Method and device for predicting residual life of lithium ion battery

Abstract

The invention provides a lithium ion battery residual life prediction method and device, and the method comprises the steps: carrying out the fitting of degradation data corresponding to the capacity of a lithium ion battery through a double-index model, so as to determine a model parameter initial value; establishing an initialized particle set through a particle filtering algorithm based on the charge-discharge cycle number and the model parameter initial value, and constructing a random particle set of the tth iteration through importance sampling; calculating the fitness value of each particle in the initialized particle set and the random particle set until t is equal to the maximum iteration number T, and constructing a target particle set based on the particle with the minimum fitness value; and eliminating and retaining each particle by adopting random resampling according to the importance weight of each particle in the target particle set, so as to determine the remaining life of the lithium ion battery based on the eliminated and retained optimal particle set. Therefore, the target particle set formed by the particles with the minimum fitness value is obtained by performing iterative optimization on the importance sampling particles, the diversity of the particles is reduced, and the prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion batteries, and in particular to a method and device for predicting the remaining life of a lithium-ion battery. Background Art

[0002] Lithium-ion batteries have the characteristics of high energy density and have become the main choice for modern electric vehicles. However, during the continuous cycle of charge and discharge, the capacity of lithium-ion batteries will decay nonlinearly, resulting in serious performance degradation, which may lead to serious accidents. In related technologies, the empirical model combined with the particle filter algorithm can fit the degradation data well to predict the remaining life of lithium-ion batteries. However, there is randomness in the particle collection process of the particle filter algorithm, which causes the problem of particle degradation in the subsequent resampling process, resulting in certain limitations in the prediction accuracy. Therefore, a more reliable method for predicting the remaining life of lithium-ion batteries is urgently needed. Summary of the invention

[0003] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0004] To this end, the first purpose of the present invention is to propose a method for predicting the remaining life of a lithium-ion battery, by iteratively optimizing the importance sampling particles to obtain a target particle set consisting of particles with the smallest fitness value, thereby reducing the diversity of particles and improving the prediction accuracy.

[0005] The second objective of the present invention is to provide a device for predicting the remaining life of a lithium-ion battery.

[0006] A third objective of the present invention is to provide an electronic device.

[0007] A fourth object of the present invention is to provide a non-transitory computer-readable storage medium storing computer instructions.

[0008] To achieve the above-mentioned purpose, a first embodiment of the present invention provides a method for predicting the remaining life of a lithium-ion battery, the method comprising:

[0009] The degradation data of the lithium-ion battery when the capacity is nonlinearly attenuated is fitted by a double exponential model to determine the initial values ​​of four model parameters of the double exponential model, wherein the four initial values ​​of the model parameters include internal impedances a0 and c0, and degradation rates b0 and d0;

[0010] Based on the number of charge and discharge cycles k of the lithium-ion battery and the a0, c0, b0, and d0, a particle filter algorithm is used to establish an initialized particle set for each charge and discharge cycle, and a set of random particle sets of the tth iteration is constructed by importance sampling, wherein the maximum number of iterations of importance sampling is T;

[0011] Calculate the fitness value of each particle in the initialization particle set and the random particle set at the tth iteration, until the number of iterations t equals T, and then construct the target particle set based on the particle with the smallest fitness value;

[0012] Substituting the target particle set into the double exponential prediction model, calculating the battery capacity prediction value corresponding to each particle in the target particle set, and normalizing the importance weight of each particle based on the battery capacity prediction value;

[0013] Random resampling is used to eliminate particles with smaller importance weights and retain particles with larger importance weights to obtain an optimal particle set, and the filter in the particle filtering algorithm is updated based on the optimal particle set to predict the remaining life of the lithium-ion battery according to the updated filter.

