Battery capacity balancing method and system based on battery health degree analysis

Abstract

The invention relates to the technical field of battery detection, and discloses a battery capacity equalization method based on battery health degree analysis, and the method comprises the steps: collecting the battery data of a to-be-equalized battery in a first charge-discharge cycle, carrying out the preprocessing and data initialization of the battery data, and obtaining a cycle parameter; obtaining the current total capacity according to the battery data, calculating the aging coefficient of the battery in combination with a battery health degree formula, and calculating the charge-discharge cycle time of the battery based on the cycle parameters and the aging coefficient in combination with Gaussian regression and extended Kalman filtering; and calculating the health condition of the battery by taking the time of the current battery charge-discharge cycle as the time of the next battery charge-discharge cycle, calculating the equalization time of the battery according to the health condition of the battery in combination with the cut-off coefficient, and adjusting the charge-discharge time of the battery in the next charge-discharge process based on the equalization time. Therefore, energy balance in the charging and discharging process of the battery is completed, and the problem that effective balance cannot be realized in the existing battery balance process is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery detection, and in particular to a battery capacity balancing method. Background Art

[0002] In lithium battery energy storage systems, single cells are connected in series to increase the output voltage and charge and discharge power, providing the energy storage converter with sufficient power and energy to meet its energy conversion requirements. However, the inherent inconsistency of single cells is caused by the inconsistency of the manufacturing process and the inconsistency of the use process environment, which will inevitably lead to the energy density, reliability, safety and other performance of the battery cells not being fully utilized after being grouped. The inconsistency between grouped battery cells will gradually expand during use, causing the capacity and power of the battery pack to decrease, and may also cause safety problems. In order to solve this problem, the use of online battery cell balancing technology after the battery is used in groups is an effective means to reduce the impact of single cell inconsistency on battery pack performance.

[0003] When balancing batteries, the existing battery balancing methods control the battery balancing degree according to parameters such as battery voltage and SOC, which improves the charging and discharging energy of the battery pack to a certain extent. However, the above parameters are only correlated with the battery energy, which is actually easy to cause under-balancing and over-balancing, and cannot achieve complete energy balanced wear between single cells. Summary of the invention

[0004] The present invention provides a battery capacity balancing method and system based on battery health analysis to solve the problem that effective balancing cannot be achieved in the existing battery balancing process.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions: In a first aspect, the present invention provides a battery capacity balancing method based on battery health analysis, comprising the following steps: Step 1: Collect battery data in the first charge and discharge cycle of the battery to be balanced, preprocess and initialize the battery data, and obtain cycle parameters; Step 2: Calculate the current total capacity of the battery based on the battery data and further calculate the battery aging coefficient based on the battery health formula, and then calculate the time of this battery charge and discharge cycle based on the cycle parameters combined with Gaussian regression and extended Kalman filtering; Step 3: Using the time of the current battery charge and discharge cycle as the time of the next battery charge and discharge cycle and calculating the health status of the battery in combination with the aging coefficient, calculating the battery balancing time according to the battery health status combined with a preset cutoff coefficient, and adjusting the charge and discharge time of the battery in the next charge and discharge process based on the balancing time to complete the energy balancing of the battery charge and discharge process.

[0006] Furthermore, the battery data includes voltage, current, capacity change and battery rated capacity; The preprocessing includes missing value processing, data deduplication, outlier processing, and data segment cutting and reconstruction; The cycle parameters are acquired based on the voltage in the battery data, and the cycle parameters include the real-time voltage of the battery collected at several fixed intervals.

[0007] Furthermore, the method of calculating the current total capacity of the battery based on the battery data and further calculating the battery aging coefficient in combination with the battery health formula includes: obtaining the battery charge change during charging and discharging based on the current, calculating the current total capacity of the battery in combination with the capacity change, and calculating the battery aging coefficient based on the current total capacity in combination with the battery health formula and the battery calibrated capacity.

