies a preset battery system parameter condition through a minimum maximum algorithm, where the target optimization variable value is used to express the first target function value and a constraint condition corresponding to the battery system parameter;
and the battery system parameter determining submodule is used for determining the battery system parameters based on the target optimization variable values.
Optionally, the reliability function establishing sub-module includes:
a reliability function unit for R ═ α × X + β × Y; wherein, R represents the reliability function, a represents the battery reliability weighting factor, X represents the battery reliability parameter, Y represents the electric vehicle reliability parameter, and β represents the electric vehicle reliability weighting factor.
Optionally, the cost function establishing sub-module includes:
cost function unit of
Wherein C represents the cost function, eiRepresenting the battery construction material coefficient; m isiRepresents the price of the battery construction material.
Optionally, the optimization variable determination sub-module includes: optimizing variable unit for F ═ W1×R-W2X θ × C; wherein F represents the optimization variable; w1Representing the reliability function weight coefficients; w2Representing the cost function weight coefficients; θ represents the order of magnitude difference constant; r represents the reliability function; c represents the cost function.
Optionally, the objective function constructing sub-module includes:
an objective function unit for S (F, C) [ (S)1(F,C1)),(S1(F,C2)),...,(S1(F,Cn))]T(ii) a S (F, C) represents the objective function; f represents the optimization variable; c represents the cost function; t represents the transpose solution of the sub-objective functions of a number of different cost function values.
Optionally, the target optimization variable value determination submodule includes:
target optimization variable value unit for [ phi ] (F) ═ min · max (S)1(F,Ci)),(i＝1,2,3,...,n)；S1(F,Ci) Representing the first objective function value; phi (F) denotesAnd the target optimization variable value min max represents the minimum maximum algorithm.
Optionally, the apparatus further comprises:
a battery design module to design a battery of the target vehicle based on battery system parameters.
In a third aspect, an embodiment of the present invention provides a vehicle, including the battery system parameter determination device according to any one of the second aspects.
Compared with the prior art, the embodiment of the invention has the following advantages:
according to the method for determining the parameters of the battery system, a battery system model is established based on a reliability function and a cost function, wherein the reliability function is used for expressing the battery of the electric vehicle and the reliability constraint condition of the electric vehicle; the cost function is used for expressing constraint conditions of the construction cost of the battery; acquiring a target battery construction cost value, a target charging speed and a target battery attribute value, wherein the target battery attribute value is a related attribute parameter in a battery charging process; and determining battery system parameters of the target vehicle through an adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value, wherein the adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator. The method considers the target charging speed, the target battery construction cost value and the target battery attribute value, avoids the problem of low reliability caused by only considering the target charging speed or the battery construction cost, namely, the method ensures the target charging speed while reducing the cost, improves the reliability of battery parameter determination, and further can improve the working efficiency of operation and maintenance personnel.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart illustrating steps of a battery system parameter determining method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a slip algorithm module signal interaction provided by an embodiment of the present application;
fig. 3 is a schematic structural diagram of a battery system parameter determination apparatus according to a third embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to fig. 1, a flowchart illustrating steps of a method for determining parameters of a battery system according to an embodiment of the present invention is shown.
As shown in fig. 1, the method for determining parameters of a battery system may specifically include the following steps:
step 101: a battery system model is established based on the reliability function and the cost function.
The reliability function is used for expressing a battery of the electric vehicle and a reliability constraint condition of the electric vehicle; the cost function is used to express constraints on the construction cost of the battery.
In the design of the battery, the charging speed of the battery and the construction cost of the battery cannot be considered at the same time, and the construction cost is high if the charging speed of the battery is increased, so that in order to realize a system corresponding to battery parameters of the designed battery considering both the construction cost and the charging speed, the charging speed of the battery is inversely related to the reliability of the battery and the reliability of the electric vehicle, therefore, constraint conditions related to the reliability, namely a reliability function, can be trained, constraint conditions related to the construction cost, namely the cost function, and a battery system model is trained, so as to finally obtain the battery parameters meeting the target charging speed and the target construction cost value of the target vehicle.