[0014] To achieve the above-mentioned purpose, a second embodiment of the present invention provides a device for predicting the remaining life of a lithium-ion battery, the device comprising:

[0015] A fitting module, used to fit the degradation data of the lithium-ion battery when the capacity undergoes nonlinear attenuation through a double exponential model to determine the initial values ​​of four model parameters of the double exponential model, wherein the four initial values ​​of the model parameters include internal impedances a0 and c0, and degradation rates b0 and d0;

[0016] The first construction module is used to establish an initialized particle set for each charge and discharge cycle through a particle filter algorithm based on the charge and discharge cycle number k of the lithium-ion battery and the a0, c0, b0, and d0, and to construct a set of random particle sets of the tth iteration through importance sampling, wherein the maximum number of iterations of importance sampling is T;

[0017] The second construction module is used to calculate the fitness value of each particle in the initialization particle set and the random particle set at the tth iteration, until the number of iterations t is equal to T, and then the target particle set is constructed based on the particle with the smallest fitness value;

[0018] A calculation module, used to substitute the target particle set into the double exponential prediction model, calculate the battery capacity prediction value corresponding to each particle in the target particle set, and normalize the importance weight of each particle based on the battery capacity prediction value;

[0019] The prediction module is used to eliminate particles with smaller importance weights by random resampling and retain particles with larger importance weights to obtain an optimal particle set, and to update the filter in the particle filter algorithm based on the optimal particle set to predict the remaining life of the lithium-ion battery according to the updated filter.

[0020] To achieve the above-mentioned purpose, the third aspect of the present invention proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0021] In order to achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the method described in the first aspect.

[0022] The method, device, electronic device and storage medium for predicting the remaining life of a lithium-ion battery provided by the embodiment of the present invention fit the degradation data corresponding to the capacity of the lithium-ion battery through a double exponential model to determine the initial value of the model parameter; based on the number of charge and discharge cycles and the initial value of the model parameter, an initialized particle set is established through a particle filter algorithm, and a random particle set of the tth iteration is constructed through importance sampling; the fitness value of each particle in the initialized particle set and the random particle set is calculated until t is equal to the maximum number of iterations T, and a target particle set is constructed based on the particle with the smallest fitness value; the importance weight of each particle in the target particle set is used, and each particle is eliminated and retained by random resampling, so as to determine the remaining life of the lithium-ion battery based on the optimal particle set after elimination and retention. Thus, by iteratively optimizing the importance sampling particles, a target particle set consisting of particles with the smallest fitness value is obtained, the diversity of particles is reduced, and the prediction accuracy is improved.

[0023] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A schematic diagram of a flow chart of a method for predicting the remaining life of a lithium-ion battery provided by an embodiment of the present invention;

[0026] Figure 2 An algorithm flow chart of a target particle set provided by an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of the structure of a lithium-ion battery remaining life prediction device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0029] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of relevant laws and regulations.

[0030] The following describes a method and device for predicting the remaining life of a lithium-ion battery according to an embodiment of the present invention with reference to the accompanying drawings.

[0031] Figure 1 A schematic diagram of a flow chart of a method for predicting the remaining life of a lithium-ion battery provided in an embodiment of the present invention.

[0032] like Figure 1 As shown, the method comprises the following steps:

[0033] Step 101, fitting the degradation data of the lithium-ion battery when the capacity undergoes nonlinear attenuation through a double exponential model to determine the initial values ​​of four model parameters of the double exponential model, the four initial values ​​of the model parameters including internal impedances a0 and c0, and degradation rates b0 and d0.

[0034] Alternatively, lithium-ion batteries may be used in electric vehicles, but are not limited thereto.

[0035] In some embodiments, the specific expression of the double exponential model can be:

[0036] Q(k)=a*exp(b*k)+c*exp(d*k)

[0037] Among them, a, b, c, and d are model parameters of the double exponential model, where a and c are internal impedances, b and d are degradation rates, k is the number of lithium-ion battery cycles, and Q is the capacity of the lithium-ion battery. Therefore, when the double exponential model is used to fit the degradation data when the capacity of the lithium-ion battery undergoes nonlinear attenuation, a0, c0, b0, and d0 are the initial values ​​of the four model parameters, a0 and c0 are the initial internal impedances, and b0 and d0 are the initial degradation rates.

[0038] Step 102, based on the number of charge and discharge cycles k of the lithium-ion battery and a0, c0, b0, d0, an initialized particle set for each charge and discharge cycle is established through a particle filtering algorithm, and a set of random particle sets for the tth iteration is constructed through importance sampling, wherein the maximum number of iterations of importance sampling is T.

[0039] Optionally, the capacity of the lithium-ion battery may be defined as Q, the initial battery capacity may be normalized to 1, and k may be set to be the number of charge and discharge cycles of the lithium-ion battery.

