Evaluation method and system for state of health of lithium battery

Abstract

Disclosed in the present application are an evaluation method and system for the state of health of a lithium battery. The method comprises: collecting in real time terminal voltage data and current data of a lithium battery at each moment during a discharge period, so as to acquire an estimated direct-current resistance value at each moment; acquiring a discharge operating condition of the lithium battery at each moment, so as to select several effective direct-current resistance values, and constructing several real-time direct-current resistance value sequences; calculating an estimated direct-current resistance sequence value of each real-time direct-current resistance value sequence; on the basis of the discharge operating conditions, segmenting the several estimated direct-current resistance sequence values and a preset discharge direct-current resistance data table, and calculating an average direct-current resistance value and a reference average direct-current resistance value within each segment; and on the basis of the average direct-current resistance values and the reference average direct-current resistance values, determining the state of health of the lithium battery. Therefore, online evaluation of the state of health of a lithium battery is realized.

Description

A method and system for assessing the health status of lithium batteries

[0001] Cross-reference of related applications

[0002] This application claims the benefit of Chinese Patent Application No. 202411532396.4, filed on October 30, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of battery health status prediction technology, and more specifically, to a method and system for assessing the health status of lithium batteries. Background Technology

[0004] With the development of technology, lithium batteries are increasingly being widely used in energy storage fields such as photovoltaics, rail transit, new energy vehicles, and communication base stations. Meanwhile, the state of health (SOH) of a battery characterizes its aging process and is crucial for improving the accuracy of lithium battery state estimation, reducing the risk of thermal runaway, and for lithium battery maintenance and residual value assessment. Therefore, improving the accuracy of online SOH estimation has become a core component of Battery Management Systems (BMS). In existing technologies, SOH for automotive lithium batteries is generally assessed through two aspects: capacity decay and internal resistance increase. SOH is characterized by the ratio of the current estimated capacity to the maximum usable capacity at the time of manufacture, defined as SOHQ, and is often used in pure electric vehicles where range is a primary concern. SOH is characterized by the rate of increase in direct current resistance (DCR), defined as SOHR, and is often used in hybrid electric vehicles where power output is a primary concern.

[0005] Currently, the mainstream SOHR estimation methods include the DC impedance method, the AC impedance method, and the model parameter identification method. The DC impedance method applies a large DC current as the input excitation to the lithium battery, typically requiring the use of Hybrid Pulse Power Characteristic (HPPC) to obtain the instantaneous voltage and current changes. The ratio of these changes yields the DC impedance of the lithium battery, providing a relatively accurate DC impedance value in offline bench testing. However, HPPC is primarily used in laboratory conditions, thus it can only be used to obtain a reference value for DCR. In real-world testing, there isn't a sufficiently long period of static operation to eliminate polarization effects, thus affecting the accuracy of the DC impedance DCR. The AC impedance method applies a small AC current at a fixed frequency as the input excitation to the battery, monitoring its terminal voltage output response. The ohmic resistance of the lithium battery, the diffusion resistance of the SEI film, and the capacitance of the SEI film are obtained from electrochemical impedance spectroscopy (EIS). This method overcomes many shortcomings of the DC impedance method, can accurately measure the corresponding parameter information, and has almost no damage to the battery due to the small input current. However, the AC impedance method can only be used under non-operating conditions and cannot measure the internal resistance of the battery during operation.

[0006] DC impedance (DCR) plays an irreplaceable role in estimating the state of health (SOHR) of lithium batteries, estimating battery power state, and diagnosing battery faults. Currently, most DCR estimations use the DC impedance method. However, this method cannot obtain relatively accurate and independent ohmic and polarization resistances, leading to inaccurate DCR calculations. Furthermore, experimental DC impedance and AC impedance methods require strict experimental conditions and complex experimental equipment, resulting in long testing cycles. Therefore, they cannot be directly applied to online estimation of lithium battery SOHR.

[0007] Application content

[0008] To address the aforementioned technical problems, this application discloses a method and system for assessing the health status of lithium batteries, which is used to estimate the health status of lithium batteries online and improve the accuracy of the estimation.

[0009] To achieve the above objectives, this application discloses a method for assessing the health status of lithium batteries, comprising:

[0010] During a single discharge cycle, the terminal voltage and current data of the lithium battery are collected in real time at each moment to obtain the estimated DC impedance of the lithium battery at each moment.

[0011] The discharge conditions of the lithium battery at each moment are obtained, and several effective DC impedance values ​​are selected from several estimated DC impedance values ​​based on the discharge conditions; wherein, the effective DC impedance values ​​include the first effective DC impedance value corresponding to each moment in the first several moments during the discharge period and the second effective DC impedance value corresponding to each moment in the remaining several moments.

[0012] Real-time acquisition of each effective value of the first DC impedance to construct an initial sequence of DC impedance values ​​corresponding to a preset time period;

[0013] The initial sequence of DC impedance values ​​is iteratively updated in real time based on each effective value of the second DC impedance to construct a real-time sequence of DC impedance values ​​corresponding to each effective value of the second DC impedance.

[0014] Calculate the DC impedance sequence estimate for each of the real-time DC impedance values;

[0015] The DC impedance sequence estimates and a preset DC impedance data table are segmented according to the discharge conditions. The average DC impedance and reference average DC impedance within each segment are calculated based on the estimated DC impedance sequence estimates and the DC impedance data corresponding to each segment. The DC impedance data table is used to represent the relationship between the state of charge and the DC impedance at different temperatures.

[0016] The health status of the lithium battery is determined based on several average DC impedance values ​​and several reference average DC impedance values.

[0017] This application discloses a method for assessing the health status of a lithium battery. First, it estimates the DC impedance by collecting terminal voltage and current data at each moment during the lithium battery's discharge period, enabling online monitoring of the lithium battery while it is in operation. Second, it acquires the discharge conditions of the lithium battery at each moment and filters the estimated DC impedance values ​​based on these conditions to remove invalid data, thus improving the efficiency of the health status assessment. Further, it collects each effective DC impedance value obtained from the filtering process in real time, first constructing an initial sequence of DC impedance values, and then iteratively updating the initial sequence using each effective DC impedance value obtained after constructing the initial sequence to obtain a real-time sequence of DC impedance values ​​at each moment. This facilitates the estimation of the DC impedance sequence value at each moment based on the real-time sequence, thereby ensuring the accuracy of the estimated DC impedance sequence value through stable sequence data. Finally, the estimated values ​​of multiple consecutive DC impedance sequences obtained under discharge conditions are segmented, and the preset discharge DC impedance data table is segmented based on the discharge conditions. Then, the average DC impedance and the reference average DC impedance within each identical segment are calculated. Finally, the health status of the lithium battery is determined based on the average DC impedance and the reference average DC impedance, thus realizing online assessment of the health status of the lithium battery.

[0018] As a preferred example, the real-time acquisition of the terminal voltage and current data of the lithium battery at each moment to obtain the estimated DC impedance value of the lithium battery at each moment includes:

[0019] Construct a second-order RC equivalent circuit model, and obtain the current terminal voltage and current value of the lithium battery at the current moment, as well as the historical terminal voltage and current value at the previous moment.

[0020] Obtain the state of charge (SOC) value of the lithium battery at the current moment, and query the preset SOC-U value based on the SOC value. OCV The relationship table is used to obtain the open-circuit voltage value of the lithium battery at the current moment;

[0021] The difference between the current terminal voltage value and the open-circuit voltage value is calculated. Based on the current terminal voltage value, the current current value, the historical terminal voltage value, the historical current value, and the difference, the parameters of the second-order RC equivalent circuit model are identified using a recursive least squares method based on an adaptive forgetting factor, thereby obtaining the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance of the lithium battery at the current moment.

[0022] This application constructs the second-order RC equivalent circuit model and, based on the real-time voltage and current data of the battery, uses a recursive least squares method based on an adaptive forgetting factor to identify the parameters of the circuit model. This allows for accurate and independent acquisition of the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance of the lithium battery. Furthermore, the accuracy of subsequent DC impedance value estimation is improved based on the accurate ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance.

[0023] As a preferred example, obtaining the estimated DC impedance of the lithium battery at each moment includes:

[0024] Based on the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance, the estimated DC impedance of the lithium battery at the current moment is calculated using the discrete-time observation equation corresponding to the second-order RC equivalent circuit model; wherein, the expression for calculating the estimated DC impedance is:

[0025] R DCR =R0+R f (1-e (-Ts / τf) )+R s (1-e (-Ts / τs) )

[0026] Wherein, the R DCR R0 represents the estimated DC impedance of the lithium battery at each moment; R0 represents the ohmic internal resistance of the lithium battery; R0 represents the DC impedance of the lithium battery at each moment ... f The R represents the concentration polarization resistance of the lithium battery; s The R represents the internal resistance of the lithium battery's electrochemical polarization; f (1-e (-Ts / τf) ) represents the high-frequency polarization internal resistance; the R s (1-e (-Ts / τs) ) represents the low-frequency polarization internal resistance; the τ f and τ s The time constant is represented by Ts; the duration is represented by Ts.

