Detection method of automobile power battery and management system based on direct current charging pile

Abstract

The invention discloses a detection method of an automobile power battery and a management system based on a direct current charging pile. The detection method comprises the following steps: (1) obtaining characteristic information of a charging process through dynamic pulse detection; (2) calculating relevant parameters of core factors which influence the battery charging and discharging safety of the battery pack and influence the service life and the driving mileage of the battery pack based on the characteristic information; and (3) analyzing a calculation result in the step (2) to obtain a result. According to the method, the characteristic value of the related aging degree of the battery cell is obtained by specifying the special pulse charging working condition, and the optimal charging current of the battery pack is estimated according to the characteristic value, so that a safe and effective charging strategy is realized.

Description

Technical Field

[0001] The present invention relates to the field of automobile power battery performance detection, and in particular to a detection method for an automobile power battery and a management system based on a DC charging pile. Background Art

[0002] In order to solve the problem of charging efficiency of new energy vehicles, most charging stations provided by commercial operation places use DC charging piles to charge electric vehicle power batteries. With the iterative development of technology, the power of DC charging piles is also constantly increasing, and even high-power DC fast charging super charging technology has emerged. Although super charging technology solves the efficiency problem, improper high-current DC charging of batteries will also have a great impact on battery life. In terms of the causes of power battery combustion accidents, it was found that more than 50% of combustion occurred during charging and when the battery was left idle after overcharging.

[0003] How to solve the safety issues of DC fast charging and realize the performance evaluation of power battery packs during the charging process have become technical issues that need to be urgently solved in the field of new energy vehicles. Summary of the invention

[0004] In response to the problems existing in the background technology, the present application implements a dynamic pulse working condition detection technology based on a DC charging pile to realize the evaluation of battery performance, thereby providing a more reasonable charging strategy to achieve the purpose of safe charging, and analyzes and models the charging data to deeply evaluate the battery pack performance, vehicle-mounted BMS hardware performance, etc., to facilitate users or third-party organizations to understand the battery performance and management system reliability of the new energy vehicle.

[0005] Technical solution:

[0006] The present invention discloses a detection method for a vehicle power battery and a management system based on a DC charging pile, the detection method comprising:

[0007] (1) Obtain characteristic information of the charging process through dynamic pulse detection;

[0008] (2) calculating relevant parameters of the core factors affecting the battery pack charging and discharging safety and the battery pack life and driving mileage based on the characteristic information;

[0009] (3) Analyze the calculation results of step (2) to obtain the result.

[0010] Preferably, step (1) specifically includes:

[0011] (1-1) Static phase: After the vehicle is connected to the DC charging pile, the pile and the onboard BMS complete the handshake according to the communication protocol, and then enter the static phase and start waiting until the set pre-detection waiting time Tw is met;

[0012] (1-2) Pulse high level period: After the resting stage, the pulse high level period begins, and the DC charging module outputs the set pulse current I to perform a constant current charging stage for the battery pack with a duration of Tp;

[0013] (1-3) Pulse low level period: After the pulse high level period ends, it enters the pulse low level period, at which time the DC charging module output is turned off until the pulse low level period ends, and the timing is Ts;

[0014] (1-4) Determine whether Tw+Tp is equal to the number of pulse cycles Cnt*pulse cycle. If not, return to step (1-2) and repeat. If equal, determine that the dynamic pulse detection is completed and record the characteristic information collected during the process.

[0015] Preferably, the characteristic information of the charging process is: multidimensional matrix data consisting of the total battery voltage V, charging current I, single cell maximum voltage Vmax, and single cell minimum voltage Vmin during the charging process according to the collected time series interval Tf.

[0016] Preferably, after the dynamic pulse detection obtains characteristic information of the charging process, the result is analyzed and a maximum charging current recommendation is given.

