Design method of fast charger for AGM lead-acid poor liquid battery and charger

Abstract

The application discloses a design method for rapid charging of an AGM lead-acid poor liquid battery and a charger thereof, and comprises the following steps: 1, determining 0.15C as the optimal charging current of the AGM lead-acid poor liquid battery through a plurality of groups of charging current comparison experiments; 2, setting the first charging stage as a waiting signal stage based on an oxygen recombination imbalance inflection point signal; 3, setting the second charging stage as a Mas curve optimization stage, and when the oxygen recombination imbalance inflection point signal is detected and the charging current is reduced to 0.05C, switching to constant current charging at a second constant current I_boost=0.1C; 4, setting the third charging stage as a floating and termination stage, and when the voltage reaches 14.65V, switching to constant voltage floating charging at a third constant voltage V_float=13.80V; and 5, setting discharge proportion adaptive logic and aging adaptive verification. According to the method, the battery can be rapidly charged according to self-expression signals of the battery in the charging process, combined with the electrochemical characteristics of the battery, the Mas curve theory and experimental data, and the service life of the battery is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of lead-acid battery charging technology, and in particular to a design method and charger for fast charging of AGM lead-acid starved electrolyte batteries. Background Technology

[0002] AGM (Adsorbed Glass Fiber) lead-acid batteries, due to their advantages such as sealed operation, maintenance-free operation, and high power output, are widely used in energy storage systems, electric vehicles, and uninterruptible power supplies. However, existing charging strategy design methods for AGM lead-acid batteries have the following drawbacks: designers consistently consider the problem from the perspective of the "charger," focusing on how to control voltage, adjust current, and set thresholds, while neglecting the battery's own "expression" during the charging process. This "charger-centric" design philosophy leads to increasingly complex charging strategies and cumbersome algorithms, yet it still fails to perfectly match the battery's actual needs.

[0003] As is well known, the internal mechanical structure of a lead-acid battery is fixed, and it has a certain threshold. Exceeding a certain current value will cause the battery to be unable to withstand it. The ideal charging current for a battery is approximately between 0.10C and 0.25C. Some designers, in order to shorten charging time, force the charging current to between 0.3C and 0.5C, or even higher, resulting in a full charge time of 5 hours, 3 hours, or even less. This type of design method completely ignores the drawbacks of high-current charging, such as increased battery temperature rise, severe oxygen evolution, impact on the mechanical structure of the lead paste causing it to soften, and even lead paste shedding with prolonged use. In addition, high-current charging will also accelerate grid corrosion, which will seriously shorten the battery's lifespan in the long run. Summary of the Invention

[0004] The purpose of this invention is to provide a design method and charger for fast charging of AGM lead-acid electrolyte-deficient batteries. This method can perform fast charging based on the battery's own self-expression signals during the charging process, combined with the battery's electrochemical characteristics, Mass curve theory, and experimental data, thereby ensuring battery life.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] This invention provides a design method for fast charging of AGM lead-acid electrolyte-deficient batteries, comprising the following steps:

[0007] Step 1: Through multiple sets of charging current comparison experiments, 0.15C was determined to be the optimal charging current for AGM lead-acid battery with low electrolyte concentration. Under the optimal charging current, the AGM lead-acid battery with low electrolyte concentration emitted an oxygen recombination imbalance inflection point signal, and the charging current showed a regular automatic decrease.

[0008] Step 2: Based on the oxygen recombination imbalance inflection point signal, the first charging stage is set as a waiting signal stage, and charging is performed with a first constant voltage V_bulk=14.40V and a first current limit value I_limit=0.15C. The decreasing signal of the charging current is monitored in real time according to the signal recognition algorithm.

[0009] Step 3: Set the second charging stage to the Mas curve optimization stage. When the oxygen recombination imbalance inflection point signal is detected and the charging current drops to 0.05C, automatically switch to constant current charging with the second constant current I_boost=0.1C. According to the power prediction algorithm and the dynamic current adjustment algorithm, predict the expected charging power ΔQ_pred when the voltage rises to the second constant voltage V_boost=14.65V in real time, and compare it with the dynamic target value Q_target2. Adjust the charging current so that the actual charging power is close to the dynamic target value Q_target2.

