CN116224126A - Method and system for estimating health state of lithium ion battery of electric automobile - Google Patents
Method and system for estimating health state of lithium ion battery of electric automobile Download PDFInfo
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Abstract
The invention discloses a method and a system for estimating the health state of a lithium ion battery of an electric automobile, wherein a battery capacity attenuation model is established according to the rated capacity of the battery, the number of charging cycles, initial parameters of the battery and parameters to be identified of the battery; using a chaotic signal to replace a random number in a suburban wolf optimization algorithm to improve the suburban wolf optimization algorithm, obtaining a chaotic suburban wolf optimization algorithm, and calculating the model by combining Chebyshev chaotic mapping; and estimating the state of health of the battery according to the optimal solution calculated by the model. The patent provides an improved meta-heuristic intelligent algorithm aiming at the identification of a mixed index-polynomial model. The method can convert the known variable of the problem into a specified data form and a specified data structure, further avoid considering the coupling between the linear parameter and the nonlinear parameter, reduce the probability of sinking into local optimum, further improve the parameter estimation precision and the convergence speed, and have a wide application range. Theory and simulation verify that the invention is convergent.
Description
Technical Field
The invention relates to the technical field of lithium ion battery health state estimation, in particular to an electric automobile lithium ion battery health state estimation method and system.
Background
With the rapid development of electric automobile technology, battery technology and battery management technology are becoming more and more important. In order to increase the cruising range of an electric vehicle and to extend the battery life, evaluation of the State of Health (SOH) has become an important research problem. The SOH of a battery can be expressed as the state of aging of the battery, and thus the rated capacity fade during cycling is modeled by a battery aging model. In the present invention, an exponential model is used to simulate the aging process of the battery, as it reflects well the variation of the measured values throughout the life cycle of the battery. The traditional identification algorithm aims at parameter identification of an index model, and has the following defects:
(1) When the least square algorithm, the gradient algorithm and the probability algorithm are applied to the parameter identification of the exponential model, the coupling between the linear parameter and the nonlinear parameter is not considered, so that the parameter estimation precision is poor.
(2) When the meta-heuristic algorithms such as a particle swarm algorithm and a suburban wolf optimization algorithm are applied to parameter identification of an index model, the meta-heuristic algorithms are easy to sink into local optimum and the convergence speed is extremely slow.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the invention provides a method and a system for estimating the health state of an electric vehicle lithium ion battery, which can solve the problems in the background technology.
In order to solve the technical problems, the invention provides a method for estimating the health state of a lithium ion battery of an electric automobile, which comprises the following steps:
establishing a battery capacity attenuation model according to the rated capacity of the battery, the number of charging cycles, the initial parameters of the battery and the parameters to be identified of the battery;
using a chaotic signal to replace a random number in a suburban wolf optimization algorithm to improve the suburban wolf optimization algorithm, obtaining a chaotic suburban wolf optimization algorithm, and calculating the model by combining Chebyshev chaotic mapping;
and estimating the state of health of the battery according to the optimal solution calculated by the model.
As a preferable scheme of the method for estimating the health state of the lithium ion battery of the electric automobile, the invention comprises the following steps: the battery capacity fade model includes,
wherein C is epk Is the rated capacity of the battery at the kth cycle, e in the subscript denotes the inclusion of the mixed exponential-polynomial model, p denotes the inclusion of the polynomial model, k denotes the kth cycle, C ek Represents the internal impedance of the battery, C pk Indicating internal aging of the battery; a, a 1 、a 2 Related to the internal impedance of the battery, a 3 、a 4 、a 5 Representing the aging rate, is an unknown parameter that the hybrid exponential-polynomial model needs to identify.