[0017] Preferably, the maximum charging current is obtained by the following steps:

[0018] ①According to the collected time series interval Tf, the total battery voltage V, charging current I, single cell maximum voltage Vmax, and single cell minimum voltage Vmin uploaded in the pulse detection process are combined into a multi-dimensional matrix data;

[0019] ② For the above data, within a certain time period dt, the cumulative charge amount of the highest single voltage, the lowest single voltage and the average single voltage are taken as the voltage change rate dQ / dV, and the characteristic results are recorded as Rq1, Rq2, Rq3, respectively, and the voltage change rate dV / dt is recorded as Rv1, Rv2, Rv3, respectively;

[0020] ③ For the above data, within the entire time range, according to the collection time interval Tf, the charging current I, the collected single-cell maximum voltage Vmax, the single-cell minimum voltage Vmin values ​​are used as the identification results, and brought into the first-order RC equivalent circuit model, and the time constants τ of the first-order RC circuit of the highest single-cell voltage cell, the lowest single-cell voltage cell, and the single-cell average value are calculated, respectively, and recorded as τ1, τ2, and τ3, and their values ​​are used as a reference for judging the degree of cell aging;

[0021] Process and judge the eigenvalues ​​calculated in steps ② and ③ above:

[0022] The first step is to determine the correlation between Rq1, Rq2, Rq3 and Rv1, Rv2, Rv3. The correlation is determined by calculating the Pearson coefficient between the two. When the correlation coefficient between the two is greater than 0.5, the characteristic result value is determined to be valid.

[0023] The second step is to calculate the standard deviation of Rv1, Rv2, and Rv3, and compare whether there is a value greater than two times the standard deviation. If so, and the corresponding τ1, τ2, and τ3 have the same corresponding relationship, then the characteristic result value is determined to be valid, indicating that there is an aging problem in the average value of the battery cell or the entire battery cell. The proportional factor is mapped out according to the ratio of the maximum aging characteristic value of the deviation and the characteristic value of voltage to time. The proportional factor is multiplied by the rated capacity of the battery and substituted into the calculation to obtain the remaining capacity of the battery, and then the recommended maximum charging current is obtained.

[0024] Preferably, the maximum charging current of the battery is obtained by multiplying the remaining capacity of the battery and the maximum charging rate Crate, that is, Imax=Crate*Cnom*proportional factor. Generally speaking, the Crate value of a lithium battery is 1, with the unit C (coulomb), so the value of Imax is equal to the current remaining capacity of the battery.

[0025] Preferably, the relevant parameters in step (2) include battery remaining SOH and battery internal resistance.

[0026] Preferably, the battery internal resistance Res is obtained by the following formula:

[0027] Res=(dVstart / dI+dVend / dI) / 2

[0028] Wherein, dVstart / dI represents the ratio of voltage to current at the moment of starting a high-level cycle, and dVend / dI represents the ratio of voltage to current at the moment of starting a low-level cycle.

[0029] Preferably, the specific process of parsing in step (3) is:

[0030] ①According to the collected time series interval Tf, the total battery voltage V, charging current I, single cell maximum voltage Vmax, single cell minimum voltage Vmin, battery pack SOC, charging gun temperature Ta, and battery pack average temperature Tc uploaded during normal charging are combined into a multi-dimensional matrix data array;

[0031] ② For the above data, within a certain time period dt, the correlation between the highest single cell voltage, the lowest single cell voltage and the average single cell voltage change rate dV / dt with the charging current I is intercepted, and the characteristic results are recorded as Vi1, Vi2, Vi3 respectively. The correlation between the charging gun temperature Ta and the battery pack average temperature Tc with the time change rate dT / dt and the charging current I is recorded as Ti1 and Ti2 respectively.

