A method and apparatus for identifying and equalizing battery EIS electrochemical parameters

Abstract

This invention provides a method and device for identifying electrochemical parameters and achieving energy balancing in battery EIS (Energy Information System), relating to the field of electrochemical analysis. The method includes: generating an impedance data set and obtaining the ohmic internal resistance of each cell; inputting the impedance data set into an electrochemical impedance model to calculate a simulated electrochemical parameter set, which includes the exchange current density of each cell; constructing a State of Health (SOH) estimator using the simulated electrochemical parameter set and building a neural network model; obtaining the State of Charge (SOC) and voltage of each cell in the battery pack using the trained neural network model; obtaining the SOH of each cell based on the SOH estimator; and performing two-layer balancing control of the battery pack based on the SOH, ohmic internal resistance, exchange current density, SOC, and voltage of each cell. This invention constructs an SOH estimator and builds a neural network model using a simulated electrochemical parameter set and proposes incorporating electrochemical parameter consistency into balancing management, achieving rapid and accurate assessment of the battery state.

Description

Technical Field

[0001] This invention relates to the field of electrochemical analysis, and in particular to a method and apparatus for identifying and balancing the electrochemical parameters and energy of a battery using EIS. Background Technology

[0002] Battery pack balancing technology is one of the key technologies to ensure battery pack performance and lifespan. Its purpose is to ensure that the charge, voltage, and other parameters of each individual cell within the battery pack remain consistent. Currently, battery pack balancing technology is mainly divided into two types: passive balancing and active balancing. Passive balancing technology achieves balancing by using components such as resistors to dissipate energy during battery discharge. Active balancing technology, on the other hand, uses control circuits and algorithms to manage batteries in a targeted manner through energy storage components such as inductors and capacitors to achieve balancing. In recent years, various balancing algorithms have been proposed, including classical control algorithms and modern intelligent algorithms. Classical control algorithms, such as PID control, are widely used due to their simple structure and stable reliability, but they require the selection of appropriate parameters and cannot quickly stabilize after sudden changes. Intelligent algorithms, such as fuzzy logic control and neural networks, can provide more flexible and efficient balancing strategies, but existing electrochemical analysis methods still have the following problems:

[0003] (1) Traditional battery pack balancing technology is mainly divided into two types: passive balancing and active balancing. Passive balancing uses components such as resistors to discharge or charge, while active balancing is managed by control circuits and algorithms. The electrochemical parameters of the battery obtained by traditional balancing methods are still not accurate enough.

[0004] (2) Although EIS technology can provide a wealth of electrochemical information, it may have problems such as complex data interpretation and long testing time in practical applications, which limits its widespread application in battery management systems.

[0005] (3) Traditional SOH assessment methods may rely on a single parameter or simple voltage and capacity measurements, which may not accurately reflect the internal state and performance changes of the battery.

[0006] (4) Existing balancing strategies may be mainly based on the consistency of voltage or SOC, which may not fully take into account the electrochemical characteristics and internal differences of the battery.

[0007] (5) Traditional battery management systems may be complex in structure, costly, and have limitations in battery state monitoring and equalization control. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide a method and device for identifying and balancing the electrochemical parameters of batteries using EIS, in order to solve the problem that the electrochemical parameters of batteries obtained by existing electrochemical analysis methods are not accurate enough.

[0009] This invention provides a method for identifying and balancing the electrochemical parameters (EIS) of a battery, comprising the following steps:

[0010] S1: Electrochemical impedance spectroscopy is generated using a variable frequency AC impedance scanning device to detect the impedance data set of individual battery cells and obtain the ohmic internal resistance of each cell.

[0011] S2: Construct an electrochemical impedance model, input the impedance data set into the electrochemical impedance model to calculate the simulated electrochemical parameter set, which includes the exchange current density of each cell;

[0012] S3: Construct an SOH estimator by simulating a set of electrochemical parameters, and build a neural network model for pre-training and transfer learning to obtain a trained neural network model;

[0013] S4: Identify the health status of battery cells using a trained neural network model to obtain the SOC and voltage of each cell in the battery pack;

[0014] S5: Obtain the SOH of each cell based on the SOH estimator, and perform dual-layer equalization control on the battery pack based on the SOH, ohmic internal resistance, exchange current density, SOC and voltage of each cell, so that the battery pack can perform charging and discharging operations.