As a preferable scheme of the method for estimating the health state of the lithium ion battery of the electric automobile, the invention comprises the following steps: the chaotic suburban wolf optimization algorithm comprises,
dividing suburban wolf population into N p Groups, each group having N c Suburban wolves only, and at the kth iteration, the c suburban wolves of the p-th group are defined as:
i.e. decision vectors, wherein D is the dimension of the search space, i.e. the total number of decision variables in the same decision vector, ">
The j-th component formula of this decision vector is:
wherein l j Is the lower bound of the j-th decision variable, u j Is the upper bound of the j-th decision variable, chaotic j Is a chaotic sequence number replacing 0-1 distributed random numbers, wherein the randomness of the social condition is reflected in the chaotic sequence number through a decision vector
Defining social behavior is:
as a preferable scheme of the method for estimating the health state of the lithium ion battery of the electric automobile, the invention comprises the following steps: the suburban wolf optimization algorithm further comprises,
the suburban wolves with best social performance in each group are called alpha, and the alpha of the p-th group at the k moment p,k Is defined as follows:
the above expression shows that at the kth iteration, the fitness function is calculated for all suburban wolves of the p-th group, and the suburban wolves with the smallest fitness function value are selected as alpha p,k Wherein
This means that, in the kth iteration, the suburban wolf with the smallest fitness function is selected from the N suburban wolves in the p-th group.
As a preferable scheme of the method for estimating the health state of the lithium ion battery of the electric automobile, the invention comprises the following steps: the suburban wolf optimization algorithm further comprises,
at time k, the group cultural trend of the p group suburban wolves is defined as cult p,k The jth component of the method is the jth component of all decision vectors of the p-th group, and the group median is taken, and the whole is defined as:
at time k, the calculation formula of the j suburban wolf of the p-th group is as follows:
here, the
And->
Randomly selected nth in the p-th group at time k 1 And the (r) 2 Only suburban social conditions, j-th component, gamma j Is a chaotic sequence for replacing 0-1 distributed random numbers, R j Representing random numbers within a decision variable range, p s Is the dispersion probability, p a The joint probabilities are expressed as follows: />
As a preferable scheme of the method for estimating the health state of the lithium ion battery of the electric automobile, the invention comprises the following steps: the suburban wolf optimization algorithm further comprises,
through alpha p,k And group cultural trend cult p,k The information transfer impact within a group of a population is described:
wherein delta 1 Representing alpha within group p p,k For the c1 suburban wolves after random selection
The influence of the information transfer of (a); delta 2 Group cultural trend cult for group p p,k For c2 suburban wolf after random selection>
Is used for the information transfer influence of the (a).
As a preferable scheme of the method for estimating the health state of the lithium ion battery of the electric automobile, the invention comprises the following steps: the suburban wolf optimization algorithm further comprises,
at the kth iteration, the updated social condition is the decision vector:
wherein, chaotic 1 And Chaotic 2 Respectively measure alpha p,k And cult p,k The information transmission degree of suburban wolves in the group is a chaotic map generated by a chaotic sequence;
at the kth iteration, the new fitness function of the c suburban wolf of the p-th group is calculated as follows:
the fitness function value at the current k moment determines the selection of the social condition decision vector at the k+1 moment, and the social condition of the c suburban wolf of the p-th group at the k+1 moment is calculated as follows:
after the iteration is completed, the suburban wolves with the minimum fitness value are taken as global optimal solutions, namely the parameter vector [ a ] to be estimated 1 ,a 2 ,a 3 ,a 4 ,a 5 ]Is used for the final estimation result of the (a).
An electric automobile lithium ion battery health state estimation system which is characterized in that: comprises a model building module, an optimization calculation module and an estimation module,
the model building module is used for building a battery capacity attenuation model according to the rated capacity of the battery, the number of charging cycles, the initial parameters of the battery and the parameters to be identified of the battery;
the optimizing calculation module is used for replacing random numbers in the suburban wolf optimization algorithm with the chaotic signal to improve the suburban wolf optimization algorithm, so as to obtain the chaotic suburban wolf optimization algorithm, and calculating the model by combining Chebyshev chaotic mapping;
and the estimation module is used for estimating the state of health of the battery according to the optimal solution calculated by the model.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method as described above when executing the computer program.
