A method and apparatus for real-time lithium plating detection of lithium-ion batteries based on equivalent circuit model

Abstract

This invention proposes a real-time lithium plating detection method and device for lithium-ion batteries based on an equivalent circuit model. The method detects lithium plating reactions in lithium-ion batteries using an equivalent circuit model that integrates mechanistic reaction information. During battery charging, this method can automatically and in real-time detect lithium plating based on a lithium plating detection threshold set in offline experiments. This guides battery users to adjust their charging strategies, preventing the continued lithium plating reaction and effectively improving battery safety and lifespan. This method relies solely on the voltage characteristics of the lithium-ion battery, which can be accurately and frequently sampled in existing battery management systems. Furthermore, the simulation model used in this method is an equivalent circuit model, requiring relatively low computational power. Therefore, this method is applicable to existing battery management systems for real-time lithium plating detection and has strong practicality.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery technology, and in particular to a method and apparatus for real-time lithium plating detection of lithium-ion batteries based on an equivalent circuit model. Background Technology

[0002] Lithium-ion batteries are widely used in 3C digital products, electric vehicles, and energy storage power stations. In these applications, lithium-ion batteries often require fast charging and low-temperature charging. However, improper charging strategies can easily trigger lithium plating, a side reaction that occurs during charging. This is a critical side reaction where lithium ions, which should be embedded in the active particles of the negative electrode, are directly reduced to lithium metal on the negative electrode surface. As the lithium plating reaction continues, the lithium metal on the negative electrode surface often grows in a dendritic pattern, eventually potentially puncturing the separator, causing an internal short circuit, and leading to thermal runaway, fire, explosion, and other safety accidents. Therefore, accurately and in real-time detecting the lithium plating reaction during the charging process is crucial for the safe application of lithium-ion batteries.

[0003] Currently, researchers both domestically and internationally can categorize lithium plating reaction detection in lithium-ion batteries into offline and online detection methods. Offline detection primarily involves disassembling the lithium-ion battery and employing techniques such as scanning electron microscopy and titration to assess the lithium metal content on the negative electrode surface. Given the irreversible damage that offline detection inflicts on lithium-ion batteries, it is mainly used in laboratory settings to achieve accurate lithium plating reaction detection. Online detection mainly utilizes mechanistic modeling and artificial intelligence to analyze the changes in characteristics such as relaxation voltage, impedance, and thickness of lithium-ion batteries to assess lithium plating status. Since most current lithium-ion battery management systems (BMS) can only capture voltage, current, and temperature signals, some online detection methods are currently insufficient to cover online lithium plating detection for all commercially available lithium-ion batteries. Furthermore, lithium plating detection methods based on lithium-ion battery relaxation voltage can only be performed after battery charging has ended, at which point the lithium plating reaction has already occurred, and its impact on battery safety and lifespan is irreversible. Therefore, there is an urgent need to develop an online lithium plating detection method based on the characteristics of the battery voltage curve, so as to realize the real-time detection of lithium plating reaction during battery charging, thereby adjusting the battery charging strategy in a timely manner and ensuring the safety of battery use. Summary of the Invention

[0004] To address the lack of existing real-time lithium plating detection technologies for lithium-ion batteries based on voltage characteristics, this invention provides a method and apparatus for real-time lithium plating detection of lithium-ion batteries based on an equivalent circuit model.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] On one hand, the present invention provides a real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model, comprising:

[0007] Construct an equivalent circuit model that integrates information on the reaction mechanism of lithium-ion batteries;

[0008] Hybrid pulse tests at different rates were conducted on lithium-ion batteries to obtain the actual battery voltage; based on the constructed equivalent circuit model, the same hybrid pulse test simulations at different rates were carried out to obtain the simulated battery voltage.

[0009] With the goal of minimizing the error between the simulated battery voltage and the actual battery voltage, the equivalent circuit model is parameterized by an optimization algorithm to obtain the parameterized equivalent circuit model.