[0015] Preferably, step S2 specifically includes:

[0016] S21: Electrochemical impedance models include: EIS fractional-order model and finite element battery physical field impedance model;

[0017] S22: Obtain the frequency set, real part set, and negative imaginary part set of the impedance data set. Input the frequency set, real part set, and negative imaginary part set into the EIS fractional-order model and the finite element battery physical field impedance model to calculate and obtain the simulated electrochemical parameter set.

[0018] Preferred:

[0019] The formula for calculating the exchange current density is:

[0020]

[0021] Where I0 is the exchange current density, R is the ideal gas constant, F represents the Faraday constant, T is the thermodynamic temperature, and α a k is the positive charge transfer coefficient. rate E is the electrochemical reaction rate constant. a The activation energy is c1, the solid-phase lithium-ion concentration is c2, and the liquid-phase lithium-ion concentration is c3. 2,max c is the maximum lithium-ion concentration in the liquid phase. 1,max c is the maximum lithium-ion concentration in the solid phase.1,surf The concentration of lithium ions on the solid surface is denoted as .

[0022] Preferably, step S3 specifically includes:

[0023] S31: Obtain the set of internal resistance parameters from the set of simulated electrochemical parameters, and construct the SOH estimator using the set of internal resistance parameters;

[0024] S32: Use the ohmic internal resistance and exchange current density of each cell as training data to pre-train the neural network model;

[0025] S33: Repeat step S32 until the training fitting error is lower than the training threshold to obtain the pre-trained model;

[0026] S34: Using the pre-trained model as the basic model and the cloud database as the target model data, the pre-trained model is transferred to the local end. At the same time, the ohmic internal resistance, exchange current density and SOH of each cell are reported to the local end during the transfer learning process.

[0027] S35: Repeat step S34 until the transfer learning is completed and a trained neural network model is obtained.

[0028] Preferably, step S5 specifically includes:

[0029] S51: Charge the battery pack, and start discharging the battery pack after it is fully charged.

[0030] S52: Input the ohmic internal resistance and exchange current density of each cell into the SOH estimator to calculate the SOH of each cell;

[0031] S53: Calculate the SOC dispersion of each cell. If the SOC dispersion exceeds the equalization threshold, proceed to step S54 after completing the upper-level equalization; otherwise, return to step S52.

[0032] S54: If the SOC of each cell is 20-80%, proceed to step S55; otherwise, proceed to step S57 after completing the voltage difference balancing.

[0033] S55: After completing the voltage difference equalization, proceed to step S56;

[0034] S56: Calculate the SOH range of each cell. If the SOH range exceeds the range threshold, proceed to step S57 after completing the lower-level equalization; otherwise, return to step S55.

[0035] S57: End the discharge operation and return to step S51.

[0036] Preferred:

[0037] The formula for calculating the SOH of a battery cell is:

[0038]

[0039] Among them, SOH i Let R be the SOH of the i-th cell. End R is the ohmic internal resistance of the battery when its remaining capacity reaches 80% of its factory rated capacity. New The ohmic internal resistance, R, measured for the new battery c,i Let be the current ohmic internal resistance of the i-th cell in the battery pack. Let be the exchange current density of the i-th cell, R be the ideal gas constant, F be the Faraday constant, T be the thermodynamic temperature, and α be the estrangement of the i-th cell. a is the charge transfer coefficient in the positive direction.

[0040] The preferred lower-level balancing process is as follows:

[0041] S561: Obtain the ohmic internal resistance and exchange current density of each cell;

[0042] S562: Obtain the maximum ohmic internal resistance Minimum Ohmic internal resistance Ohmic internal resistance equalization threshold S Ω Maximum exchange current density Minimum exchange current density and the equalization threshold S of the exchange current density I ,like and Then proceed to step S563; otherwise, return to step S561.

[0043] S563: Calculate the correction factor and the balance center point, and perform a balance operation on the battery pack using the correction factor and the balance center point.

[0044] Preferred:

[0045] The formula for calculating the correction factor is:

[0046]

[0047] Where f(R0) is the correction factor, R0 is the ohmic internal resistance of the cell to be corrected, I, Π, and III are the ranges of SOC parameters, and R Π Let l be the ohmic internal resistance of the cell starting in the Π region, and l be the length of the charge range in the Π region.