A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method as described above.
The invention has the beneficial effects that: the invention provides a method and a system for estimating the health state of a lithium ion battery of an electric automobile, and provides an improved meta heuristic intelligent algorithm aiming at the identification of a hybrid index-polynomial model. The method can convert the known variable of the problem into a specified data form and a specified data structure, further avoid considering the coupling between the linear parameter and the nonlinear parameter, reduce the probability of sinking into local optimum, further improve the parameter estimation precision and the convergence speed, and have a wide application range. Theory and simulation verify that the invention is convergent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is a flowchart of a method and a system for estimating a health state of a lithium ion battery of an electric vehicle according to an embodiment of the present invention;
fig. 2 is an iteration comparison chart of a method and a system for estimating the health state of a lithium ion battery of an electric vehicle according to an embodiment of the present invention, which are specific to the same system, between a particle swarm optimization algorithm (PSO, particle swarm optimization algorithm) and CCOA;
fig. 3 is an iteration contrast chart of the method and the system for estimating the health state of the lithium ion battery of the electric vehicle according to an embodiment of the present invention, which aims at the same system, wherein the iteration contrast chart is between suburban wolf optimization algorithm (COA, coyote optimization algorithm) and CCOA;
fig. 4 is a comparison chart of PSO, COA and CCOA for the same system of the method and system for estimating the health status of a lithium ion battery of an electric vehicle according to an embodiment of the present invention;
fig. 5 is an internal structure diagram of a computer device of a method and a system for estimating a health state of a lithium ion battery of an electric vehicle according to an embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1-5, a method and a system for estimating a health state of a lithium ion battery of an electric vehicle according to a first embodiment of the present invention are provided, including:
102, establishing a battery capacity attenuation model according to the rated capacity of the battery, the number of charging cycles, initial parameters of the battery and parameters to be identified of the battery;
the invention combines the index model and the polynomial model for the first time to establish a mixed index-polynomial model.
Specifically, the lithium ion battery model is NCR18650PF, from the battery capacity index: lithium ion battery data are collected from normal voltage, minimum/maximum voltage, standard charge/fast charge, maximum charge current, maximum discharge current, operating discharge temperature, and size indicators. :
still further, the battery capacity fade model includes,
wherein C is epk Is the rated capacity of the battery at the kth cycle, e in the subscript denotes the inclusion of the mixed exponential-polynomial model, p denotes the inclusion of the polynomial model, k denotes the kth cycle, C ek Represents the internal impedance of the battery, C pk Indicating internal aging of the battery; a, a 1 、a 2 Related to the internal impedance of the battery, a 3 、a 4 、a 5 Representing the aging rate, is an unknown parameter that the hybrid exponential-polynomial model needs to identify.
because of the non-repeatability and the ergodic property of chaos, the method can perform overall search at a higher speed than random search depending on probability, reduces the probability of sinking into local optimum, and ensures that the parameter estimation precision of a battery capacity model is higher.
For the lithium ion battery data structure, the input is battery current, the output is terminal voltage, and the built model is a mixed exponential-polynomial model, so that capacity attenuation in the battery recycling process can be well simulated.
Wherein the chaotic suburban wolf optimization algorithm comprises,
dividing suburban wolf population into N p Groups, each group having N c Suburban wolves only, and at the kth iteration, the c suburban wolves of the p-th group are defined as:
i.e. decision vectors, wherein D is the dimension of the search space, i.e. the total number of decision variables in the same decision vector, ">
The j-th component formula of this decision vector is:
wherein l j Is the lower bound of the j-th decision variable, u j Is the upper bound of the j-th decision variable, chaotic j Is a chaotic sequence number replacing 0-1 distributed random numbers, wherein the randomness of the social condition is reflected in the chaotic sequence number through a decision vector
Defining social behavior is:
further, in the above process, suburban wolves in the population are randomly initialized, and suburban wolves are forced to leave the previous wolves or enter a new population according to the life habits of the wolves, so that the suburban wolves are evicted with probability
This relates to the number of suburban wolves in the group, and such a mechanism facilitates information exchange between suburban wolves, thereby increasing population diversity.