[0032] ③ For the above data, within the entire time range, according to the collection time interval Tf, charging current I, single cell maximum voltage Vmax, single cell minimum voltage Vmin value, and battery pack current SOC as the identification result, calculate the cell SOH increment ΔSOH and estimate the remaining SOH. The calculation formula is:

[0033] SOH g =ΔSOH / ΔSOC

[0034] Among them, SOH g represents the remaining SOH, ΔSOC represents the SOC increment;

[0035] Process and judge the eigenvalues ​​calculated in steps ② and ③ above:

[0036] The first step is to calculate whether there is a positive correlation between Vi1, Vi2, and Vi3 and the value of the correlation coefficient. If there is no positive correlation or the correlation value is less than the threshold, it is determined that there is a problem with the vehicle BMS voltage detection hardware;

[0037] The second step is to determine whether there is a positive correlation between Ti1 and Ti2 and the size of the correlation value. If not or the correlation value is less than the threshold, it is determined that there is a problem with the on-board BMS temperature detection hardware or the charging gun temperature detection.

[0038] Preferably, ③ also includes: SOH g The battery aging degree is calculated by comparing it with the rated SOH, and data mapping is performed with the aging parameter value calculated in the pulse detection to iteratively update the equivalent circuit model.

[0039] Beneficial effects of the present invention

[0040] The present invention specifies a special pulse charging condition to obtain the characteristic value of the cell-related aging degree, and estimates the optimal charging current of the battery pack based on this characteristic value, thereby realizing a safe and effective charging strategy. Secondly, combined with the complete charging process, the effectiveness of the on-board BMS hardware detection, the estimation of the remaining SOH of the battery pack, and other key evaluation data related to battery safety, service life, and mileage are realized, providing strong support for the safety of power batteries and the performance evaluation of battery packs. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a timing diagram of the dynamic pulse detection working condition in the embodiment.

[0042] Figure 2 The present invention is a flow chart of the detection method. DETAILED DESCRIPTION

[0043] The present invention will be further described below in conjunction with embodiments, but the protection scope of the present invention is not limited thereto:

[0044] To achieve safe charging, before starting charging, a dynamic pulse charging process is added to detect the current state and performance of the battery pack, which is used to formulate charging current and charging protection strategies. Figure 1 , the pulse charging detection function parameters are:

[0045] (1) Waiting time before detection Tr, in seconds.

[0046] (2) Pulse high level period Tp, in seconds.

[0047] (3) Pulse low level period Ts, in seconds.

[0048] (4) Number of pulse cycles Cnt, unit: times.

[0049] (5) Pulse current I, unit: ampere (A).

[0050] (6) BMS data reading and uploading time interval Tf, in seconds.

[0051] (7) Waiting time after detection Tw, in seconds.

[0052] The above parameters (1)-(6) can be initialized or modified during execution by sending instructions through the human-computer interaction interface or the cloud platform. The maximum setting value of Tr, Tp, and Ts is 1200 seconds, the maximum setting value of the pulse cycle number is 100, and the maximum value of the pulse current must be less than the maximum allowable charging current value of the battery uploaded by the BMS.

[0053] Based on the above functional parameters, combined with Figure 2 , the pulse detection execution process is the following 6 steps:

[0054] Step 1: After the vehicle is connected to the DC charging pile, the pile and the onboard BMS complete the handshake according to the communication protocol, and then start the timing waiting until the set pre-test waiting time Tw is met. The test waiting time is to ensure that the power battery has a period of rest time before the test to ensure that the battery pack voltage tends to be stable.

[0055] Step 2: After the static stage, the pulse high level period is entered, and the DC charging module outputs the set pulse current I to perform a constant current charging stage for the battery pack with a duration of Tp.

[0056] Step 3: After the pulse high level cycle ends, it enters the pulse low level cycle, at which time the DC module output is turned off until the low level cycle ends.

[0057] Step 4: A pulse cycle is the sum of the high level time and the low level time. If the pulse cycle number Cnt is 1, after the low level cycle ends, it means that the dynamic condition diagnosis is completed. If the Cnt number is greater than 1, repeat Step 2 and Step 3 until the number is equal to the set Cnt value.