[0040] In some embodiments, based on the number of charge and discharge cycles k of the lithium-ion battery and a0, c0, b0, d0, a particle filter algorithm is used to establish an initialized particle set for each charge and discharge cycle, and a set of random particle sets of the tth iteration is constructed by importance sampling, wherein an implementation method of importance sampling with a maximum number of iterations of T can be: based on the number of charge and discharge cycles k of the battery capacity and a0, c0, b0, d0, a particle filter algorithm is used to establish a particle set for each charge and discharge cycle, and a0, c0, b0, d0 are added with process noise to initialize the state of the particle set to obtain an initialized particle set; based on the number of charge and discharge cycles k of the battery capacity and a0, c0, b0, d0, a set of random particle sets of the tth iteration is constructed by importance sampling. Thus, the initialization of the particle set and the rapid generation of the random particle set are achieved.

[0041] Furthermore, by setting the number of charge and discharge cycles to k = n, the particle set is When n=1, extract m initial state particles (initialized particle set) Where i=1,2...n.

[0042] Furthermore, by setting the maximum number of iterations of importance sampling to T, at the tth iteration, a set of random particles is The random particle set is initialized as

[0043] Among them, the position of the random particle set at the tth iteration is the maximum value d of the particle set at the tth iteration t and the minimum value c of the random particle set at the tth iteration t Within the range, the maximum value d t is determined by the maximum value of the random parameter and the initial iteration of the particle set under the tth iteration, and the minimum value c t It is determined by the minimum value of the random parameters at the tth iteration and the initial iteration of the random particle set.

[0044] Specifically, the minimum value formula of the random particle set at the tth iteration is as follows:

[0045] Where w is the random parameter under different iteration numbers; c1 is the minimum value of the initial iteration of the random particle set.

[0046] The maximum value formula of the particle set at the tth iteration is as follows:

[0047] Where w is the random parameter under different iteration numbers; d1 is the maximum value of the initial iteration of the particle set.

[0048] Among them, the random particle set The position of the maximum value d t With the minimum value c t Within the range of , the expression of the position of the random particle set after the tth iteration is as follows:

[0049]

[0050] Where cumsum is the cumulative function, and the expression of r(t) is:

[0051] z is a random number between 0 and 1.

[0052] Step 103, calculate the fitness value of each particle in the initialization particle set and the random particle set at the tth iteration, until the number of iterations t equals T, and construct the target particle set based on the particle with the smallest fitness value.

[0053] In some embodiments, the fitness values ​​of the initialized particle set and each particle in the random particle set at the tth iteration are calculated, until the number of iterations t is equal to T, and an implementation method of constructing the target particle set based on the particle with the smallest fitness value can be to construct a fitness function based on the Gaussian function, the battery capacity prediction value after filtering by the filter of the particle filter algorithm, and the lithium-ion battery capacity observation value; based on the fitness function, the fitness values ​​of the initialized particle set and each particle in the random particle set at the tth iteration are calculated, and the fitness values ​​of the initialized particle set and each particle in the random particle set are compared, and the m particles with small fitness values ​​are determined as the position of the particle set, until the number of iterations t is equal to T, the target particle with the smallest fitness value and the particle set position of the target particle are obtained; according to the target particle and the particle set position of the target particle, the target particle set is constructed. Thus, by comparing the fitness function of the particle set with the fitness function of the random particle set, it is determined that the value with a smaller fitness value replaces the position of the particle set, and the position of the particle set is adjusted, and after iteration to the maximum value, the optimal N solutions are obtained. All particles are optimized to be the solution set with the best fitness value.

[0054] Specifically, the battery capacity prediction value after filtering by the particle filter algorithm is Z pf , and the observed value of lithium-ion battery capacity is Z, the fitness function expression is:

[0055]

[0056] In the formula, x is a random number between -4 and 4, F is a Gaussian function, and F obeys the standard normal distribution. The purpose of introducing the Gaussian function F is to iterate t times to prevent the fitness function from falling into local convergence. Sort the particle set from small to large, and the one with the smallest fitness value is the target particle set.

[0057] Optionally, the position of the particle set is less than The specific position of the particle set can be: after the tth iteration, take the average value of the target particle set position and the particle position with a small fitness value.