[0048] The formula for calculating the equilibrium center point is:

[0049]

[0050] Among them, R c As the equilibrium center point, Δ(R) cR represents the degree of deviation from the equilibrium center point. i R is the ohmic internal resistance of the i-th cell. j Let δ be the ohmic internal resistance of the j-th cell, and take a value of 5% of R. c η is the energy conversion efficiency, and h is the equilibrium threshold correction factor.

[0051] A storage medium storing instructions and data for implementing the battery EIS electrochemical parameter identification and energy balancing method.

[0052] A battery EIS electrochemical parameter identification and energy balancing device includes: a processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement the battery EIS electrochemical parameter identification and energy balancing method.

[0053] The present invention has the following beneficial effects:

[0054] (1) Combine electrochemical impedance spectroscopy (EIS) to obtain the impedance data of the battery pack. The impedance data can more accurately identify and evaluate the battery state, thereby achieving more effective energy balance management.

[0055] (2) An electrochemical impedance model consisting of an EIS fractional-order model and a finite element battery physical field impedance model was constructed. The impedance data set was input into the electrochemical impedance model to calculate the simulated electrochemical parameter set. An SOH estimator was constructed and a neural network model was built through the simulated electrochemical parameter set. It was proposed to incorporate the consistency of electrochemical parameters into the balance management, thereby realizing a rapid and accurate assessment of the battery state.

[0056] (3) An equalization threshold correction factor is introduced to adaptively optimize the equalization threshold according to the working conditions of the battery pack, thereby improving the accuracy and efficiency of equalization.

[0057] (4) A two-layer balancing strategy based on the consistency of SOC, voltage and electrochemical parameters was proposed. Combined with the characteristics of battery mechanism, a more refined and effective battery pack energy balance management was achieved.

[0058] (5) By using only ohmic internal resistance and exchange current density as electrochemical parameters, the process of battery state assessment and equalization control is simplified, the system complexity and cost are reduced, and the system reliability and efficiency are improved. Attached Figure Description

[0059] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0060] Figure 2 This is a structural diagram of the device according to an embodiment of the present invention;

[0061] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0063] Reference Figure 1 This invention provides a method for identifying and balancing the electrochemical parameters (EIS) of a battery, comprising the following steps:

[0064] S1: Electrochemical impedance spectroscopy is generated using a variable frequency AC impedance scanning device to detect the impedance data set of individual battery cells and obtain the ohmic internal resistance of each cell.

[0065] As one embodiment, the operating current of the lithium battery pack is monitored by the energy management system. If the following sampling conditions are met, an online impedance measurement device applies an AC current excitation signal of a certain frequency to each cell in the pack, observes and records the response voltage of each cell at the same frequency, and the ratio of the response voltage to the excitation current is taken as the electrochemical impedance at that frequency. Two sampling conditions are defined as follows:

[0066] (1) The fluctuation of the working current does not exceed the sampling threshold and the duration exceeds a certain value.

[0067] (2) The lithium battery pack has been charged and discharged a certain number of times.

[0068] If only sampling condition (1) is met, the mid-frequency and high-frequency impedances of each battery cell in the group are sampled respectively. If both sampling conditions (1) and (2) are met, the low-frequency and mid-frequency impedances of each battery cell in the group are sampled. The i-th battery in the battery group is sorted according to its frequency, and its sampling data format is as follows: Where ν n The nth frequency point within the specified frequency interval, divided according to the sampling time. Let the real part of the impedance be at a given frequency. This represents the negative imaginary part of the impedance at a given frequency.

[0069] The average of the two points to the left and right of the zero-crossing point in the extracted real part data of the i-th battery cell is taken as the ohmic internal resistance R. Ω .

