A method for evaluating the state of health of a lead storage battery

Abstract

This invention discloses a method for evaluating the health status of lead-acid batteries, relating to the field of lead-acid battery testing technology. This invention measures the duration for which the charging voltage remains above 13.6V after each use, compares this to the percentage of the total charging time above 13.6V throughout the battery's lifespan, and corrects for this by considering the temperature rise during each charge. This allows for accurate prediction of the battery's state of health (SOH), with reliable results and an error within 5%. This method can be applied to smart batteries, enabling the monitoring of their SOH status.

Description

Technical Field

[0001] This invention relates to the field of lead-acid battery testing technology, and specifically to a method for evaluating the health status of lead-acid batteries. Background Technology

[0002] Lead-acid batteries have a history of over 150 years and are widely used. In recent years, low-speed electric vehicles have developed rapidly in my country due to their good transportation performance, low storage requirements, and excellent price advantages. Benefiting from this, the battery industry has also developed rapidly.

[0003] Battery state of health (SOH) can be understood as the percentage of a battery's current capacity relative to its original factory capacity. While end-users are becoming increasingly knowledgeable about batteries, they often lack a clear understanding of their health status. This is especially true for Zhongda Mi New Energy batteries, where a battery pack is highly valuable, making battery health a primary concern for users. When problems arise in a battery pack, it's often due to one or two faulty cells, but identifying the specific cell can lead to communication breakdowns or significant financial losses from replacing the entire pack. Therefore, battery SOH assessment, as a crucial task in battery condition evaluation, is receiving increasing attention from researchers both domestically and internationally.

[0004] Currently, common methods for assessing the health status of automotive power batteries all involve estimating and identifying internal battery parameters. These methods mainly fall into two categories: one assesses battery health status by estimating the battery's rated capacity, and the other assesses battery health status by estimating the battery's internal resistance. However, in practice, it is difficult to obtain accurate rated capacity and internal resistance values ​​for batteries, hindering their practical application.

[0005] During use, a battery's health deteriorates, primarily manifested as a decrease in rated capacity and an increase in internal resistance. Simultaneously, its internal temperature, state of charge (SOC), voltage, and current also change to varying degrees. Therefore, a simple, efficient, and accurate method for assessing battery health is particularly important to facilitate more effective battery use and management.

[0006] The lifespan and performance status of a battery pack are related not only to the stability of the internal electrochemical system but also to the operating environment and conditions, particularly the charge / discharge rate and operating temperature. Excessive charge / discharge rates accelerate battery degradation. The degradation of battery life and its impact on battery performance also vary under different operating temperatures. Large temperature differences between individual cells within the battery pack can exacerbate performance variations, worsening inconsistencies and further affecting the overall performance status of the battery pack. These changes in battery performance cannot be directly reflected in measured physical quantities; therefore, a method is needed to assess the state of health (SOH) of the battery pack.

[0007] Although battery packs, especially lithium-ion battery packs, are currently equipped with battery management systems to ensure real-time monitoring of the battery pack's status and safe operation, battery performance evaluation mainly relies on the battery management system's SOC estimation and measurements of individual cell voltage, battery temperature, voltage difference between individual cells, and temperature difference within the battery pack. This evaluation method has limitations and is highly dependent on the knowledge and experience of specialized technicians. Summary of the Invention

[0008] In view of this, the present invention provides a method for evaluating the health status of lead-acid batteries. By measuring the duration during which the charging voltage is not lower than 13.6V after each use, comparing it with the percentage of the total duration during which the charging voltage is not lower than 13.6V throughout the battery's lifespan, and correcting for it by considering the temperature rise during each charge, the SOH state of the battery can be accurately predicted, and the results are true and reliable.

[0009] A method for evaluating the health status of a lead-acid battery includes the following steps:

[0010] (1) Statistical duration t, where duration t is the time from the first time the lead-acid battery under test reaches the limit voltage of 14.6 to 14.8V until the end of charging when the charging voltage is not lower than 13.6V, and the temperature rise value T on the surface of the lead-acid battery under test is recorded at the same time.