Furthermore, the suburban wolf with best social performance in each group is called alpha, and the alpha of the p-th group at the time k is recorded p ,k Is defined as follows:
the above expression shows that at the kth iteration, the fitness function is calculated for all suburban wolves of the p-th group, and the suburban wolves with the smallest fitness function value are selected as alpha p,k Wherein
This means that, in the kth iteration, the suburban wolf with the smallest fitness function is selected from the N suburban wolves in the p-th group.
The chaos suburban wolf optimization algorithm also includes,
further, at time k, the group cultural trend of the p-th suburban wolf is defined as cult p,k The jth component of the method is the jth component of all decision vectors of the p-th group, and the group median is taken, and the whole is defined as:
further, at the time k, the calculation formula of the j suburban wolf of the p-th group is as follows:
here, the
And->
Randomly selected nth in the p-th group at time k 1 And the (r) 2 Only suburban social conditions, j-th component, gamma j Is a chaotic sequence for replacing 0-1 distributed random numbers, R j Representing random numbers within a decision variable range, p s Is the dispersion probability, p a The joint probabilities are expressed as follows:
further, through alpha p,k And group cultural trend cult p,k The information transfer impact within a group of a population is described:
wherein delta 1 Representing alpha within group p p,k For the c1 suburban wolves after random selection
The influence of the information transfer of (a); delta 2 Group cultural trend cult for group p p,k For c2 suburban wolf after random selection>
Is used for the information transfer influence of the (a). />
Further, at the kth iteration, the updated social condition is the decision vector:
wherein, chaotic 1 And Chaotic 2 Respectively measure alpha p,k And cult p,k The information transmission degree of suburban wolves in the group is a chaotic map generated by a chaotic sequence;
further, at the kth iteration, the new fitness function of the c suburban wolf of the p-th group is calculated as follows:
furthermore, the fitness function value at the current k moment determines the selection of the social condition decision vector at the k+1 moment, and the social condition of the c suburban wolf of the p-th group at the k+1 moment is calculated as follows:
it should be noted that after the iteration is completed, suburban wolves with the smallest fitness value are taken as the global optimal solution, namely the parameter vector [ a ] to be estimated 1 ,a 2 ,a 3 ,a 4 ,a 5 ]Is used for the final estimation result of the (a).
And 106, estimating the state of health of the battery according to the optimal solution calculated by the model.
An electric automobile lithium ion battery health state estimation system which is characterized in that: comprises a model building module, an optimization calculation module and an estimation module,
the model building module is used for building a battery capacity attenuation model according to the rated capacity of the battery, the number of charging cycles, the initial parameters of the battery and the parameters to be identified of the battery;
the optimizing calculation module is used for replacing a random number in the suburban wolf optimizing algorithm with the chaotic signal to improve the suburban wolf optimizing algorithm, so as to obtain the chaotic suburban wolf optimizing algorithm, and calculating the model by combining with chebyshev chaotic mapping;
and the estimation module is used for estimating the state of health of the battery according to the optimal solution calculated by the model.
The above unit modules may be embedded in hardware or independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above units.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program when executed by a processor is used for realizing a method for estimating the health state of the lithium ion battery of the electric automobile. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
establishing a battery capacity attenuation model according to the rated capacity of the battery, the number of charging cycles, the initial parameters of the battery and the parameters to be identified of the battery;
using a chaotic signal to replace a random number in a suburban wolf optimization algorithm to improve the suburban wolf optimization algorithm, obtaining the chaotic suburban wolf optimization algorithm, and calculating a model by combining Chebyshev chaotic mapping;
and estimating the state of health of the battery according to the optimal solution calculated by the model.