[0027] This application calculates the DC impedance value at each moment based on the parameters obtained by identifying the model and the discrete equation of the second-order equivalent circuit model, thereby realizing the online estimation of the DC impedance value.

[0028] As a preferred example, the step of obtaining the discharge condition of the lithium battery at each moment, and selecting several effective DC impedance values ​​from several DC impedance estimates based on the discharge condition, includes:

[0029] Obtain the state of charge, temperature, and charge / discharge state of the lithium battery at each moment;

[0030] When the state of charge value at any given moment is within a preset state of charge range, the corresponding temperature value is within a preset temperature range, and the charging / discharging state is a discharging state, the estimated DC impedance value at that moment is determined as the effective value of the DC impedance.

[0031] This application sets an effective data range by defining a state of charge range, a temperature range, and limiting the lithium battery to a discharge state. Then, by combining the effective data range with the real-time state of charge value, real-time temperature value, and real-time charge / discharge state of the lithium battery at each moment, each DC impedance estimate is filtered to obtain a stable DC impedance value. This facilitates improving the accuracy of lithium battery health status assessment based on stable data.

[0032] As a preferred example, the real-time acquisition of each effective value of the first DC impedance to construct an initial sequence of DC impedance values ​​includes:

[0033] Each effective value of the first DC impedance collected in real time is sequentially saved to a cache array with a preset dimension to construct an initial sequence of DC impedance values ​​corresponding to the preset dimension.

[0034] This application utilizes a cache array of preset dimensions to store each real-time estimated effective DC impedance value. When the number of stored data reaches the dimension of the cache array, initial data storage is completed to construct an initial sequence of DC impedance values. This allows for iterative updates to the initial sequence of DC impedance values ​​based on each real-time acquired effective DC impedance value, ensuring that a corresponding real-time sequence of DC impedance values ​​exists for each acquired effective DC impedance value at any given time. This enables the subsequent acquisition of stable DC impedance values ​​based on this sequence.

[0035] As a preferred example, the calculation of the estimated DC impedance sequence value for each of the real-time DC impedance values ​​includes:

[0036] Calculate the mean and standard deviation of each real-time sequence of DC impedance values, and construct an outlier screening interval for each real-time sequence of DC impedance values ​​based on the mean and standard deviation corresponding to each real-time sequence of DC impedance values.

[0037] For each of the real-time DC impedance sequences, each effective value of the DC impedance in the real-time DC impedance sequence is compared with the outlier filtering interval corresponding to the real-time DC impedance sequence.

[0038] When the effective value of the DC impedance is not within the outlier filtering range, the current effective value of the DC impedance is determined to be an outlier value of the DC impedance, and the outlier value of the DC impedance is replaced with the average value corresponding to the real-time sequence of the DC impedance value in which it is located.

[0039] After the abnormal DC impedance values ​​are replaced with the average values, the real-time sequence of each DC impedance value is sorted.

[0040] Obtain the median value in each sorted real-time sequence of DC impedance values, and determine the median value corresponding to each real-time sequence of DC impedance values ​​as the estimated value of the DC impedance sequence for each real-time sequence of DC impedance values.

[0041] This application filters outlier DC impedance values ​​in the sequence data by calculating the mean and standard deviation, thus preventing these outlier values ​​from affecting the estimation accuracy of DCR. Then, after removing outliers, the sequence is sorted so that the median value is used as the stable DC impedance estimate at the current moment, thereby improving the accuracy of the DC impedance estimate.

[0042] As a preferred example, the step of segmenting the estimated DC impedance sequence values ​​and a preset discharge DC impedance data table according to the discharge conditions, and calculating the average DC impedance and reference average DC impedance within each segment based on the estimated DC impedance sequence values ​​and discharge DC impedance data corresponding to each segment, includes:

[0043] Based on the discharge conditions, obtain the state of charge and temperature values ​​corresponding to each estimated value of the DC impedance sequence.

[0044] Based on the state of charge value corresponding to each DC impedance sequence estimate, the DC impedance sequence estimates are segmented at a preset interval of state of charge change to obtain the DC impedance sequence estimates corresponding to each segment.

[0045] Based on the estimated state of charge and temperature values ​​corresponding to each DC impedance sequence value within each segment, the SOC-T-DCR can be queried. Bd Offline data table, obtain several DC impedance reference values ​​corresponding to each segment;

[0046] Based on the estimated values ​​of the DC impedance sequence corresponding to each segment, the average DC impedance within each segment is calculated; wherein, the expression for calculating the average DC impedance is:

[0047] Time k =Time k-1 +Ts

[0048] DCR sum,k =DCR sum,k-1 +DCR k

[0049] DCR Avg,k =DCR sum,k / Time k

[0050] Among them, DCR sum,k The sum of estimated values ​​for several DC impedance sequences within each segment; DCR k The DC impedance sequence estimate at time k; DCR Avg,k The average DC impedance within each segment; Time k is the number of samples; Ts is the sampling step size;

[0051] Based on the several DC impedance reference values ​​corresponding to each segment, the average DC impedance reference value for each segment is calculated; wherein, the expression for calculating the average DC impedance value is:

[0052] Time k =Time k-1 +Ts

[0053] DCR sumBol,k =DCR sumBol,k-1 +DCR k

[0054] DCR AvgBol,k =DCR sumBol,k / Time k

[0055] Among them, DCR sumBol,k The sum of several DC impedance reference values ​​of the lithium battery at the time of manufacture for each segment; DCR Bol,k The DC impedance reference value at time k; DCR AvgBol,k This is the reference average value of the DC impedance for each segment.

[0056] This application performs segmented processing based on the change in state of charge, and averages the data in each segment to ensure that a relatively stable average DC impedance and a reference average DC impedance are obtained throughout the process.

[0057] As a preferred example, determining the health status of the lithium battery based on a plurality of average DC impedance values ​​and a plurality of reference average DC impedance values ​​includes:

[0058] Based on the average DC impedance in each segment, the sum of the average DC impedance of all segments is calculated, and the ratio of the sum of the average DC impedance to the number of segments is calculated to obtain the total average DC impedance of the lithium battery during discharge.

[0059] Based on the average DC impedance reference value within each segment, the sum of the average DC impedance reference values ​​of all segments is calculated, and the ratio of the sum of the average DC impedance reference values ​​to the number of segments is calculated to obtain the total average DC impedance reference value of the lithium battery during discharge.

[0060] The ratio of the average total DC impedance to the reference average total DC impedance is obtained to determine the health status of the lithium battery based on the ratio.

[0061] As a preferred example, determining the health status of the lithium battery based on the ratio includes:

[0062] When the ratio is greater than a preset threshold, the health status of the lithium battery is determined to be the end of its lifespan.

[0063] As a preferred example, the preset state of charge range is [30, 60].

[0064] As a preferred example, the preset temperature range is (20, 50).

[0065] As a preferred example, the preset dimension is [1, 50].

[0066] As a preferred example, when the effective value of the DC impedance is not within the outlier screening range, the following formula is satisfied:

[0067] |DCR k -DCR k,μ |>2σ k

[0068] Among them, DCR k Let DCR be the effective value of the DC impedance at time k. k,μ σ is the average value corresponding to the real-time sequence of DC impedance values ​​at time k. k The standard deviation is the real-time sequence of the DC impedance values ​​at time k.

[0069] This application assesses the health status of the lithium battery based on the stable average total DC impedance and the reference average total DC impedance throughout the entire process, ensuring the accuracy of the lithium battery health status assessment.

[0070] On the other hand, this application discloses a lithium battery health status assessment system, including an impedance estimation module, a data filtering module, a sequence construction module, a sequence update module, a sequence estimation module, a data segmentation module, and a health assessment module;

[0071] The impedance estimation module is used to collect the terminal voltage and current data of the lithium battery at each moment in real time during a single discharge to obtain the estimated DC impedance value of the lithium battery at each moment.

[0072] The data filtering module is used to obtain the discharge condition of the lithium battery at each moment, and filter several effective DC impedance values ​​from several estimated DC impedance values ​​according to the discharge condition; wherein, the effective DC impedance values ​​include the first effective DC impedance value corresponding to each moment in the first several moments during the discharge period and the second effective DC impedance value corresponding to each moment in the remaining several moments.

[0073] The sequence construction module is used to collect each effective value of the first DC impedance in real time to construct an initial sequence of DC impedance values;

[0074] The sequence update module is used to iteratively update the DC impedance estimation value sequence in real time according to each second DC impedance effective value, so as to construct a real-time sequence of DC impedance values ​​corresponding to each second DC impedance effective value.

[0075] The sequence estimation module is used to calculate the DC impedance sequence estimate value for each of the real-time DC impedance values.

[0076] The data segmentation module is used to segment several estimated DC impedance sequences and a preset discharge DC impedance data table according to the discharge conditions, and to calculate the average DC impedance and the reference average DC impedance in each segment; wherein, the discharge DC impedance data table is used to represent the relationship between the state of charge value and the DC impedance value at different temperatures.

[0077] The health assessment module is used to determine the health status of the lithium battery based on several average DC impedance values ​​and several reference average DC impedance values.