[0011] (2) Calculate the cumulative value of duration t, ∑t 历次 This value is the cumulative value of the battery cycle up to the current cycle, including data from the current cycle and previous cycles.

[0012] (3) Constructing the SOH model:

[0013] SOH=((1-∑t 历次 / ∑t 总 )×Cn×20%+Cn×80%) / Cn;where Cn is the rated capacity value of the lead-acid battery to be tested, ∑t 总This is the cumulative total charging time of the lead-acid battery under test; battery life is generally evaluated when it reaches 80% of its rated capacity, so ∑t 总 The statistics are based on the cumulative value within 20% of the rated capacity;

[0014] (4) SOH Correction: No correction is needed if the temperature rise of the lead-acid battery surface in step (1) is not higher than the reference value; otherwise, correction is required. The SOH correction model is as follows:

[0015] SOH correction value = SOH - (T - T1) × ρ, where T1 is the baseline value and ρ is the correction coefficient.

[0016] Batteries typically employ a constant current-limiting voltage + constant voltage-limiting current charging method. The initial voltage limit and constant voltage values ​​are set at 14.7±0.1V. The final stage of charging is trickle charging, with the constant voltage value set at 13.8±0.1V. This is to account for temperature compensation of the charging voltage (reducing it by 3-5mV / cell / ℃ when the temperature is above 25℃) and to account for the fact that the battery terminal voltage will gradually drop from 13.6V after charging, with the rate of drop slowing down with increasing battery usage. Therefore, 13.6V is set as the statistical baseline.

[0017] Every time a battery is charged, it loses a certain amount of water. The longer it is charged at high voltage (above 13.6V), the more water is lost. Strictly controlling charging parameters and time is beneficial to battery life, but it cannot change the trend of water loss during charging, it can only slow down the trend. Therefore, controlling the charging high voltage (above 13.6V) time after full charge will extend the service life.

[0018] In step (1), the time t from the initial voltage limit of 14.6-14.8V until the end of charging, during which the charging voltage is not lower than 13.6V, is not included in ∑t. This period, from the initial voltage limit of 13.6V to the voltage limit of 14.6-14.8V, is not included in the calculation of the battery voltage as it increases from low to high during charging. 历次 .like Figure 1 The figure shown is ∑t 当次 Statistical period.

[0019] In step (1), the duration t is no more than 4.5h.

[0020] When the statistical duration t is higher than 4.5 hours, it is counted as 4.5 hours. When it is lower than 4.5 hours, the actual data is counted. Because there are many types of chargers on the market, some have a timer function to turn off the charger while charging, while others continue charging even after the charging indicator light turns green unless the power is manually disconnected. Therefore, a maximum duration value is set for this specific time to exclude such occasional situations.

[0021] In step (3), the ∑t 总 It is obtained through the following method:

[0022] (a) Take several lead-acid batteries of the same model as the battery pack to be tested, and perform cycle life tests using a discharge current of 0.3 to 0.7C3A respectively;

[0023] (b) Stop the test when the cycle life ends, calculate the cumulative value of the total charging time and perform a weighted average to obtain ∑t. 总 .

[0024] The discharge current in step (a) is 0.5C3A. The operating current for low-speed electric vehicles and special-purpose vehicles (such as site vehicles, sightseeing vehicles, and aerial work platform vehicles) is generally between 0.3 and 0.7C3A, so 0.5C3A was selected as the test operating current.

[0025] Preferably, the cycle life test method in step (a) is as follows:

[0026] (I) Discharge: Discharge to 10.5V with a constant current of 0.5C3A;

[0027] (II) Charging:

[0028] S1: Charge at 0.1C3A for 0.5 hours or charge to 12V;

[0029] S2: Charge to 14.4V at 0.18C3A, charging time not exceeding 6 hours;

[0030] S3: Charge to 14.8V at 0.12C3A, charging time not exceeding 2 hours;

[0031] S4: Charge at a constant voltage of 14.8V with a current limit of 0.05C3A until the current is less than 0.01C3A, or charge for 2 hours;

[0032] S5: Charge at a constant voltage of 13.8V with a limit of 0.01C3A for 2 hours.

