A method for detecting lithium plating of a lithium-ion battery based on pressure signals

Abstract

The present invention relates to the technical field of lithium-ion battery detection, and discloses a method for detecting lithium deposition in a lithium-ion battery based on a pressure signal, including the following steps: charging a lithium-ion battery with lithium deposition in a fixture at a temperature T to obtain a pressure signal set Y' and a voltage signal set X'. This detection method simulates the usage of a lithium-ion battery in a fixed constraint scenario. By charging 4 experimental lithium-ion batteries in an incubator at different charging rates, the pressure information and voltage information during the charging process of the lithium-ion battery at different rates are determined, and the differential pressure curve during the charging process of each battery is calculated. By determining whether a lithium deposition characteristic peak appears in the differential pressure curve, it can be judged whether lithium deposition occurs in the lithium-ion battery, and then the lithium-ion battery with unqualified lithium deposition can be detected. It can not only avoid damaging the lithium-ion battery, but also reflect the phase change process of the graphite electrode material during the charging process of the lithium-ion battery through the differential pressure curve.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery detection, and specifically to a method for detecting lithium plating of a lithium-ion battery based on a pressure signal. Background Art

[0002] Lithium-ion power batteries have been widely used in electric vehicles, military and civilian small and medium-sized electrical appliances due to their high energy and pollution-free characteristics. In actual applications, lithium-ion batteries are often used in a fixed constraint environment. Lithium-ion batteries are subject to an initial pre-tightening force given by the fixed constraint. Since the volume of the battery expands during the lithiumation process of the lithium battery, abnormal expansion caused by side reactions inside the battery (lithium plating, SEI growth, and gas generation) will lead to a series of safety problems such as a sharp decline in battery capacity and internal short circuit. Graphite is the most commonly used material for the negative electrode of lithium batteries. During the process of lithium deintercalation (intercalation), the change in the lattice spacing in the positive and negative electrode materials causes a change in the electrode volume. Existing research shows that during the charging process, the expansion of the graphite anode is significantly higher than that of the LFP cathode. Therefore, the change in the expansion force of the lithium-ion battery is essentially dominated by the graphite anode. When charging at a high current, lithium deposition occurs on the anode, and the elemental lithium and the SEI formed on the anode will cause an additional increase in the battery thickness and irreversible changes, resulting in increased expansion.

[0003] The existing methods for detecting lithium plating in lithium-ion batteries generally perform a first-order derivative operation on the open-circuit voltage and the rest time during the rest stage after charging. This method requires a long rest time after charging, and when the amount of lithium plating is small, it may not be detectable, affecting the effectiveness of lithium plating detection in lithium-ion batteries; another type of method is to judge whether lithium plating has occurred by observing the lithium deposition on the negative electrode plate after disassembling the battery. This is a destructive detection method. To improve the efficiency of lithium plating detection, the present invention proposes a method for detecting lithium plating of a lithium-ion battery based on a pressure signal. During the charging process of the lithium-ion battery, the actual use scenario is simulated, a pressure signal is introduced, and based on the pressure and voltage information during the charging stage, a first-order differential curve of pressure and voltage, that is, a differential pressure curve, is proposed. In the differential pressure curve, if a lithium plating characteristic peak appears within a specific voltage range, it indicates that lithium plating occurs under this condition. The proposed method can effectively detect lithium plating phenomena under different temperatures, rates, and battery types; it avoids the disadvantages of detecting lithium plating by long-term rest after charging, will greatly improve the efficiency of lithium plating detection, support the development of new fast charging methods to optimize and obtain the optimal charging time, and therefore a method for detecting lithium plating of a lithium-ion battery based on a pressure signal is proposed to solve the above problems. Summary of the Invention

[0004] In view of the deficiencies of the prior art, the present invention provides a method for detecting lithium plating of a lithium-ion battery based on pressure signals, which has the advantages of being able to simulate the actual application scenario of the lithium-ion battery, realizing lithium plating detection through the change of the battery expansion force under fixed constraints, avoiding damage to the lithium-ion battery, and avoiding long-term open-circuit static placement, and can improve the lithium plating detection efficiency of the lithium-ion battery, etc., and solves the problems mentioned in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for detecting lithium plating of a lithium-ion battery based on pressure signals, comprising the following steps:

[0006] S1: Place the lithium-plated lithium-ion battery in a fixture for charging at temperature T, and obtain a set of pressure signals Y' and a set of voltage signals X';

[0007] S2: Perform locally weighted regression processing on the obtained set of pressure signals Y' and set of voltage signals X', interpolate and extrapolate the correlation function of the set of pressure signals Y' and set of voltage signals X', calculate the first derivative of the correlation function, and fit the differential pressure curve L1;

[0008] S3: Determine the optimal pre-tightening force F0 of a normal lithium-ion battery, place the normal lithium-ion battery in a fixture for charging at temperature T and the optimal pre-tightening force F0, and obtain a set of pressure signals Y'' and a set of voltage signals X'';

[0009] S4: Perform locally weighted regression processing on the obtained set of pressure signals Y'' and set of voltage signals X'', interpolate and extrapolate the correlation function of the set of pressure signals Y'' and set of voltage signals X'', calculate the first derivative of the correlation function, and fit the differential pressure curve L2;

[0010] S5: Screen out the characteristic peak voltage intervals of the differential pressure curve L1 and the differential pressure curve L2 by the slope method;

[0011] S6: Perform peak detection by the Hilbert transform method to determine whether a lithium plating characteristic peak appears. If so, it is determined that the lithium-ion battery has lithium plating, otherwise it is determined that the lithium-ion battery has no lithium plating.

[0012] Preferably, the step of determining the optimal pre-tightening force F0 in step S3 is:

[0013] Set the initial pre-tightening force F1, and calculate the optimal pre-tightening force F0 through the initial pre-tightening force F1, the proportionality factor K1, the proportionality factor K2, and the contact area S between the pressure sensor and the battery. The expression is:

[0014] F0 = K1 * K2 * F1 * S

[0015] Preferably, the specific steps for obtaining the differential pressure curve L1 are:

[0016] S2.1: Perform weighted regression on the data points (X i , Y i ) in the pressure signal set Y' and the voltage signal set X', and the expression is:

[0017]

[0018] where W i is the weight vector, X i is a voltage value in the voltage signal set X', Y i is a pressure value in the pressure signal set Y', W T x i +b is the predicted value, and b is the intercept;

[0019] S2.2: With a voltage value X i in the voltage signal set X' as the center point, define the tricube weight function, and the expression is:

[0020]

[0021] where c i is the predicted value corresponding to a voltage value X i in the voltage signal set X' by the weighted linear regression in step S2.1, and d i is the distance of the farthest neighboring point along the horizontal axis within the span;

[0022] S2.3: For each voltage value X i in the voltage signal set X', obtain the corresponding predicted value c i through weighted linear regression, calculate the weight W i of the i-th data point of the predicted value c i and the intercept b, and for each voltage value X i in the voltage set X', obtain a predicted dependent variable H pred by fitting a linear model through weighted least squares, and the expression is:

[0023] H pred i =W i T x i +b

[0024] S2.4: Repeat step S2.3, and obtain multiple predicted pressure values H pred i through the local weighted linear model, form a set of data points (X, H pred i ) and fit them into a pressure-voltage curve Q;

[0025] S2.5: Interpolate and extrapolate the correlation function of the pressure-voltage curve Q after local weighted regression processing, calculate the first derivative of the correlation function, and fit to obtain the differential pressure curve L1.

[0026] Preferably, the steps for obtaining the differential pressure curve L2 are the same as those for the differential pressure curve L1.

[0027] Preferably, the specific steps in step S5 are as follows:

[0028] S5.1: Store the voltage data in the differential pressure curves L1 and L2 in the voltage signal sets X' and X'', and store the first derivative data of the differential pressure curves L1 and L2 in the arrays P1 and P2.

[0029] S5.2: Define a difference function diff(x) to represent the difference between the two arrays P1 and P2, and the function expression is:

[0030] diff(x) = P1 - P2

[0031] S5.3: Calculate the slope of the difference function diff(x), that is, the first derivative D1(x).