[0008] Furthermore, the current total capacity is calculated by the following formula: ; in, Indicates the current total capacity; and Indicates different moments in the charging or discharging process, For the corresponding The remaining battery capacity; For the corresponding The remaining battery capacity; for The current of the battery at the moment; The aging coefficient is calculated by the following formula: ; in, Calibrate the capacity of the battery.

[0009] Furthermore, in step 2, the time of the current battery charge and discharge cycle is calculated based on the cycle parameters and the aging coefficient in combination with Gaussian regression and extended Kalman filtering, including: performing Gaussian regression on the cycle parameters in step 1, and then using the cycle parameters after Gaussian regression as the cycle parameters of the next charge and discharge cycle to perform extended Kalman filtering to calculate the time of the current battery charge and discharge cycle.

[0010] Furthermore, the Gaussian regression uses a linear function As the mean function, the squared exponential covariance function was chosen as the kernel function.

[0011] The extended Kalman filter is updated in conjunction with the difference quotient approximation Jacobian matrix.

[0012] Furthermore, the health status of the battery is calculated by the following formula: ; in, For the Battery health status at each cycle; Time to fully charge the battery; is a given charge and discharge current.

[0013] Furthermore, the equalization time includes a charge equalization time and a discharge equalization time, and the cutoff coefficient includes a charge cutoff coefficient and a discharge cutoff coefficient; The charge equalization time is calculated by the following formula: ; in, Indicates the charge equalization time; Indicates the amount of power transferred during battery balancing; Said Calculated by the following formula: ; in, represents the charge cut-off coefficient, Indicates Moment Save the remaining battery power. Indicates the average remaining chargeable capacity of the battery; Said Calculated by the following formula: ; in, Indicates the number of cycles; The discharge balance time is calculated by the following formula: ; in Indicates the amount of electricity transferred by battery discharge; Said Calculated by the following formula: ; in, represents the discharge cut-off coefficient; Indicates the average discharge capacity of the battery at the end of discharge; Said Calculated by the following formula: .

[0014] In a second aspect, the present invention provides a battery capacity balancing system based on battery health analysis, comprising a processor and a memory, The memory is used to store computer programs; The processor is used to implement any of the method steps described above when executing the program stored in the memory.

[0015] Beneficial effects: The present invention provides a battery capacity balancing method and system based on battery health analysis, which provides a quantitative analysis through the battery data collected during the first charge and discharge cycle, so as to accurately calculate the current total capacity of the battery, and then perform battery balancing according to the current total capacity of the battery. This makes the battery balancing process more accurate, avoiding the error in the balancing process caused by the estimation error of the actual battery capacity in the previous balancing method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The present invention is a flow chart of a battery capacity balancing method based on battery health analysis. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the usual meanings understood by persons with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "one" do not indicate quantity restrictions, but indicate the existence of at least one. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0019] See also Figure 1 The present application embodiment provides a battery capacity balancing method based on battery health analysis, including: Step 1: Collect battery data in the first charge and discharge cycle of the battery to be balanced, preprocess and initialize the battery data, and obtain cycle parameters; Specifically, the battery data in the first charge and discharge cycle of the battery to be balanced is collected. The battery data includes voltage, current, capacity change and battery calibration capacity. After the battery data is collected, missing value processing, data deduplication, outlier processing and data segment cutting and reconstruction are performed on the battery data. Among them, missing value processing: when there are a lot of missing data in the time dimension, a certain data item or all data items in the time period are deleted; when there are only a small amount of missing data in the time dimension, considering the continuity of each parameter data, the interpolation method is used to fill the data to ensure data integrity and data quality; Data deduplication: locate based on time data, sort all data by time, select the first item in the duplicate data to save, and delete the rest of the duplicate data; Abnormal value processing: due to abnormal collection, abnormal communication and other problems, abnormal values ​​that significantly exceed the adjacent data are generated. The processing method is to interpolate based on the adjacent data; if the charging or discharging end data is abnormal, the entire data is directly deleted; Data segment cutting and reconstruction: Data segment cutting and reconstruction is to intercept different data items for classification in different dimensions, and divide the data set into time spans according to the screening conditions.