[0033] In S4, there are two scenarios as the decision-making principle, with the scenario where the condition is met taking precedence.

[0034] In step (4), the reference value is 7°C. When the temperature rise is within 1.5°C of the reference value but not including 1.5°C, the correction factor ρ = 1.5%; when the temperature rise is 1.5°C or more of the reference value, the correction factor ρ = 3%.

[0035] It is normal for a battery to heat up during charging. When the electrode saturation is 94%, the temperature rise is generally within 7°C when charging using the above charging curve; therefore, a baseline value of 7°C is set. When the temperature rise exceeds the baseline value, it indirectly indicates that the electrode saturation is decreasing. The higher the temperature rise, the lower the electrode saturation, which means a higher water loss rate. In the later stages of charging, most of the electrical energy is used for useless work (electrolysis of water), so the duration of high voltage (above 13.6V) directly reflects the battery's health status.

[0036] The beneficial effects of this invention are:

[0037] This invention accurately predicts the State of Health (SOH) of a battery by measuring the duration during which the charging voltage remains above 13.6V after each use, comparing this to the percentage of the total charging time above 13.6V throughout the battery's lifespan, and adjusting for temperature rise during each charge. The results are reliable with an error within 5%. This method is relatively easy to apply to smart batteries, requiring only the acquisition of voltage and temperature values, and can effectively monitor the SOH state of smart batteries. Attached Figure Description

[0038] Figure 1 The charging curve is shown for the 6-EVF-100 battery as an example. Detailed Implementation

[0039] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Operating methods not specifically specified in the following embodiments are generally performed under conventional conditions or as recommended by the manufacturer.

[0040] Example 1

[0041] Take three new 6-EVF-100 batteries, label them #1, #2, and #3 respectively, and perform charge-discharge cycles according to the following process:

[0042] (a) Discharge: Discharge to 10.5V at a constant current of 50A;

[0043] (b) Charging: The charging curve is as follows Figure 1 As shown;

[0044] S1: Charge at 10A for 0.5 hours or charge to 12V;

[0045] S2: Charge at 18A to 14.4V, charging time not exceeding 6 hours;

[0046] S3: Charge at 12A to 14.8V, charging time not exceeding 2 hours;

[0047] S4: Charge at a constant voltage of 14.8V with a current limit of 5A until the current is less than 1A;

[0048] S5: Charge at a constant voltage of 13.8V with a limit of 1A for 2 hours.

[0049] The cyclic statistical results are shown in Table 1. The laboratory ambient temperature was 25±2℃, and the charging temperature rise during the entire cycle was not higher than the baseline value (7℃).

[0050] Table 1

[0051] Battery number Number of loops, times <![CDATA[∑t 总 h]]> 1# 358 1298 2# 372 1312 3# 377 1359 weighted average 369 1323

[0052] Therefore, the ∑t of the 6-EVF-100 model battery 总 It is 1323h.

[0053] Example 2

[0054] Two 6-EVF-100 smart batteries that had been in use on the market for 3 months were taken and labeled as #4 and #5 respectively. The battery status is shown in Table 2:

[0055] Table 2

[0056]

[0057] Calculation process for #4:

[0058] SOH=((1-∑t 历次 / ∑t 总 ()×Cn×20%+Cn×80%) / Cn

[0059] = ((1-204 / 1323)×100×20%+100×80%) / 100

[0060] =96.92%;

[0061] Calculation process for #5:

[0062] SOH=((1-∑t 历次 / ∑t 总 ()×Cn×20%+Cn×80%) / Cn

[0063] = ((1-207 / 1323)×100×20%+100×80%) / 100

[0064] =96.87%.