[0032] S5.4: Find the X value at which the first derivative D1(x) first exceeds the threshold τ, which is the starting value of the voltage interval of the lithium plating characteristic peak of the differential pressure curves L1 and L2. voltage This value is the starting value of the voltage interval of the lithium plating characteristic peak of the differential pressure curves L1 and L2.

[0033] Preferably, the specific steps in step S6 are as follows:

[0034] S6.1: Store the voltage data in the differential pressure curves L1 and L2 in the voltage signal sets X' and X'', and store the first derivative data of the differential pressure curves L1 and L2 in the arrays P1 and P2.

[0035] S6.2: Denote each data point in the differential pressure curve L1 as P1(x), and convert P1(x) into a complex signal U(x), and the expression is:

[0036] U1(x) = P1(x) + iH(P1(x))

[0037] where the imaginary part is the Hilbert transform H(P1(x)) of the original signal.

[0038] S6.3: The definition of the Hilbert transform H(P1(x)) is:

[0039]

[0040] Among them, P.V. represents the principal value of the integral;

[0041] S6.4: The complex signal U1(x) gives the envelope of the signal;

[0042]

[0043] S6.5: By detecting the local maximum value in the modulus |U1(x)| of the complex signal, the peak position of the signal is found, and the expression is;

[0044] ||U1(N i )|| > |U1(N i-1 )| and |U1(N i )| > |U1(N i+1 )|

[0045] Among them, N i is the voltage where the peak is located;

[0046] S6.6: Traverse all the data and eliminate the voltage values that are not within the voltage characteristic interval;

[0047] S6.7: The modulus of the complex signal U1(x) is the intensity of the characteristic peak, denoted as R1;

[0048] S6.8: If the peak intensity R1 > R in a specific interval of the differential pressure curve of the experimental lithium-ion battery max it is considered that lithium plating has occurred.

[0049] Compared with the prior art, the present invention provides a method for detecting lithium plating of a lithium-ion battery based on a pressure signal, having the following beneficial effects:

[0050] 1. The method for detecting lithium plating of a lithium-ion battery based on a pressure signal, by simulating the use of a lithium-ion battery in a fixed constraint scenario, charging 4 experimental lithium-ion batteries in an incubator at different charging rates, determining the pressure information and voltage information during the charging process of the lithium-ion battery at different rates, and calculating the differential pressure curve during the charging process of each battery, and determining whether a lithium plating characteristic peak appears in the differential pressure curve, can determine whether lithium plating occurs in the lithium-ion battery, and further detect the lithium-ion battery with unqualified lithium plating. It can not only avoid damaging the lithium-ion battery, but also reflect the phase change process of the graphite electrode material during the charging process of the lithium-ion battery through the differential pressure curve.

[0051] 2. The lithium plating detection method for lithium-ion batteries based on pressure signals. Due to the special nature of LFP batteries, the conventional relaxation voltage method (VRP) cannot be used for detection. Therefore, a pressure signal is introduced as a detection means in multiple physical fields. The effectiveness of this method under different charging currents is verified using 4 different charging currents. Compared with the above VRP method, the results from a three-electrode battery show that lithium plating has occurred during charging at a high current (≥0.75C), but the conventional VRP method did not detect the lithium plating. On the contrary, the corresponding characteristics appear in the differential pressure curve, enabling precise detection. This method is applicable to various charging temperatures, rates, and battery types, and these advantages are particularly useful for electric vehicles, which will greatly reduce the incidence of lithium plating and support the development of new fast charging methods to optimize and obtain the optimal charging time. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic flow chart of a lithium plating detection method for lithium-ion batteries based on pressure signals proposed by the present invention;

[0053] Figure 2 Relationship curve diagram of the pressure change and full voltage of 4 experimental lithium-ion batteries of the same model under different charging rates in the lithium plating detection method for lithium-ion batteries based on pressure signals proposed by the present invention;

[0054] Figure 3 Schematic diagram of the differential pressure curve of 4 experimental lithium-ion batteries of the same model under different charging rates in the lithium plating detection method for lithium-ion batteries based on pressure signals proposed by the present invention;