[0058] Step 5: After the pulse detection process is completed, wait for the calculation result of the cloud platform or local computing device within the specified waiting time Tf, and ensure charging safety according to the maximum charging current recommendation issued in the result. After receiving this current value, the charging pile starts the charging process. If the result is not obtained after the timeout, read the maximum charging current parameter from the vehicle BMS to start the charging process.

[0059] Step 6: After the charging process is completed, combined with the characteristic results of the pulse detection, the relevant parameters that affect the battery pack charging and discharging safety and the core factors that affect the battery pack life and mileage, namely the battery remaining SOH, battery internal resistance, etc. are calculated and analyzed, and the calculated result values ​​are analyzed, which are finally reflected in the estimation of the battery remaining SOH and the explanation of battery charging safety.

[0060] Step7: The process ends.

[0061] In the process of executing Step 1 to Step 5, the charging pile reads data from the on-board BMS in real time. The data content includes at least the total voltage of the battery pack, the total current, the highest single cell voltage, the lowest single cell voltage, the battery pack SOC, the average temperature of the battery pack, the temperature of the charging gun body, etc. The real-time data is uploaded to the cloud platform or the local computing device according to the upload time interval specified in the detection. The cloud platform or the local computing device parses and saves the real-time data. After the Step 5 and Step 6 processes are completed, the relevant data are integrated and analyzed.

[0062] After Step 5, the data integration and analysis process is as follows:

[0063] (1) According to the collected time series interval Tf, the total battery voltage V, charging current I, single cell maximum voltage Vmax, and single cell minimum voltage Vmin uploaded in the pulse detection process are combined into a multi-dimensional matrix data.

[0064] (2) For the above data, within a certain time period dt, the rate of change of the accumulated charge to the voltage dQ / dV of the highest single voltage, the lowest single voltage and the average single voltage are respectively recorded as Rq1, Rq2, and Rq3, and the rate of change of the voltage to time dV / dt is respectively recorded as Rv1, Rv2, and Rv3.

[0065] (3) For the above data, within the entire time range, according to the collection time interval Tf, the charging current I, the collected single cell maximum voltage Vmax, the single cell minimum voltage Vmin values ​​are used as the identification results, and are brought into the first-order RC equivalent circuit model to calculate the time constant τ of the first-order RC circuit of the highest single cell voltage cell, the lowest single cell voltage cell, and the single cell average value, respectively. The time constants are recorded as τ1, τ2, and τ3, respectively, and their values ​​are used as a reference for judging the degree of cell aging (the smaller the value, the higher the degree of aging).

[0066] (4) The characteristic values ​​calculated in the above steps (2) and (3) are processed and judged. The first step is to judge the correlation between Rq1, Rq2, Rq3 and Rv1, Rv2, Rv3. The correlation is determined by calculating the Pearson coefficient between the two. When the correlation coefficient between the two is greater than 0.5, the characteristic result value is determined to be valid. The second step is to calculate the standard deviation of Rv1, Rv2, and Rv3, and compare whether there is a value greater than two times the standard deviation. If there is, and the corresponding τ1, τ2, and τ3 have the same corresponding relationship, if there is, then the characteristic result value is determined to be valid, indicating that the average value of the battery cell or the entire battery cell has an aging problem, and the range is mapped out according to the ratio of the maximum aging characteristic value of the deviation and the characteristic value of voltage to time. The proportional factor is multiplied by the rated capacity of the battery and substituted into the calculation to obtain the recommended maximum charging current. This current is usually less than the maximum charging current uploaded by the BMS. When calculating this value, the maximum charging current that the charging pile power module can output is not considered.

[0067] After Step 6, the data integration and analysis process is as follows:

[0068] (1) According to the collected time series interval Tf, the total battery voltage V, charging current I, single cell maximum voltage Vmax, single cell minimum voltage Vmin, battery pack SOC, charging gun temperature Ta, and battery pack average temperature Tc uploaded during normal charging are combined into a multi-dimensional matrix data array.

