Excitation signal optimization method capable of improving lithium battery thermal characteristic test efficiency, electronic equipment and medium

Abstract

The invention discloses an excitation signal optimization method capable of improving lithium battery thermal characteristic parameter test efficiency, electronic equipment and a medium. According to the excitation signal optimization method capable of improving the thermal characteristic test efficiency of the lithium battery, the optimized frequency distribution and phase are obtained through frequency distribution optimization and phase optimization, so that the crest factor is reduced, and a new idea is provided for improving the thermal characteristic test efficiency of the lithium battery. The invention further provides a collaborative optimization algorithm integrating the genetic algorithm and the particle swarm optimization algorithm, the global search capability of the genetic algorithm and the rapid convergence of the particle swarm optimization algorithm are both considered, the defects of a single algorithm are overcome, and the optimal solution is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium battery thermal characteristic measurement, and in particular relates to an excitation signal optimization method, electronic equipment and medium capable of improving the efficiency of lithium battery thermal characteristic parameter testing. Background Art

[0002] Electrochemical impedance spectroscopy (EIS) testing is a test method for thermal characteristics testing of lithium batteries. This test method requires applying a small-amplitude sinusoidal AC voltage or current excitation signal to the battery and measuring the battery's response signal. The excitation signal can simulate the dynamic changes of the battery in actual use, such as current and voltage fluctuations during charging and discharging. By analyzing the battery's response to the excitation signal, we can have a more comprehensive understanding of the battery's thermal characteristics. In addition, lithium batteries exhibit nonlinear behaviors during charging and discharging, such as changes in internal resistance and polarization phenomena. The excitation signal can cover a wide frequency range, thereby revealing the battery's response characteristics at different frequencies, which helps to further study the nonlinear behavior of the battery.

[0003] At present, the excitation signal for the thermal characteristics test of lithium batteries mainly adopts the signal generation algorithm. The algorithm generates the required waveform by defining the frequency and phase parameters of the signal, and can optimize the excitation signal by optimizing the frequency, phase and other parameters of the signal. The algorithm needs to select a suitable frequency measurement range in terms of frequency, and select the excitation signal frequency points with a relatively uniform distribution; for the phase, the current mainstream method is to optimize it through a deterministic method. The deterministic method usually uses strict mathematical analysis to find the optimal solution. Although the accuracy of this method is acceptable, the quality of the optimal solution is heavily dependent on the setting of the initial conditions. Therefore, in order to improve the thermal characteristics test effect of lithium batteries, an efficient signal optimization method is urgently needed.

[0004] A Chinese invention patent with publication number CN103488078A discloses an excitation signal optimization method for improving the accuracy of closed-loop identification of power systems. It uses a Kalman filter algorithm to optimize the excitation signal of the power system and achieves good results. However, this method has the disadvantages of relying on linear models, complex calculations, and sensitivity to noise.

[0005] A Chinese patent with publication number CN106501616 A discloses a method for optimizing the waveform parameters of a multi-frequency sinusoidal voltage excitation for a fast frequency-domain dielectric response test. The core of the method is to optimize the frequency, amplitude and phase of the multi-frequency sinusoidal voltage excitation. However, the fminsearch function method of the patent has the disadvantages of being dependent on initial values, being prone to falling into local optimality and having slow convergence. Summary of the invention

[0006] In order to solve the deficiencies of the above-mentioned existing methods, the present invention provides an excitation signal optimization method, an electronic device and a medium which can improve the efficiency of lithium battery thermal characteristic testing.

[0007] The purpose of the present invention is to improve the efficiency of lithium battery thermal characteristics testing. A multi-frequency superimposed sinusoidal discrete signal is used as the initial excitation signal, and the multi-frequency superimposed sinusoidal excitation signal is optimized from the perspective of frequency distribution optimization and phase optimization, thereby improving the phase and frequency of the excitation signal, and ultimately improving the efficiency of lithium battery thermal characteristics testing.

[0008] A first aspect of the present invention provides an excitation signal optimization method for improving the efficiency of testing thermal characteristic parameters of a lithium battery, comprising the following steps:

[0009] Step 1: Optimize the frequency distribution, determine the range of the measurement frequency, use logarithmic frequency intervals and determine the specific excitation signal frequency points based on the relationship between the frequency points.

[0010] Step 2: Phase optimization, phase optimization is performed through a collaborative optimization algorithm that integrates genetic algorithm and particle swarm algorithm.