[0020] After completing the above operations, Collect data at fixed intervals The real-time voltage of each battery constitutes the cycle parameters, which are as follows: ; in, Indicates the specific data sampling time, that is ; Indicates the real-time voltage of the battery to be balanced.

[0021] Step 2: Calculate the current total capacity of the battery based on the battery data and further calculate the battery aging coefficient based on the battery health formula, and then calculate the time of this battery charge and discharge cycle based on the cycle parameters combined with Gaussian regression and extended Kalman filtering; The battery to be balanced starts charging from 0 dehumidification capacity, then: ; in, For the Battery health status at each cycle; is a given charge and discharge current; The rated capacity of the battery to be balanced when it is put into operation; The time required for the battery to be balanced to charge from the initial time 0 to the cut-off voltage; is the aging coefficient.

[0022] As the battery to be balanced continues to charge and discharge, Time has come The battery power changes at all times are: ; in, and for Time has come The internal charge of the battery changes at all times. During this period of time, from Changes to The total capacity of the above formula Calculate and get: ; Based on total capacity , combined with the rated capacity Then we have: ; For loop parameters Perform Gaussian regression and use linear function As the mean function, the square exponential covariance function is selected as the kernel function, and the cycle parameters after Gaussian regression are used as the cycle parameters of the next charge and discharge cycle, that is, , perform extended Kalman filtering to calculate the time of this battery charge and discharge cycle, and update the difference quotient near-view Jacobian matrix in the process of Kalman filtering calculation. The specific formula is as follows: ; ; in, Represents the state matrix, as the identity matrix; Indicates the use of Perform a Gaussian regression process and predict The voltage value, Indicates The observed values ​​of the cycle parameters obtained after Gaussian regression in the charge and discharge cycle are and denote state noise and measurement noise respectively; Extended Kalman filter loop recursion: ; ; in, Represents state noise The covariance of Represents the measurement noise The covariance of Represents the predicted covariance matrix, which is used to represent the covariance of the uncertainty or error of the state estimate; Update the difference quotient approximate Jacobian matrix: ; ; ; ; in, represents the difference quotient approximate Jacobian matrix; Represents the filter gain in the Kalman filter, which combines the observed value and the predicted value to update the state estimate; Indicates The covariance of the sub-cycle estimation errors; Step 3: Use the time of this battery charge and discharge cycle as the time of the next battery charge and discharge cycle and calculate the health status of the battery in combination with the aging coefficient. Calculate the battery balancing time based on the battery health status and the cutoff coefficient. Adjust the charge and discharge time of the battery in the next charge and discharge process based on the balancing time to complete the energy balancing of the battery charge and discharge process.

[0023] The time of this battery charge and discharge cycle is , that is, wait for the balanced battery to be charged from the minimum capacity to The time of the moment, based on this, The full charge time is expressed as: ; Will , and the full charge time is Thus, the full charge time of all cycles is obtained, that is, ; Combined with the aging coefficient, the health status of the battery is calculated: ; Combined with the set charging cut-off coefficient and discharge cut-off coefficient , calculate the battery balancing time; The charge equalization time is calculated by the following formula: ; in, Indicates the charge equalization time; Indicates the amount of power transferred during battery balancing; ; in, express Moment Save the remaining battery power. Indicates the average remaining chargeable capacity of the battery; ; in, Indicates the number of cycles; The discharge balance time is calculated by the following formula: ; in Indicates the amount of electricity transferred by battery discharge; ; in, Indicates the average discharge capacity of the battery at the end of discharge; .

[0024] In this embodiment, a battery capacity balancing method based on battery health analysis provided in this embodiment is compared with a conventional battery voltage, SOC parameter control balancing method and a non-balancing method, see Table 1; Table 1: Comparison of solutions

[0025] As can be seen from Table 1, this solution is significantly better than the non-balancing and voltage and SOC control balancing solutions in terms of discharge capacity, and can more effectively solve the problem of under-balancing. It has a smaller elevator voltage difference peak, effectively and fundamentally eliminating the problems of under-balancing and over-balancing.