[0069] (2) For the above data, within a certain time period dt, the correlation between the highest single cell voltage, the lowest single cell voltage and the rate of change dV / dt of the average single cell voltage with respect to time and the charging current I is captured, and the characteristic results are recorded as Vi1, Vi2, and Vi3 respectively. The correlation between the charging gun temperature Ta and the rate of change dT / dt of the average battery pack temperature Tc with respect to time and the charging current I is recorded as Ti1 and Ti2 respectively.

[0070] (3) For the above data, within the entire time range, based on the collection time interval Tf, charging current I, single cell maximum voltage Vmax, single cell minimum voltage Vmin, and the current SOC of the battery pack as the identification result, the cell SOH increment is calculated and the remaining SOH is estimated. The battery aging is calculated based on the SOH and data mapping is performed with the aging parameter value calculated in the pulse detection for iterative updating of the equivalent circuit model.

[0071] The characteristic values ​​calculated in the above steps (2) and (3) are processed and judged. The first step is to calculate whether there is a positive correlation between Vi1, Vi2, and VI3 and the value of the correlation coefficient. If not or the correlation value is too small, it is judged that there is a problem with the vehicle BMS voltage detection hardware. The second step is to judge whether there is a positive correlation between Ti1 and Ti2 and the size of the correlation value. If not or the correlation value is too small, it is judged that there is a problem with the vehicle BMS temperature detection hardware or the charging gun temperature detection.

[0072] The specific embodiments described herein are merely examples of the spirit of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in similar ways, but they will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A detection method for automobile power battery and management system based on DC charging pile, characterized in that Detection methods include: (1) Obtain characteristic information of the charging process through dynamic pulse detection; (2) calculating relevant parameters of the core factors affecting the battery pack charging and discharging safety and the battery pack life and driving mileage based on the characteristic information; (3) Analyze the calculation results of step (2) to obtain the result.

2. The method according to claim 1, characterized in that Step (1) specifically includes: (1-1) Static phase: After the vehicle is connected to the DC charging pile, the pile and the onboard BMS complete the handshake according to the communication protocol, and then enter the static phase and start waiting until the set pre-detection waiting time Tw is met; (1-2) Pulse high level period: After the resting stage, the pulse high level period begins, and the DC charging module outputs the set pulse current I to perform a constant current charging stage for the battery pack with a duration of Tp; (1-3) Pulse low level period: After the pulse high level period ends, it enters the pulse low level period, at which time the DC charging module output is turned off until the pulse low level period ends, and the timing is Ts; (1-4) Determine whether Tw+Tp is equal to the number of pulse cycles Cnt*pulse cycle. If not, return to step (1-2) and repeat. If equal, determine that the dynamic pulse detection is completed and record the characteristic information collected during the process.

3. The method according to claim 1 or 2, characterized in that The characteristic information of the charging process is: according to the collected time series interval Tf, the multi-dimensional matrix data composed of the total battery voltage V, the charging current I, the maximum cell voltage Vmax, and the minimum cell voltage Vmin during the charging process.

4. The method according to claim 1, characterized in that After the dynamic pulse detection obtains characteristic information of the charging process, the result is analyzed and a maximum charging current recommendation is given.