[0011] The second aspect of the present invention provides an electronic device for optimizing excitation signals that can improve the efficiency of testing thermal characteristic parameters of lithium batteries, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the above-mentioned method for optimizing excitation signals that can improve the efficiency of testing thermal characteristic parameters of lithium batteries is implemented.

[0012] A third aspect of the present invention provides a computer-readable storage medium storing a computer program for executing the above-mentioned method for optimizing an excitation signal that can improve the efficiency of testing thermal characteristic parameters of a lithium battery.

[0013] Features and beneficial effects of the present invention:

[0014] The present invention proposes an excitation signal optimization method that can improve the efficiency of lithium battery thermal characteristics testing. Through frequency distribution optimization and phase optimization, optimized frequency distribution and phase are obtained, thereby reducing the crest factor, and proposing a new idea for improving the efficiency of lithium battery thermal characteristics testing.

[0015] The present invention also proposes a collaborative optimization algorithm that integrates the genetic algorithm and the particle swarm optimization algorithm, taking into account the global search capability of the integrated genetic algorithm and the rapid convergence of the particle swarm optimization algorithm, overcoming the shortcomings of a single algorithm and obtaining the optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a specific flow chart of phase optimization in the embodiment of the present application;

[0017] Figure 2 It is a comparison diagram of the phase of the excitation signal before and after optimization in the embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The present invention provides an excitation signal optimization method for improving the efficiency of lithium battery thermal characteristic parameter testing, comprising the following steps:

[0020] Step 1: Optimize the frequency distribution, determine the range of the measurement frequency, use logarithmic frequency intervals and determine the specific excitation signal frequency points based on the relationship between the frequency points.

[0021] Step 2: Phase optimization, phase optimization is performed through a collaborative optimization algorithm that integrates genetic algorithm and particle swarm algorithm.

[0022] Furthermore, the optimization algorithm uses a multi-frequency superimposed discrete sinusoidal signal as an initial excitation signal.

[0023] Further, the step 2 comprises the following steps:

[0024] Step 2-1: Initialization: Set basic parameters, initialize the population of the genetic algorithm and the particle swarm of the particle swarm optimization algorithm, and randomly generate the initial phase.

[0025] Step 2-2: Genetic algorithm operation: perform selection, crossover and mutation operations on the genetic algorithm population to generate a new population, evaluate the fitness of the new population and retain excellent individuals.

[0026] Step 2-3: Particle swarm optimization algorithm operation: Use the excellent individuals generated by the genetic algorithm as the initial population of the particle swarm optimization algorithm, update the speed and position of the particles, and adjust the global optimal solution and individual optimal solution according to the fitness.

[0027] Step 2-4: Alternating / mixed operations: forming a collaborative optimization algorithm based on the fusion strategy.

[0028] Step 2-5: Determine the termination condition: If the termination condition is met, output the optimal solution, otherwise return to step 2-2 to continue iterating.

[0029] Furthermore, the basic parameters in step 2-1 are specifically population size, number of iterations, individual learning factor, social learning factor, crossover rate and mutation rate.

[0030] Furthermore, the fusion strategy in steps 2-4 is specifically to perform alternating or mixed operations, alternately using the genetic algorithm and the particle swarm optimization algorithm or fusing the operation rules of the genetic algorithm and the particle swarm optimization algorithm.

[0031] Furthermore, the termination condition in step 2-5 is specifically that the maximum number of iterations is reached.

[0032] The implementation steps of the present invention are further described below with reference to the accompanying drawings and specific examples:

[0033] The present application embodiment provides an excitation signal optimization method that can improve the efficiency of lithium battery thermal characteristic parameter testing, including the following steps:

[0034] Step 1: Frequency distribution optimization, determine the measurement frequency to be 0.1Hz~1KHz, use logarithmic frequency intervals and determine the specific excitation signal frequency points based on the relationship between the frequency points. Frequency distribution optimization aims to obtain a more uniform frequency distribution and avoid problems such as too many frequency points in a single group, which makes subsequent FFT analysis difficult.

[0035] Further, the step 1 comprises the following steps:

[0036] Step 1-1: Determine the upper and lower limits of the measurement frequency.

[0037] According to the prior art, when the impedance spectrum frequency is higher than 1kHz, the deviation caused by the inductance effect can be clearly observed; the impedance data at 0.1mHz to 0.1Hz will cause the real part of the impedance to increase due to the lag of the open-circuit voltage. Based on the above, the upper limit of the measurement frequency is determined to be 1kHz and the lower limit is 0.1Hz.