[0010] Step 4: Set the third charging stage as a float charging and termination stage. When the voltage reaches 14.65V, it will automatically switch to constant voltage float charging with a third constant voltage V_float=13.80V. The total power monitoring logic is designed. When the cumulative total charging power reaches 110%-125% of the discharged power and the current drops to the termination threshold I_end=0.01C, the charging will automatically terminate.

[0011] Step 5: Set the discharge ratio adaptive logic and aging adaptive verification. Based on the oxygen recombination imbalance inflection point signal mentioned in Step 1, make the charging current automatically terminate at the 80% inflection point. Combined with the dynamic target calculation in Step 3, ensure that the total charging capacity is controlled within the range of 110%-125% of the discharged capacity under any discharge ratio and aging degree.

[0012] Preferably, the comparative experiment in step 1 includes four sets of charging currents: 0.10C, 0.15C, 0.20C, and 0.25C. By comparing the charging characteristics of the four sets of charging currents, it is shown that when the charging current is 0.15C, the best balance is achieved between charging time and full charge, and the inflection point is closest to the ideal value of 80%.

[0013] Preferably, in step 1, the regular automatic decrease of the charging current is manifested as follows: constant current is maintained for approximately 6 hours, the inflection point occurs at approximately 80% SOC, and the current decreases to 0.05C in approximately 30 minutes.

[0014] Preferably, the selection of the second constant current I_boost=0.1C in step 3 is based on the Mas curve theory, and the second constant current is in the optimal range of battery charging acceptance and the dynamic adjustment range is 0.08C to 0.15C.

[0015] Preferably, the formula for calculating the dynamic target value in step 3 is:

[0016] Q_target2=K_target×Q_discharged-Q1-Q3_est;

[0017] Wherein, K_target is 110%-125%, Q_discharged is the discharged power, Q1 is the actual charged power in the first charging stage, and Q3_est is the expected charged power in the third charging stage.

[0018] Preferably, in step 4, charging is forcibly terminated when the termination threshold I_end = 0.01C and the total charging amount reaches 125% of the discharged amount.

[0019] Preferably, the aging adaptive verification in step 5 includes adaptive verification of the experimental battery capacity of three groups of batteries: new battery, moderately aged battery, and severely aged battery. The experimental data shows that even when the battery capacity decays to 80%, the oxygen recombination imbalance inflection point signal can still be clearly identified.

[0020] Another aspect of the present invention provides a fast charger for AGM lead-acid low-electrode batteries, including a microcontroller, a voltage and current sampling circuit, and a PWM drive circuit; the microcontroller is configured to execute the response logic based on the oxygen recombination imbalance inflection point signal described above, the dynamic current adjustment algorithm in step 3, and the discharge ratio adaptive calculation in step 5.

[0021] The present invention achieves the following beneficial technical effects compared to the prior art:

[0022] This invention provides a fast-charging design method for AGM lead-acid batteries with low electrolyte levels. It uses the battery's self-expressed signals as the natural switching point for the first charging stage, and uses Mascherano's curve theory to guide the current design for the second charging stage. Optimal parameters are determined through comparative experiments, and precise power allocation is ensured through dynamic adjustments. Ultimately, the total charging capacity automatically adapts to any discharge ratio and aging level, and is stably controlled within a safe range of 110%-125% of the discharged capacity. This design method automatically adapts to capacity decay based on the battery's self-expressed signals, eliminating the need for parameter recalibration. This invention uses 110%-125% of the discharged capacity as the control target, ensuring both full battery saturation (110% lower limit) and preventing overcharging (125% upper limit), providing sufficient safety redundancy. Fast charging is performed based on the battery's self-expressed signals during the charging process, combined with the battery's electrochemical characteristics, Mascherano's curve theory, and experimental data, ensuring battery lifespan. Attached Figure Description

[0023] Figure 1This is a flowchart of the design method for fast charging of AGM lead-acid electrolyte-deficient batteries in this invention. Detailed Implementation