[0078] This application discloses a lithium battery health status assessment system. First, it estimates the DC impedance by collecting terminal voltage and current data at each moment during the lithium battery's discharge period, enabling online monitoring of the lithium battery while it is in operation. Second, it acquires the discharge conditions of the lithium battery at each moment and filters the estimated DC impedance values ​​based on these conditions to remove invalid data, improving the efficiency of the health status assessment. Further, it collects each filtered effective DC impedance value in real time, constructs an initial sequence of DC impedance values, and then iteratively updates the initial DC impedance values ​​using each effective DC impedance value obtained after constructing the initial sequence, obtaining a real-time sequence of DC impedance values ​​for each moment. This facilitates the estimation of the DC impedance sequence value at each moment based on the real-time sequence, thereby ensuring the accuracy of the estimated DC impedance sequence value through stable sequence data. Finally, the estimated values ​​of multiple consecutive DC impedance sequences obtained under discharge conditions are segmented, and the preset discharge DC impedance data table is segmented based on the discharge conditions. Then, the average DC impedance and the reference average DC impedance within each identical segment are calculated. Finally, the health status of the lithium battery is determined based on the average DC impedance and the reference average DC impedance, thus realizing online assessment of the health status of the lithium battery.

[0079] As a preferred example, the impedance estimation module includes a circuit unit, a data acquisition unit, a parameter identification unit, and an estimation unit;

[0080] The circuit unit is used to construct a second-order RC equivalent circuit model and obtain the current terminal voltage and current value of the lithium battery at the current moment, as well as the historical terminal voltage and current value at the previous moment.

[0081] The data acquisition unit is used to obtain the state of charge (SOC-U) value of the lithium battery at the current moment, and query the preset SOC-U value based on the SOC-U value. OCV The relationship table is used to obtain the open-circuit voltage value of the lithium battery at the current moment;

[0082] The parameter identification unit is used to calculate the difference between the current terminal voltage value and the open circuit voltage value, and to identify the parameters of the second-order RC equivalent circuit model based on the current terminal voltage value, the current current value, the historical terminal voltage value, the historical current value and the difference using the recursive least squares method based on the adaptive forgetting factor, so as to obtain the ohmic internal resistance, high-frequency polarization internal resistance and low-frequency polarization internal resistance of the lithium battery at the current moment.

[0083] The estimation unit is used to calculate the estimated DC impedance of the lithium battery at the current moment based on the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance, using the discrete-time observation equation corresponding to the second-order RC equivalent circuit model; wherein, the calculation expression for the estimated DC impedance is:

[0084] R DCR =R0+R f (1-e (-Ts / τf) )+R s (1-e (-Ts / τs) )

[0085] Wherein, the R DCR R0 represents the estimated DC impedance of the lithium battery at each moment; R0 represents the ohmic internal resistance of the lithium battery; R0 represents the DC impedance of the lithium battery at each moment ... f The R represents the concentration polarization resistance of the lithium battery; s The R represents the internal resistance of the lithium battery's electrochemical polarization; f (1-e (-Ts / τf) ) represents the high-frequency polarization internal resistance; the R s (1-e( -Ts / τs) ) represents the low-frequency polarization internal resistance; the τ f and τ s The time constant is represented by Ts; the duration is represented by Ts.

[0086] This application constructs the second-order RC equivalent circuit model and, based on the real-time voltage and current data of the battery, uses a recursive least squares method based on an adaptive forgetting factor to identify the parameters of the circuit model. This allows for accurate and independent acquisition of the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance of the lithium battery. Furthermore, the accuracy of subsequent DC impedance estimation is improved based on these accurate ohmic, high-frequency, and low-frequency polarization internal resistances. Further, the DC impedance value at each moment is calculated based on the parameters obtained from the model identification and the discrete equations of the second-order equivalent circuit model, achieving online estimation of the DC impedance value. Attached Figure Description

[0087] Figure 1: A flowchart illustrating a method for assessing the health status of a lithium battery as disclosed in an embodiment of this application;

[0088] Figure 2: A schematic diagram of the structure of a lithium battery health status assessment system disclosed in an embodiment of this application;

[0089] Figure 3: A flowchart illustrating a method for assessing the health status of a lithium battery according to another embodiment of this application;

[0090] Figure 4: A schematic diagram of a second-order RC equivalent circuit model disclosed in another embodiment of this application;

[0091] Figure 5: A schematic diagram of parameter identification based on recursive least squares method with adaptive forgetting factor disclosed in another embodiment of this application;

[0092] Figure 6: A schematic diagram of a DCR outlier removal process based on 2Sigma filtering disclosed in another embodiment of this application. Detailed Implementation

[0093] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0094] Example 1

[0095] This embodiment discloses a method for assessing the health status of a lithium battery. The specific implementation flow of the method is shown in Figure 1, and mainly includes steps 101 to 107. These steps are as follows:

[0096] Step 101: During a single discharge cycle, the terminal voltage and current data of the lithium battery are collected in real time at each moment to obtain the estimated DC impedance of the lithium battery at each moment.

[0097] In this embodiment, the main steps are: constructing a second-order RC equivalent circuit model, and obtaining the current terminal voltage and current value of the lithium battery at the current moment, as well as the historical terminal voltage and current values ​​at the previous moment; obtaining the state of charge (SOC) value of the lithium battery at the current moment, and querying the preset SOC-U value based on the SOC value. OCV The relationship table is used to obtain the open-circuit voltage value of the lithium battery at the current moment; the difference between the current terminal voltage value and the open-circuit voltage value is calculated; based on the current terminal voltage value, the current current value, the historical terminal voltage value, the historical current value and the difference, the parameters of the second-order RC equivalent circuit model are identified using the recursive least squares method based on the adaptive forgetting factor, and the ohmic internal resistance, high-frequency polarization internal resistance and low-frequency polarization internal resistance of the lithium battery at the current moment are obtained.

[0098] Furthermore, based on the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance, the estimated DC impedance of the lithium battery at the current moment is estimated using the discrete-time observation equation corresponding to the second-order RC equivalent circuit model; wherein, the expression for calculating the estimated DC impedance is:

[0099] R DCR =R0+R f (1-e(-Ts / τf) )+R s (1-e (-Ts / τs) )

[0100] Wherein, the R DCR R0 represents the estimated DC impedance of the lithium battery at each moment; R0 represents the ohmic internal resistance of the lithium battery; R0 represents the DC impedance of the lithium battery at each moment ... f The R represents the concentration polarization resistance of the lithium battery; s The R represents the internal resistance of the lithium battery's electrochemical polarization; f (1-e (-Ts / τf) ) represents the high-frequency polarization internal resistance; the R s (1-e (-Ts / τs) ) represents the low-frequency polarization internal resistance; the τ f and τ s The time constant is represented by Ts; the duration is represented by Ts.

[0101] In this embodiment, the second-order RC equivalent circuit model is constructed in this step. Based on the real-time voltage and current data of the battery, the recursive least squares method based on the adaptive forgetting factor is used to identify the parameters of the circuit model. This can accurately and independently obtain the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance of the lithium battery. In turn, the accuracy of subsequent DC impedance value estimation is improved based on the accurate ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance.

[0102] Step 102: Obtain the discharge condition of the lithium battery at each moment, and select several effective DC impedance values ​​from several DC impedance estimates based on the discharge condition; wherein, the effective DC impedance values ​​include the first effective DC impedance value corresponding to each moment in the first several moments during the discharge period and the second effective DC impedance value corresponding to each moment in the remaining several moments.

[0103] In this embodiment, the step mainly includes: obtaining the state of charge value, temperature value and charge / discharge state of the lithium battery at each moment; when the state of charge value at any moment is in a preset state of charge range, the corresponding temperature value is in a preset temperature range and the charge / discharge state is a discharge state, the estimated DC impedance value at that moment is determined as the effective value of the DC impedance.

[0104] In this embodiment, this step sets an effective data range by defining a state of charge range, a temperature range, and limiting the lithium battery to a discharge state. Then, by combining the effective data range with the real-time state of charge value, real-time temperature value, and real-time charge / discharge state of the lithium battery at each moment, each DC impedance estimate is screened to obtain a stable DC impedance value. This facilitates improving the accuracy of lithium battery health status assessment based on stable data.

[0105] Step 103: Collect each effective value of the first DC impedance in real time to construct an initial sequence of DC impedance values.

[0106] In this embodiment, the step mainly includes: saving each of the first DC impedance effective values ​​collected in real time to a cache array with a preset dimension in sequence, so as to construct an initial sequence of DC impedance values ​​corresponding to the preset dimension.

[0107] In this embodiment, this step utilizes a cache array of preset dimensions to store each of the real-time estimated effective DC impedance values. Initial data storage is completed and an initial sequence of DC impedance values ​​is constructed when the number of stored data reaches the dimension of the cache array. This allows for iterative updates to the initial sequence of DC impedance values ​​based on each real-time acquired effective DC impedance value, ensuring that a corresponding real-time sequence of DC impedance values ​​exists for each acquired effective DC impedance value at any given time. This enables the subsequent acquisition of stable DC impedance values ​​based on the sequence.

[0108] Step 104: Iteratively update the DC impedance estimation sequence in real time based on each effective value of the second DC impedance to construct a real-time sequence of DC impedance values ​​corresponding to each effective value of the second DC impedance.