[0010] Multiple safe charging experiments at different rates were conducted on lithium-ion batteries to obtain the actual measured voltage under each charge. Simulations of the multiple safe charging experiments at different rates were performed on lithium-ion batteries based on parameterized equivalent circuit models to obtain the simulated voltage under each charge.

[0011] Calculate the error between the actual measured voltage and the simulated voltage for each charging cycle to obtain the error sequence;

[0012] The lithium plating detection threshold is determined based on the distribution of the error sequence;

[0013] Real-time lithium plating detection is performed on lithium-ion batteries during the charging process based on lithium plating detection thresholds.

[0014] On the other hand, a real-time lithium plating detection device for lithium-ion batteries based on an equivalent circuit model is provided, comprising:

[0015] The first module is used to construct an equivalent circuit model that integrates information on the reaction mechanism of lithium-ion batteries.

[0016] The second module is used to obtain the actual battery voltage of the lithium-ion battery when the lithium-ion battery is subjected to mixed pulse tests at different rates; and to conduct the same mixed pulse test simulation at different rates based on the constructed equivalent circuit model to obtain the model simulated battery voltage.

[0017] The third module is used to identify parameters of the equivalent circuit model with the goal of minimizing the error between the simulated battery voltage and the actual battery voltage, and to obtain the parameterized equivalent circuit model by means of an optimization algorithm.

[0018] The fourth module is used to obtain the actual measured voltage of each charge under various safe charging conditions when the lithium-ion battery is subjected to multiple safe charging conditions at different rates. Based on the parameterized equivalent circuit model, the lithium-ion battery is simulated under various safe charging conditions at different rates, and the simulated voltage under each charge is obtained.

[0019] The fifth module is used to calculate the error between the actual measured voltage and the simulated voltage under each charging cycle, and to obtain the error sequence.

[0020] The sixth module is used to determine the lithium plating detection threshold based on the distribution of the error sequence;

[0021] The seventh module is used to perform real-time lithium plating detection on lithium-ion batteries during the charging process based on the lithium plating detection threshold.

[0022] On the other hand, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-mentioned lithium-ion battery real-time lithium plating detection method based on an equivalent circuit model.

[0023] On the other hand, the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described lithium-ion battery real-time lithium plating detection method based on an equivalent circuit model.

[0024] On the other hand, the present invention provides a computer program product stored on a computer-readable storage medium and including computer instructions that, when executed by a processor, cause a computer device to implement the steps of the above-described lithium-ion battery real-time lithium plating detection method based on an equivalent circuit model.

[0025] Compared with the prior art, the technical effects of the present invention are as follows:

[0026] To address the lack of existing real-time lithium plating detection technologies for lithium-ion batteries based on voltage characteristics, this invention provides a real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model. This method constructs an equivalent circuit model that integrates information about the lithium-ion battery's mechanistic reaction. Combined with experiments such as lithium-ion battery hybrid pulse testing and positive / negative half-cell testing, and with intelligent optimization algorithms, unknown parameters in the constructed equivalent circuit model can be accurately identified. In this case, the parameterized equivalent circuit model can accurately model the voltage curve during charging when the lithium-ion battery has not undergone lithium plating. However, when lithium plating occurs, an additional lithium plating polarization process is added to the charging process, causing the constructed equivalent circuit model to fail to accurately model the charging voltage curve under this condition, resulting in a significant increase in modeling error. By analyzing the modeling error of the equivalent circuit model regarding the charging voltage curve using Laida's rule in statistics, the initial state of charge (SOC) of the lithium-ion battery during lithium plating can be accurately identified. This guides the adjustment of charging strategies to prevent the continued occurrence of lithium plating, ensuring safe and lossless charging of the battery. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0028] Figure 1 This is a flowchart of a real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model in one embodiment.

[0029] Figure 2 This is a flowchart illustrating the construction and parameter identification of an equivalent circuit model that integrates lithium-ion battery mechanism reaction information in one embodiment. Figure 2 (a) Flowchart for parameter identification of equivalent circuit model that integrates lithium-ion battery mechanism reaction information. Figure 2 (b) is a first-order equivalent circuit model diagram;

[0030] Figure 3 This is a flowchart of setting the lithium plating detection threshold based on a parameterized equivalent circuit model in one embodiment.