5. The method according to claim 4, characterized in that The maximum charging current is obtained by the following steps: ①According to the collected time series interval Tf, the total battery voltage V, charging current I, single cell maximum voltage Vmax, and single cell minimum voltage Vmin uploaded in the pulse detection process are combined into a multi-dimensional matrix data; ② For the above data, within a certain time period dt, the cumulative charge amount of the highest single voltage, the lowest single voltage and the average single voltage are taken as the voltage change rate dQ / dV, and the characteristic results are recorded as Rq1, Rq2, Rq3, respectively, and the voltage change rate dV / dt is recorded as Rv1, Rv2, Rv3, respectively; ③ For the above data, within the entire time range, according to the collection time interval Tf, the charging current I, the collected single-cell maximum voltage Vmax, the single-cell minimum voltage Vmin values ​​are used as the identification results, and brought into the first-order RC equivalent circuit model, and the time constants τ of the first-order RC circuit of the highest single-cell voltage cell, the lowest single-cell voltage cell, and the single-cell average value are calculated, respectively, and recorded as τ1, τ2, and τ3, and their values ​​are used as a reference for judging the degree of cell aging; Process and judge the eigenvalues ​​calculated in steps ② and ③ above: The first step is to determine the correlation between Rq1, Rq2, Rq3 and Rv1, Rv2, Rv3. The correlation is determined by calculating the Pearson coefficient between the two. When the correlation coefficient between the two is greater than 0.5, the characteristic result value is determined to be valid. The second step is to calculate the standard deviation of Rv1, Rv2, and Rv3, and compare whether there is a value greater than two times the standard deviation. If so, and the corresponding τ1, τ2, and τ3 have the same corresponding relationship, then the characteristic result value is determined to be valid, indicating that there is an aging problem in the average value of the battery cell or the entire battery cell. The proportional factor is mapped out according to the ratio of the maximum aging characteristic value of the deviation and the characteristic value of voltage to time. The proportional factor is multiplied by the rated capacity of the battery and substituted into the calculation to obtain the remaining capacity of the battery, and then the recommended maximum charging current is obtained.

6. The method according to claim 5, characterized in that The recommended maximum charging current Imax of the battery is obtained by multiplying the remaining capacity and the maximum charging rate Crate.

7. The method according to claim 1, characterized in that The relevant parameters in step (2) include the battery remaining SOH and the battery internal resistance.

8. The method according to claim 7, characterized in that The specific process of parsing in step (3) is as follows: ①According to the collected time series interval Tf, the total battery voltage V, charging current I, single cell maximum voltage Vmax, single cell minimum voltage Vmin, battery pack SOC, charging gun temperature Ta, and battery pack average temperature Tc uploaded during normal charging are combined into a multi-dimensional matrix data array; ② For the above data, within a certain time period dt, the correlation between the highest single cell voltage, the lowest single cell voltage and the average single cell voltage change rate dV / dt with the charging current I is intercepted, and the characteristic results are recorded as Vi1, Vi2, Vi3 respectively. The correlation between the charging gun temperature Ta and the battery pack average temperature Tc with the time change rate dT / dt and the charging current I is recorded as Ti1 and Ti2 respectively. ③ For the above data, within the entire time range, according to the collection time interval Tf, charging current I, single cell maximum voltage Vmax, single cell minimum voltage Vmin value, and battery pack current SOC as the identification result, calculate the cell SOH increment ΔSOH and estimate the remaining SOH. The calculation formula is: SOH g =ΔSOH / ΔSOC Among them, SOH g represents the remaining SOH, ΔSOC represents the SOC increment; Process and judge the eigenvalues ​​calculated in steps ② and ③ above: The first step is to calculate whether there is a positive correlation between Vi1, Vi2, and Vi3 and the value of the correlation coefficient. If there is no positive correlation or the correlation value is less than the threshold, it is determined that there is a problem with the vehicle BMS voltage detection hardware; The second step is to determine whether there is a positive correlation between Ti1 and Ti2 and the size of the correlation value. If not or the correlation value is less than the threshold, it is determined that there is a problem with the on-board BMS temperature detection hardware or the charging gun temperature detection.

9. The method according to claim 8, characterized in that ③ Also includes: SOH g The battery aging degree is calculated by comparing it with the rated SOH, and data mapping is performed with the aging parameter value calculated in the pulse detection to iteratively update the equivalent circuit model.

10. The method according to claim 7, characterized in that The battery internal resistance Res is obtained by the following formula: Res=(dVstart / dI+dVend / dI) / 2 Wherein, dVstart / dI represents the ratio of voltage to current at the moment of starting a high-level cycle, and dVend / dI represents the ratio of voltage to current at the moment of starting a low-level cycle.

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