[0058] In summary, the present invention proposes an algorithm flow chart of a target particle set, such as Figure 2 As shown, at the beginning of the nth charge and discharge cycle, the particle set initialized for each charge and discharge cycle is established by the particle filter algorithm, and a set of random particle sets for the tth iteration is constructed by importance sampling. When t is equal to 1, the fitness values ​​of the particle set and each particle in the random particle set are calculated respectively, and the sizes of the fitness values ​​are compared. The m particles with small fitness values ​​are determined as the positions of the particle set, and the records are adjusted. Then, the next iteration is performed. At the tth iteration, random particles are reselected, and the fitness values ​​of the random particles of the tth iteration and the particles of the t-1th iteration are compared. The particles with small fitness values ​​in the tth iteration are determined as the positions of the particle set, and the records are adjusted. t=t+1, until t=T, after the Tth iteration, the target particle set is constructed based on the particles with the smallest fitness values.

[0059] Step 104 , substitute the target particle set into the double exponential prediction model, calculate the battery capacity prediction value corresponding to each particle in the target particle set, and normalize the importance weight of each particle based on the battery capacity prediction value.

[0060] In some embodiments, the importance weight of each particle is determined by comparing the similarity between the observed data (battery capacity prediction value) and the actual observed data (battery capacity observation value Z) of the particle.

[0061] Step 105, using random resampling to eliminate particles with smaller importance weights and retain particles with larger importance weights to obtain an optimal particle set, and updating the filter in the particle filter algorithm based on the optimal particle set to predict the remaining life of the lithium-ion battery according to the updated filter.

[0062] Optionally, the predicted remaining life of the lithium-ion battery refers to the number of charge and discharge cycles required from the current moment of the lithium-ion battery to the end of the life of the lithium-ion battery.

[0063] In some embodiments, random resampling is used to eliminate particles with smaller importance weights, and particles with larger importance weights are retained to obtain an optimal particle set, and a filter in a particle filtering algorithm is updated based on the optimal particle set, so as to predict the remaining life of the lithium-ion battery according to the updated filter. An implementation method may be to use random resampling to eliminate particles with smaller importance weights, and particles with larger importance weights are retained to obtain an optimal particle set, and filter X in a particle filtering algorithm is updated based on the optimal particle set. pf ; Determine the four target model parameter values ​​in the double exponential model according to the updated filter, so as to calculate the battery capacity prediction value Z based on the four target model parameter values pf ; Based on the predicted value of battery capacity, predict the remaining life of the lithium-ion battery.

[0064] Furthermore, we can set k=k+1 until k=N to complete the particle filter X pf And the battery capacity prediction value Z after filtering pf Updated, accurate prediction of remaining life of lithium-ion batteries in real time.

[0065] In addition, the cycle start period can also be set to R = NF, where N represents the total number of cycle periods, and F represents the predicted number of cycle periods. pf , determine the four target model parameter values ​​in the double exponential model, according to the battery capacity prediction value Z pf , determine the remaining life of the lithium-ion battery.

[0066] The remaining life prediction method of a lithium-ion battery in an embodiment of the present invention fits the degradation data corresponding to the capacity of the lithium-ion battery through a double exponential model to determine the initial values ​​of the model parameters; based on the number of charge and discharge cycles and the initial values ​​of the model parameters, an initialized particle set is established through a particle filter algorithm, and a random particle set of the tth iteration is constructed through importance sampling; the fitness value of each particle in the initialized particle set and the random particle set is calculated until t is equal to the maximum number of iterations T, and a target particle set is constructed based on the particle with the smallest fitness value; the importance weight of each particle in the target particle set is used, and each particle is eliminated and retained by random resampling, so as to determine the remaining life of the lithium-ion battery based on the optimal particle set after elimination and retention. Thus, by iteratively optimizing the importance sampling particles, a target particle set consisting of particles with the smallest fitness value is obtained, the diversity of particles is reduced, and the prediction accuracy is improved.

[0067] In order to implement the above embodiment, the present invention also provides a device for predicting the remaining life of a lithium-ion battery.

[0068] Figure 3 A schematic diagram of the structure of a lithium-ion battery remaining life prediction device provided by an embodiment of the present invention.

[0069] like Figure 3 As shown, the lithium-ion battery remaining life prediction device 30 includes: a fitting module 31, a first construction module 32, a second construction module 33, a calculation module 34, and a prediction module 35.