[0031] Figure 4 This is a flowchart of a real-time lithium plating detection method for batteries based on a set lithium plating detection threshold, as described in one embodiment. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0033] To address the lack of existing real-time lithium plating detection technologies for lithium-ion batteries based on voltage characteristics, this paper refers to... Figure 1 One embodiment provides a real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model, comprising:

[0034] Construct an equivalent circuit model that integrates information on the reaction mechanism of lithium-ion batteries;

[0035] Hybrid pulse tests at different rates were conducted on lithium-ion batteries to obtain the actual battery voltage; based on the constructed equivalent circuit model, the same hybrid pulse test simulations at different rates were carried out to obtain the simulated battery voltage.

[0036] With the goal of minimizing the error between the simulated battery voltage and the actual battery voltage, the equivalent circuit model is parameterized by an optimization algorithm to obtain the parameterized equivalent circuit model.

[0037] Multiple safe charging experiments at different rates were conducted on lithium-ion batteries to obtain the actual measured voltage under each charge. Simulations of the multiple safe charging experiments at different rates were performed on lithium-ion batteries based on parameterized equivalent circuit models to obtain the simulated voltage under each charge.

[0038] Calculate the error between the actual measured voltage and the simulated voltage for each charging cycle to obtain the error sequence;

[0039] The lithium plating detection threshold is determined based on the distribution of the error sequence;

[0040] Real-time lithium plating detection is performed on lithium-ion batteries during the charging process based on lithium plating detection thresholds.

[0041] Among them, the construction and parameter identification of the equivalent circuit model that integrates the mechanism reaction information of lithium-ion batteries is achieved by incorporating the open-circuit potential of the positive and negative electrodes and the electrochemical reaction equations into the component equations of the equivalent circuit model, thereby improving the ability of the equivalent circuit model to simulate the battery voltage response. Based on the designed battery identification test, with the goal of minimizing the error between the simulated voltage of the equivalent circuit model and the actual battery voltage, a genetic algorithm is used as the optimizer to parameterize the equivalent circuit model.

[0042] If lithium plating occurs during the charging process of a lithium-ion battery, its internal physicochemical reaction processes will change. Specifically, a lithium-ion deposition reaction will be added to the original lithium-ion insertion reaction, thus affecting the voltage curve during charging. However, since the lithium plating reaction accounts for a relatively small proportion, its impact on the voltage curve is also relatively minor. Existing equivalent circuit models can only simulate the electrical characteristics of lithium-ion batteries and cannot simulate their internal physicochemical reactions, making it difficult to identify such subtle changes. To address this, by integrating the open-circuit potential information of the positive and negative electrodes and electrochemical reaction information of the lithium-ion battery into the equivalent circuit model, the voltage simulation capability of the equivalent circuit model is enhanced. This allows the model to capture the regular differences in the voltage curve during lithium-ion battery charging, enabling real-time lithium plating detection.

[0043] Reference Figure 2 The flowchart illustrates the construction and parameter identification of an equivalent circuit model that integrates information on the reaction mechanism of lithium-ion batteries. Figure 2 (a) Flowchart for parameter identification of the equivalent circuit model that integrates lithium-ion battery mechanism reaction information. Figure 2 (b) is a first-order equivalent circuit model diagram.

[0044] The equivalent circuit model includes an open-circuit voltage source (Uocv), an ohmic internal resistance (R0), and a polarization internal resistance (R0). The first-order equivalent circuit model of four circuit elements: ) and polarization capacitor (C1);

[0045] A lithium-ion battery was disassembled and half-cells with positive and negative electrodes were fabricated. The open-circuit potential Uocv of the positive electrode was obtained through small-current testing. p The open-circuit potential Uocv of the negative electrode n Among them: Uocv p Uocv represents the open-circuit potential of the positive electrode, in V. n This represents the open-circuit potential of the negative electrode, measured in V.