[0038] Step 1-2: Use an excitation signal with a logarithmic frequency interval, and the frequency points satisfy the relationship (1):

[0039] f i =a i-1 f min i=1,2,L,N (1)

[0040] Furthermore, α in the formula (1) satisfies the relationship (2):

[0041]

[0042] Among them, f max is the upper limit of the frequency range to be measured, f min is the lower limit of the frequency range to be measured. When the total number of frequency points N is 37, the frequency constant α=1.2915.

[0043] Step 2: Phase optimization, phase optimization is performed by integrating the collaborative optimization algorithm of genetic algorithm and particle swarm algorithm. The specific process is as follows Figure 1 shown.

[0044] Further, the step 2 specifically includes the following steps:

[0045] Step 2-1: Initialization: Set basic parameters, such as population size, number of iterations, individual learning factor, social learning factor, crossover rate and mutation rate, etc. Initialize the population of the genetic algorithm and the particle swarm of the particle swarm optimization algorithm, and randomly generate the initial phase.

[0046] In particular, for multi-frequency sinusoidal signals, the crest factor (CF) is defined as the ratio of the peak-to-peak value of the signal to the effective value, that is:

[0047]

[0048] The smaller the value of the crest factor, the higher the energy excited at each frequency. From formula (3), it can be seen that if the crest factor is reduced, a reasonable set of initial phases should be selected to reduce the peak-to-peak value of the signal s(n).

[0049] Step 2-2: Genetic algorithm operation: perform selection, crossover and mutation operations on the genetic algorithm population to generate a new population, evaluate the fitness of the new population and retain excellent individuals.

[0050] Step 2-3: Particle swarm optimization algorithm operation: Use the excellent individuals generated by the genetic algorithm as the initial population of the particle swarm optimization algorithm, update the speed and position of the particles, and adjust the global optimal solution and individual optimal solution according to the fitness.

[0051] Step 2-4: Alternating / mixed operations: forming a collaborative optimization algorithm based on the fusion strategy.

[0052] Furthermore, the fusion strategy is specifically to perform alternating or mixed operations, alternately using the genetic algorithm and the particle swarm optimization algorithm or fusing the operation rules of the genetic algorithm and the particle swarm optimization algorithm.

[0053] The fusion strategy can make full use of the global search capability of the genetic algorithm and the fast convergence characteristics of the particle swarm optimization algorithm, merge the operations of the genetic algorithm and the particle swarm optimization algorithm, form a new selection and update strategy, improve the search capability and convergence speed of the algorithm; and according to the characteristics and needs, adjust the parameters of the genetic algorithm and the particle swarm optimization algorithm to achieve the best fusion effect.

[0054] Preferably, parameter adjustment may include optimal settings of key parameters such as inertia coefficient, learning factor, crossover rate, mutation rate, etc.

[0055] Step 2-5: Determine the termination condition: If the termination condition is met and the maximum number of iterations is reached, output the optimal solution, otherwise return to step 2-2 to continue iterating.

[0056] Figure 2 This is a comparison diagram of the phase optimization of the excitation signal of the embodiment of the present application. In this embodiment, the amplitude is 30mV under a single frequency. At the same time, when the sampling frequency of the 0.1Hz~10Hz frequency segment is 1k, the sampling rate of the 10Hz~1k Hz frequency segment is 100k, and the total number of points N=100k, the following can be obtained: Figure 2 Comparison of the excitation signal before and after phase optimization is shown.

[0057] in, Figure 2 (a) shows the relationship between the crest factor and the number of iterations when the collaborative optimization algorithm is used in different frequency bands. Figure 2 (b) and Figure 2 (c) shows the excitation signals before and after initial phase optimization in the frequency range of 0.1 Hz to 10 Hz and 10 Hz to 1 kHz respectively.

[0058] Depend on Figure 2 It can be seen that compared with the original excitation signal with an initial phase of zero, the peak-to-peak value of the optimized 0.1Hz~10Hz frequency range is reduced from 0.3123V to 0.2175V, a decrease of 30.36%, and the peak-to-peak value of the optimized 10Hz~1kHz frequency range is reduced from 0.3123V to 0.2161V, a decrease of 30.80%.