[0109] Step 105: Calculate the DC impedance sequence estimate for each of the real-time DC impedance values.

[0110] In this embodiment, the step mainly includes: calculating the average value and standard deviation of each real-time DC impedance value sequence, and constructing an outlier screening interval for each real-time DC impedance value sequence based on the average value and standard deviation corresponding to each real-time DC impedance value sequence; for each real-time DC impedance value sequence, comparing each effective DC impedance value in the real-time DC impedance value sequence with the outlier screening interval corresponding to the real-time DC impedance value sequence; when the effective DC impedance value is not within the outlier screening interval, determining the current effective DC impedance value as an outlier DC impedance value, and replacing the outlier DC impedance value with the average value corresponding to the real-time DC impedance value sequence in which it is located; after replacing the outlier DC impedance value with the average value, sorting each real-time DC impedance value sequence; obtaining the median value in each sorted real-time DC impedance value sequence, and determining the median value corresponding to each real-time DC impedance value sequence as the estimated value of the DC impedance sequence for each real-time DC impedance value sequence.

[0111] In this embodiment, this step filters out outlier DC impedance values ​​in the sequence data by calculating the mean and standard deviation, thus preventing these outlier DC impedance values ​​from affecting the estimation accuracy of DCR. Next, after removing outliers, the sequence is sorted so that the median value is used as the stable DC impedance estimate at the current moment, thereby improving the accuracy of the DC impedance estimate.

[0112] Step 106: Divide the estimated values ​​of several DC impedance sequences and the preset DC impedance data table into segments according to the discharge conditions, and calculate the average DC impedance and the reference average DC impedance in each segment based on the estimated values ​​of several DC impedance sequences and the DC impedance data corresponding to each segment; wherein, the DC impedance data table is used to represent the relationship between the state of charge value and the DC impedance value at different temperatures.

[0113] In this embodiment, the step mainly includes: obtaining the state of charge (SOC) value and temperature value corresponding to each DC impedance sequence estimation value according to the discharge condition; dividing the DC impedance sequence estimation values ​​into segments with a preset SOC change interval according to the SOC value corresponding to each DC impedance sequence estimation value, to obtain a number of DC impedance sequence estimation values ​​corresponding to each segment; and querying the SOC-T-DCR value based on the SOC value and temperature value corresponding to each DC impedance sequence estimation value in each segment. Bd Offline data table, obtain several DC impedance reference values ​​corresponding to each segment;

[0114] Furthermore, based on the estimated values ​​of the DC impedance sequence corresponding to each segment, the average DC impedance within each segment is calculated; wherein, the expression for calculating the average DC impedance is:

[0115] Time k =Time k-1 +Ts

[0116] DCR sum,k =DCR sum,k-1 +DCR k

[0117] DCR Avg,k =DCR sum,k / Time k

[0118] Among them, DCR sum,k The sum of estimated values ​​for several DC impedance sequences within each segment; DCR k The DC impedance sequence estimate at time k; DCR Avg,k The average DC impedance within each segment; Timek is the number of samples; Ts is the sampling step size;

[0119] Based on the several DC impedance reference values ​​corresponding to each segment, the average DC impedance reference value for each segment is calculated; wherein, the expression for calculating the average DC impedance value is:

[0120] Time k =Time k-1 +Ts

[0121] DCR sumBol,k =DCR sumBol,k-1 +DCR k

[0122] DCR AvgBol,k =DCR sumBol,k / Time k

[0123] Among them, DCR sumBol,k The sum of several DC impedance reference values ​​of the lithium battery at the time of manufacture for each segment; DCR Bol,k The DC impedance reference value at time k; DCR AvgBol,k This is the reference average value of the DC impedance for each segment.

[0124] In this embodiment, the step is segmented based on the change in state of charge, and the data in each segment is averaged to ensure that a relatively stable average DC impedance and a reference average DC impedance are obtained throughout the process.

[0125] Step 107: Determine the health status of the lithium battery based on several average DC impedance values ​​and several reference average DC impedance values.

[0126] In this embodiment, the step mainly includes: calculating the sum of the average DC impedance values ​​of all segments based on the average DC impedance value within each segment, calculating the ratio of the sum of the average DC impedance values ​​to the number of segments, and obtaining the total average DC impedance value of the lithium battery during discharge; calculating the sum of the reference average DC impedance values ​​of all segments based on the reference average DC impedance value within each segment, calculating the ratio of the sum of the reference average DC impedance values ​​to the number of segments, and obtaining the total reference average DC impedance value of the lithium battery during discharge; obtaining the ratio of the total average DC impedance value to the total reference average DC impedance value, and determining the health status of the lithium battery based on the ratio.

[0127] In this embodiment, the health status is assessed based on the stable average total DC impedance and the reference average total DC impedance throughout the process, ensuring the accuracy of the lithium battery health status assessment.

[0128] In one embodiment, after step 107, the method for assessing the health status of the lithium battery further includes: controlling the battery management system to switch the operating state of the lithium battery based on its health status. Specifically, when the lithium battery's health status is at the end of its lifespan, the battery management system is controlled to switch the lithium battery's operating state to shutdown.

[0129] In one embodiment, the method for assessing the health status of a lithium battery can be performed by a controller, a server, or a cloud platform.

[0130] On the other hand, this embodiment also discloses a lithium battery health status assessment system. The specific structure of the assessment system is shown in Figure 2, including an impedance estimation module 201, a data filtering module 202, a sequence construction module 203, a sequence update module 204, a sequence estimation module 205, a data segmentation module 206, and a health assessment module 207.

[0131] The impedance estimation module 201 is used to collect the terminal voltage and current data of the lithium battery at each moment during a single discharge cycle in real time, so as to obtain the estimated DC impedance value of the lithium battery at each moment. The data filtering module 202 is used to obtain the discharge condition of the lithium battery at each moment, and filter several effective DC impedance values ​​from several estimated DC impedance values ​​according to the discharge condition; wherein, the effective DC impedance values ​​include the first effective DC impedance value corresponding to each moment in the first several moments during the discharge cycle and the second effective DC impedance value corresponding to each moment in the remaining several moments.

[0132] The sequence construction module 203 is used to collect each effective value of the first DC impedance in real time to construct an initial sequence of DC impedance values.

[0133] The sequence update module 204 is used to iteratively update the initial sequence of DC impedance values ​​in real time according to each effective value of the second DC impedance, so as to construct a real-time sequence of DC impedance values ​​corresponding to each effective value of the second DC impedance.

[0134] The sequence estimation module 205 is used to calculate the DC impedance sequence estimate for each real-time sequence of the DC impedance value.

[0135] The data segmentation module 206 is used to segment several estimated DC impedance sequences and a preset DC impedance data table according to the discharge conditions, and to calculate the average DC impedance and the reference average DC impedance in each segment; wherein, the DC impedance data table is used to represent the relationship between the state of charge value and the DC impedance value at different temperatures.

[0136] The health assessment module 207 is used to determine the health status of the lithium battery based on several average DC impedance values ​​and several reference average DC impedance values.

[0137] In this embodiment, the impedance estimation module 201 includes a circuit unit, a data acquisition unit, a parameter identification unit, and an estimation unit;

[0138] The circuit unit is used to construct a second-order RC equivalent circuit model and obtain the current terminal voltage and current value of the lithium battery at the current moment, as well as the historical terminal voltage and current value at the previous moment.

[0139] The data acquisition unit is used to obtain the state of charge (SOC-U) value of the lithium battery at the current moment, and query the preset SOC-U value based on the SOC-U value. OCV The relationship table is used to obtain the open-circuit voltage value of the lithium battery at the current moment;

[0140] The parameter identification unit is used to calculate the difference between the current terminal voltage value and the open circuit voltage value. Based on the current terminal voltage value, the current current value, the historical terminal voltage value, the historical current value, and the difference, the recursive least squares method based on the adaptive forgetting factor is used to identify the parameters of the second-order RC equivalent circuit model to obtain the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance of the lithium battery at the current moment.

[0141] The estimation unit is used to calculate the estimated DC impedance of the lithium battery at the current moment based on the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance, using the discrete-time observation equation corresponding to the second-order RC equivalent circuit model; wherein, the calculation expression for the estimated DC impedance is:

[0142] R DCR =R0+R f (1-e (-Ts / τf) )+R s (1-e (-Ts / τs) )

[0143] Wherein, the R DCR R0 represents the estimated DC impedance of the lithium battery at each moment; R0 represents the ohmic internal resistance of the lithium battery; R0 represents the DC impedance of the lithium battery at each moment ... f The R represents the concentration polarization resistance of the lithium battery; s The R represents the internal resistance of the lithium battery's electrochemical polarization; f (1-e (-Ts / τf) ) represents the high-frequency polarization internal resistance; the R s (1-e (-Ts / τs) ) represents the low-frequency polarization internal resistance; the τ f and τ sThe time constant is represented by Ts; the duration is represented by Ts.