[0070] S2: Construct an electrochemical impedance model, input the impedance data set into the electrochemical impedance model to calculate the simulated electrochemical parameter set, which includes the exchange current density of each cell;

[0071] As one example,

[0072] Step S2 is as follows:

[0073] S21: Electrochemical impedance models include: EIS fractional-order model and finite element battery physical field impedance model;

[0074] Specifically, the finite element method for the physical field impedance model of a battery:

[0075] The basic components of a lithium battery include positive and negative current collectors, positive and negative porous electrodes, and a separator. First, from the perspective of the electrochemical process, the lithium battery is simplified to one dimension, comprising three domains of different thicknesses (positive electrode, negative electrode, and separator). The size of the current collector is negligible, as is the longitudinal variation of lithium-ion concentration within the battery. An electrochemical impedance model is established using simulation software, employing a typical electrochemical process. The frequency, real part, and negative imaginary part of the impedance data are input into the model, and global least squares optimization is performed, with the ohmic internal resistance R... Ω The exchange current density I0 is used as an electrochemical parameter to describe the characteristics of the battery. In the electrochemical reaction of the experimental lithium battery, its local current density I0 is... n The expression is as follows:

[0076]

[0077] Where I0 is the exchange current density, a function of battery temperature and lithium-ion concentrations in the electrolyte and solid active material, R is the ideal gas constant, F represents the Faraday constant, T is the thermodynamic temperature, and α... a and α b represents the charge transfer coefficients in the positive (negative) direction, respectively, and γ is the activation overpotential.

[0078] Mean absolute error (MAE) and mean relative error (MSE) are used as standards to evaluate the accuracy of electrochemical model simulations. The calculation formulas are as follows:

[0079]

[0080] In the formula Z measurement and Z emulation These represent the impedances of the experimentally measured EIS and the simulated EIS at different frequencies, respectively. MAE z and MRE z If the value is below a given threshold, it indicates a good model fit. The ohmic internal resistance R is then calculated during the optimization process. Ω The exchange current density I0 is an important parameter for battery state assessment and equalization management.

[0081] S22: Obtain the frequency set, real part set, and negative imaginary part set of the impedance data set. Input the frequency set, real part set, and negative imaginary part set into the EIS fractional-order model and the finite element battery physical field impedance model to calculate and obtain the simulated electrochemical parameter set.

[0082] As one example,

[0083] The formula for calculating the exchange current density is:

[0084]

[0085] Where I0 is the exchange current density, R is the ideal gas constant, F represents the Faraday constant, T is the thermodynamic temperature, and α a k is the positive charge transfer coefficient. rate E is the electrochemical reaction rate constant. a The activation energy is c1, the solid-phase lithium-ion concentration is c2, and the liquid-phase lithium-ion concentration is c3. 2,max c is the maximum lithium-ion concentration in the liquid phase. 1,max c is the maximum lithium-ion concentration in the solid phase. 1 ,sur f represents the lithium ion concentration on the solid surface.

[0086] S3: Construct an SOH estimator by simulating a set of electrochemical parameters, and build a neural network model for pre-training and transfer learning to obtain a trained neural network model;

[0087] As one example:

[0088] Step S3 is as follows:

[0089] S31: Obtain the set of internal resistance parameters from the set of simulated electrochemical parameters, and construct the SOH estimator using the set of internal resistance parameters;

[0090] Specifically, the internal resistance of a lithium battery in its State of Health (SOH) is defined as follows:

[0091]

[0092] In the formula: R EOL R is the internal resistance at the end of the battery's lifespan. C R is the current internal resistance of the battery. new This refers to the internal resistance of the new battery.

[0093] S32: Use the ohmic internal resistance and exchange current density of each cell as training data to pre-train the neural network model;

[0094] Specifically, a small-sample experiment was conducted on a single model of lithium battery. The battery underwent a cross-test of cyclic charge-discharge and EIS testing. A battery tester was used for cyclic charge-discharge, during which the state of charge (SOH) gradually decreased. EIS testing was performed at 2% SOH intervals using an AC impedance scanner. The impedance data extraction format is as shown in step S1, using SOH as the label and the impedance processing data of each sample cell as input, and was trained using a neural network algorithm.

[0095] During the training process, the exchange current density is obtained by solving the SOH estimator, and the ohmic internal resistance under each specific SOH is obtained based on the original EIS data. If the training fitting error is lower than the training threshold, the training requirements are met, and the model and the obtained electrochemical parameters are saved.

[0096] S33: Repeat step S32 until the training fitting error is lower than the training threshold to obtain the pre-trained model;

[0097] S34: Using the pre-trained model as the basic model and the cloud database as the target model data, the pre-trained model is transferred to the local end. At the same time, the ohmic internal resistance, exchange current density and SOH of each cell are reported to the local end during the transfer learning process.