[0065] The two batteries underwent a 3-hour capacity test, and the test method is as follows:

[0066] (a) Discharge: Discharge to 10.5V at a constant current of 33.4A (1.0I3A);

[0067] (b) Charging: The charging curve is as follows Figure 1 As shown;

[0068] S1: Charge at 10A for 0.5 hours or charge to 12V;

[0069] S2: Charge at 18A to 14.4V, charging time not exceeding 6 hours;

[0070] S3: Charge at 12A to 14.8V, charging time not exceeding 2 hours;

[0071] S4: Charge at a constant voltage of 14.8V with a current limit of 5A until the current is less than 1A;

[0072] S5: Charge at a constant voltage of 13.8V with a limit of 1A for 2 hours.

[0073] The test results are shown in Table 3.

[0074] Table 3

[0075] Battery number 3hr capacity Ah Charging temperature rise ℃ Measured SOH value 4# 101.5 5.3 101.5％ 5# 100.2 5.5 102.2％

[0076] Since the denominator is the rated capacity Cn, and the actual battery capacity Ca will be slightly larger and tend to increase in capacity during the early stages of cycling (when the battery has no abnormal faults), the measured SOH value here may exceed 100%. Comparing the results in Tables 2 and 3, it can be seen that the difference between the displayed SOH value and the measured value is within 5%, indicating that the results are accurate and demonstrating that the SOH evaluation method provided by this invention is feasible.

[0077] Example 3

[0078] Two 6-EVF-100 smart batteries that had been in use on the market for 6 months were taken and labeled as 6# and 7# respectively. The battery status is shown in Table 4:

[0079] Table 4

[0080] Battery number <![CDATA[∑t 历次 h]]> SO₂ display value Current temperature rise 6# 465 92.97％ Not exceeding the benchmark value 7# 468 92.93％ Not exceeding the benchmark value

[0081] Calculation process for #6:

[0082] SOH=((1-∑t 历次 / ∑t 总 ()×Cn×20%+Cn×80%) / Cn

[0083] = ((1-465 / 1323)×100×20%+100×80%) / 100

[0084] =92.97%;

[0085] Calculation process for #7:

[0086] SOH=((1-∑t 历次 / ∑t 总 ()×Cn×20%+Cn×80%) / Cn

[0087] = ((1-468 / 1323)×100×20%+100×80%) / 100

[0088] =92.97%.

[0089] The two batteries were subjected to a 3-hour capacity test (test method as in Example 2), and the results are shown in Table 5.

[0090] Table 5

[0091] Battery number 3hr capacity Ah Charging temperature rise ℃ Measured SOH value 6# 95.5 5.8 95.5％ 7# 95.2 5.9 95.2％

[0092] The results in Tables 4 and 5 show that the difference between the displayed SOH value and the measured value is within 3%, indicating that the SOH evaluation method provided by the present invention is feasible.

[0093] Example 4

[0094] Two 6-EVF-100 smart batteries that have been in use on the market for 12 months were taken and labeled as 8# and 9# respectively. The battery status is shown in Table 6:

[0095] Table 6

[0096] Battery number <![CDATA[∑t 历次 h]]> SO₂ display value Current temperature rise 8# 1020 80.35％ 8.5℃ 9# 1023 78.54％ 9.0℃

[0097] Calculation process for #8:

[0098] SOH=((1-∑t 历次 / ∑t 总 ()×Cn×20%+Cn×80%) / Cn

[0099] = ((1-1020 / 1323)×100×20%+100×80%) / 100

[0100] =84.58%.

[0101] The SOH correction method requires no correction when the temperature rise is below the reference value, but a correction is necessary when the temperature rise exceeds the reference value. The SOH correction value is calculated as: SOH - (Measured Temperature Rise T - Reference Value) × ρ. The reference value is 7℃. When the temperature rise exceeds the reference value by less than 1.5℃, the correction factor ρ = 1.5%. When the temperature rise exceeds the reference value by 1.5℃ or more, the correction factor ρ = 3%.