[0024] This invention provides a design method for fast charging of AGM lead-acid electrolyte-deficient batteries, such as... Figure 1 As shown. Through in-depth research on the oxygen recombination mechanism of AGM batteries, the inventors discovered a key physical phenomenon that had been long overlooked: AGM batteries possess a natural "self-expression" capability during charging. When the charged capacity reaches approximately 80% of its current capacity, the oxygen evolution rate at the positive electrode begins to exceed the recombination rate at the negative electrode, resulting in a decrease in oxygen recombination efficiency. Consequently, the battery's charging acceptance capacity naturally decreases, manifested as an automatic drop in current during the constant-voltage charging phase. This current drop signal is the battery sending a clear instruction to the charger—"I have reached the oxygen recombination imbalance inflection point; please switch charging phases." The inventors also verified this pattern through numerous experiments, using a 100Ah rated AGM battery as an example, conducting a series of comparative experiments at 25°C:

[0025] Charging current Time to reach constant pressure Constant current sustaining time When the inflection point occurs, SOC Time for the current to drop to 0.05C 0.10C 35 8.5 81.5% 25 minutes 0.15C 24 6 80.2% 30 minutes 0.20C 18 4 79.5% 35 minutes 0.25C 12 2.8 78% 40 minutes

[0026] The experimental data above show that at 0.15C charging, the inflection point is closest to the ideal value of 80%, the constant current maintenance time is moderate, and the current decrease process is smooth. This finding provides a key natural switching point and a basis for optimal current selection in the design of the first-stage charging strategy.

[0027] Meanwhile, this invention incorporates the Mass curve theory as the basis for the current design in the second stage. The Mass curve reveals the relationship between battery charge acceptance rate and depth of discharge, indicating that the optimal charging current should gradually decrease as the battery approaches full charge. Based on this theory, this invention employs a 0.1C constant current charging in the second stage, which can be dynamically adjusted as needed, conforming to the battery's charge acceptance characteristics while ensuring sufficient charge input.

[0028] Furthermore, through testing batteries at different aging stages, the inventors discovered that even when the battery capacity decays to 80% of its rated capacity, the aforementioned pattern still holds true. Only the absolute SOC at the inflection point decreases slightly (approximately 78%-79%), but the battery's self-expression signal remains clear and reliable. This demonstrates the universality of the method of this invention.

[0029] Most charging strategies target the battery's nominal capacity (100%), but the charging efficiency of lead-acid batteries is usually between 80% and 90%. This means that the amount of charge input needs to be 110% to 125% of the amount of charge output, so that the total charge output is controlled within the range of 110% to 125% of the charge output.

[0030] Based on this, the design method for fast charging of AGM lead-acid electrolyte-deficient batteries provided by the present invention includes the following steps:

[0031] Step 1: Through multiple sets of charging current comparison experiments, 0.15C was determined to be the optimal charging current for AGM lead-acid battery with low electrolyte concentration. Under the optimal charging current, the AGM lead-acid battery with low electrolyte concentration emits an oxygen recombination imbalance inflection point signal (i.e., the battery's self-expression signal), and the charging current exhibits a regular automatic decrease. The physical essence of the oxygen recombination imbalance inflection point signal is that when the battery is charged to 80% of its current capacity, the oxygen evolution rate at the positive electrode exceeds the recombination rate at the negative electrode, the oxygen recombination efficiency decreases, and the charging acceptance capacity naturally decreases.

[0032] Step 2: Based on the oxygen recombination imbalance inflection point signal, the first charging stage is set as a waiting signal stage. The circuit topology and control logic for charging with a first constant voltage V_bulk = 14.40V and a first current limit I_limit = 0.15C are implemented. The charging current decrease signal is monitored in real time according to a signal recognition algorithm, including:

[0033] Design a current monitoring circuit to detect the charging current I(t) in real time;

[0034] Design a signal recognition algorithm that determines that a battery inflection point signal has been received when the current continuously decreases from I_limit and the rate of decrease exceeds a preset threshold.

[0035] At the end of the first charging stage, the cumulative charge should reach about 80% of the battery's current capacity (determined by the battery itself), at which point the current has dropped to about 0.05C.