Reliability parameters and cost parameters of batteries of vehicles can be collected based on a large number of sample vehicles, reliability constraint conditions and cost constraint conditions are obtained based on respective analysis of the reliability parameters and the cost parameters, namely, a reliability function and a cost function are obtained, functions are established according to the reliability constraint conditions and the cost constraint conditions, and the functions are integrated to obtain a battery system model. The battery system model reflects constraints between the construction cost and the charging speed of the battery.
In the invention, the reliability function can be established based on the battery reliability parameter, the electric vehicle reliability parameter, the battery reliability weight coefficient and the electric vehicle reliability weight coefficient; establishing the cost function based on the price of the battery construction material and the coefficient of the battery construction material; determining an optimization variable based on the reliability function, the reliability function weight coefficient, the cost function weight coefficient and the magnitude difference constant; and forming an objective function based on the optimization variables and the cost function so as to complete the establishment of the battery system model.
After the battery system model is built based on the reliability function and the cost function, step 102 is performed.
Step 102: and acquiring a target battery construction cost value, a target charging speed and a target battery attribute value.
And the target battery attribute value is a related attribute parameter in the battery charging process.
The target battery construction cost value refers to a predetermined battery cost value, and a specific numerical value may be set according to an actual application scenario, which is not specifically limited in this embodiment of the application.
The target charging speed refers to an expected value of the target charging speed in a current application scenario, and a specific numerical value may be set according to an actual application scenario, which is not specifically limited in this embodiment of the present application.
The target battery attribute value may include a transmission delay value, a battery reliability parameter, an electric vehicle reliability parameter, and other related attribute values.
The target battery construction cost value, the target charging speed, and the target battery attribute value may be set in advance, and after the target battery construction cost value, the target charging speed, and the target battery attribute value are acquired, step 103 is performed.
Step 103: and determining battery system parameters of the target vehicle through a self-adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value.
The adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator; the battery system parameter is a parameter necessary for reflecting the battery of the target vehicle at the time of design.
The adaptive immune algorithm adopts an optimal storage strategy in the immune algorithm to ensure that the algorithm is converged with probability 1, and adopts the optimal crossover operator and mutation operator all the time, so that the convergence and feasible solution diversity are well balanced.
The battery system parameters may include current magnitude and battery material, the target battery construction cost value refers to a predetermined battery cost value, and the battery system parameters meeting the target battery construction cost value and the target charging speed may be determined by calculating a battery system model through an adaptive immune algorithm according to the target battery construction cost value, the target charging speed and the target battery attribute value.
According to the method for determining the parameters of the battery system, a battery system model is established based on a reliability function and a cost function, wherein the reliability function is used for expressing the battery of the electric vehicle and the reliability constraint condition of the electric vehicle; the cost function is used for expressing constraint conditions of the construction cost of the battery; acquiring a target battery construction cost value, a target charging speed and a target battery attribute value, wherein the target battery attribute value is a related attribute parameter in a battery charging process; and determining battery system parameters of the target vehicle through an adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value, wherein the adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator. The method considers the target charging speed, the target battery construction cost value and the target battery attribute value, avoids the problem of low reliability caused by only considering the target charging speed or the battery construction cost, namely, the method ensures the target charging speed while reducing the cost, improves the reliability of battery parameter determination, and further can improve the working efficiency of operation and maintenance personnel.
Referring to fig. 2, a flowchart illustrating steps of a battery system parameter determining method according to a second embodiment of the present invention is shown.
As shown in fig. 2, the method for determining parameters of a battery system may specifically include the following steps:
step 201: and establishing the reliability function based on the battery reliability parameters, the electric vehicle reliability parameters, the battery reliability weight coefficients and the electric vehicle reliability weight coefficients.
The battery reliability parameter is a parameter for representing the service life of the battery, and the electric vehicle reliability parameter is a parameter for representing that the electric vehicle smoothly completes the power-off process after charging is completed.