[0102] The temperature rose by 8.5℃.

[0103] SOH correction value = SOH - (8.5 - 7) × 3%

[0104] =84.85% - (8.5 - 7) × 3%

[0105] =80.35%.

[0106] Calculation process for #9:

[0107] SOH=((1-∑t 历次 / ∑t 总 ()×Cn×20%+Cn×80%) / Cn

[0108] = ((1-1023 / 1323)×100×20%+100×80%) / 100

[0109] =84.54%;

[0110] The temperature rose by 9.0℃.

[0111] SOH correction value = SOH - (9-7) × 3%

[0112] =84.54% - (9-7) × 3%

[0113] =78.54%.

[0114] The two batteries were subjected to a 3-hour capacity test (test method as in Example 2), and the results are shown in Table 7.

[0115] Table 7

[0116] Battery number 3hr capacity Ah Charging temperature rise ℃ Measured SOH value 8# 81.5 8.5 81.5％ 9# 79.1 9.0 79.1％

[0117] The results in Tables 6 and 7 show that the difference between the displayed SOH value and the measured value is within 1%, indicating that the SOH evaluation method provided by the present invention is feasible.

[0118] As can be seen from the above embodiments, the SOH value becomes more accurate with the increase of usage times. The difference between the displayed SOH value and the measured value is within 5% throughout the entire battery operation and production cycle, indicating that the SOH evaluation method provided by the present invention is reliable.

[0119] Furthermore, it should be understood that after reading the above description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A method for evaluating the health status of a lead-acid battery, characterized in that, Includes the following steps: (1) Calculate the time from the first time the charging voltage reaches the limit value of 14.6~14.8V to the end of the charging process when the lead-acid battery under test is charged, and record it as t. At the same time, record the temperature rise value T on the surface of the lead-acid battery under test. The duration t is no more than 4.5 hours; when the duration t is higher than 4.5 hours, it is counted as 4.5 hours. (2) Calculate the cumulative value of time t 历次 ; (3) Constructing the SOH model: SOH = ((1- 历次 / 总 ()×Cn×20%+Cn×80%) / Cn; where Cn is the rated capacity of the lead-acid battery to be tested. 总 The cumulative value of the total charging time of the lead-acid battery to be tested; The 总 It is obtained through the following method: (a) Take several lead-acid batteries of the same model as the battery pack to be tested, and perform cycle life tests using a discharge current of 0.3~0.7C3A respectively; (b) Stop the test when the cycle life ends, calculate the cumulative value of the total charging time and perform a weighted average to obtain the result. 总 ; (4) SOH Correction: No correction is needed when the temperature rise of the lead-acid battery surface in step (1) is not higher than the reference value; otherwise, correction is required. The SOH correction model is as follows: SOH correction value = SOH - (T - T1) × ρ, where T1 is the baseline value and ρ is the correction coefficient; The reference value is 7℃; when the temperature rise is within 1.5℃ of the reference value but not including 1.5℃, the correction factor ρ = 1.5%; when the temperature rise is 1.5℃ or more of the reference value, the correction factor ρ = 3%.

2. The method for evaluating the health status of a lead-acid battery as described in claim 1, characterized in that, The discharge current in step (a) is 0.5C3A.

3. The method for evaluating the health status of a lead-acid battery as described in claim 1, characterized in that, The cycle life test method in step (a) is as follows: (I) Discharge: Discharge to 10.5V with a constant current of 0.5C3A; (II) Charging: S1: Charge at 0.1C3A for 0.5 hours or charge to 12V; S2: Charge to 14.4V at 0.18C3A, charging time not exceeding 6 hours; S3: Charge to 14.8V at 0.12C3A, charging time not exceeding 2 hours; S4: Charge at a constant voltage of 14.8V with a current limit of 0.05C3A until the current is less than 0.01C3A, or charge for 2 hours; S5: Charge at a constant voltage of 13.8V with a limit of 0.01C3A for 2 hours.

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