[0046] The open-circuit voltage Uocv of a lithium-ion battery is obtained based on a small current test. ;in This represents the open-circuit voltage of the full battery, measured in volts (V).

[0047] Equivalent replacement of open-circuit voltage source: Based on a genetic algorithm, the open-circuit voltage of the full cell is mapped to the open-circuit potential Uocv of the positive electrode. p Open circuit potential Uocv of the negative electrode n The difference is used to construct a mechanistic open-circuit voltage source model: .

[0048] The open-circuit potential Uocv of the positive electrode of a half-cell based on positive and negative electrodes obtained under different current I (current applied in charge-discharge tests, in A) test conditions. p The open-circuit potential Uocv of the negative electrode n Fitting the Tafel equation: .in: Represents overpotential, which is the difference between the actual positive / negative electrode potential and the open-circuit potential of the positive / negative electrode, in V; 'b' represents the Tafel slope, reflecting the sensitivity of the overpotential to logarithmic changes in current; 'b' represents the Tafel intercept, in V, which is related to the exchange current density of the electrochemical reaction and describes the intrinsic rate of the reaction.

[0049] Polarization resistance modeling: Incorporate the Tafel slope k and Tafel intercept b from the Tafel equation into the polarization branch of the first-order equivalent circuit model to model the polarization resistance. (Unit: Ω) is expressed as a function of current I:

[0050] ;

[0051] in: Represents the polarization resistance, which varies with current; I represents the measured or simulated applied DC or pulse current, in amperes (A).

[0052] Parameters to be identified: In the first-order equivalent circuit model, the parameters to be identified include:

[0053] R0: Ohmic internal resistance, in Ω, representing the internal and contact resistance of the lithium-ion battery;

[0054] C1: Polarization capacitance, unit F, describes the double layer and mass transfer effect;

[0055] k: Tafel slope;

[0056] b: Tafel intercept.

[0057] By identifying the aforementioned unknown parameters, the dynamics of the electrochemical mechanism of the battery during charging and discharging can be reflected, and this information can be used for lithium plating risk assessment.

[0058] Hybrid pulse tests at different rates were conducted on lithium-ion batteries to obtain the actual battery voltage. Simulations of the same hybrid pulse tests at different rates were then performed based on the constructed equivalent circuit model to obtain the simulated battery voltage. Minimizing the error between the simulated battery voltage and the actual battery voltage was the optimization objective. Unknown parameters R0, C1, k, and b in the equivalent circuit model were used as decision variables. A genetic algorithm was used to identify the parameters of the equivalent circuit model, resulting in a parameterized equivalent circuit model that incorporates mechanistic reaction information.

[0059] Assuming no lithium plating reaction occurs in the lithium-ion battery during charging, the error between the simulated voltage response of the equivalent circuit model and the measured voltage response of the target battery mainly comes from two parts: the inherent error of the model simulation and the instrument measurement error. Since the equivalent circuit model incorporates information such as the open-circuit potentials of the positive and negative electrodes and the electrochemical reaction process, the inherent error of the model simulation can be effectively reduced. Instrument measurement error is unavoidable, but it generally follows a normal distribution; therefore, increasing the number of test samples can effectively avoid the interference of instrument measurement error on setting the lithium plating detection threshold.

[0060] The first step in setting the lithium plating detection threshold is to determine safe charging conditions at various rates based on the manual, that is, to ensure that the battery will not undergo lithium plating under these charging rates. The second step is to conduct experiments and simulations using the above safe charging conditions for the target battery and the parameterized equivalent circuit model, and obtain the measured voltage and simulated voltage values ​​during the charging process.

[0061] Specifically, refer to Figure 3 The flowchart for setting the lithium plating detection threshold based on the parameterized equivalent circuit model shows that determining the lithium plating detection threshold includes the following steps:

[0062] Multiple safe charging experiments at different rates were conducted on lithium-ion batteries to obtain the actual measured voltage under each charge. Simulations of the multiple safe charging experiments at different rates were performed on lithium-ion batteries based on parameterized equivalent circuit models to obtain the simulated voltage under each charge.