In the design of the battery, the charging speed of the battery and the construction cost of the battery cannot be considered at the same time, and the construction cost is high if the charging speed of the battery is increased, so that in order to realize a system corresponding to battery parameters of the designed battery considering both the construction cost and the charging speed, the charging speed of the battery is inversely related to the reliability of the battery and the reliability of the electric vehicle, therefore, constraint conditions related to the reliability, namely a reliability function, can be trained, constraint conditions related to the construction cost, namely the cost function, and a battery system model is trained, so as to finally obtain the battery parameters meeting the target charging speed and the target construction cost value of the target vehicle.
Reliability parameters and cost parameters of batteries of vehicles can be collected based on a large number of sample vehicles, reliability constraint conditions and cost constraint conditions are obtained based on respective analysis of the reliability parameters and the cost parameters, namely, a reliability function and a cost function are obtained, functions are established according to the reliability constraint conditions and the cost constraint conditions, and the functions are integrated to obtain a battery system model. The battery system model reflects constraints between the construction cost and the charging speed of the battery.
Specifically, the reliability function may be: r ═ α × X + β × Y; wherein, R represents the reliability function, a represents the battery reliability weighting factor, X represents the battery reliability parameter, Y represents the electric vehicle reliability parameter, and β represents the electric vehicle reliability weighting factor.
Wherein, the value of the sum of alpha and beta is always 1, that is, the value ranges of alpha and beta are both more than or equal to 0 and less than or equal to 1.
X represents the battery reliability parameter, wherein the battery reliability parameter is set based on the service life of the battery and other parameters; and Y represents the reliability parameter of the electric vehicle, wherein the reliability parameter of the electric vehicle can be set based on relevant parameters such as whether the power-off parameter can be smoothly completed after the electric vehicle is charged.
After the reliability function is established based on the battery reliability parameter, the electric vehicle reliability parameter, the battery reliability weight coefficient, and the electric vehicle reliability weight coefficient, step 202 is performed.
Step 202: the cost function is established based on the battery construction material price and the battery construction material coefficient.
In particular, the cost function may be
Wherein C represents the cost function, eiRepresenting the battery construction material coefficient; m isiRepresents the price of the battery construction material.
Wherein the price of the battery construction material refers to the price of the material corresponding to different materials, and the coefficient e of the battery construction materialiIs 0 or 1, when eiWhen the value of (A) is 0, it represents the corresponding miFor the neutral state, when eiWhen the value of (1) isDenotes the corresponding miIs the selected state.
After the cost function is established based on the battery construction material price and the battery construction material coefficient, step 203 is performed.
Step 203: and determining an optimization variable based on the reliability function, the reliability function weight coefficient, the cost function weight coefficient and the magnitude difference constant.
The optimization variables are used to express constraints on the difference relationship between the reliability function and the cost function.
Specifically, the optimization variables may be: f ═ W1×R-W2X θ × C; wherein F represents the optimization variable; w1Representing the reliability function weight coefficients; w2Representing the cost function weight coefficients; θ represents the order of magnitude difference constant; r represents the reliability function; c represents the cost function.
W1And W2The attention degree of the target charging speed and the target battery construction cost value can be adjusted according to different use scenes, and the specific numerical values of the charging speed and the construction cost value are not limited in the embodiment of the application. In the embodiment of the invention, W can be obtained by adopting a neural network algorithm1And W2。
Theta represents the order of magnitude difference constant, namely, the order of magnitude difference of the cost function and the reliability function is represented, and when the order of magnitude of the cost function is larger, theta should be a value which can make the order of magnitude of the cost function and the order of magnitude of the reliability function consistent.
Optimizing variables may enable a larger reliability function to be achieved with a lower cost function.
After determining the optimization variables based on the reliability function, reliability function weight coefficients, the cost function, cost function weight coefficients, and magnitude difference constants, step 204 is performed.
Step 204: and forming an objective function based on the optimization variables and the cost function so as to complete the establishment of the battery system model.