Example 2
Referring to fig. 1-5, for one embodiment of the present invention, a method and a system for estimating the health status of a lithium ion battery of an electric vehicle are provided, and in order to verify the beneficial effects of the present invention, a scientific demonstration is performed through a comparative experiment.
Aiming at the parameter identification of the exponential model, the traditional identification algorithm such as a least square algorithm, a gradient algorithm and a probability algorithm does not consider the coupling between linear parameters and nonlinear parameters when being applied, so that the parameter estimation precision is poor; when the meta-heuristic algorithms such as a particle swarm algorithm and a suburban wolf optimization algorithm are applied to parameter identification of an index model, the meta-heuristic algorithms are prone to being trapped into local optimum, the convergence speed is extremely low, and the calculation efficiency is low. Therefore, the patent provides a novel meta-heuristic method aiming at the identification of the mixed index-polynomial model for simulating the battery aging process.
Since the battery health of a battery is expressed as a change in its rated capacity, estimating the battery health requires building a model to estimate the rated capacity of the battery, i.e., a capacity model. Since the change in the rated capacity of the battery is small in a short time, the change in the capacity can be expressed as follows:
C k+1 ＝C k +r k ,
wherein C is k Representing t k Rated capacity of battery at moment, r k Is t k Process noise of time modelThe sound has a mean value of zero and a variance of sigma r Is a gaussian white noise of (c). In a typical battery system, current and terminal voltage may be measured directly. In the above capacity system, the terminal voltage is selected as the system output variable and the current is the input variable.
The battery aging model simulates the decrease in battery rated capacity during cycling, i.e., the change in battery rated capacity with cycling times. The invention combines an index model with a polynomial model for the first time to establish a hybrid index-polynomial model.
First, a mixed exponential-polynomial model is constructed as follows:
wherein C is epk Is the rated capacity of the battery at the kth cycle, e in the subscript denotes the inclusion of the mixed exponential-polynomial model, p denotes the inclusion of the polynomial model, k denotes the kth cycle, C ek Represents the internal impedance of the battery, C pk Indicating internal aging of the battery; a, a 1 、a 2 Related to the internal impedance of the battery, a 3 、a 4 、a 5 Representing the aging rate, is an unknown parameter that the hybrid exponential-polynomial model needs to identify.
The chebyshev chaotic mapping to be used is determined as follows:
γ k+1 ＝cos(kcos -1 (γ k )).
in the initialization of the population, a Chaotic signal Chaotic is adopted by a CCOA algorithm j Instead of the random number r j Therefore, the j-th component formula of the decision vector is:
the social condition of each suburban wolf (each decision vector) is updated using two chaotic sequences generated by the chaotic map. Thus, the new social condition of suburban wolves at the kth iteration is updated as:
finally, in the setting of suburban wolf outlier probability, the chaotic number generated by the chaotic sequence is adopted to replace the random number distributed in 0-1.
In the technical scheme of the invention, an improved meta-heuristic algorithm CCOA is provided, which can convert known variables of problems into a specified data form and a specified data structure, so that the coupling between linear parameters and nonlinear parameters is avoided, the performance and the estimation precision of a particle swarm algorithm and a suburban wolf optimization algorithm are improved by utilizing chaotic mapping, the probability of sinking into local optimum is reduced, and the parameter estimation precision and the convergence speed are improved.
Referring to fig. 2 to 4 of the drawings, in fig. 2 to 4, the ordinate is the error between the normalized estimated parameter and the true parameter, and the abscissa refers to the number of iterations; fig. 2 is a comparison between PSO and CCOA, fig. 3 is a comparison between COA and CCOA, and fig. 4 is an overall comparison of three types of algorithms.
As can be seen from fig. 2, the CCOA algorithm converges faster than the PSO algorithm as the number of iterations increases.