[0063] The error between the actual measured voltage and the simulated voltage under each charging cycle is calculated to obtain the error sequence. The calculation of the error between the actual measured voltage and the simulated voltage under each charging cycle is as follows:

[0064] ;

[0065] In the formula For the first m Number of sampling points for voltage during the second charge For the first m The first charge The simulated voltage value corresponding to each sampling point For the first m The first charge The actual measured voltage corresponding to each sampling point , M This represents the total number of charging cycles. To ensure sufficient fitting error samples when setting subsequent thresholds, the number of charging cycles performed in this step must be no less than 50.

[0066] Determining the lithium plating detection threshold based on the distribution of the error sequence includes:

[0067] Determine the error sequence Does it follow a normal distribution?

[0068] If the error sequence L If it follows a normal distribution, then for the error sequence L The lithium plating detection threshold is set based on the 3σ rule. For error sequence The mean, σ, is used to represent the level of equilibrium error. L ) is the error sequence The standard deviation is used to measure the dispersion of errors. According to the 3σ rule, if the error sequence... L If it follows a normal distribution, then the lithium detection threshold is = μ ( L )+3σ( L This ensures that the error under normal fluctuations is almost (99.7%) within this range, and if it exceeds this range, it is judged that the risk of "lithium plating" is high.

[0069] If the error sequence L The error sequence does not follow a normal distribution. L Perform a logarithmic transformation to obtain ,based on Set the lithium plating detection threshold, i.e. And for Perform an inverse transform to obtain the lithium plating detection threshold. .

[0070] Reference Figure 4 This is a flowchart of real-time lithium plating detection for batteries based on a set lithium plating detection threshold. Real-time lithium plating detection is performed on lithium-ion batteries during charging based on the lithium plating detection threshold, including:

[0071] Experimental tests were conducted on the lithium-ion battery under the test charging conditions, and the measured voltage value of the battery was obtained in real time. At the same time, the experimental tests were simulated on the lithium-ion battery under the test charging conditions based on the parameterized equivalent circuit model, and the simulated voltage value of the model was obtained in real time.

[0072] The error between the current measured battery voltage and the simulated voltage is compared with a lithium plating detection threshold. If the error exceeds the threshold, it indicates a high risk of lithium plating within the lithium-ion battery or that a lithium plating reaction has already occurred, and the charging strategy should be adjusted promptly. Conversely, if the error is within the threshold, the battery is in a safe charging state. Based on this, real-time lithium plating detection during the charging process can be achieved.

[0073] Addressing the issue of lithium plating reactions easily induced in fast charging at room temperature and low-temperature charging scenarios for lithium-ion batteries, this invention utilizes an equivalent circuit model that integrates mechanistic reaction information to detect lithium plating reactions in lithium-ion batteries. Compared to previous techniques that rely on relaxation voltage characteristics after charging, this method can perform real-time and automatic lithium plating detection during battery charging, based on a lithium plating detection threshold set in offline experiments. This guides battery users to adjust their charging strategies, preventing the continued lithium plating reaction and effectively improving battery safety and lifespan. This method relies solely on the voltage characteristics of the lithium-ion battery, which can be accurately and frequently sampled in existing battery management systems. Furthermore, the simulation model used in this method is an equivalent circuit model, requiring relatively low computational power. Therefore, this method is capable of real-time lithium plating detection in existing battery management systems and has strong practical applicability.

[0074] In one embodiment, a real-time lithium plating detection device for lithium-ion batteries based on an equivalent circuit model is provided, comprising:

[0075] The first module is used to construct an equivalent circuit model that integrates information on the reaction mechanism of lithium-ion batteries.

[0076] The second module is used to obtain the actual battery voltage of the lithium-ion battery when the lithium-ion battery is subjected to mixed pulse tests at different rates; and to conduct the same mixed pulse test simulation at different rates based on the constructed equivalent circuit model to obtain the model simulated battery voltage.

[0077] The third module is used to identify parameters of the equivalent circuit model with the goal of minimizing the error between the simulated battery voltage and the actual battery voltage, and to obtain the parameterized equivalent circuit model by means of an optimization algorithm.