[0036] Step 3: Set the second charging stage as the Mas curve optimization stage and design the corresponding logic: When the oxygen recombination imbalance inflection point signal is detected and the charging current drops to 0.05C, automatically switch to constant current charging with the second constant current I_boost=0.1C. The second charging stage is initially set to constant current charging with the second constant current I_boost=0.1C. This current value conforms to the Mas curve theory and is in the optimal range for battery charging acceptance. Based on the power prediction algorithm and the dynamic current adjustment algorithm, predict the expected charging power ΔQ_pred when the voltage rises to the second constant voltage V_boost=14.65V in real time, compare it with the dynamic target value Q_target2, and adjust the charging current so that the actual charging power is close to the dynamic target value Q_target2.

[0037] The power prediction algorithm includes: during the second charging stage, real-time monitoring of the terminal voltage V(t) and the cumulative charging power ΔQ (relative to the power at the end of the first charging stage), and prediction of the expected charging power ΔQ_pred when the voltage rises to the second constant voltage V_boost=14.65V based on the battery model.

[0038] The design of the dynamic current adjustment algorithm includes:

[0039] The dynamic target value Q_target2 is set to 32% of the rated capacity, which is a reasonable range calculated based on the fact that 80% has been charged in the first charging stage and the total target is 110%-125%.

[0040] If the expected charge amount ΔQ_pred < the dynamic target value Q_target2, then appropriately increase the charging current (in steps of 0.01C, not exceeding 0.15C) to increase the charge amount;

[0041] If the expected charge ΔQ_pred > the dynamic target value Q_target2, the charging current can be appropriately reduced (in steps of 0.01C, with a minimum of 0.08C) to slow down the voltage rise and ensure that it does not exceed the safety limit.

[0042] Adjustments are made based on the Mass curve theory to ensure that the current is always within the acceptable range of the battery.

[0043] Repeat the prediction-adjustment process until the voltage reaches 14.65V, at which point the second charging stage ends, ensuring that the actual charge ΔQ_actual is close to the dynamic target value Q_target2.

[0044] Step 4: Set the third charging stage as a float charging and termination stage, and design the response logic: when the voltage reaches 14.65V, automatically switch to the third charging stage. The third charging stage uses a third constant voltage V_float=13.80V for constant voltage float charging, and design the total power monitoring logic. When the cumulative total charging power reaches 110%-125% of the discharged power and the current drops to the termination threshold I_end=0.01C, the charging will automatically terminate.

[0045] Among them, the cumulative total charging power Q_total (relative to before charging started) is monitored in real time and compared with the discharged power.

[0046] When the total accumulated charging capacity Q_total reaches 110% of the discharged capacity, start monitoring the termination conditions;

[0047] When the current drops to the preset termination threshold I_end=0.01C and the cumulative total charging capacity Q_total is within the range of 110%-125% of the discharged capacity, charging will automatically terminate.

[0048] If the current has not dropped to I_end when the total accumulated charging capacity Q_total reaches 125% of the discharged capacity, charging will be forcibly terminated to prevent overcharging.

[0049] Step 5: Set the discharge ratio adaptive logic and aging adaptive verification. Using the oxygen recombination imbalance inflection point signal mentioned in Step 1, the charging current automatically terminates at the 80% inflection point. Combined with the dynamic target calculation in Step 3, ensure that the total charging capacity is controlled within the range of 110%-125% of the discharged capacity under any discharge ratio and aging degree.

[0050] In designing the discharge ratio adaptive logic, since the first charging stage relies on the battery's own 80% inflection point signal, regardless of the initial discharge ratio of the battery (0%, 50%, or others), the battery will be charged to 80% of its current capacity at the end of the first charging stage, which is also the moment when the oxygen evolution rate is higher than the negative electrode recombination rate. Therefore, the proportion of additional charge required from 80% to full charge is fixed (approximately 30%-45%). Through the 0.1C constant current and dynamic adjustment in the second charging stage, it can be ensured that the total charge input is always controlled within the range of 110%-125% of the discharge output, without the need for additional calculation of the initial SOC.