As can be seen from fig. 3, the COA algorithm is easily trapped in the local optimum, and the probability of the CCOA algorithm being trapped in the local optimum is lower.
As can be seen from fig. 4, as the iteration number increases, the convergence rate of the CCOA algorithm increases, so that the CCOA algorithm is less prone to falling into local optimum, and the parameter estimation accuracy is highest.
The first table below is a comparison between the parameter estimation accuracy and the iteration error value as the iteration number increases by the PSO algorithm, COA algorithm and CCOA algorithm.
Table one: PSO, COA, CCOA three algorithms convergence accuracy and iteration error comparison, wherein a 1 、a 2 、a 3 、a 4 Is the parameter which the system needs to identify, error is the iteration error after normalization, is the square of the two norms of the normalized real output and the noise-containing output, and k is the iteration number.
Table one: PSO, COA, CCOA comparison of convergence accuracy and iteration error of three algorithms
From the contents of table one, PSO, COA, CCOA estimation accuracy is compared to normalized iteration error: the CCOA parameter estimation accuracy is higher, even the parameter true value is approximated, and the normalized iteration error is smaller.
And dividing the data into training data and test data by utilizing the acquired lithium ion battery data, wherein the training data is used for building a model, after the health state estimation model is built, the test data is divided into 5 groups, and the test data is compared with the models built by the three algorithms to measure the health state of the battery. The comparison is as follows:
and (II) table: three algorithms are applied to test data for comparison of battery state of health
| | Experiment | 1 | |
Experiment 3 | |
Experiment 5 |
| PSO | Sub-health | Sub-health | Unhealthy | Sub-health | Health care | |
| COA | Sub-health | Unhealthy | Unhealthy | Unhealthy | Sub-health | |
| CCOA | Health care | Sub-health | Health care | Unhealthy | Unhealthy | |
| True state | Health care | Sub-health | Health care | Unhealthy | Unhealthy |
From the table, the proposed CCOA method has better effect on estimating the health state of the lithium battery on test data and more accurate result.
In summary, the parameter identification method (Chaotic Coyote Optimization Algorithm: CCOA) of the mixed exponential-polynomial model for simulating the battery aging process can convert known variables of problems into specified data forms and data structures, so that the coupling between linear parameters and nonlinear parameters is avoided, meanwhile, the probability of sinking into local optimum is reduced, and further, the parameter estimation precision and convergence speed are improved.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (10)
1. The method for estimating the health state of the lithium ion battery of the electric automobile is characterized by comprising the following steps of: comprising the steps of (a) a step of,
establishing a battery capacity attenuation model according to the rated capacity of the battery, the number of charging cycles, the initial parameters of the battery and the parameters to be identified of the battery;
using a chaotic signal to replace a random number in a suburban wolf optimization algorithm to improve the suburban wolf optimization algorithm, obtaining a chaotic suburban wolf optimization algorithm, and calculating the model by combining Chebyshev chaotic mapping;
and estimating the state of health of the battery according to the optimal solution calculated by the model.
2. The method for estimating the health state of the lithium ion battery of the electric automobile according to claim 1, wherein: the battery capacity fade model includes,
wherein C is epk Is the rated capacity of the battery at the kth cycle, e in the subscript denotes the inclusion of the mixed exponential-polynomial model, p denotes the inclusion of the polynomial model, k denotes the kth cycle, C ek Represents the internal impedance of the battery, C pk Indicating internal aging of the battery, a 1 、a 2 Related to the internal impedance of the battery, a 3 、a 4 、a 5 Representing the aging rate, is an unknown parameter that the hybrid exponential-polynomial model needs to identify.