[0078] The fourth module is used to obtain the actual measured voltage of each charge under various safe charging conditions when the lithium-ion battery is subjected to multiple safe charging conditions at different rates. Based on the parameterized equivalent circuit model, the lithium-ion battery is simulated under various safe charging conditions at different rates, and the simulated voltage under each charge is obtained.

[0079] The fifth module is used to calculate the error between the actual measured voltage and the simulated voltage under each charging cycle, and to obtain the error sequence.

[0080] The sixth module is used to determine the lithium plating detection threshold based on the distribution of the error sequence;

[0081] The seventh module is used to perform real-time lithium plating detection on lithium-ion batteries during the charging process based on the lithium plating detection threshold.

[0082] In the fifth module, the error between the actual measured voltage and the simulated voltage under each charging cycle is calculated as follows:

[0083] ;

[0084] In the formula For the first m Number of sampling points for voltage during the second charge For the first m The first charge The simulated voltage value corresponding to each sampling point For the first m The first charge The actual measured voltage corresponding to each sampling point , M This represents the total number of charging cycles.

[0085] Furthermore, the sixth module includes:

[0086] The judgment module is used to judge the error sequence. Does it follow a normal distribution?

[0087] The first lithium detection threshold setting module is used to set the error sequence. L Setting the lithium detection threshold under the condition that it follows a normal distribution, including the error sequence. L The lithium plating detection threshold is set based on the 3σ rule, where the lithium detection threshold is: threshold = μ ( L )+3σ( L ), For error sequence The mean, σ( L ) is the error sequence Standard deviation;

[0088] The second lithium detection threshold setting module is used to set the error sequence. L Setting lithium detection thresholds when the distribution does not conform to a normal distribution, including for error sequences. L Perform a logarithmic transformation to obtain ,based on Set the lithium plating detection threshold, i.e. And for Perform an inverse transform to obtain the lithium plating detection threshold. .

[0089] Furthermore, the seventh module includes:

[0090] The battery voltage measurement module is used to acquire the battery voltage value of the lithium-ion battery under test in real time during the experimental test under the charging condition under test.

[0091] The model simulation voltage acquisition module is used to simulate the experimental test of the lithium-ion battery under test under the test charging condition based on the parameterized equivalent circuit model, and to acquire the model simulation voltage value in real time.

[0092] The real-time lithium plating detection module compares the error between the current measured battery voltage and the simulated voltage with the lithium plating detection threshold. If the error is greater than the lithium plating detection threshold, it indicates a high risk of lithium plating reaction occurring inside the lithium-ion battery or that a lithium plating reaction has already occurred, and the charging strategy for the lithium-ion battery should be adjusted promptly. Conversely, if the error is not greater than the lithium plating detection threshold, it indicates that the lithium-ion battery is in a safe charging state.

[0093] On the other hand, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model provided in any of the above embodiments. The computer device can be a server. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores sample data. The network interface of the computer device is used for communication with external terminals via a network connection.

[0094] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model provided in any of the above embodiments.

[0095] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0096] Matters not covered in this invention are common knowledge.