[0098] Specifically, transfer learning of models:

[0099] A pre-trained model is implanted into the cloud-based battery energy management system. The pre-trained model is used as the basic model, and the cloud database is used as the target model data for transfer learning. The ohmic internal resistance and AC current density during the training process are saved to complete the health status assessment from the battery mechanism level, and the SOH of each cell is saved.

[0100] Report SOH to the local end:

[0101] To accurately obtain the health status value of each cell in a battery pack, the most direct method is to design a state estimator for each cell. However, multiple estimators working simultaneously would place a huge burden on the system's microprocessor. This invention patent employs a dual-parallel EKF-global least squares algorithm to estimate the health status of cells within a lithium battery pack. This algorithm tracks the SOH values ​​of all cells in the pack at a microscale (i.e., at each sampling time point), storing the corresponding SOH values ​​and cell serial numbers in a corresponding sequence. Next, a sorting traversal algorithm is used to sort the cells by SOH value, finding the first and second largest, and the last and second-to-last cell serial numbers, respectively. The largest and second-largest SOH values ​​and their corresponding cell serial numbers, as well as the smallest and second-smallest SOH values ​​and their corresponding cell serial numbers, are then reported to the local terminal.

[0102] By using the dual-parallel EKF-global least squares algorithm, the health status estimation of the battery pack can be guaranteed with low computational cost and high reliability, and the inconsistency information of individual cells within the pack can be accurately obtained.

[0103] S35: Repeat step S34 until the transfer learning is completed and a trained neural network model is obtained.

[0104] S4: Identify the health status of battery cells using a trained neural network model to obtain the SOC and voltage of each cell in the battery pack;

[0105] S5: Obtain the SOH of each cell based on the SOH estimator, and perform dual-layer equalization control on the battery pack based on the SOH, ohmic internal resistance, exchange current density, SOC and voltage of each cell, so that the battery pack can perform charging and discharging operations.

[0106] As one example, during the charging and discharging process of a battery pack, in order to maximize the battery pack capacity, it is necessary to perform discharge equalization on the cells that first reach the charge / discharge cutoff voltage. Existing research mostly classifies cells within the pack based on their operating voltage and performs battery equalization based on the charge / discharge cutoff voltage or the difference in State of Charge (SOC) within the pack. That is, it judges whether equalization is needed based on the consistency of operating voltage or SOC. This invention, combining battery mechanism characteristics, proposes a two-layer equalization control method based on the consistency of SOC, voltage, and electrochemical parameters.

[0107] Step S5 is as follows:

[0108] S51: Charge the battery pack, and start discharging the battery pack after it is fully charged.

[0109] S52: Input the ohmic internal resistance and exchange current density of each cell into the SOH estimator to calculate the SOH of each cell;

[0110] Specifically:

[0111] The formula for calculating the SOH of a battery cell is:

[0112]

[0113] Among them, SOH i Let R be the SOH of the i-th cell. End R is the ohmic internal resistance of the battery when its remaining capacity reaches 80% of its factory rated capacity. New The ohmic internal resistance, R, measured for the new battery c,i Let be the current ohmic internal resistance of the i-th cell in the battery pack. Let be the exchange current density of the i-th cell, R be the ideal gas constant, F be the Faraday constant, T be the thermodynamic temperature, and α be the estrangement of the i-th cell. a is the charge transfer coefficient in the positive direction.

[0114] S53: Calculate the SOC dispersion of each cell. If the SOC dispersion exceeds the equalization threshold, proceed to step S54 after completing the upper-level equalization; otherwise, return to step S52.

[0115] Specifically, the upper-level equilibrium strategy:

[0116] The battery management system collects parameters such as battery pack voltage, current, and temperature, performs SOC evaluation on n batteries, and records the SOC.i (i = 1, 2, 3, ..., n). Sort the obtained SOC values ​​and find the single unit with the largest remaining capacity SOC. Max SOC of the smallest remaining capacity unit Min Calculate the SOC dispersion ΔSOC of the battery pack. Max Simultaneously determine ΔSOC Max Is it greater than or equal to the set equalization activation threshold S? t .

[0117] When the SOC of the battery is between 20% and 80%, cells with the highest SOC values ​​are discharged, while cells with the lowest SOC values ​​are charged. If the SOC is below 20% or above 80%, voltage difference is used for balancing, and cells with the highest voltage values ​​are discharged, while cells with the lowest voltage values ​​are charged.