[0144] This embodiment discloses a method and system for assessing the health status of a lithium battery. First, it estimates the DC impedance by collecting terminal voltage and current data at each moment during the lithium battery's discharge period, enabling online monitoring of the lithium battery while it is in operation. Second, it acquires the discharge conditions of the lithium battery at each moment and filters the estimated DC impedance values ​​based on these conditions to remove invalid data, improving the efficiency of the health status assessment. Further, it collects each filtered effective DC impedance value in real time, constructs an initial sequence of DC impedance values ​​corresponding to a preset time period, and then iteratively updates the initial sequence using each remaining effective DC impedance value to obtain a real-time sequence of DC impedance values ​​for each moment. This facilitates the estimation of the DC impedance sequence value at each moment based on the real-time sequence, thereby ensuring the accuracy of the estimated DC impedance sequence value through stable sequence data. Finally, the estimated values ​​of multiple consecutive DC impedance sequences obtained under discharge conditions are segmented, and the preset discharge DC impedance data table is segmented based on the discharge conditions. Then, the average DC impedance and the reference average DC impedance within each identical segment are calculated. Finally, the health status of the lithium battery is determined based on the average DC impedance and the reference average DC impedance, thus realizing online assessment of the health status of the lithium battery.

[0145] Example 2

[0146] Please refer to Figure 3, which illustrates a method for assessing the health status of a lithium battery provided in this embodiment. The implementation process of the assessment method includes steps 301 to 306, and the main steps are as follows:

[0147] Step 301: Construct a second-order equivalent circuit model, collect voltage and current data of the lithium battery at each moment during discharge, and identify the parameters of the second-order equivalent circuit model based on the voltage and current data by using the recursive least squares method based on the adaptive forgetting factor to obtain the estimated DC impedance value of the lithium battery at each moment.

[0148] Specifically, a second-order RC equivalent circuit model is constructed, and the current voltage and current data of the lithium battery at the current moment are obtained. At the same time, the historical voltage and current data of the lithium battery at the previous moment are also obtained. Based on the current voltage and current data and the historical voltage and current data, the parameters of the second-order RC equivalent circuit model are identified by the recursive least squares method based on the adaptive forgetting factor to obtain the ohmic internal resistance, high-frequency polarization internal resistance and low-frequency polarization internal resistance of the lithium battery at the current moment. Then, the estimated value of the DC impedance of the lithium battery at the current moment is calculated based on the ohmic internal resistance, high-frequency polarization internal resistance and low-frequency polarization internal resistance.

[0149] Optionally, a second-order RC equivalent circuit model is first constructed, which encompasses the battery's polarization characteristics (fast / slow) and ohmic internal resistance. The internal components of this second-order RC equivalent circuit model can be seen in Figure 4, mainly including an open-circuit voltage source, an ohmic internal resistance R0, and a battery terminal voltage U. t ;R f and C f These are the concentration polarization resistance and polarization capacitance of the battery, respectively, characterizing the rapid electrode reactions inside the battery. The voltage across them is U. f ;R s and C s These are the battery's electrochemical polarization resistance and polarization capacitance, respectively, characterizing the slow electrode reactions inside the battery. The voltage across the terminals is U. s .

[0150] By mathematically representing the second-order RC equivalent circuit model shown in Figure 4, and according to Kirchhoff's laws, the system equations and observation equations of the second-order RC equivalent circuit model are obtained. The system equations and observation equations are as follows:

[0151] U t =U oc -i L R0-U f -U s

[0152] In the above equations, the direction of the discharge current is defined as positive, and the direction of the charging current is defined as negative. Further, the system equations and observation equations are converted into discrete-time form, and [U... f,k U s,k Let ] be the state variable, and establish the following state-space equation and observation equation:

[0153] U t,k =U OCV,k -U f,k -U s,k -R0i L,k

[0154] Referring to the above equation, these are the state-space equation and observation equation corresponding to the second-order RC equivalent circuit model.

[0155] Secondly, an adaptive forgetting factor-based recursive least squares algorithm (AFFRLS algorithm) is constructed; wherein, the construction process of the adaptive forgetting factor-based recursive least squares algorithm is as follows:

[0156] 1) The least squares method is used; wherein, the mathematical expression of the least squares method is:

[0157] In the formula, e Ls,k It is stationary zero-mean white noise.

[0158] 2) The core of the least squares (RLS) method is to minimize the sum of squared errors between the predicted and measured data. Therefore, a cost function J is constructed to evaluate the least squares method; the expression for the cost function J is:

[0159] Among them, the parameter to be identified θ k As the independent variable, in order to obtain the parameter vector θ k The optimal estimate of the cost function J is obtained by applying the principle of least squares to the independent variable θ. k The minimum value of θ, that is, the value of the construction cost function that is minimized. k The value of θ is the optimal parameter solution; then, when the cost function J is minimized, it is related to the independent variable θ. k The relational expression is:

[0160] When the cost function J is minimized, the independent variable θ k When solving the relational expression, the expression to be solved is:

[0161] 3) Transform the solved expression into a recursive form, and add a forgetting factor during the transformation process to construct a forgetting factor-based recursive least squares method (FFRLS); the expression of the forgetting factor-based recursive least squares method is as follows:

[0162] Where λ is the forgetting factor, and when this value is 1, the recursive least squares method based on the forgetting factor degenerates into the traditional recursive least squares algorithm. K Ls,k P is the gain of the algorithm. Ls,k Let be the error covariance matrix of the state estimates, and I be the identity matrix. These are the parameter values ​​estimated at the previous moment. This is the observed value at this moment, yk It is the actual observed value of the system, y k and Subtracting the two gives the system's prediction error. Then, the prediction error is multiplied by the gain matrix K. Ls,k Multiplying them together yields a corrected value for the parameter estimate at this moment, compared to the previous estimate K. Ls,k The work was carried out and the final estimated value was obtained.

[0163] Fixed forgetting factor λ k When the value is close to 1, the parameter identification results are more stable, converge slowly, and are less affected by new data; λ k When the value is far from 1, the greater the fluctuation of the parameter identification result, the faster the convergence speed, and the greater the influence of new data on the identification result. The large fluctuation of the identification parameters reflects the time-varying characteristics of the model parameters as the working conditions change.

[0164] To further improve the accuracy of parameter identification, a voltage error dynamic adjustment forgetting factor λ is introduced. k The magnitude of the voltage error is the difference between the actual voltage and the identified voltage. This value reflects the degree of consistency between the parameters of the equivalent circuit for parameter identification and the actual parameters. When the voltage error is large, the forgetting factor needs to be reduced to improve the algorithm's tracking performance and thus reduce the voltage error. When the voltage error is small, the forgetting factor needs to be increased to ensure the algorithm's stability and thus stabilize the parameters.

[0165] 4) Considering that the frequently changing forgetting factor affects the convergence of the identification parameters, and that the strategy of adjusting the forgetting factor for individual data is sensitive to interference, the sliding window theory is adopted. Instead of using the single-step voltage error, the mean square value of the multi-step voltage error within the window time is used to adjust the magnitude of the forgetting factor. Therefore, the adjustment expression for the forgetting factor is:

[0166] In the formula, λ k Let λ represent the forgetting factor at time k. max and λ min These represent the maximum and minimum values ​​of the forgetting factor, respectively, where ρ represents the sensitivity coefficient, and α... k This represents the mean square value of the multi-step voltage error within the time window, where M represents the size of the time window, and e k Let λ represent the voltage error at time k. The maximum forgetting factor λ in this scheme is... max =1, minimum forgetting factor λ min =0.98, time window M=30, that is, the average voltage error within 30 consecutive seconds is taken, and the sensitivity coefficient is taken as 500000≤ρ≤1000000.

[0167] Furthermore, the AFFRLS algorithm is applied to the parameter identification of the second-order RC equivalent circuit model of the lithium battery constructed as shown in Figure 4. The specific process of the AFFRLS algorithm for parameter identification of the second-order RC equivalent circuit model is as follows:

[0168] 1) Based on the system equations and observation equations of the second-order RC equivalent circuit model, the transfer function between the output voltage and input current of the lithium battery is obtained by Kirchhoff's laws and Laplace transform; then the transfer function is:

[0169] 2) Discretize the transfer function of the second-order equivalent circuit model in the frequency domain, transforming the battery model into a least-squares mathematical form. The expression of this least-squares mathematical form is:

[0170] E L (s)=U t (s)-U OCV (s),

[0171] 3) By combining the parameters of the least squares mathematical expression, we get:

[0172] 4) Define the equivalent intermediate parameter D i (i = 1, 2, ..., 5), by substituting the parameters in the least squares mathematical form of the expression after parameter merging, the mathematical expression of the defined equivalent intermediate parameter is:

[0173] 5) Simplify the formula in step 3 based on the equivalent intermediate parameters in step 4. The simplified formula expression is:

[0174] 6) Based on the bilinear transformation rule, the frequency domain transfer function can be mapped to the Z-plane for time-domain discretization, as shown in the equation: Substituting into the formula in step 5, we get:

[0175] Therefore, the discrete expression of the transfer function of the second-order equivalent circuit model of the battery in the z-plane is as follows:

[0176] Where, θ i (i = 1, 2, ..., 5) are the parameters to be identified.