[0055] Figure 4 Potential diagram of the graphite negative electrode in the lithium plating detection method for lithium-ion batteries based on pressure signals proposed by the present invention, where 4 experimental lithium-ion batteries of the same model are subjected to in-situ three-electrode processing;

[0056] Figure 5 Relaxation voltage curve diagram of 4 experimental lithium-ion batteries of the same model under different charging rates in the lithium plating detection method for lithium-ion batteries based on pressure signals proposed by the present invention;

[0057] Figure 6 Schematic diagram of large-area lithium plating after charging 4 experimental lithium-ion batteries of the same model at different charging rates in the lithium plating detection method for lithium-ion batteries based on pressure signals proposed by the present invention;

[0058] Figure 7 Schematic diagram of the experimental equipment in the lithium plating detection method for lithium-ion batteries based on pressure signals proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0060] Please refer to Figure 1-7 , a method for detecting lithium deposition in a lithium-ion battery based on a pressure signal, comprising the following steps:

[0061] S1: Place the lithium-deposited lithium-ion battery in a fixture for charging at temperature T, and obtain a pressure signal set Y' and a voltage signal set X';

[0062] S2: Perform locally weighted regression processing on the obtained pressure signal set Y' and voltage signal set X', interpolate and extrapolate the correlation function of the pressure signal set Y' and voltage signal set X', calculate the first derivative of the correlation function, and fit the differential pressure curve L1;

[0063] S3: Determine the optimal pre-tightening force F0 of a normal lithium-ion battery, place the normal lithium-ion battery in a fixture for charging at temperature T and the optimal pre-tightening force F0, and obtain a pressure signal set Y'' and a voltage signal set X'';

[0064] S4: Perform locally weighted regression processing on the obtained pressure signal set Y'' and voltage signal set X'', interpolate and extrapolate the correlation function of the pressure signal set Y'' and voltage signal set X'', calculate the first derivative of the correlation function, and fit the differential pressure curve L2;

[0065] S5: Screen out the characteristic peak voltage intervals of the differential pressure curve L1 and the differential pressure curve L2 by the slope method;

[0066] S6: Perform peak detection by the Hilbert transform method to determine whether a lithium deposition characteristic peak appears. If so, it is determined that the lithium-ion battery has lithium deposition; otherwise, it is determined that the lithium-ion battery has no lithium deposition.

[0067] In a constant temperature incubator at room temperature of 25°C, charge 4 three-electrode soft-pack batteries of the same specification at different charging rates (0.2C, 0.5C, 0.75C, 1C) to obtain the pressure signal and voltage signal of the lithium-ion battery during charging.

[0068] It should be noted that among the currently common battery materials, including but not limited to the cathode materials such as lithium iron phosphate, lithium manganese iron phosphate, and ternary materials, and the anode materials such as graphite and silicon-based materials, during the process of lithium deintercalation (intercalation), the change in the lattice spacing in the positive and negative electrode materials causes a change in the electrode volume, which is then converted into the pressure change collected by the sensor.

[0069] The capacity of the lithium-ion battery with a lithium metal reference electrode used in this embodiment is 10,000 mAh. This battery contains a positive electrode plate, two negative electrode plates, and a reference electrode. The lithium-ion battery with a copper-gold (Cu-Au) alloy reference electrode is a three-electrode battery. The material of the positive electrode plate is LiFePO4, the material of the negative electrode plate is graphite, and the reference electrode is a lithium metal-coated conductive copper wire.

[0070] If the pre-tightening force is too small, it cannot ensure good contact between battery components; if the pre-tightening force is too large, the battery electrodes will be overly squeezed, resulting in an impact on ion transmission and causing excessive capacity attenuation.