[0097] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

[0099] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for real-time lithium plating detection of lithium-ion batteries based on an equivalent circuit model, characterized in that, include: Construct an equivalent circuit model that integrates information on the mechanism and reaction of lithium-ion batteries; Hybrid pulse tests at different rates were conducted on lithium-ion batteries to obtain the actual battery voltage. Based on the constructed equivalent circuit model, the same mixed pulse test simulation at different rates was carried out to obtain the simulated battery voltage. With the goal of minimizing the error between the simulated battery voltage and the actual battery voltage, the equivalent circuit model is parameterized by an optimization algorithm to obtain the parameterized equivalent circuit model. Multiple safe charging experiments at different rates were conducted on lithium-ion batteries to obtain the actual measured voltage under each charge. Simulations of the multiple safe charging experiments at different rates were performed on lithium-ion batteries based on parameterized equivalent circuit models to obtain the simulated voltage under each charge. Calculate the error between the actual measured voltage and the simulated voltage for each charging cycle to obtain the error sequence; Determining the lithium plating detection threshold based on the distribution of the error sequence includes: Determine the error sequence L =[ L 1, L 2, …, L M Does it follow a normal distribution? For the first m The error between the actual measured voltage and the simulated voltage during the first charge. m= 1,2..., M , M This represents the total number of charging cycles. If the error sequence L If it follows a normal distribution, then for the error sequence L The lithium plating detection threshold is set based on the 3σ rule, where the lithium detection threshold is: threshold = μ ( L )+3σ( L ), For error sequence The mean, σ( L ) is the error sequence Standard deviation; If the error sequence L The error sequence does not follow a normal distribution. L Perform a logarithmic transformation to obtain ,based on Set the lithium plating detection threshold, i.e. And for Perform an inverse transform to obtain the lithium plating detection threshold. ; Real-time lithium plating detection is performed on lithium-ion batteries during the charging process based on lithium plating detection thresholds.

2. The real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model according to claim 1, characterized in that, Constructing an equivalent circuit model that integrates information on the mechanism and reaction of lithium-ion batteries, including: The equivalent circuit model is a first-order equivalent circuit model containing four circuit elements: an open-circuit voltage source, an ohmic internal resistance, a polarization internal resistance, and a polarization capacitor. A lithium-ion battery was disassembled and half-cells with positive and negative electrodes were fabricated. The open-circuit potential Uocv of the positive electrode was obtained through small-current testing. p The open-circuit potential Uocv of the negative electrode n ; The open-circuit voltage Uocv of a lithium-ion battery is obtained based on a small current test. ; Based on a genetic algorithm, the open-circuit voltage of the full cell is mapped to the open-circuit potential Uocv of the positive electrode. p Open circuit potential Uocv of the negative electrode n The difference is used to construct a mechanistic open-circuit voltage source model: ; The open-circuit potential Uocv of the positive electrode obtained from a half-cell with positive and negative electrodes under different current I test conditions. p The open-circuit potential Uocv of the negative electrode n Fitting the Tafel equation: ,in: Represents overpotential, which is the difference between the actual positive / negative electrode potential and the open-circuit potential of the positive / negative electrode, in V; b represents the Tafel slope; b represents the Tafel intercept. By incorporating the Tafel slope k and Tafel intercept b from the Tafel equation into the polarization branch of the first-order equivalent circuit model, the polarization internal resistance is reduced. Expressed as a function of current I, i.e. : The parameters to be identified in the first-order equivalent circuit model include the ohmic internal resistance R0, the polarization capacitance C1, the Tafel slope k, and the Tafel intercept b.

3. The real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model according to claim 1, characterized in that, The error between the actual measured voltage and the simulated voltage for each charging cycle is calculated as follows: In the formula For the first m Number of sampling points for voltage during the second charge For the first m The first charge i The simulated voltage value corresponding to each sampling point For the first m The first charge i The actual measured voltage corresponding to each sampling point m= 1,2..., M , M This represents the total number of charging cycles.

4. The real-time lithium plating detection method for lithium-ion batteries based on an equivalent circuit model according to claim 1, characterized in that, Real-time lithium plating detection of lithium-ion batteries during charging is performed based on lithium plating detection thresholds, including: Experimental tests were conducted on the lithium-ion battery under the test charging conditions, and the measured voltage value of the battery was obtained in real time. At the same time, the experimental tests were simulated on the lithium-ion battery under the test charging conditions based on the parameterized equivalent circuit model, and the simulated voltage value of the model was obtained in real time. The error between the current measured battery voltage and the simulated voltage is compared with the lithium plating detection threshold. If the error is greater than the lithium plating detection threshold, it indicates that the risk of lithium plating reaction occurring inside the current lithium-ion battery is high or that lithium plating reaction has already occurred, and the charging strategy of the lithium-ion battery should be adjusted in a timely manner. Conversely, if the error is not greater than the lithium plating detection threshold, it indicates that the lithium-ion battery is in a safe charging state.