[0051] In the design of the aging adaptive verification, the universality of this design method is verified by testing batteries at different aging levels:

[0052] New battery (100% capacity): The inflection point appears at 80% SOC, and the current decrease follows a clear pattern;

[0053] Moderate aging (capacity 90%): The inflection point occurs at 79% SOC, and the signal is still recognizable;

[0054] Severe aging (capacity 80%): The inflection point occurs at 78% SOC, the current drops slightly faster, but the signal remains valid;

[0055] The core concept of this design method is to use the battery's self-expressed signal as the natural switching point for the first charging stage, guide the current design for the second charging stage with Mass curve theory, determine the optimal parameters through comparative experiments, and ensure precise power allocation through dynamic adjustment. Ultimately, the total charging capacity automatically adapts to any discharge ratio and aging level, and is stably controlled within a safe range of 110%-125% of the discharged capacity. This design method automatically adapts to capacity decay through the battery's self-expressed signal, eliminating the need for parameter recalibration. This invention uses 110%-125% of the discharged capacity as the control target, ensuring both full battery saturation (110% lower limit) and preventing overcharging (125% upper limit), providing sufficient safety redundancy.

[0056] The comparative experiment in step 1 included four sets of charging currents: 0.10C, 0.15C, 0.20C, and 0.25C. Comparison of the charging characteristics of these four sets of currents showed that a charging current of 0.15C achieved the best balance between charging time and full charge, with the inflection point closest to the ideal 80%. Real-world comparative experiments proved that 0.15C is the optimal choice, with the inflection point closest to 80% and the current decrease process most smoothly, demonstrating a solid and reliable design basis.

[0057] In step 1, the regular automatic decrease of the charging current is manifested as follows: constant current is maintained for approximately 6 hours, the inflection point appears at approximately 80% SOC, and the current drops to 0.05C in approximately 30 minutes.

[0058] The selection of the second constant current I_boost = 0.1C in step 3 is based on the Mas curve theory. The second constant current is within the optimal range of battery charge acceptance and its dynamic adjustment range is 0.08C to 0.15C. By introducing the Mas curve theory, the selection of a 0.1C current in the second charging stage conforms to the charge acceptance law revealed by the Mas curve, ensuring that the charging process is always within the battery's optimal acceptance range, thus guaranteeing efficiency while avoiding damage.

[0059] The formula for calculating the dynamic target value in step 3 is as follows:

[0060] Q_target2=K_target×Q_discharged-Q1-Q3_est;

[0061] Wherein, K_target is 110%-125%, Q_discharged is the discharged power, Q1 is the actual charged power in the first charging stage, and Q3_est is the expected charged power in the third charging stage.

[0062] In step 4, charging is forcibly terminated when the termination threshold I_end = 0.01C and the total charging amount reaches 125% of the discharged amount.

[0063] The aging adaptive verification in step 5 includes adaptive verification of the experimental battery capacity of three groups of batteries: new battery, moderately aged battery, and severely aged battery. The experimental data shows that even when the battery capacity decays to 80%, the oxygen recombination imbalance inflection point signal can still be clearly identified.

[0064] The design method of the present invention will be described in detail below with reference to specific embodiments. This embodiment takes an AGM battery with a rated voltage of 12V and a rated capacity of 100Ah as an example, but the design method of the present invention is not limited to this.

[0065] Comparative experimental data verification

[0066] The inventors conducted a comparative experiment on the same batch of new batteries at different charging currents in an environment of 25°C, and the results are as follows:

[0067] Charging current Time to reach constant pressure Constant current sustaining time When the inflection point occurs, SOC Time for the current to drop to 0.05C Total charging time Fullness 0.10C 35 minutes 8.5 hours 81.5% 25 minutes 12.5 hours 98.5% 0.15C 24 minutes 6 hours 80.2% 30 minutes 9.5 hours 98.2% 0.20C 18 minutes 4 hours 79.5% 35 minutes 8.2 hours 96.5% 0.25C 12 minutes 2.8 hours 78% 40 minutes 7.5 hours 94.0%

[0068] Experimental data show that:

[0069] 0.10C charging takes too long (12.5 hours) and is inefficient;

[0070] Although the total charging time is short at 0.20C and 0.25C, the full charge level decreases and the inflection point is reached earlier, requiring a longer period of replenishment later on.