Wherein the objective functionCan be as follows: s (F, C) [ (S)1(F,C1)),(S1(F,C2)),...,(S1(F,Cn))]T(ii) a S (F, C) represents the objective function; f represents the optimization variable; c represents the cost function; t represents the transpose solution of the sub-objective functions of a number of different cost function values.
After an objective function is constructed based on the optimization variables and the cost function to complete the building of the battery system model, step 205 is performed.
Step 205: and acquiring a target battery construction cost value, a target charging speed and a target battery attribute value.
The target battery construction cost value refers to a predetermined battery cost value, and a specific numerical value may be set according to an actual application scenario, which is not specifically limited in this embodiment of the application.
The target charging speed refers to an expected value of the target charging speed in a current application scenario, and a specific numerical value may be set according to an actual application scenario, which is not specifically limited in this embodiment of the present application.
The target battery attribute value may include a transmission delay value, a battery reliability parameter, an electric vehicle reliability parameter, and other related attribute values.
The target battery construction cost value, the target charging speed, and the target battery attribute value may be set in advance, and after the target battery construction cost value, the target charging speed, and the target battery attribute value are acquired, step 206 is performed.
Step 206: determining a first objective function value satisfying the target battery construction cost value and the target charging speed through an adaptive immune algorithm based on the target function, the target battery construction cost value, the target charging speed, and the target battery attribute value.
The adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator; the battery system parameter is a parameter necessary for reflecting the battery of the target vehicle at the time of design.
The adaptive immune algorithm adopts an optimal storage strategy in the immune algorithm to ensure that the algorithm is converged with probability 1, and adopts the optimal crossover operator and mutation operator all the time, so that the convergence and feasible solution diversity are well balanced.
The battery system parameters may include current magnitude and battery material, the target battery construction cost value refers to a predetermined battery cost value, and S (F, C) [ (S) may be set by an adaptive immune algorithm according to the target battery construction cost value, the target charging speed, and the target battery attribute value1(F,C1)),(S1(F,C2)),...,(S1(F,Cn))]TAnd calculating to determine a first objective function value which accords with the target battery construction cost value and the target charging speed.
After determining a first objective function value satisfying the target battery construction cost value and the target charging speed through an adaptive immune algorithm based on the target function, the target battery construction cost value, the target charging speed, and the target battery attribute value, step 207 is performed.
Step 207: and determining a target optimization variable value meeting the parameter condition of the preset battery system through a minimum maximum algorithm based on the first target function value.
And the target optimization variable value is used for expressing the first target function value and a constraint condition corresponding to the battery system parameter.
Wherein the target optimization variable value may be: phi (F) ═ min max (S)1(F,Ci)),(i＝1,2,3,...,n)；S1(F,Ci) Representing the first objective function value; phi (F) represents the target optimization variable value, and min max represents the infinitesimal maximum algorithm.
The first objective function value may be used to determine a target optimization variable value through a minimum maximum algorithm, and after determining a target optimization variable value satisfying a preset battery system parameter condition through a minimum maximum algorithm based on the first objective function value, step 208 may be performed.
Step 208: determining the battery system parameter based on the target optimization variable value.
The relevant current magnitude in the battery system parameters can be determined according to the target optimization variable values, and finally the selection scheme of the battery material is determined based on the construction cost values and the target charging speed. After the battery system parameters are determined, the battery of the target vehicle may be designed based on the battery system parameters.