5. A real-time lithium plating detection device for lithium-ion batteries based on an equivalent circuit model, characterized in that, include: The first module is used to construct an equivalent circuit model that incorporates information about the mechanism and reaction of lithium-ion batteries. The second module is used to obtain the actual battery voltage of the lithium-ion battery when the lithium-ion battery is subjected to mixed pulse tests at different rates; and to conduct the same mixed pulse test simulation at different rates based on the constructed equivalent circuit model to obtain the model simulated battery voltage. The third module is used to identify parameters of the equivalent circuit model by means of an optimization algorithm, with the goal of minimizing the error between the simulated battery voltage and the actual battery voltage, to obtain the parameterized equivalent circuit model. The fourth module is used to obtain the actual measured voltage of each charge under various safe charging conditions when the lithium-ion battery is subjected to multiple safe charging conditions at different rates. Based on the parameterized equivalent circuit model, the lithium-ion battery is simulated under various safe charging conditions at different rates, and the simulated voltage under each charge is obtained. The fifth module is used to calculate the error between the actual measured voltage and the simulated voltage under each charging cycle, and to obtain the error sequence. The sixth module is used to determine the lithium plating detection threshold based on the distribution of the error sequence. The sixth module includes: The judgment module is used to judge the error sequence. L =[ L 1, L 2, …, L M Does it follow a normal distribution? For the first m The error between the actual measured voltage and the simulated voltage during the first charge. m= 1,2..., M , M This represents the total number of charging cycles. The first lithium detection threshold setting module is used to set the error sequence. L Setting the lithium detection threshold under the condition that it follows a normal distribution, including the error sequence. L The lithium plating detection threshold is set based on the 3σ rule, where the lithium detection threshold is: threshold = μ ( L )+3σ( L ), For error sequence The mean, σ( L ) is the error sequence Standard deviation; The second lithium detection threshold setting module is used to set the error sequence. L Setting lithium detection thresholds when the distribution does not follow a normal distribution, including for error sequences. L Perform a logarithmic transformation to obtain ,based on Set the lithium plating detection threshold, i.e. And for Perform an inverse transform to obtain the lithium plating detection threshold. ; The seventh module is used to perform real-time lithium plating detection on lithium-ion batteries during the charging process based on the lithium plating detection threshold.

6. The real-time lithium plating detection device for lithium-ion batteries based on an equivalent circuit model according to claim 5, characterized in that, The equivalent circuit model is a first-order equivalent circuit model containing four circuit elements: an open-circuit voltage source, an ohmic internal resistance, a polarization internal resistance, and a polarization capacitor.

7. The real-time lithium plating detection device for lithium-ion batteries based on an equivalent circuit model according to claim 5, characterized in that, In the fifth module, the error between the actual measured voltage and the simulated voltage under each charging cycle is calculated as follows: In the formula For the first m Number of sampling points for voltage during the second charge For the first m The first charge i The simulated voltage value corresponding to each sampling point For the first m The first charge i The actual measured voltage corresponding to each sampling point m= 1,2..., M , M This represents the total number of charging cycles.

8. The real-time lithium plating detection device for lithium-ion batteries based on an equivalent circuit model according to claim 5, characterized in that, Module 7 includes: The battery voltage measurement module is used to acquire the battery voltage value of the lithium-ion battery under test in real time during the experimental test under the charging condition under test. The model simulation voltage acquisition module is used to simulate the experimental test of the lithium-ion battery under test under the test charging condition based on the parameterized equivalent circuit model, and to acquire the model simulation voltage value in real time. The real-time lithium plating detection module compares the error between the current measured battery voltage and the simulated voltage with the lithium plating detection threshold. If the error is greater than the lithium plating detection threshold, it indicates a high risk of lithium plating reaction occurring inside the lithium-ion battery or that a lithium plating reaction has already occurred, and the charging strategy for the lithium-ion battery should be adjusted in a timely manner. Conversely, if the error is not greater than the lithium plating detection threshold, it indicates that the lithium-ion battery is in a safe charging state.

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