3. The method for estimating the health state of the lithium ion battery of the electric automobile according to claim 2, wherein: the chaotic suburban wolf optimization algorithm comprises,
dividing suburban wolf population into N p Groups, each group having N c Suburban wolves only, and at the kth iteration, the c suburban wolves of the p-th group are defined as:
i.e. decision vectors, wherein D is the dimension of the search space, i.e. the total number of decision variables in the same decision vector, ">
The j-th component formula of this decision vector is:
wherein l j Is the lower bound of the j-th decision variable, u j Is the upper bound of the j-th decision variable, chaotic j Is a chaotic sequence number replacing 0-1 distributed random numbers, wherein the randomness of the social condition is reflected in the chaotic sequence number through decision(Vector)
Defining social behavior is:
4. the method for estimating the health state of a lithium ion battery of an electric automobile according to claim 3, wherein: the suburban wolf optimization algorithm further comprises,
the suburban wolves with best social performance in each group are called alpha, and the alpha of the p-th group at the k moment p,k Is defined as follows:
the above expression shows that at the kth iteration, the fitness function is calculated for all suburban wolves of the p-th group, and the suburban wolves with the smallest fitness function value are selected as alpha p,k Wherein
This means that, in the kth iteration, the suburban wolf with the smallest fitness function is selected from the N suburban wolves in the p-th group.
5. The method for estimating the health state of the lithium ion battery of the electric automobile according to claim 4, wherein: the suburban wolf optimization algorithm further comprises,
at time k, the group cultural trend of the p group suburban wolves is defined as cult p,k The jth component of the method is the jth component of all decision vectors of the p-th group, and the group median is taken, and the whole is defined as:
at time k, the calculation formula of the j suburban wolf of the p-th group is as follows:
here, the
And->
Randomly selected nth in the p-th group at time k 1 And the (r) 2 Only suburban social conditions, j-th component, gamma j Is a chaotic sequence for replacing 0-1 distributed random numbers, R j Representing random numbers within a decision variable range, p s Is the dispersion probability, p a The joint probabilities are expressed as follows:
6. the method for estimating the health state of the lithium ion battery of the electric automobile according to claim 5, wherein: the suburban wolf optimization algorithm further comprises,
through alpha p,k And group cultural trend cult p,k The information transfer impact within a group of a population is described:
wherein delta 1 Representing alpha within group p p,k For the c1 suburban wolves after random selection
The influence of the information transfer of (a); delta 2 Group cultural trend cult for group p p,k For c2 suburban wolf after random selection>
Is used for the information transfer influence of the (a).
7. The method for estimating the health state of the lithium ion battery of the electric automobile according to claim 6, wherein: the suburban wolf optimization algorithm further comprises,
at the kth iteration, the updated social condition is the decision vector:
wherein, chaotic 1 And Chaotic 2 Respectively measure alpha p,k And cult p,k The information transmission degree of suburban wolves in the group is a chaotic map generated by a chaotic sequence;
at the kth iteration, the new fitness function of the c suburban wolf of the p-th group is calculated as follows:
the fitness function value at the current k moment determines the selection of the social condition decision vector at the k+1 moment, and the social condition of the c suburban wolf of the p-th group at the k+1 moment is calculated as follows:
after the iteration is completed, the suburban wolves with the minimum fitness value are taken as global optimal solutions, namely the parameter vector [ a ] to be estimated 1 ,a 2 ,a 3 ,a 4 ,a 5 ]Is used for the final estimation result of the (a).
8. An electric automobile lithium ion battery health state estimation system which is characterized in that: comprises a model building module, an optimization calculation module and an estimation module,
the model building module is used for building a battery capacity attenuation model according to the rated capacity of the battery, the number of charging cycles, the initial parameters of the battery and the parameters to be identified of the battery;
the optimizing calculation module is used for replacing random numbers in the suburban wolf optimization algorithm with the chaotic signal to improve the suburban wolf optimization algorithm, so as to obtain the chaotic suburban wolf optimization algorithm, and calculating the model by combining Chebyshev chaotic mapping;
and the estimation module is used for estimating the state of health of the battery according to the optimal solution calculated by the model.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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