[0026] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A battery capacity balancing method based on battery health analysis, characterized in that: The steps include: Step 1: Collect battery data in the first charge and discharge cycle of the battery to be balanced, preprocess and initialize the battery data, and obtain cycle parameters; Step 2: Calculate the current total capacity of the battery based on the battery data and further calculate the battery aging coefficient based on the battery health formula, and then calculate the time of this battery charge and discharge cycle based on the cycle parameters combined with Gaussian regression and extended Kalman filtering; Step 3: Using the time of the current battery charge and discharge cycle as the time of the next battery charge and discharge cycle and calculating the health status of the battery in combination with the aging coefficient, calculating the battery balancing time according to the battery health status combined with a preset cutoff coefficient, and adjusting the charge and discharge time of the battery in the next charge and discharge process based on the balancing time to complete the energy balancing of the battery charge and discharge process.

2. The battery capacity balancing method based on battery health analysis according to claim 1, characterized in that: The battery data includes voltage, current, capacity change and battery rated capacity; The preprocessing includes missing value processing, data deduplication, outlier processing, and data segment cutting and reconstruction; The cycle parameters are acquired based on the voltage in the battery data, and the cycle parameters include the real-time voltage of the battery collected at several fixed intervals.

3. The battery capacity balancing method based on battery health analysis according to claim 2, characterized in that: The method of calculating the current total capacity of the battery based on the battery data and further calculating the battery aging coefficient in combination with the battery health formula includes: obtaining the battery charge change during battery charging and discharging based on the current, calculating the current total capacity of the battery in combination with the capacity change, and calculating the battery aging coefficient based on the current total capacity in combination with the battery health formula and the battery calibrated capacity.

4. The battery capacity balancing method based on battery health analysis according to claim 3, characterized in that: The current total capacity is calculated by the following formula: ； in, Indicates the current total capacity; and Indicates different moments in the charging or discharging process, For the corresponding The remaining battery capacity; For the corresponding The remaining battery capacity; for The current of the battery at the moment; The aging coefficient is calculated by the following formula: ； in, Calibrate the capacity of the battery.

5. The battery capacity balancing method based on battery health analysis according to claim 1, characterized in that: In step 2, the time of the current battery charge and discharge cycle is calculated based on the cycle parameters and the aging coefficient in combination with Gaussian regression and extended Kalman filtering, including: performing Gaussian regression on the cycle parameters in step 1, and then using the cycle parameters after Gaussian regression as the cycle parameters of the next charge and discharge cycle to perform extended Kalman filtering to calculate the time of the current battery charge and discharge cycle.

6. The battery capacity balancing method based on battery health analysis according to claim 5, characterized in that: The Gaussian regression uses a linear function As the mean function, the squared exponential covariance function was chosen as the kernel function. The extended Kalman filter is updated in conjunction with the difference quotient approximation Jacobian matrix.

7. The battery capacity balancing method based on battery health analysis according to claim 1, characterized in that: The battery health formula is specifically: ； in, For the Battery health status at each cycle; Time to fully charge the battery; is a given charge and discharge current.

8. The battery capacity balancing method based on battery health analysis according to claim 1, characterized in that: The equalization time includes a charge equalization time and a discharge equalization time, and the cutoff coefficient includes a charge cutoff coefficient and a discharge cutoff coefficient; The charge equalization time is calculated by the following formula: ； in, Indicates the charge equalization time; Indicates the amount of power transferred during battery balancing; Said Calculated by the following formula: ； in, represents the charge cut-off coefficient, express Moment Save the remaining battery power. Indicates the average remaining chargeable capacity of the battery; Said Calculated by the following formula: ； in, Indicates the number of cycles; The discharge balance time is calculated by the following formula: ； in Indicates the amount of electricity transferred by battery discharge; Said Calculated by the following formula: ； in, represents the discharge cut-off coefficient; Indicates the average discharge capacity of the battery at the end of discharge; Said Calculated by the following formula: 。 9. A battery capacity balancing system based on battery health analysis, characterized in that: including a processor and memory, The memory is used to store computer programs; The processor is used to implement the method steps described in any one of claims 1-8 when executing the program stored in the memory.

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