[0118] The upper limit of the safety threshold is set at 95% of the charging cutoff voltage of the lithium battery, and the lower limit is set at 105% of the discharging cutoff voltage. For example, if a certain model of lithium battery has a charging cutoff voltage of 4.2V, its upper limit of the safety threshold is 3.99V, and its discharging cutoff voltage is 2.6V, then its lower limit of the safety threshold is 2.73V. A cell voltage difference upper limit of 6mV is set as one of the conditions for balancing to be activated, ensuring the balancing effect while preventing frequent activation of balancing.

[0119] S54: If the SOC of each cell is 20-80%, proceed to step S55; otherwise, proceed to step S57 after completing the voltage difference balancing.

[0120] S55: After completing the voltage difference equalization, proceed to step S56;

[0121] S56: Calculate the SOH range of each cell. If the SOH range exceeds the range threshold, proceed to step S57 after completing the lower-level equalization; otherwise, return to step S55.

[0122] Specifically, the lower-level equilibrium strategy:

[0123] If the cells with the highest and lowest SOH values ​​exceed a given threshold, the lower-level equalization is activated to achieve a consistent balance of cell health status from the perspective of battery mechanism.

[0124] Let the internal resistance of the i-th cell be ohmic. Exchange current density Health Status Value (SOH) i Local terminal obtains information about each cell in the battery pack. and And mark the maximum value. and and minimum value and Set the ohmic internal resistance equalization threshold S Ω Similarly, set the equalization threshold S for the exchange current density. I The specific threshold is determined based on the battery model. For example, for a certain type of 18650 battery, if... Equalization is initiated and ends when the current is less than 1mΩ, stopping charging or discharging. The current threshold setting is similar.

[0125] After the lower-level equalization is enabled, equalization control and management are performed on the four cells with the largest, second largest, smallest, and second smallest SOH mentioned in steps 2-3.

[0126] The specific process of lower-level balancing is as follows:

[0127] S561: Obtain the ohmic internal resistance and exchange current density of each cell;

[0128] S562: Obtain the maximum ohmic internal resistance Minimum Ohmic internal resistance Ohmic internal resistance equalization threshold S Ω Maximum exchange current density Minimum exchange current density and the equalization threshold S of the exchange current density I ,like and Then proceed to step S563; otherwise, return to step S561.

[0129] S563: Calculate the correction factor and the balance center point, and perform a balance operation on the battery pack using the correction factor and the balance center point.

[0130] Specifically, the equilibrium threshold is calculated as follows:

[0131] This patent introduces a balanced threshold correction factor h to adaptively optimize the set threshold. The threshold calculation equation is as follows:

[0132] R set =R c +(1+h)δ

[0133] I 0,set =λI c +(1+h)δ

[0134] In the formula: R set Set the threshold value for ohmic resistance equalization; R c The target cell's internal resistance is the midpoint; h is the equalization threshold correction factor, whose value is determined based on the battery pack's operating conditions; δ is the equalization accuracy, set at 5% of R. c I 0,set This is the exchange current density threshold setting value, I cλ is the median point of the target cell's exchange current density; λ is the hardware condition compensation point.

[0135] By fitting the charge-discharge characteristics of the target battery, a one-dimensional equation for the ohmic internal resistance is obtained:

[0136] V cells (R)=a i R i +a i-1 R i-1 +L+a1R 1 +a0

[0137] In the formula: a i It is a polynomial; i is the number of battery cells.

[0138] To calculate the battery equalization threshold correction factor h, we set e = |V′(R) c Solve the inequality for |V′(Q)<1.5e|. The correction factor is shown in the following equation:

[0139] The formula for calculating the correction factor is:

[0140]

[0141] Where f(R0) is the correction factor, R0 is the ohmic internal resistance of the cell to be corrected, I, Π, and III are the ranges of SOC parameters, and R Π Let l be the ohmic internal resistance of the cell starting in the Π region, and l be the length of the charge range in the Π region.

[0142] Specifically, considering the differences between individual battery cells, the ohmic internal resistance value of the battery module is corrected, and a safety factor σ is set at the boundary of adjacent regions to improve the balancing accuracy of the battery module. Region III represents cells with a SOC of 20-80% and excludes cells of type Π, while Region I represents cells with an SOC of less than 20% or more than 80%.