[0071] The steps to determine the optimal pre-tightening force F0 in step S3 are as follows:

[0072] Set the initial pre-tightening force F1 = 110 kPa. Calculate the optimal pre-tightening force F0 through the initial pre-tightening force F1, the proportionality factor K1, the proportionality factor K2, and the contact area S between the pressure sensor and the battery. K1*K2 is taken as 1.5, and the contact area between the pressure sensor and the battery is a circle with a diameter of 3 cm. The expression is:

[0073] F0 = K1*K2*F1*S = 116.632 N

[0074] Conduct a charging experiment on the above lithium-ion battery with a lithium metal reference electrode under the condition of the optimal pre-tightening force F0. Charge it to 3.65 V at four different rates of 0.2C, 0.5C, 0.75C, and 1C respectively, and then charge it at a constant voltage of 3.65 V until the current is less than or equal to 0.1 A to obtain the pressure signal and voltage signal of the experimental lithium-ion battery during the charging process.

[0075] The specific steps to obtain the differential pressure curve L1 are as follows:

[0076] S2.1: Perform weighted regression processing on the data points (X i , Y i ) of the pressure signal set Y' and the voltage signal set X'. The expression is:

[0077]

[0078] Among them, W i is the weight vector, X i is a voltage value in the voltage signal set X', Y i is a pressure value in the pressure signal set Y', W T x i + b is the predicted value, and b is the intercept;

[0079] S2.2: Take a voltage value X in the voltage signal set X' i As the center point, define the three-cube weight function, the expression is:

[0080]

[0081] Among them, c i Step S2.1 is to perform weighted linear regression on a voltage value X in the voltage signal set X' i The corresponding predicted value, d i is the distance along the horizontal axis from the farthest neighboring point within the span;

[0082] S2.3: Set each voltage value X in the voltage signal set X' i The corresponding predicted value c is obtained by weighted linear regression i , calculate the predicted value c i The weight W of the i-th data point i and intercept b, convert each voltage value X in the voltage set X' i The linear model is fitted by weighted least squares method to obtain a predicted dependent variable H pred , the expression is:

[0083] H pred i =W i T x i +b

[0084] S2.4: Repeat step S2.3 to obtain multiple predicted pressure values ​​H through the local weighted linear model pred i , forming a set of data points (X,H pred i ) and fitted into a pressure-voltage curve Q;

[0085] S2.5: For the pressure-voltage curve Q after the local weighted regression processing, interpolate and extrapolate the correlation function of the pressure-voltage curve Q, calculate the first-order derivative of the correlation function, and fit to obtain the differential pressure curve L1. The steps for obtaining the differential pressure curve L2 are the same as those for the differential pressure curve L1.

[0086] The specific steps in step S5 are:

[0087] S5.1: storing the voltage data in the differential pressure curve L1 and the differential pressure curve L2 in the voltage signal set X' and the voltage signal set X'', and storing the first-order derivative data of the differential pressure curve L1 and the differential pressure curve L2 in the arrays P1 and P2;

[0088] S5.2: Define a difference function diff(x) to represent the difference between two arrays P1 and P2. The function expression is:

[0089] diff(x) = P1 - P2

[0090] S5.3: Calculate the slope of the difference function diff(x), i.e., the first derivative D1(x);

[0091] S5.4: Find the first X value where the first derivative D1(x) exceeds the threshold τ, voltage which is the starting value of the voltage interval of the lithium plating characteristic peak of the differential pressure curve L1 and the differential pressure curve L2.

[0092] Specifically in step S6:

[0093] S6.1: Store the voltage data in the differential pressure curves L1 and L2 in the voltage signal sets X' and X'', and store the first derivative data in the differential pressure curves L1 and L2 in the arrays P1 and P2;

[0094] S6.2: Denote each data point in the differential pressure curve L1 as P1(x), and convert P1(x) into a complex signal U(x). The expression is:

[0095] U1(x) = P1(x) + iH(P1(x))

[0096] where the imaginary part is the Hilbert transform H(P1(x)) of the original signal;

[0097] S6.3: The definition of the Hilbert transform H(P1(x)) is:

[0098]

[0099] where P.V. represents the principal value of the integral;

[0100] S6.4: The complex signal U1(x) gives the envelope of the signal;

[0101]

[0102] S6.5: By detecting the local maximum in the modulus value |U1(x)| of the complex signal, find the peak position of the signal. The expression is;

[0103] ||U1(N i )|| > |U1(N i-1 )| and |U1(N i )| > |U1(N i+1 )|

[0104] where N iis the voltage at the peak;

[0105] S6.6: Traverse all the data and eliminate the voltage values outside the voltage characteristic range;

[0106] S6.7: The modulus value of the complex signal U1(x) is the intensity of the characteristic peak, denoted as R1;

[0107] S6.8: If the peak intensity R1 of the differential pressure curve in a specific interval of the experimental lithium-ion battery > R max it is considered that lithium plating occurs.