[0177] 7) The parameter θ to be identified can be obtained from the correspondence of the formulas in step 6. i The analytical expression, i.e., the parameter θ to be identified. i The analytical expression is:

[0178] 8) Based on the discrete expression of the transfer function of the second-order equivalent circuit model of the battery constructed in step 6 in the z-plane, the mathematical analytical expression of the least squares difference equation can be obtained:

[0179] E L (k)=θ1E L (k-1)+θ2E L (k-2)+θ3I L (k)+θ4I L (k-1)+θ5I L (k-2)

[0180] Based on the mathematical analytical expression, the parameter matrix (system output) and data matrix (system input) of the second-order RC equivalent circuit model are determined as follows:

[0181] 9) After parameter identification, θ can be obtained at different stages. i The parameter θ to be identified in step 7 i The analytical expression can be used to inversely analyze the equivalent intermediate parameter D. i ,Right now

[0182] 10) Based on the mathematical expression of the equivalent intermediate parameter defined in step 4, it can be obtained through the equivalent intermediate parameter D. i The actual equivalent circuit parameters were analyzed.

[0183] Based on the above AFFRLS algorithm, the parameter identification process of the second-order RC equivalent circuit model is carried out as follows: In one embodiment, referring to Figure 5, the current voltage data and current current data of the lithium battery at the current moment are collected; wherein, the current voltage data and current current data are set as the current i at time k. L (k), voltage U at time k t (k). Simultaneously, acquire the historical voltage data and historical current data of the lithium battery at the time preceding the current time; wherein, the historical voltage data and historical current data are set as the current i at time k-1. L Current i at times (k-1) and (k-2) L Voltage U at times (k-2) and (k-1) t Voltage U at times (k-1) and (k-2) t (k-2), and obtain the state of charge (SOC(k)) of the lithium battery at the current moment, and obtain the U corresponding to the SOC(k) by looking up the table. OCV (SOC k );

[0184] The voltage and current data collected above, along with the state of charge and U, are used to... OCV(SOC k Using θ as the input parameter of the second-order RC equivalent circuit model, adaptive forgetting factor recursive least squares parameter identification is performed, and the output is θ. i (i = 1, 2, ..., 5).

[0185] Specifically, in the parameter identification process, the algorithm to be identified is first initialized by setting the simulation step size to Ts = 1s, reading the initial SOC value, initializing the parameter matrix to be identified θ = [0 0 0 0 0], and initializing the error covariance matrix P. Ls,k =10 6 *eye(6); Based on the real-time SOC estimate, through SOC-U OCV Look up the table to obtain the current terminal voltage U. t Calculate the terminal voltage error at this moment: E L =U t -U OCV The adaptive forgetting factor is calculated based on the terminal voltage error; based on the current, voltage, and E at the current moment... L The value and the adaptive forgetting factor are used to obtain the identification parameter value θ in real time based on the constructed AFFRLS algorithm. i Based on the obtained identification parameter value θ i Based on the formulas constructed in steps 9 and 10 during the parameter identification process, the equivalent circuit parameters R0 and R are analyzed. f and R s Based on the equivalent circuit parameters, the DC impedance of the lithium battery at the current time K is calculated using the discrete-time observation equation of the second-order RC equivalent circuit; the expression for calculating the DC impedance is then:

[0186] R DCR =R0+R f (1-e (-Ts / τf) )+R s (1-e (-Ts / τs) )

[0187] In the formula, R0 is the ohmic internal resistance, R f (1-e (-Ts / τf) R represents the high-frequency polarization internal resistance. s (1-e (-Ts / τs) Low-frequency polarization internal resistance, R f and R s Initial value of polarization impedance, τ f and τ s Ts is the time constant, and Ts is the duration. In this embodiment, Ts = 1s.

[0188] Step 302: Obtain the discharge condition of the lithium battery at each moment, so as to judge the validity of each of the real-time estimated DC impedance estimation values according to the discharge condition, and screen the effective DC impedance values according to the judgment results.

[0189] Specifically, in this embodiment, obtain the state of charge value, temperature value and charge-discharge state corresponding to the lithium battery at each moment; when the state of charge value corresponding to any moment is within a preset state of charge interval, the corresponding temperature value is within a preset temperature interval, and the charge-discharge state is a discharge state, determine the DC impedance estimation value corresponding to this moment as the effective DC impedance value.

[0190] Optionally, by testing the change law of the internal resistance of the lithium battery at different SOCs and temperatures using the HPPC method, it is found that the DC impedance of the lithium battery is relatively stable when the state of charge is within the working interval of 30% < SOC < 80%. At the same time, in order to enable the lithium battery to have better buffering ability, in one implementation manner, the state of charge interval [30, 60] is selected as the state of charge interval for screening stable DCR values. In actual process, the state of charge interval can be adjusted according to the DCR characteristics of the battery cell.

[0191] Furthermore, with the change of temperature, the ohmic impedance and high / low-frequency polarization impedance of the lithium battery have different change amplitudes. In order to ensure the stability of the DCR value, in one implementation manner, the temperature interval (20, 50) is selected as the temperature interval for screening stable DCR values.

[0192] Based on the good parameter identification effect of AFFRLS under the discharge condition, thus, in one implementation manner, it is set to perform parameter identification only on the battery in the discharge state. Among them, the discharge state is identified by the battery working state.

[0193] Based on the set temperature interval, state of charge interval and the restricted condition of setting to only evaluate the battery in the discharge state, judge the validity of the DC impedance estimation value estimated in real time at each moment based on the real-time SOC, temperature and charge-discharge state at each moment. When it is judged that the real-time SOC, temperature and charge-discharge state corresponding to any moment are within the corresponding interval ranges, judge the DC impedance estimation value corresponding to this moment as the effective DC impedance value.

[0194] Step 303: Save each of the effective DC impedance values in real time until an initial sequence of DC impedance values is constructed, and iteratively update the initial sequence of DC impedance values according to each of the effective DC impedance values obtained after constructing the initial sequence of DC impedance values, so as to construct a real-time sequence of DC impedance values corresponding to each of the effective DC impedance values obtained after constructing the initial sequence of DC impedance values.

[0195] Specifically, in this embodiment, each of the real-time collected effective DC impedance values ​​is sequentially saved into a cache array with a preset dimension to construct an initial sequence of DC impedance values ​​corresponding to the preset dimension. The initial sequence of DC impedance values ​​is then updated based on the remaining real-time collected effective DC impedance values ​​to construct a real-time sequence of DC impedance values ​​corresponding to each effective DC impedance value.

[0196] Optionally, in this embodiment, referring to Figure 6, a buffer array with a preset dimension of [1, 50] is used to store the real-time estimated effective DC impedance values ​​to construct an initial sequence of DC impedance values ​​with the same dimension. In one implementation, if the sampling step size is set to 1 second, that is, the effective DC impedance value of the lithium battery is calculated in each second. When it is determined that the effective DC impedance values ​​corresponding to each second are all valid data, then based on the dimension of the buffer array of 50, it is necessary to continuously collect the data for the first 50 seconds to construct an initial sequence of DC impedance values ​​based on the effective DC impedance values ​​estimated in each second within the first 50 seconds. Then, the initial sequence of DC impedance values ​​is iteratively updated based on the effective DC impedance values ​​obtained in each second after 50 seconds that are within the range of valid data, thereby ensuring that there is a real-time sequence of DC impedance values ​​of [1, 50] at every moment.

[0197] Step 304: Remove abnormal data from each of the real-time DC impedance value sequences, and after removing the abnormal data, calculate the estimated DC impedance sequence value corresponding to each of the real-time DC impedance value sequences.

[0198] Specifically, in this embodiment, the average value and standard deviation of each real-time sequence of DC impedance values ​​are calculated, and abnormal data are filtered based on the average value and standard deviation. After the abnormal data is filtered out, the abnormal data is replaced with the average value to complete the removal of the abnormal data.

[0199] Optionally, the outlier degree of each effective DC impedance value in the real-time DC impedance value sequence can be obtained using the normal distribution principle. If the outlier degree of an effective DC impedance value is too large, it indicates that the effective DC impedance value has an outlier effect, which will affect the estimation accuracy of DCR. To address this, the 1-Sigma criterion, 2-Sigma criterion, or 3-Sigma criterion in the normal distribution principle can be used to filter out outlier data.

[0200] The 1Sigma criterion defines that approximately 68.27% of the data values ​​fall within the range of the mean (μ) plus or minus one standard deviation (σ), and the mathematical expression is: , then the DCR values ​​that are not in this range are outliers.

[0201] The 2Sigma criterion defines that approximately 95.45% of the data values ​​fall within the range of the mean (μ) plus or minus two standard deviations (σ). The mathematical expression is: , then the DCR values ​​that are not in this range are outliers.

[0202] The 3Sigma criterion defines that approximately 99.73% of the data values ​​fall within the range of the mean (μ) plus or minus three standard deviations (σ). The mathematical expression is: , then the DCR value that is not in this range is an outlier.

[0203] In one implementation, a 2Sigma filter is used, with the confidence interval set within the range of the mean plus or minus two standard deviations. Outliers are defined as those where the deviation between the real-time DCR and the DCR series mean exceeds two standard deviations. A schematic diagram of the DCR outlier removal process based on 2Sigma filtering is shown in Figure 6.