[0143]

[0144] In the formula: R * R represents the corrected ohmic internal resistance of the battery. i This represents the ohmic internal resistance value of each battery cell;

[0145] To determine the battery balance center point R c Solve for |Δ(R) c The minimum value of |) is calculated by the following equation:

[0146] The formula for calculating the equilibrium center point is:

[0147]

[0148] Among them, Rc As the equilibrium center point, Δ(R) c R represents the degree of deviation from the equilibrium center point. i R is the ohmic internal resistance of the i-th cell. j Let δ be the ohmic internal resistance of the j-th cell, and take a value of 5% of R. c η is the energy conversion efficiency, and h is the equilibrium threshold correction factor.

[0149] Specifically, R c In [R] min ,R max Internal fluctuations, further derivation yields:

[0150]

[0151] In the formula: u represents the internal resistance of u cells in the target module that exceeds the set equalization threshold; v represents the current density of u cells in the target module that exceeds the set equalization threshold.

[0152] Determine R C Then, a battery pack balancing model is established, with its charge transfer equation as follows:

[0153]

[0154] In the formula: t i Δq represents the time required for a high-SOC cell i to transfer C units of charge to a low-SOC cell j within the battery pack; T is the pulse signal period; Δq i,j This represents the amount of electricity transferred between individual cells within a given period.

[0155] S57: End the discharge operation and return to step S51.

[0156] Please see Figure 2 , Figure 2 This is a schematic diagram of the hardware device in operation according to an embodiment of the present invention. The hardware device specifically includes: a battery EIS electrochemical parameter identification and energy balancing device 401, a processor 402, and a storage medium 403.

[0157] A battery EIS electrochemical parameter identification and energy balancing device 401: The battery EIS electrochemical parameter identification and energy balancing device 401 realizes the battery EIS electrochemical parameter identification and energy balancing method.

[0158] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the battery EIS electrochemical parameter identification and energy balancing method.

[0159] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the battery EIS electrochemical parameter identification and energy balancing method.

[0160] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0161] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the unit claims listing several devices, several of these devices may be embodied by the same hardware item. The use of the terms first, second, and third, etc., does not indicate any order and can be interpreted as identifiers.

[0162] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for identifying and balancing the electrochemical parameters (EIS) of a battery, characterized in that, Including the following steps: S1: Electrochemical impedance spectroscopy is generated using a variable frequency AC impedance scanning device to detect the impedance data set of individual battery cells and obtain the ohmic internal resistance of each cell. S2: Construct an electrochemical impedance model, input the impedance data set into the electrochemical impedance model to calculate the simulated electrochemical parameter set, which includes the exchange current density of each cell; S3: Construct an SOH estimator by simulating a set of electrochemical parameters, and build a neural network model for pre-training and transfer learning to obtain a trained neural network model; S4: Identify the health status of battery cells using a trained neural network model to obtain the SOC and voltage of each cell in the battery pack; S5: Obtain the SOH of each cell based on the SOH estimator, and perform dual-layer equalization control on the battery pack based on the SOH, ohmic internal resistance, exchange current density, SOC and voltage of each cell, so that the battery pack can perform charging and discharging operations. Two-layer equalization control includes: upper-layer equalization and lower-layer equalization; Step S5 is as follows: S51: Charge the battery pack, and start discharging the battery pack after it is fully charged. S52: Input the ohmic internal resistance and exchange current density of each cell into the SOH estimator to calculate the SOH of each cell; S53: Calculate the SOC dispersion of each cell. If the SOC dispersion exceeds the equalization threshold, proceed to step S54 to perform upper-level equalization; otherwise, return to step S52. S54: If at least one cell has a SOC of less than 20% or more than 80%, proceed to step S55. If the SOC of each cell is 20-80%, then the cells with the top three SOC values ​​are discharged and the cells with the bottom three SOC values ​​are charged. The SOC dispersion is calculated based on the SOC of each cell until the SOC dispersion does not exceed the equalization threshold, then proceed to step S57. S55: After completing the voltage difference equalization, proceed to step S56; The specific steps for voltage difference equalization include: discharging cells with voltage values ​​in the top three and charging cells with voltage values ​​in the bottom three. S56: Calculate the SOH range of each cell. If the SOH range exceeds the range threshold, proceed to step S57 after completing the lower-level equalization; otherwise, return to step S55. The specific process of lower-level balancing is as follows: S561: Obtain the ohmic internal resistance and exchange current density of each cell; S562: Obtain the maximum ohmic internal resistance Minimum Ohmic internal resistance Ohmic internal resistance equalization threshold S Ω Maximum exchange current density Minimum exchange current density and the equalization threshold S of the exchange current density I ,like and Then proceed to step S563; otherwise, return to step S561. S563: Calculate and obtain the correction factor and the balance center point, and perform balance electric action on the battery pack using the correction factor and the balance center point; S57: End the discharge operation and return to step S51.