The method for determining battery system parameters provided by the embodiment of the invention establishes the reliability function based on the battery reliability parameters, the electric vehicle reliability parameters, the battery reliability weight coefficients and the electric vehicle reliability weight coefficients, establishes the cost function based on the battery construction material price and the battery construction material coefficients, determines the optimization variables based on the reliability function, the reliability function weight coefficients, the cost function weight coefficients and the magnitude difference constant, forms the objective function based on the optimization variables and the cost function to complete the establishment of the battery system model, obtains the target battery construction cost value, the target charging speed and the target battery attribute value, determines the first cost value satisfying the target battery construction value and the target charging speed based on the objective function, the target battery construction cost value, the target charging speed and the target battery attribute value through the adaptive immune algorithm And determining a target optimization variable value meeting the preset battery system parameter condition through a minimum maximum algorithm based on the first target function value, and determining the battery system parameter based on the target optimization variable value. The method considers the target charging speed, the target battery construction cost value and the target battery attribute value, avoids the problem of low reliability caused by only considering the target charging speed or the battery construction cost, namely, the method ensures the target charging speed while reducing the cost, improves the reliability of battery parameter determination, and further can improve the working efficiency of operation and maintenance personnel.
Referring to fig. 3, a schematic structural diagram of a battery system parameter determining apparatus according to a third embodiment of the present invention is shown, where the battery system parameter determining apparatus 300 includes:
a battery system model establishing module 301, configured to establish a battery system model based on a reliability function and a cost function, where the reliability function is used to express a battery of an electric vehicle and a reliability constraint condition of the electric vehicle; the cost function is used for expressing constraint conditions of construction cost of the battery;
a battery attribute value obtaining module 302, configured to obtain a target battery construction cost value, a target charging speed, and a target battery attribute value, where the target battery attribute value is a related attribute parameter in a battery charging process;
a battery system parameter determining module 303, configured to determine a battery system parameter of a target vehicle through an adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed, and the target battery attribute value; the adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator; the battery system parameter is a parameter necessary for reflecting the battery of the target vehicle at the time of design.
Optionally, the battery system modeling module includes:
the reliability function establishing submodule is used for establishing the reliability function based on a battery reliability parameter, an electric vehicle reliability parameter, a battery reliability weight coefficient and an electric vehicle reliability weight coefficient, wherein the battery reliability parameter is a parameter for representing the service life of a battery, and the electric vehicle reliability parameter is a parameter for representing that the electric vehicle successfully completes the power-off process after charging is completed;
the cost function establishing submodule is used for establishing the cost function based on the price of the battery construction material and the coefficient of the battery construction material;
an optimized variable determining submodule, configured to determine an optimized variable based on the reliability function, the reliability function weight coefficient, the cost function weight coefficient, and the magnitude difference constant, where the optimized variable is used to express a constraint condition of a difference relationship between the reliability function and the cost function;
and the objective function construction submodule is used for constructing an objective function based on the optimization variables and the cost function so as to complete the establishment of the battery system model.
Optionally, the battery system parameter determination module includes:
a first objective function value determination submodule configured to determine, based on the objective function, the target battery construction cost value, the target charging speed, and the target battery attribute value, a first objective function value that satisfies the target battery construction cost value and the target charging speed by an adaptive immune algorithm;
a target optimization variable value determining submodule, configured to determine, based on the first target function value, a target optimization variable value that satisfies a preset battery system parameter condition through a minimum maximum algorithm, where the target optimization variable value is used to express the first target function value and a constraint condition corresponding to the battery system parameter;
and the battery system parameter determining submodule is used for determining the battery system parameters based on the target optimization variable values.
Optionally, the reliability function establishing sub-module includes:
a reliability function unit for R ═ α × X + β × Y; wherein, R represents the reliability function, a represents the battery reliability weighting factor, X represents the battery reliability parameter, Y represents the electric vehicle reliability parameter, and β represents the electric vehicle reliability weighting factor.
Optionally, the cost function establishing sub-module includes:
cost function unit of
Wherein C represents the cost function, eiRepresenting the battery construction material coefficient; m isiRepresents the price of the battery construction material.
Optionally, the optimization variable determination sub-module includes: optimizing variable unit for F ═ W1×R-W2X θ × C; wherein F represents the optimization variable; w1Representing the reliability function weight coefficients; w2Representing the cost function weight coefficients; θ represents the order of magnitude difference constant; r represents the reliability function; c represents the cost function.