[0070] A fitting module 31 is used to fit the degradation data of the lithium-ion battery when the capacity is nonlinearly attenuated by a double exponential model to determine the initial values ​​of four model parameters of the double exponential model, wherein the four initial values ​​of the model parameters include internal impedances a0 and c0, and degradation rates b0 and d0;

[0071] A first construction module 32 is used to establish an initialized particle set for each charge and discharge cycle through a particle filter algorithm based on the charge and discharge cycle number k of the lithium-ion battery and the a0, c0, b0, and d0, and to construct a set of random particle sets of the tth iteration through importance sampling, wherein the maximum number of iterations of importance sampling is T;

[0072] The second construction module 33 is used to calculate the fitness value of each particle in the initialization particle set and the random particle set at the tth iteration, until the number of iterations t is equal to T, and then the target particle set is constructed based on the particle with the smallest fitness value;

[0073] A calculation module 34 is used to substitute the target particle set into the double exponential prediction model, calculate the battery capacity prediction value corresponding to each particle in the target particle set, and normalize the importance weight of each particle based on the battery capacity prediction value;

[0074] The prediction module 35 is used to eliminate particles with smaller importance weights by random resampling and retain particles with larger importance weights to obtain an optimal particle set, and to update the filter in the particle filter algorithm based on the optimal particle set to predict the remaining life of the lithium-ion battery according to the updated filter.

[0075] Further, in a possible implementation of the embodiment of the present invention, the first building module 32 is specifically used to:

[0076] Based on the number of charge and discharge cycles k of the battery capacity and the a0, c0, b0, and d0, a particle set for each charge and discharge cycle is established by a particle filtering algorithm, and a0, c0, b0, and d0 are added with process noise to initialize the state of the particle set to obtain an initialized particle set;

[0077] Based on the number k of charge and discharge cycles of the battery capacity and a0, c0, b0, and d0, a set of random particle sets of the tth iteration is constructed by importance sampling.

[0078] Further, in a possible implementation of the embodiment of the present invention, the position of the random particle set at the tth iteration is at the maximum value d of the particle set at the tth iteration. t and the minimum value c of the random particle set at the tth iteration t Within the range, the maximum value d t is determined by the maximum value of the random parameter and the initial iteration of the particle set under the tth iteration, and the minimum value c t It is determined by the minimum value of the random parameters at the tth iteration and the initial iteration of the random particle set.

[0079] Further, in a possible implementation manner of the embodiment of the present invention, the second building module 33 is specifically used to:

[0080] A fitness function is constructed based on the predicted battery capacity value after filtering by the Gaussian function and the particle filter algorithm, and the observed capacity value of the lithium-ion battery;

[0081] Based on the fitness function, the fitness values ​​of the initialized particle set and each particle in the random particle set at the tth iteration are calculated, and the fitness values ​​of the initialized particle set and each particle in the random particle set are compared, and the m particles with the smallest fitness values ​​are determined as the position of the particle set, until the number of iterations t equals T, the target particle with the smallest fitness value and the particle set position of the target particle are obtained;

[0082] A target particle set is constructed according to the target particles and the particle set positions of the target particles.

[0083] Further, in a possible implementation of the embodiment of the present invention, the position of the particle set is less than

[0084] Furthermore, in a possible implementation of the embodiment of the present invention, the prediction module 35 is specifically used to:

[0085] Using random resampling to eliminate particles with smaller importance weights and retain particles with larger importance weights to obtain an optimal particle set, and updating the filter in the particle filtering algorithm based on the optimal particle set;

[0086] Determining four target model parameter values ​​in the double exponential model according to the updated filter, so as to calculate a battery capacity prediction value based on the four target model parameter values;

[0087] The remaining life of the lithium-ion battery is predicted based on the battery capacity prediction value.

[0088] The remaining life prediction device of the lithium-ion battery of the embodiment of the present invention fits the degradation data corresponding to the capacity of the lithium-ion battery through a double exponential model to determine the initial value of the model parameter; based on the number of charge and discharge cycles and the initial value of the model parameter, an initialized particle set is established through a particle filter algorithm, and a random particle set of the tth iteration is constructed through importance sampling; the fitness value of each particle in the initialized particle set and the random particle set is calculated until t is equal to the maximum number of iterations T, and a target particle set is constructed based on the particle with the smallest fitness value; the importance weight of each particle in the target particle set is used, and each particle is eliminated and retained by random resampling, so as to determine the remaining life of the lithium-ion battery based on the optimal particle set after elimination and retention. Thus, by iteratively optimizing the importance sampling particles, a target particle set consisting of particles with the smallest fitness value is obtained, the diversity of particles is reduced, and the prediction accuracy is improved.