[0071] 0.15C achieves the best balance between charging time (9.5 hours) and full charge (98.2%), with the inflection point closest to the ideal value of 80% and the current drop process being smooth (30 minutes).

[0072] Therefore, the present invention selects 0.15C as the first stage charging current.

[0073] Aging battery verification experiment

[0074] The inventors tested batteries of the same model but with different service lives to verify the universality of this method:

[0075] Battery status Actual capacity 0.15C charging inflection point SOC Current fall time Signal clarity New battery 100Ah 80.2% 30 minutes Very clear Moderate aging (2 years) 92Ah 79.5% 28 minutes Clear Severe aging (4 years) 82Ah 78.3% 25 minutes Recognizable

[0076] Experimental data shows that even when the battery capacity decays to 82%, the battery's self-expressive signal remains and is identifiable, with only a slight decrease in the inflection point SOC. This design method naturally adapts to this change by recognizing the signal itself, rather than a preset absolute SOC value.

[0077] Example 1: Battery discharged 100%, new battery

[0078] First charging phase:

[0079] Voltage and current settings: 14.40V constant voltage, 0.15C (15A) current limiting.

[0080] Charging begins, and the voltage reaches 14.40V in about 24 minutes, while the current is maintained at 15A.

[0081] After about 6 hours of continuous charging, when the charge reaches about 80Ah (80% of the rated capacity), the current begins to decrease automatically.

[0082] The current gradually decreased from 15A to 5A (0.05C) over a period of approximately 30 minutes.

[0083] A signal indicating a continuous decrease in current was detected, marking the end of the first charging phase. A total of 80Ah of electricity was charged, with a total charging time of approximately 6.5 hours.

[0084] Second charging phase:

[0085] Switch to constant current charging with an initial current of 0.1C (10A).

[0086] Real-time monitoring of voltage and cumulative charge (starting from 80Ah).

[0087] The system predicts the amount of charge to be delivered when the voltage reaches 14.65V during charging. If the predicted value is below 32Ah, the current will be increased appropriately (up to 0.15C); if it is above 32Ah, the current will be decreased appropriately (down to 0.08C).

[0088] Finally, when the voltage reached 14.65V, the total charge was 112Ah (80Ah + 32Ah), taking about 3.5 hours (average current adjustment of about 0.09C).

[0089] The total capacity is 112Ah, which is 112% of the discharged capacity of 100Ah, within the range of 110%-125%.

[0090] Third charging stage:

[0091] Switch to 13.80V constant voltage float charging.

[0092] The float charging current gradually decreases. When the current drops to 1A (0.01C), the total charge is 115Ah (115% of the discharged charge), and charging automatically terminates.

[0093] Total charging time: 6.5h + 3.5h + 1.0h = 11 hours.

[0094] Example 2: Battery discharged 50%, new battery

[0095] First charging phase:

[0096] Voltage and current settings (same as in Example 1): 14.40V constant voltage, 0.15C (15A) current limiting.

[0097] The initial SOC is 50%, meaning the battery has 50Ah of remaining charge.

[0098] Charging begins, and the voltage reaches 14.40V in about 10 minutes, while the current is maintained at 15A.

[0099] After charging for about 3 hours, when the cumulative charge reaches 30Ah (reaching 80% absolute SOC, i.e., 80Ah), the current begins to decrease automatically.

[0100] The current dropped from 15A to 5A in about 30 minutes.

[0101] The first charging phase has ended, with a total charge of 30Ah (from 50% to 80%), taking approximately 3.5 hours.

[0102] Second charging phase:

[0103] Target total charging calculation: Discharged capacity = 50Ah, take K_target = 115%, then the target total charging capacity = 57.5Ah.

[0104] Subtracting the 30Ah from the first charging stage, the remaining amount is 27.5Ah. Subtracting the estimated float charge of approximately 3Ah from the third charging stage, the target for the second charging stage is approximately 24.5Ah.