[0204] Referring to Figure 6, calculate the average value (DCR) of the real-time sequence for each of the DC impedance values. AVG,K and standard deviation DCR Del,K K represents the time corresponding to the real-time sequence of DC impedance values. Each effective DC impedance value in the real-time sequence is iterated over and compared with the average value DCR. AVG,K and standard deviation DCR Del,K The value is compared to a standard range to determine if it falls within that range. If the effective value of the DC impedance is not within the range, then the average value DCR is used. AVG,K Replace the effective value of the DC impedance to obtain the sequence data after removing abnormal data from the real-time sequence of each DC impedance value.

[0205] Outliers in the DCR sequence are replaced by the average value of the DCR sequence.

[0206] In the formula, DCR k Here is the calculated DCR value at time k. k,μ Let σ be the average value of the DCR sequence at time k. k Let be the standard deviation of the DCR sequence at time k.

[0207] After removing outliers from the DCR sequence, the valid DCR sequences in the [1,50] buffer are sorted, and the intermediate value of the DCR sequence is output as the stable DCR estimate at the current moment for subsequent calculations.

[0208] Step 305: Based on the SOC change, segment each DC impedance sequence estimate and each pre-saved DC impedance reference value to calculate the estimated average value corresponding to several DC impedance sequence estimates and the reference average value corresponding to several DC impedance reference values ​​within each segment.

[0209] Specifically, in this embodiment, the DC impedance reference value is the DC impedance DCR of the battery under the corresponding operating conditions at the time of manufacture. Bol During the segmentation process, the effective DCR sequence of the discharge process is segmented based on the SOC change DeltaSOC, where DeltaSOC is defined as 1% / 2% / 5%, with appropriate intervals defined according to requirements. This scheme uses DeltaSOC = 1% as the interval. When the DCR estimation result is valid, several valid DCR estimation values ​​are recorded within each DeltaSOC = 1% segment, the average DCR value within that segment is calculated, and this average DCR value is defined as the actual DCR output value for that segment. Simultaneously, within the same segment, based on SOC-T-DCR... Bol Offline data table, using SOC and temperature to obtain DCR within this segment Bol Value, calculate DCR within this segment Bol The average value, and define the DCR. Bol The average value is the DCR output reference value within this segment.

[0210] The average DCR is calculated as follows:

[0211] Time k =Time k-1 +Ts

[0212] DCR sum,k =DCR sum,k-1 +DCR k

[0213] DCR Avg,k =DCR sum,k / Time k

[0214] In the formula, DCR sum,k The sum of the calculated DCR values ​​within each segment, DCR k The effective DCR calculation value at time k, DCR Avg,k The effective DCR average within the segment, Time k Ts represents the number of samples, and Ts represents the sampling step size, i.e., how often the DCR value is sampled.

[0215] Among them, DCR Bol The average value is calculated as follows:

[0216] Time k =Time k-1 +Ts

[0217] DCR sumBol,k =DCR sumBol,k-1 +DCR k

[0218] DCR AvgBol,k =DCR sumBol,k / Time k

[0219] In the formula, DCR sumBol,k The sum of the factory-set DCR reference values ​​for each segment. Bol,k The DCR value at time k is the DCR lookup table value at the time of manufacture. AvgBol,k This is the average reference value of DCR at the time of manufacture within each segment.

[0220] Step 306: Determine the health status of the lithium battery based on the multiple estimated average values ​​and the multiple reference average values.

[0221] Specifically, in this embodiment, based on the average DC impedance within each segment, the reference average DC impedance, and the number of segments, the total average DC impedance and the reference average DC impedance of the lithium battery during the entire discharge period are determined. The health status of the lithium battery is determined based on the ratio of the total average DC impedance to the reference average DC impedance.

[0222] Optionally, as the battery discharges, the average DCR value within each segment is gradually averaged, and simultaneously, the DCR value within each segment is gradually adjusted. Bol The average value is averaged based on the mean DCR and mean DCR during the discharge cycle. Bol The ratio of the two values ​​is used to obtain the state of health (SOHR) of the lithium battery based on its DC impedance.

[0223] SOHR = DCR Avg,k / DCR AvgBol,k

[0224] In specific implementations, different manufacturers define the degree of battery aging differently. Preferably, the battery life can be considered to have ended when the SOHR is stably greater than 2.

[0225] In another embodiment, the above-mentioned lithium battery health status assessment system includes a processor, wherein the processor is used to execute the above-mentioned program modules and units stored in memory, including: impedance estimation module 201, data filtering module 202, sequence construction module 203, sequence update module 204, sequence estimation module 205, data segmentation module 206, and health assessment module 207, as well as the circuit units, data acquisition unit, parameter identification unit, and estimation unit in the impedance estimation module 201.

[0226] This embodiment discloses a method for assessing the health status of a lithium battery. Under dynamic discharge conditions, firstly, the terminal voltage and total current of the lithium battery are collected to obtain the current operating state value of the battery. Based on continuously collected data, an adaptive forgetting factor recursive least squares identification algorithm is used to obtain the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance of the lithium battery when the "terminal voltage square error is minimized". Then, the DCR estimate is calculated based on the second-order equivalent circuit discrete equation, thereby obtaining a continuously changing DCR sequence. Secondly, based on the battery SOC, temperature, and charge / discharge state, the effective data range is divided. Within the effective data range, the DCR sequence is preprocessed, and abnormal data is removed by filtering using the 2Sigma principle. Next, based on the change in SOC, segmented processing is performed to obtain the average value of the relatively stable DCR estimate and the DCR reference value throughout the process. Finally, the SOHR of the lithium battery is obtained through a health status estimation model, realizing online health status assessment of the lithium battery.

[0227] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application for those skilled in the art.

Claims

1. A method for assessing the health status of a lithium battery, wherein, include: During a single discharge cycle, the terminal voltage and current data of the lithium battery are collected in real time at each moment to obtain the estimated DC impedance of the lithium battery at each moment. The discharge conditions of the lithium battery at each moment are obtained, and several effective DC impedance values ​​are selected from several estimated DC impedance values ​​based on the discharge conditions; wherein, the effective DC impedance values ​​include the first effective DC impedance value corresponding to each moment in the first several moments during the discharge period and the second effective DC impedance value corresponding to each moment in the remaining several moments. Real-time acquisition of each effective value of the first DC impedance to construct an initial sequence of DC impedance values; The initial sequence of DC impedance values ​​is iteratively updated in real time based on each effective value of the second DC impedance to construct a real-time sequence of DC impedance values ​​corresponding to each effective value of the second DC impedance. Calculate the DC impedance sequence estimate for each of the real-time DC impedance values; The DC impedance sequence estimates and a preset DC impedance data table are segmented according to the discharge conditions. The average DC impedance and reference average DC impedance within each segment are calculated based on the estimated DC impedance sequence estimates and the DC impedance data corresponding to each segment. The DC impedance data table is used to represent the relationship between the state of charge and the DC impedance at different temperatures. The health status of the lithium battery is determined based on several average DC impedance values ​​and several reference average DC impedance values.

2. The method for assessing the health status of a lithium battery according to claim 1, wherein, The real-time acquisition of the terminal voltage and current data of the lithium battery at each moment to obtain the estimated DC impedance value of the lithium battery at each moment includes: Construct a second-order RC equivalent circuit model, and obtain the current terminal voltage and current value of the lithium battery at the current moment, as well as the historical terminal voltage and current value at the previous moment. Obtain the state of charge (SOC) value of the lithium battery at the current moment, and query the preset SOC-U value based on the SOC value. OCV The relationship table is used to obtain the open-circuit voltage value of the lithium battery at the current moment; The difference between the current terminal voltage value and the open-circuit voltage value is calculated. Based on the current terminal voltage value, the current current value, the historical terminal voltage value, the historical current value, and the difference, the parameters of the second-order RC equivalent circuit model are identified using a recursive least squares method based on an adaptive forgetting factor, thereby obtaining the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance of the lithium battery at the current moment.

3. The method for assessing the health status of a lithium battery according to claim 2, wherein, The step of obtaining the estimated DC impedance value of the lithium battery at each moment includes: Based on the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance, the estimated DC impedance of the lithium battery at the current moment is calculated using the discrete-time observation equation corresponding to the second-order RC equivalent circuit model; wherein, the expression for calculating the estimated DC impedance is: R DCR NR0+R f (1-e (-Ts / τf) )+R s (1-e (-Ts / τs) ) Wherein, the R DCR R0 represents the estimated DC impedance of the lithium battery at each moment; R0 represents the ohmic internal resistance of the lithium battery; R0 represents the DC impedance of the lithium battery at each moment ... f The R represents the concentration polarization resistance of the lithium battery; s The R represents the internal resistance of the lithium battery's electrochemical polarization; f (1-e (-Ts / τf) ) represents the high-frequency polarization internal resistance; the R s (1-e (-Ts / τs) ) represents the low-frequency polarization internal resistance; the τ f and τ s The time constant is represented by Ts; the duration is represented by Ts.