[0089] In order to implement the above embodiment, the present invention further provides an electronic device, including:

[0090] at least one processor; and

[0091] a memory communicatively connected to the at least one processor; wherein,

[0092] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the aforementioned method.

[0093] In order to implement the above embodiment, the present invention further proposes a non-transitory computer-readable storage medium storing computer instructions, where the computer instructions are used to enable the computer to execute the above method.

[0094] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0095] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0096] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0098] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0099] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0101] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for predicting the remaining life of a lithium-ion battery, characterized in that: The method comprises: The degradation data of the lithium-ion battery when the capacity is nonlinearly attenuated is fitted by a double exponential model to determine the initial values ​​of four model parameters of the double exponential model, wherein the four initial values ​​of the model parameters include internal impedances a0 and c0, and degradation rates b0 and d0; Based on the number of charge and discharge cycles k of the lithium-ion battery and the a0, c0, b0, and d0, a particle filter algorithm is used to establish an initialized particle set for each charge and discharge cycle, and a set of random particle sets of the tth iteration is constructed by importance sampling, wherein the maximum number of iterations of importance sampling is T; Calculate the fitness value of each particle in the initialization particle set and the random particle set at the tth iteration, until the number of iterations t equals T, and then construct the target particle set based on the particle with the smallest fitness value; Substituting the target particle set into the double exponential prediction model, calculating the battery capacity prediction value corresponding to each particle in the target particle set, and normalizing the importance weight of each particle based on the battery capacity prediction value; Random resampling is used to eliminate particles with smaller importance weights and retain particles with larger importance weights to obtain an optimal particle set, and the filter in the particle filtering algorithm is updated based on the optimal particle set to predict the remaining life of the lithium-ion battery according to the updated filter.

2. The method for predicting the remaining life of a lithium-ion battery according to claim 1, characterized in that: The number of charge and discharge cycles k of the lithium-ion battery and the a0, c0, b0, d0 are used to establish an initialized particle set for each charge and discharge cycle through a particle filter algorithm, and a set of random particle sets for the tth iteration are constructed through importance sampling, including: Based on the number of charge and discharge cycles k of the battery capacity and the a0, c0, b0, and d0, a particle set for each charge and discharge cycle is established by a particle filtering algorithm, and a0, c0, b0, and d0 are added with process noise to initialize the state of the particle set to obtain an initialized particle set; Based on the number k of charge and discharge cycles of the battery capacity and a0, c0, b0, and d0, a set of random particle sets of the tth iteration is constructed by importance sampling.

3. The method for predicting the remaining life of a lithium-ion battery according to claim 2, characterized in that: in, The position of the random particle set at the tth iteration is the maximum value d of the particle set at the tth iteration. t and the minimum value c of the random particle set at the tth iteration t Within the range, the maximum value d t is determined by the maximum value of the random parameter and the initial iteration of the particle set under the tth iteration, and the minimum value c t It is determined by the minimum value of the random parameters at the tth iteration and the initial iteration of the random particle set.

4. The method for predicting the remaining life of a lithium-ion battery according to claim 1, characterized in that: The step of calculating the fitness value of each particle in the initialization particle set and the random particle set at the t-th iteration until the number of iterations t is equal to T, and constructing the target particle set based on the particle with the smallest fitness value, includes: A fitness function is constructed based on the predicted battery capacity value after filtering by the Gaussian function and the particle filter algorithm, and the observed capacity value of the lithium-ion battery; Based on the fitness function, the fitness values ​​of the initialized particle set and each particle in the random particle set at the tth iteration are calculated, and the fitness values ​​of the initialized particle set and each particle in the random particle set are compared, and the m particles with the smallest fitness values ​​are determined as the position of the particle set, until the number of iterations t equals T, the target particle with the smallest fitness value and the particle set position of the target particle are obtained; A target particle set is constructed according to the target particles and the particle set positions of the target particles.