[0105] Charged at a constant current of 0.1C (10A), with the current dynamically adjusted to ensure approximately 24.5Ah of charge is delivered.

[0106] When the voltage reaches 14.65V, the cumulative charge is 54.5Ah (30Ah + 24.5Ah). Adding the initial 50Ah, the absolute SOC is 104.5Ah (104.5%).

[0107] The second charging phase takes approximately 2.5 hours.

[0108] Third charging stage:

[0109] The float charge is approximately 3Ah, and the total charging capacity is 57.5Ah, which is 115% of the discharge capacity of 50Ah.

[0110] Total duration: 3.5h + 2.5h + 1.0h = 7 hours.

[0111] Example 3: Severely aged battery (actual capacity 82Ah) discharged to 100%.

[0112] First charging phase:

[0113] Voltage and current settings: (same as in Example 1): 14.40V constant voltage, 0.15C (15A) current limit (equivalent to 0.183C relative to 82Ah).

[0114] When the charge reaches approximately 78% of the absolute SOC (78% of the actual capacity of 82Ah, which is 64Ah), the current begins to decrease.

[0115] The current drop time is approximately 25 minutes.

[0116] The first charging phase has ended, with a total charge of 64Ah (starting from 0%), taking approximately 5.2 hours.

[0117] Second charging phase:

[0118] Target total charging calculation: Discharged capacity = 82Ah, take K_target = 115%, then the target total charging capacity = 94.3Ah.

[0119] Subtracting the first phase's 64Ah, the remaining amount is 30.3Ah. Subtracting the estimated floating charge of the third phase of approximately 3Ah, the target for the second phase is approximately 27.3Ah.

[0120] Charged at a constant current of 0.1C (8.2A), with the current dynamically adjusted to ensure approximately 27Ah of charge.

[0121] When the voltage reached 14.65V, a total of 91Ah of electricity was charged, taking approximately 3.3 hours.

[0122] Third charging stage:

[0123] The float charge is approximately 3.3Ah, and the total charging capacity is 94.3Ah, which is 115% of the discharged capacity of 82Ah.

[0124] Total duration: 5.2h + 3.3h + 1.2h = 9.7 hours.

[0125] Comparative Example: Traditional Fixed-Parameter Three-Stage Charging

[0126] The severely aged battery (same as in Example 3) was charged using conventional fixed parameters of 0.20C / 0.15C / 13.8V:

[0127] The first charging stage, 14.40V / 20A: Due to the relatively high current, the inflection point was reached earlier at 68% of the actual SOC (56Ah), taking 3.5 hours.

[0128] The second charging stage is 0.15C (12.3A) constant current charging: the voltage quickly reaches 14.65V, only 18Ah is charged, and a total of 74Ah is charged;

[0129] The third charging stage is 13.80V float charging: After 6 hours of float charging, the cumulative capacity is 82Ah, only 100% (relative to the actual capacity), but the charging time is as long as 11.5 hours.

[0130] The comparison shows that the method of the present invention achieves 115% charge capacity in a similar time period (9.7h vs 11.5h), the battery is more saturated, and it automatically adapts to the aging state.

[0131] Another aspect of the present invention provides a fast charger for lead-acid low-emission batteries, including a microcontroller, a voltage and current sampling circuit, and a PWM drive circuit; the microcontroller is configured to execute the response logic based on the oxygen recombination imbalance inflection point signal described above, the dynamic current adjustment algorithm in step 3, and the discharge ratio adaptive calculation in step 5.

[0132] This invention has illustrated its principles and implementation methods using specific examples. The descriptions of these embodiments are merely illustrative of the method and its core ideas; furthermore, those skilled in the art will recognize that modifications may be made to the specific implementation methods and application scope based on the principles of this invention. Therefore, the content of this specification should not be construed as limiting the invention.