4. The method for assessing the health status of a lithium battery according to claim 1, wherein, The process of obtaining the discharge condition of the lithium battery at each moment, and selecting several effective DC impedance values ​​from several DC impedance estimates based on the discharge condition, includes: Obtain the state of charge, temperature, and charge / discharge state of the lithium battery at each moment; When the state of charge value at any given moment is within a preset state of charge range, the corresponding temperature value is within a preset temperature range, and the charging / discharging state is a discharging state, the estimated DC impedance value at that moment is determined as the effective value of the DC impedance.

5. The method for assessing the health status of a lithium battery according to claim 1, wherein, The real-time acquisition of each effective value of the first DC impedance to construct an initial sequence of DC impedance values ​​includes: Each effective value of the first DC impedance collected in real time is sequentially saved to a cache array with a preset dimension to construct an initial sequence of DC impedance values ​​corresponding to the preset dimension.

6. The method for assessing the health status of a lithium battery according to claim 1, wherein, The calculation of the estimated DC impedance sequence value for each of the real-time DC impedance values ​​includes: Calculate the mean and standard deviation of each real-time sequence of DC impedance values, and construct an outlier screening interval for each real-time sequence of DC impedance values ​​based on the mean and standard deviation corresponding to each real-time sequence of DC impedance values. For each of the real-time DC impedance sequences, each effective value of the DC impedance in the real-time DC impedance sequence is compared with the outlier filtering interval corresponding to the real-time DC impedance sequence. When the effective value of the DC impedance is not within the outlier filtering range, the current effective value of the DC impedance is determined to be an outlier value of the DC impedance, and the outlier value of the DC impedance is replaced with the average value corresponding to the real-time sequence of the DC impedance value in which it is located. After the abnormal DC impedance values ​​are replaced with the average values, the real-time sequence of each DC impedance value is sorted. Obtain the median value in each sorted real-time sequence of DC impedance values, and determine the median value corresponding to each real-time sequence of DC impedance values ​​as the estimated value of the DC impedance sequence for each real-time sequence of DC impedance values.

7. The method for assessing the health status of a lithium battery according to claim 1, wherein, The step of segmenting several estimated DC impedance sequences and a preset discharge DC impedance data table according to the discharge conditions, and calculating the average DC impedance and reference average DC impedance within each segment based on the estimated DC impedance sequences and discharge DC impedance data corresponding to each segment, includes: Based on the discharge conditions, obtain the state of charge and temperature values ​​corresponding to each estimated value of the DC impedance sequence. Based on the state of charge value corresponding to each DC impedance sequence estimate, the DC impedance sequence estimates are segmented at a preset interval of state of charge change to obtain the DC impedance sequence estimates corresponding to each segment. Based on the estimated state of charge and temperature values ​​corresponding to each DC impedance sequence value within each segment, the SOC-T-DCR can be queried. Bd Offline data table, obtain several DC impedance reference values ​​corresponding to each segment; Based on the estimated values ​​of the DC impedance sequence corresponding to each segment, the average DC impedance within each segment is calculated; wherein, the expression for calculating the average DC impedance is: Time k =Time k-1 +Ts DCR sum,k =DCR sum,k-1 +DCR k DCR Avg,k =DCR sum,k / Time k Among them, DCR sum,k The sum of estimated values ​​for several DC impedance sequences within each segment; DCR k The DC impedance sequence estimate at time k; DCR Avg,k The average DC impedance within each segment; Time k is the number of samples; Ts is the sampling step size; Based on the several DC impedance reference values ​​corresponding to each segment, the average DC impedance reference value within each segment is calculated; wherein, the calculation expression for the average DC impedance reference value is: Time k =Time k-1 +Ts DCR sumBol,k =DCR sumBol,k-1 +DCR k DCR AvgBol,k =DCR sumBol,k / Time k Among them, DCR sumBol,k The sum of several DC impedance reference values ​​of the lithium battery at the time of manufacture for each segment; DCR Bol,k The DC impedance reference value at time k; DCR AvgBol,k This is the reference average value of the DC impedance for each segment.

8. The method for assessing the health status of a lithium battery according to claim 7, wherein, The step of determining the health status of the lithium battery based on a plurality of average DC impedance values ​​and a plurality of reference average DC impedance values ​​includes: Based on the average DC impedance in each segment, the sum of the average DC impedance of all segments is calculated, and the ratio of the sum of the average DC impedance to the number of segments is calculated to obtain the total average DC impedance of the lithium battery during discharge. Based on the average DC impedance reference value within each segment, the sum of the average DC impedance reference values ​​of all segments is calculated, and the ratio of the sum of the average DC impedance reference values ​​to the number of segments is calculated to obtain the total average DC impedance reference value of the lithium battery during discharge. The ratio of the average total DC impedance to the reference average total DC impedance is obtained to determine the health status of the lithium battery based on the ratio.

9. The method for assessing the health status of a lithium battery according to claim 8, wherein, Determining the health status of the lithium battery based on the ratio includes: When the ratio is greater than a preset threshold, the health status of the lithium battery is determined to be the end of its lifespan.

10. The method for assessing the health status of a lithium battery according to claim 4, wherein, The preset state of charge range is [30, 60].

11. The method for assessing the health status of a lithium battery according to claim 4, wherein, The preset temperature range is (20, 50).

12. The method for assessing the health status of a lithium battery according to claim 5, wherein, The preset dimension is [1, 50].

13. The method for assessing the health status of a lithium battery according to claim 6, wherein, When the effective value of the DC impedance is not within the outlier screening range, the following formula is satisfied: |DCR k -DCR k,μ |>2σ k Among them, DCR k Let DCR be the effective value of the DC impedance at time k. k,μ σ is the average value corresponding to the real-time sequence of DC impedance values ​​at time k. k The standard deviation is the real-time sequence of the DC impedance values ​​at time k.

14. A system for assessing the health status of a lithium battery, wherein, It includes an impedance estimation module, a data filtering module, a sequence construction module, a sequence update module, a sequence estimation module, a data segmentation module, and a health assessment module; The impedance estimation module is used to collect the terminal voltage and current data of the lithium battery at each moment in real time during a single discharge to obtain the estimated DC impedance value of the lithium battery at each moment. The data filtering module is used to obtain the discharge condition of the lithium battery at each moment, and filter several effective DC impedance values ​​from several estimated DC impedance values ​​according to the discharge condition; wherein, the effective DC impedance values ​​include the first effective DC impedance value corresponding to each moment in the first several moments during the discharge period and the second effective DC impedance value corresponding to each moment in the remaining several moments. The sequence construction module is used to collect each effective value of the first DC impedance in real time to construct an initial sequence of DC impedance values; The sequence update module is used to iteratively update the initial sequence of DC impedance values ​​in real time according to each effective value of the second DC impedance, so as to construct a real-time sequence of DC impedance values ​​corresponding to each effective value of the second DC impedance. The sequence estimation module is used to calculate the DC impedance sequence estimate value for each of the real-time DC impedance values. The data segmentation module is used to segment several estimated DC impedance sequences and a preset discharge DC impedance data table according to the discharge conditions, and to calculate the average DC impedance and the reference average DC impedance in each segment; wherein, the discharge DC impedance data table is used to represent the relationship between the state of charge value and the DC impedance value at different temperatures. The health assessment module is used to determine the health status of the lithium battery based on several average DC impedance values ​​and several reference average DC impedance values.

15. The lithium battery health status assessment system according to claim 14, wherein, The impedance estimation module includes a circuit unit, a data acquisition unit, a parameter identification unit, and an estimation unit. The circuit unit is used to construct a second-order RC equivalent circuit model and obtain the current terminal voltage and current value of the lithium battery at the current moment, as well as the historical terminal voltage and current value at the previous moment. The data acquisition unit is used to obtain the state of charge (SOC-U) value of the lithium battery at the current moment, and query the preset SOC-U value based on the SOC-U value. OCV The relationship table is used to obtain the open-circuit voltage value of the lithium battery at the current moment; The parameter identification unit is used to calculate the difference between the current terminal voltage value and the open circuit voltage value. Based on the current terminal voltage value, the current current value, the historical terminal voltage value, the historical current value, and the difference, the recursive least squares method based on the adaptive forgetting factor is used to identify the parameters of the second-order RC equivalent circuit model to obtain the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance of the lithium battery at the current moment. The estimation unit is used to calculate the estimated DC impedance of the lithium battery at the current moment based on the ohmic internal resistance, high-frequency polarization internal resistance, and low-frequency polarization internal resistance, using the discrete-time observation equation corresponding to the second-order RC equivalent circuit model; wherein, the calculation expression for the estimated DC impedance is: R DCR NR0+R f (1-e (-Ts / τf) )+R s (1-e (-Ts / τs) ) Wherein, the R DCR R0 represents the estimated DC impedance of the lithium battery at each moment; R0 represents the ohmic internal resistance of the lithium battery; R0 represents the DC impedance of the lithium battery at each moment ... f The R represents the concentration polarization resistance of the lithium battery; s The R represents the internal resistance of the lithium battery's electrochemical polarization; f (1-e (-Ts / τf) ) represents the high-frequency polarization internal resistance; the R s (1-e (-Ts / τs) ) represents the low-frequency polarization internal resistance; the τ f and τ s The time constant is represented by Ts; the duration is represented by Ts.

Images