5. The method for predicting the remaining life of a lithium-ion battery according to claim 4, characterized in that: The position of the particle set is less than 6. The method for predicting the remaining life of a lithium-ion battery according to claim 1, characterized in that: The random resampling is used to eliminate particles with smaller importance weights and retain particles with larger importance weights to obtain an optimal particle set, and the filter in the particle filter algorithm is updated based on the optimal particle set to predict the remaining life of the lithium-ion battery according to the updated filter, including: Using random resampling to eliminate particles with smaller importance weights and retain particles with larger importance weights to obtain an optimal particle set, and updating the filter in the particle filtering algorithm based on the optimal particle set; Determining four target model parameter values ​​in the double exponential model according to the updated filter, so as to calculate a battery capacity prediction value based on the four target model parameter values; The remaining life of the lithium-ion battery is predicted based on the battery capacity prediction value.

7. A lithium-ion battery remaining life prediction device, characterized in that: The device comprises: A fitting module, used to fit the degradation data of the lithium-ion battery when the capacity undergoes nonlinear attenuation through a double exponential model to determine the initial values ​​of four model parameters of the double exponential model, wherein the four initial values ​​of the model parameters include internal impedances a0 and c0, and degradation rates b0 and d0; The first construction module is used to establish an initialized particle set for each charge and discharge cycle through a particle filter algorithm based on the charge and discharge cycle number k of the lithium-ion battery and the a0, c0, b0, and d0, and to construct a set of random particle sets of the tth iteration through importance sampling, wherein the maximum number of iterations of importance sampling is T; The second construction module is used to calculate the fitness value of each particle in the initialization particle set and the random particle set at the tth iteration, until the number of iterations t is equal to T, and then the target particle set is constructed based on the particle with the smallest fitness value; A calculation module, used to substitute the target particle set into the double exponential prediction model, calculate the battery capacity prediction value corresponding to each particle in the target particle set, and normalize the importance weight of each particle based on the battery capacity prediction value; The prediction module is used to eliminate particles with smaller importance weights by random resampling and retain particles with larger importance weights to obtain an optimal particle set, and to update the filter in the particle filter algorithm based on the optimal particle set to predict the remaining life of the lithium-ion battery according to the updated filter.

8. The device for predicting the remaining life of a lithium-ion battery according to claim 7, characterized in that: The first building module is specifically used for: Based on the number of charge and discharge cycles k of the battery capacity and the a0, c0, b0, and d0, a particle set for each charge and discharge cycle is established by a particle filtering algorithm, and a0, c0, b0, and d0 are added with process noise to initialize the state of the particle set to obtain an initialized particle set; Based on the number k of charge and discharge cycles of the battery capacity and a0, c0, b0, and d0, a set of random particle sets of the tth iteration is constructed by importance sampling.

9. The device for predicting the remaining life of a lithium-ion battery according to claim 8, characterized in that: in, The position of the random particle set at the tth iteration is the maximum value d of the particle set at the tth iteration. t and the minimum value c of the random particle set at the tth iteration t Within the range, the maximum value d t is determined by the maximum value of the random parameter and the initial iteration of the particle set under the tth iteration, and the minimum value c t It is determined by the minimum value of the random parameters at the tth iteration and the initial iteration of the random particle set.

10. The lithium-ion battery remaining life prediction device according to claim 7, characterized in that: The second building block is specifically used for: A fitness function is constructed based on the predicted battery capacity value after filtering by the Gaussian function and the particle filter algorithm, and the observed capacity value of the lithium-ion battery; Based on the fitness function, the fitness values ​​of the initialized particle set and each particle in the random particle set at the tth iteration are calculated, and the fitness values ​​of the initialized particle set and each particle in the random particle set are compared, and the m particles with the smallest fitness values ​​are determined as the position of the particle set, until the number of iterations t equals T, the target particle with the smallest fitness value and the particle set position of the target particle are obtained; A target particle set is constructed according to the target particles and the particle set positions of the target particles.

11. The lithium-ion battery remaining life prediction device according to claim 10, characterized in that: The position of the particle set is less than 12. The lithium-ion battery remaining life prediction device according to claim 7, characterized in that: The prediction module is specifically used for: Using random resampling to eliminate particles with smaller importance weights and retain particles with larger importance weights to obtain an optimal particle set, and updating the filter in the particle filtering algorithm based on the optimal particle set; Determining four target model parameter values ​​in the double exponential model according to the updated filter, so as to calculate a battery capacity prediction value based on the four target model parameter values; The remaining life of the lithium-ion battery is predicted based on the battery capacity prediction value.

13. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

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