Claims

1. A design method for fast charging of AGM lead-acid thin-liquid batteries, characterized in that: Includes the following steps: Step 1: Through multiple sets of charging current comparison experiments, 0.15C was determined to be the optimal charging current for AGM lead-acid battery with low electrolyte concentration. Under the optimal charging current, the AGM lead-acid battery with low electrolyte concentration emitted an oxygen recombination imbalance inflection point signal, and the charging current showed a regular automatic decrease. Step 2: Based on the oxygen recombination imbalance inflection point signal, the first charging stage is set as a waiting signal stage, and charging is performed with a first constant voltage V_bulk=14.40V and a first current limit value I_limit=0.15C. The decreasing signal of the charging current is monitored in real time according to the signal recognition algorithm. Step 3: Set the second charging stage to the Mas curve optimization stage. When the oxygen recombination imbalance inflection point signal is detected and the charging current drops to 0.05C, automatically switch to constant current charging with the second constant current I_boost=0.1C. According to the power prediction algorithm and the dynamic current adjustment algorithm, predict the expected charging power ΔQ_pred when the voltage rises to the second constant voltage V_boost=14.65V in real time, and compare it with the dynamic target value Q_target2. Adjust the charging current so that the actual charging power is close to the dynamic target value Q_target2. Step 4: Set the third charging stage as a float charging and termination stage. When the voltage reaches 14.65V, it will automatically switch to constant voltage float charging with a third constant voltage V_float=13.80V. The total power monitoring logic is designed. When the cumulative total charging power reaches 110%-125% of the discharged power and the current drops to the termination threshold I_end=0.01C, the charging will automatically terminate. Step 5: Set the discharge ratio adaptive logic and aging adaptive verification. Based on the oxygen recombination imbalance inflection point signal mentioned in Step 1, make the charging current automatically terminate at the 80% inflection point. Combined with the dynamic target calculation in Step 3, ensure that the total charging capacity is controlled within the range of 110%-125% of the discharged capacity under any discharge ratio and aging degree.

2. The design method of AGM lead-acid poor liquid battery fast charging according to claim 1, characterized in that: The comparative experiment in step 1 includes four sets of charging currents: 0.10C, 0.15C, 0.20C, and 0.25C. By comparing the charging characteristics of the four sets of charging currents, it is shown that when the charging current is 0.15C, the best balance is achieved between charging time and full charge, and the inflection point is closest to the ideal value of 80%.

3. The design method of AGM lead-acid poor liquid battery fast charging according to claim 1, characterized in that: In step 1, the regular automatic decrease of the charging current is manifested as follows: constant current is maintained for approximately 6 hours, the inflection point appears at approximately 80% SOC, and the current drops to 0.05C in approximately 30 minutes.

4. The design method for fast charging of AGM lead-acid electrolyte-deficient batteries according to claim 1, characterized in that: The selection of the second constant current I_boost=0.1C in step 3 is based on the Mas curve theory. The second constant current is in the optimal range of battery charging acceptance and the dynamic adjustment range is 0.08C to 0.15C.

5. The design method for fast charging of AGM lead-acid electrolyte-deficient batteries according to claim 1, characterized in that: The formula for calculating the dynamic target value in step 3 is as follows: Q_target2=K_target×Q_discharged-Q1-Q3_est; Wherein, K_target is 110%-125%, Q_discharged is the discharged power, Q1 is the actual charged power in the first charging stage, and Q3_est is the expected charged power in the third charging stage.

6. The design method for fast charging of AGM lead-acid electrolyte-deficient batteries according to claim 1, characterized in that: In step 4, charging is forcibly terminated when the termination threshold I_end = 0.01C and the total charging amount reaches 125% of the discharged amount.

7. The design method for fast charging of AGM lead-acid electrolyte-deficient batteries according to claim 1, characterized in that: The aging adaptive verification in step 5 includes adaptive verification of the experimental battery capacity of three groups of batteries: new battery, moderately aged battery, and severely aged battery. The experimental data shows that even when the battery capacity decays to 80%, the oxygen recombination imbalance inflection point signal can still be clearly identified.

8. A fast charger for AGM lead-acid electrolyte-deficient batteries, characterized in that: It includes a microcontroller, a voltage and current sampling circuit, and a PWM drive circuit; the microcontroller is configured to execute the response logic based on the oxygen recombination imbalance inflection point signal as described in any one of claims 1 to 7, the dynamic current adjustment algorithm in step 3, and the discharge ratio adaptive calculation in step 5.

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