[0108] Similar to the incremental capacity curve (IC curve), the differential pressure curve of a normal battery (≤C / 2) shows 5 stages of graphite phase change, as shown in the following formula, where (0 < a < 1). When a = 1, it means that lithium ions are completely embedded in the graphite negative electrode. At this time, the graphite electrode is transformed from C to LiC6, reaching the maximum expansion state. When lithium deposition occurs at the negative electrode, it causes an abnormal increase in the battery thickness, and the battery expansion force shows a difference from that of a normal battery. This also explains the reason for the appearance of a new peak in the differential pressure curve of a lithium-plated battery.

[0109]

[0110] During specific verification, the negative electrode potential during the charging process is tested using a three-electrode (the manufacturing method of the three-electrode battery has been described in other patents). As shown in the figure, the negative electrode potential is significantly lower than 0V at the new peak of the differential pressure curve, indicating that lithium plating occurs at the negative electrode. The detection method in this embodiment has good detection accuracy.

[0111] In view of existing research, Figure 6 The following are the detection results of using the relaxation voltage method to detect the above 4 types of batteries under the same charging conditions. As shown by the results of the above in-situ three-electrode, when the charging rate (≥0.75C), lithium plating occurs in the experimental battery, but no corresponding characteristics (peak valleys appear in the relaxation voltage diagram of the lithium-plated battery) appear in the relaxation voltage diagram. This shows that for LFP batteries, the method described can bring higher detection accuracy.

[0112] The three-electrode verification results are in line with the experimental expectations, that is, within the voltage range greater than 3.49V, a lithium-plating characteristic peak appears in the lithium-plated lithium-ion battery.

[0113] The detection results of the experiment are shown in the following table:

[0114] Experiment Charging condition Intensity of lithium plating characteristic peak Voltage range Conclusion 1 25℃-0.2C No characteristic peak - No lithium plating occurred 2 25℃-0.5C No characteristic peak - No lithium plating occurred 3 25℃-0.75C 480 N / V 3.49-3.65V Lithium plating occurred 4 25℃-1C 510 N / V 3.51-3.65V Lithium plating occurred

[0115] In summary, the lithium plating detection method for lithium-ion batteries based on pressure signals can accurately determine whether lithium plating occurs in lithium-ion batteries by determining the lithium plating characteristic peaks in a specific voltage range of the differential pressure curve of lithium-ion batteries. Compared with the prior art, the lithium plating detection method of the present invention can not only avoid damaging lithium-ion batteries, but also reflect the phase change process of the graphite electrode material during the charging process of lithium-ion batteries through the differential pressure curve, and infer the end time of lithium deposition.