Optionally, the objective function constructing sub-module includes:
an objective function unit for S (F, C) [ (S)1(F,C1)),(S1(F,C2)),...,(S1(F,Cn))]T(ii) a S (F, C) represents the objective function; f represents the optimization variable; c represents the cost function; t represents the transpose solution of the sub-objective functions of a number of different cost function values.
Optionally, the target optimization variable value determination submodule includes:
target optimization variable value unit for [ phi ] (F) ═ min · max (S)1(F,Ci)),(i＝1,2,3,...,n)；S1(F,Ci) Representing the first objective function value; phi (F) represents the target optimization variable value, and min max represents the infinitesimal maximum algorithm.
Optionally, the apparatus further comprises:
a battery design module to design a battery of the target vehicle based on battery system parameters.
The specific implementation of the battery system parameter determination apparatus in the embodiment of the present invention has been described in detail at the method side, and therefore, the detailed description thereof is omitted here.
The battery system parameter determining device provided by the embodiment of the invention can establish a battery system model based on a reliability function and a cost function through a battery system model establishing module, wherein the reliability function is used for expressing a battery of an electric vehicle and a reliability constraint condition of the electric vehicle; the cost function is used for expressing constraint conditions of construction cost of a battery, acquiring a target battery construction cost value, a target charging speed and a target battery attribute value, wherein the target battery attribute value is a related attribute parameter in the battery charging process, and finally determining battery system parameters of a target vehicle through a self-adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value, wherein the self-adaptive immune algorithm is used for determining the battery system parameters of which the battery system model meets the target battery construction cost value and the target charging speed through a self cross operator and a mutation operator; the battery system parameter is a parameter necessary for reflecting the battery of the target vehicle at the time of design. The method considers the target charging speed, the target battery construction cost value and the target battery attribute value, avoids the problem of low reliability caused by only considering the target charging speed or the battery construction cost, namely, the method ensures the target charging speed while reducing the cost, improves the reliability of battery parameter determination, and further can improve the working efficiency of operation and maintenance personnel.
The embodiment of the invention also provides a vehicle which comprises the battery system parameter determining device in the third embodiment.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A battery system parameter determination method, the method comprising:
establishing a reliability function based on a battery reliability parameter, an electric vehicle reliability parameter, a battery reliability weight coefficient and an electric vehicle reliability weight coefficient, wherein the battery reliability parameter is a parameter for representing the service life of a battery, the electric vehicle reliability parameter is a parameter for representing that an electric vehicle successfully completes a power-off process after charging is completed, and the reliability function is used for expressing the battery of the electric vehicle and the reliability constraint condition of the electric vehicle;
establishing a cost function based on the price of the battery construction material and the coefficient of the battery construction material, wherein the cost function is used for expressing the constraint condition of the construction cost of the battery;
determining an optimization variable based on the reliability function, the reliability function weight coefficient, the cost function weight coefficient and the magnitude difference constant, wherein the optimization variable is used for expressing a constraint condition of a difference relation between the reliability function and the cost function;
constructing an objective function based on the optimization variables and the cost function to complete the establishment of the battery system model, wherein the objective function is used for expressing constraint conditions between the optimization variables and the cost function;
acquiring a target battery construction cost value, a target charging speed and a target battery attribute value, wherein the target battery attribute value is a related attribute parameter in the battery charging process;
determining battery system parameters of a target vehicle through a self-adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value;
the adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator; the battery system parameter is a parameter necessary for reflecting the battery of the target vehicle at the time of design.
2. The method of claim 1, wherein determining battery system parameters of a target vehicle via an adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charge rate, and the target battery attribute value comprises:
determining, by the adaptive immune algorithm, a first objective function value of the target vehicle that satisfies the target battery construction cost value and the target charging speed based on the target function, the target battery construction cost value, the target charging speed, and the target battery attribute value;
determining a target optimization variable value meeting preset battery system parameter conditions through a minimum maximum algorithm based on the first target function value, wherein the target optimization variable value is used for expressing the first target function value and constraint conditions corresponding to the battery system parameters;
determining the battery system parameter based on the target optimization variable value.