[0059] The embodiment of the present application also discloses an electronic device for optimizing excitation signals that can improve the efficiency of testing the thermal characteristic parameters of lithium batteries, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of an excitation signal optimization method that can improve the efficiency of testing the thermal characteristic parameters of lithium batteries are implemented, wherein the memory may include a memory, such as a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk memory, etc.; the processor, the network interface, and the memory are interconnected through an internal bus, and the internal bus may be an industrial standard architecture bus, a peripheral component interconnection standard bus, an extended industrial standard architecture bus, etc., and the bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs, and specifically, the program may include a program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0060] The embodiment of the present application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of an excitation signal optimization method that can improve the efficiency of lithium battery thermal characteristic parameter testing are implemented. Specifically, the computer-readable storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include a read-only memory (ROM), a hard disk, a flash memory, an optical disk, a magnetic disk, etc.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. An excitation signal optimization method for improving the efficiency of lithium battery thermal characteristic parameter testing, characterized in that: The following steps are involved: Step 1: Optimize the frequency distribution, determine the range of the measurement frequency, use logarithmic frequency intervals and determine the specific excitation signal frequency points based on the relationship between the frequency points; Step 2: Phase optimization, phase optimization is performed through a collaborative optimization algorithm that integrates genetic algorithm and particle swarm algorithm.

2. The method for optimizing the excitation signal for improving the efficiency of testing the thermal characteristic parameters of a lithium battery according to claim 1, characterized in that: The collaborative optimization algorithm uses a multi-frequency superimposed discrete sinusoidal signal as an initial excitation signal.

3. The method for optimizing the excitation signal for improving the efficiency of testing the thermal characteristic parameters of a lithium battery according to claim 2, characterized in that: The step 1 comprises the following steps: Step 1-1: Determine the upper and lower limits of the measurement frequency; Step 1-2: Use an excitation signal with logarithmic frequency intervals, and the relationship between each frequency point satisfies: f i =a i-1 f min i=1,2,L,N Among them, f min is the lower limit of the frequency range to be measured, α is the frequency constant, and N is the total number of frequency points.

4. The method for optimizing the excitation signal for improving the efficiency of testing the thermal characteristic parameters of a lithium battery according to claim 3, characterized in that: The frequency constant α satisfies the relationship: Among them, f max is the upper limit of the frequency range to be measured, f min is the lower limit of the frequency range to be measured.

5. The method for optimizing the excitation signal for improving the efficiency of testing the thermal characteristic parameters of a lithium battery according to claim 1, characterized in that: The step 2 comprises the following steps: Step 2-1: Initialization: set basic parameters, initialize the population of the genetic algorithm and the particle swarm of the particle swarm optimization algorithm, and randomly generate the initial phase; Step 2-2: Genetic algorithm operation: perform selection, crossover and mutation operations on the genetic algorithm population to generate a new population, evaluate the fitness of the new population and retain excellent individuals; Step 2-3: Particle swarm optimization algorithm operation: Use the excellent individuals generated by the genetic algorithm as the initial population of the particle swarm optimization algorithm, update the speed and position of the particles, and adjust the global optimal solution and the individual optimal solution according to the fitness; Step 2-4: Alternating / mixed operation: forming a collaborative optimization algorithm based on the fusion strategy; Step 2-5: Determine the termination condition: If the termination condition is met, output the optimal solution, otherwise return to step 2-2 to continue iterating.

6. The method for optimizing the excitation signal for improving the efficiency of testing the thermal characteristic parameters of a lithium battery according to claim 5, characterized in that: The basic parameters in step 2-1 are specifically population size, number of iterations, individual learning factor, social learning factor, crossover rate and mutation rate.

7. The method for optimizing the excitation signal for improving the efficiency of testing the thermal characteristic parameters of a lithium battery according to claim 5, characterized in that: The fusion strategy in steps 2-4 is specifically to perform alternating or mixed operations, alternately using the genetic algorithm and the particle swarm optimization algorithm or integrating the operation rules of the genetic algorithm and the particle swarm optimization algorithm.

8. The method for optimizing the excitation signal for improving the efficiency of testing the thermal characteristic parameters of a lithium battery according to claim 5, characterized in that: The termination condition in step 2-5 is specifically that the maximum number of iterations is reached.

9. An electronic device with excitation signal optimization that can improve the efficiency of testing thermal characteristic parameters of lithium batteries, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an excitation signal optimization method for improving the efficiency of testing thermal characteristic parameters of a lithium battery as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the excitation signal optimization method for improving the efficiency of lithium battery thermal characteristic parameter testing as described in any one of claims 1 to 8 above.

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