[0116] It should be noted that the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0117] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A lithium ion battery lithium deposition detection method, characterized in that: The following steps are involved: S1: placing the lithium-ion battery that has been lithium-deposited in a fixture for charging at a temperature T, and obtaining a pressure signal set Y' and a voltage signal set X'; S2: Perform local weighted regression processing on the acquired pressure signal set Y' and voltage signal set X', interpolate and extrapolate the correlation function of the pressure signal set Y' and the voltage signal set X', calculate the first-order derivative of the correlation function, and fit the differential pressure curve L1; S3: Determine the optimal preload force F0 for a normal lithium-ion battery, place the normal lithium-ion battery in a fixture for charging at temperature T and the optimal preload force F0, and obtain a pressure signal set Y'' and a voltage signal set X''; S4: performing local weighted regression processing on the acquired pressure signal set Y'' and voltage signal set X'', interpolating and extrapolating the correlation function of the pressure signal set Y'' and the voltage signal set X'', calculating the first-order derivative of the correlation function, and fitting the differential pressure curve L2; S5: Screening out characteristic peak voltage intervals of the differential pressure curve L1 and the differential pressure curve L2 by using a slope method; S6: Perform peak detection by Hilbert transform method to determine whether a lithium deposition characteristic peak appears, if yes, it is determined that lithium deposition occurs in the lithium ion battery, otherwise, it is determined that lithium deposition does not occur in the lithium ion battery; The steps for determining the optimal preload force F0 in step S3 are: Set the initial preload force F1, and calculate the optimal preload force F0 through the initial preload force F1, proportional factor K1, proportional factor K2, and the contact area S between the pressure sensor and the battery. The expression is: The specific steps of obtaining the differential pressure curve L1 are: S2.1: The data points (X i , Y i ) is used for weighted regression processing, and the expression is: Among them, W i is the weight vector, X i is a voltage value in the voltage signal set X', Y i is a pressure value in the pressure signal set Y', W T x i +b is the predicted value, b is the intercept; S2.2: Take a voltage value X in the voltage signal set X' i As the center point, define the three-cube weight function, the expression is: Among them, c i Step S2.1 is to perform weighted linear regression on a voltage value X in the voltage signal set X' i The corresponding predicted value, d i is the distance along the horizontal axis from the farthest neighboring point within the span; S2.3: Set each voltage value X in the voltage signal set X' i The corresponding predicted value c is obtained by weighted linear regression i , calculate the predicted value c i The weight W of the i-th data point i and intercept b, each voltage value in the voltage set X' is converted to i The linear model is fitted by weighted least squares method to obtain a predicted dependent variable H pred , the expression is: S2.4: Repeat step S2.3 to obtain multiple predicted pressure values ​​through the local weighted linear model , forming a set of data points And fit into the pressure-voltage curve Q; S2.5: For the pressure-voltage curve Q after the local weighted regression processing, interpolate and extrapolate the correlation function of the pressure-voltage curve Q, calculate the first-order derivative of the correlation function, and fit to obtain the differential pressure curve L1.

2. The method for detecting lithium deposition of a lithium ion battery according to claim 1, characterized in that: The steps for obtaining the differential pressure curve L2 are the same as those for obtaining the differential pressure curve L1.

3. The method for detecting lithium deposition of a lithium ion battery according to claim 1, characterized in that: The specific steps in step S5 are: S5.1: storing the voltage data in the differential pressure curve L1 and the differential pressure curve L2 in the voltage signal set X' and the voltage signal set X'', and storing the first-order derivative data of the differential pressure curve L1 and the differential pressure curve L2 in the arrays P1 and P2; S5.2: Define a difference function diff(x) to represent the difference between two arrays P1 and P2. The function expression is: diff(x)=P1-P2 S5.3: Calculate the slope of the difference function diff(x), that is, the first-order derivative D1(x); S5.4: Find the first X whose first-order derivative D1(x) exceeds the threshold τ voltage The value is the starting value of the voltage interval of the lithium deposition characteristic peak between the differential pressure curve L1 and the differential pressure curve L2.

4. The method for detecting lithium deposition of a lithium ion battery according to claim 1, characterized in that: The specific steps of step S6 are as follows: S6.1: storing the voltage data in the differential pressure curve L1 and the differential pressure curve L2 in the voltage signal set X' and the voltage signal set X'', and storing the first-order derivative data in the differential pressure curve L1 and the differential pressure curve L2 in the arrays P1 and P2; S6.2: Each data point in the differential pressure curve L1 is recorded as P1(x), and P1(x) is converted into a complex signal U(x), which is expressed as: Where the imaginary part is the Hilbert transform of the original signal ; S6.3: Hilbert transform is defined as: Among them, PV represents the principal value of the integral; S6.4: The complex signal U1(x) gives the envelope of the signal; S6.5: By detecting the modulus of the complex signal The local maximum value in , find the peak position of the signal, the expression is; Among them, N i is the voltage at the peak value; S6.6: traverse all data and eliminate voltage values ​​that are not within the voltage characteristic range; S6.7: The modulus of the complex signal U1(x) is the intensity of the characteristic peak, denoted as R1; S6.8: If the peak intensity R1>R max That is, lithium deposition is considered to have occurred.

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