3. The method of claim 1, wherein establishing a reliability function based on the battery reliability parameter, the electric vehicle reliability parameter, the battery reliability weight coefficient, and the electric vehicle reliability weight coefficient comprises:
r ═ α × X + β × Y; wherein, R represents a reliability function, alpha represents a battery reliability weight coefficient, X represents a battery reliability parameter, Y represents an electric vehicle reliability parameter, and beta represents an electric vehicle reliability weight coefficient.
4. The method of claim 1, wherein establishing a cost function based on battery construction material prices and battery construction material coefficients comprises:
5. The method of claim 1, wherein determining an optimization variable based on the reliability function, reliability function weight coefficients, the cost function, cost function weight coefficients, and magnitude difference constants comprises:
F＝W1×R-W2x θ × C; wherein F represents an optimization variable; w1Representing a reliability function weight coefficient; w2Representing a cost function weight coefficient; θ represents an order of magnitude difference constant; r represents the reliability function; c represents the cost function.
6. The method of claim 1, wherein constructing an objective function based on the optimization variables and the cost function to complete the building of the battery system model comprises:
S(F,C)＝[(S1(F,C1)),(S1(F,C2)),...,(S1(F,Cn))]T(ii) a S (F, C) represents an objective function; f represents the optimization variable; c represents the cost function; t represents the transpose solution of the sub-objective functions of a number of different cost function values.
7. The method of claim 2, wherein the determining, based on the first objective function value, a target optimization variable value that satisfies a preset battery system parameter condition through a minimum maximum algorithm comprises:
φ(F*)＝min·max(S1(F,Ci)),(i＝1,2,3,...,n)；S1(F,Ci) Representing the first objective function value; phi (F) represents the target optimization variable value, and min max represents the infinitesimal maximum algorithm.
8. The method of claim 1, further comprising, after said determining battery system parameters of a target vehicle by an adaptive immune algorithm based on said battery system model, said target battery construction cost value, said target charge rate, and said target battery property value, further comprising:
designing a battery of the target vehicle based on battery system parameters.
9. A battery system parameter determination apparatus, the apparatus comprising:
the reliability function establishing module is used for establishing a reliability function based on a battery reliability parameter, an electric vehicle reliability parameter, a battery reliability weight coefficient and an electric vehicle reliability weight coefficient, wherein the battery reliability parameter is a parameter for representing the service life of a battery, the electric vehicle reliability parameter is a parameter for representing that an electric vehicle successfully completes a power-off process after charging is completed, and the reliability function is used for expressing the battery of the electric vehicle and the reliability constraint condition of the electric vehicle;
the cost function establishing module is used for establishing a cost function based on the price of the battery construction material and the coefficient of the battery construction material, and the cost function is used for expressing the constraint condition of the construction cost of the battery;
an optimized variable determining module, configured to determine an optimized variable based on the reliability function, the reliability function weight coefficient, the cost function weight coefficient, and the magnitude difference constant, where the optimized variable is used to express a constraint condition of a difference relationship between the reliability function and the cost function;
an objective function construction module, configured to construct an objective function based on the optimized variable and the cost function to complete the building of the battery system model, where the objective function is used to express a constraint condition between the optimized variable and the cost function;
the battery attribute value acquisition module is used for acquiring a target battery construction cost value, a target charging speed and a target battery attribute value, wherein the target battery attribute value is a related attribute parameter in the battery charging process;
the battery system parameter determining module is used for determining battery system parameters of a target vehicle through a self-adaptive immune algorithm based on the battery system model, the target battery construction cost value, the target charging speed and the target battery attribute value; the adaptive immune algorithm is used for determining the battery system parameters of the battery system model meeting the target battery construction cost value and the target charging speed through a self crossover operator and a mutation operator; the battery system parameter is a parameter necessary for reflecting the battery of the target vehicle at the time of design.
10. A vehicle characterized by comprising the battery system parameter determination device according to claim 9.
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