2. The battery EIS electrochemical parameter identification and energy equalization method according to claim 1, characterized in that, Step S2 is as follows: S21: Electrochemical impedance models include: EIS fractional-order model and finite element battery physical field impedance model; S22: Obtain the frequency set, real part set, and negative imaginary part set of the impedance data set. Input the frequency set, real part set, and negative imaginary part set into the EIS fractional-order model and the finite element battery physical field impedance model to calculate and obtain the simulated electrochemical parameter set.

3. The battery EIS electrochemical parameter identification and energy equalization method according to claim 1, characterized in that: The formula for calculating the exchange current density is: Where I0 is the exchange current density, R is the ideal gas constant, F represents the Faraday constant, T is the thermodynamic temperature, and α a k is the positive charge transfer coefficient. rate E is the electrochemical reaction rate constant. a The activation energy is c1, the solid-phase lithium-ion concentration is c2, and the liquid-phase lithium-ion concentration is c3. 2,max c is the maximum lithium-ion concentration in the liquid phase. 1,max c is the maximum lithium-ion concentration in the solid phase. 1,surf The concentration of lithium ions on the solid surface is denoted as .

4. The battery EIS electrochemical parameter identification and energy equalization method according to claim 1, characterized in that, Step S3 is as follows: S31: Obtain the set of internal resistance parameters from the set of simulated electrochemical parameters, and construct the SOH estimator using the set of internal resistance parameters; S32: Use the ohmic internal resistance and exchange current density of each cell as training data to pre-train the neural network model; S33: Repeat step S32 until the training fitting error is lower than the training threshold to obtain the pre-trained model; S34: Using the pre-trained model as the basic model and the cloud database as the target model data, the pre-trained model is transferred to the local end. At the same time, the ohmic internal resistance, exchange current density and SOH of each cell are reported to the local end during the transfer learning process. S35: Repeat step S34 until the transfer learning is completed and a trained neural network model is obtained.

5. The battery EIS electrochemical parameter identification and energy equalization method according to claim 1, characterized in that: The formula for calculating the SOH of a battery cell is: Among them, SOH i Let R be the SOH of the i-th cell. End R is the ohmic internal resistance of the battery when its remaining capacity reaches 80% of its factory rated capacity. New The ohmic internal resistance, R, measured for the new battery c,i Let be the current ohmic internal resistance of the i-th cell in the battery pack. Let be the exchange current density of the i-th cell, R be the ideal gas constant, F be the Faraday constant, T be the thermodynamic temperature, and α be the estrangement of the i-th cell. a is the charge transfer coefficient in the positive direction.

6. The battery EIS electrochemical parameter identification and energy equalization method according to claim 1, characterized in that: The formula for calculating the correction factor is: Where f(R0) is the correction factor, R0 is the ohmic internal resistance of the cell to be corrected, I, Π, and III are the ranges of SOC parameters, and R Π Let l be the ohmic internal resistance of the cell starting in the Π region, and l be the length of the charge range in the Π region. The formula for calculating the equilibrium center point is: Among them, R c As the equilibrium center point, Δ(R) c R represents the degree of deviation from the equilibrium center point. i R is the ohmic internal resistance of the i-th cell. j Let δ be the ohmic internal resistance of the j-th cell, and take a value of 5% of R. c η is the energy conversion efficiency, and h is the equilibrium threshold correction factor.

7. A storage medium, characterized in that: The storage medium stores instructions and data to implement the battery EIS electrochemical parameter identification and energy balancing method according to any one of claims 1 to 6.

8. A battery EIS electrochemical parameter identification and energy balancing device, characterized in that: include: A processor and a storage medium; the processor loads and executes instructions and data in the storage medium to implement the battery EIS electrochemical parameter identification and energy balancing method according to any one of claims 1 to 6.

Images