Battery thermal-electric balance dynamic adjustment method and system based on multi-tab structure cooperation

Abstract

The application provides a battery thermal and electrical balance dynamic adjustment method and system based on a multi-tab structure cooperation, relates to the technical field of dynamic adjustment, and comprises the following steps: dividing the battery into regions based on tab design parameters, and collecting temperature information, cell voltage and current data of each region in real time; identifying different hot spot scenarios, combining the current state of the battery and the aging state of the battery, and constructing a thermal and electrical risk index; evaluating, obtaining corresponding adjustment instructions, and formulating adjustment strategies combined with user habits; collecting the adjusted data in real time, calculating multiple indexes, and evaluating; if any item is unqualified, constructing an adjustment score coefficient, and modifying the corresponding adjustment strategy. The application can accurately adjust according to the actual situation and user demand, improve user experience and the use efficiency of the battery, timely find problems in the adjustment process and optimize, and ensure that the battery is always in a good thermal and electrical balance state.

Description

Technical Field

[0001] This invention relates to the field of dynamic adjustment technology, and in particular to a method and system for dynamic adjustment of battery thermoelectric balance based on multi-tab structure synergy. Background Technology

[0002] With the rapid development of fast charging technology for smartphones The above-mentioned high-rate charging (5C and above) has become the mainstream demand, but the thermal imbalance of batteries caused by high-rate charging and discharging is becoming increasingly serious. Traditional single-tab battery design has inherent defects: the current is concentrated near the tab, and the current density at the edge of the electrode can be more than three times that of the central area. The temperature at the root of the tab is prone to exceed 55°C, while the temperature in the middle of the battery is only 38°C. A temperature difference of more than 15°C will accelerate the growth of lithium dendrites and the decomposition of electrolyte, seriously affecting safety and cycle life.

[0003] Existing technologies rely on a single temperature threshold (such as 45°C) to determine hot spots, which cannot distinguish between different causes such as high-rate current concentration, increased internal resistance due to aging, and poor heat dissipation. False judgments are often caused by sensor misalignment or casing obstruction. Furthermore, they do not integrate the coupled effects of multiple factors such as charge / discharge rate, ambient temperature, and battery aging (cycle count, internal resistance increase). For example, when an aged battery is charged at 3C, even if the surface temperature does not exceed the standard, local internal resistance heat generation has already significantly increased, and existing technologies cannot extract early warnings.

[0004] The "one-size-fits-all" adjustment, without taking into account user habits (such as nighttime charging and gaming load) and battery status, leads to user experience issues such as nighttime cooling noise interference and a sharp drop in fast charging speed. Furthermore, excessive adjustment wastes the performance of non-aged areas, further shortening the cycle life.

[0005] To address the aforementioned issues, there is an urgent need for a method and system for dynamic adjustment of battery thermoelectric balance based on multi-tab structure collaboration, in order to promote the safe application of high-power fast charging technology. Summary of the Invention

[0006] This invention provides a method and system for dynamic adjustment of battery thermoelectric balance based on multi-tab structure collaboration, which solves the defects of the prior art in inaccurate hot spot scene identification and lack of personalized adjustment according to user habits.

[0007] On one hand, the present invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation, including:

[0008] Collect electrode design parameters, divide the battery into regions based on the electrode design parameters, and collect temperature information, cell voltage and current data of each region in real time.

[0009] Based on temperature information, cell voltage and current data, different hotspot scenarios are identified. Combined with the current state of the battery and the battery aging state, a thermoelectric risk index is constructed for different hotspot scenarios.

[0010] We assess different hotspot scenarios and thermal power risk indices, obtain corresponding adjustment instructions, and formulate adjustment strategies based on user habits.

[0011] Real-time data collection of temperature distribution, current distribution uniformity, and cooling system operation in each region after adjustment; calculation of temperature deviation index, current balance index, and cooling efficiency index; evaluation; and construction of adjustment score coefficients to correct the corresponding adjustment strategy if any one of them fails to meet the requirements.

[0012] This invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation, wherein the step of dividing the battery into regions includes:

[0013] The number and distribution of electrodes, geometric dimensions, material and process parameters, and electrical performance parameters are collected as electrode design parameters.

[0014] Based on the battery capacity and target charge / discharge rate, the number of tabs is determined to meet the current density requirements, and the number of zones is determined by uniformly cutting the positive and negative electrode plates based on the number of tabs.

[0015] Based on the fixed distance between the sub-electrode and the center of the electrode tab, radial partitions centered on the electrode tab are formed to determine the partition boundaries.

[0016] Voltage sampling points are deployed at the non-welded end of each sub-electrode based on the number of partitions and partition boundaries, and temperature sensors are embedded in partitions.

[0017] This invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation, and the steps for identifying different hotspot scenarios include:

[0018] Temperature information, cell voltage and current data of each area are collected synchronously at a preset frequency, and high-frequency interference is eliminated and abnormal data is removed.

[0019] Temperature characteristics are obtained by extracting the absolute value of the monopole temperature and the temperature difference between the monopole from the preprocessed temperature information and calculating the rate of temperature change.

[0020] Voltage characteristics are obtained by analyzing the regional voltage drop and polarization voltage in the pre-processed cell voltage, and current characteristics are obtained by analyzing the current distribution deviation and current change rate in the pre-processed current data.

[0021] Based on temperature characteristics and the thermal safety requirements of multi-tab batteries, and to meet the constraints of temperature compliance and duration, a three-level hot spot threshold is set.

[0022] Different hotspot scenarios are obtained by cross-validating the three-level hotspot thresholds based on voltage and current characteristics.

[0023] This invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation. The steps for constructing thermoelectric risk indices under different hotspot scenarios include:

[0024] Based on the current state of the battery, the charging status and high-temperature environment are determined, and the real-time charging current, rated current, ambient temperature and the battery's maximum tolerance temperature under the current state are extracted to calculate the high-temperature thermoelectric risk index.

[0025] The battery aging status is obtained based on the cumulative number of charge-discharge cycles, and the aging thermoelectric risk index is calculated by extracting the internal resistance growth rate, capacity decay rate, and tab temperature standard deviation.

[0026] The thermoelectric risk index is obtained by weighting and summing the high-temperature thermoelectric risk index and the aging thermoelectric risk index.

[0027] This invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation, wherein the steps for obtaining the corresponding adjustment command include:

[0028] The identified hotspot scenarios are linked to the corresponding thermal power risk indices, and association rules are formulated.

[0029] The risk level is determined by combining the thermal power risk index associated with different hotspot scenarios with preset thresholds.

[0030] Based on the risk level and different hot-spot scenario types, and combined with the adjustment capabilities of the multipole structure, targeted adjustment commands are output.

[0031] This invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation. The steps for formulating the adjustment strategy include:

[0032] Charging behavior data is obtained based on users' time preferences and charging scenarios, while usage habit data is obtained from users' load preferences and temperature sensitivity.

[0033] User habits are derived by collecting charging behavior data and usage habit data from areas where users are frequently active.

[0034] Develop adjustment strategies by matching user habits with adjustment instructions.

[0035] This invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation. The steps for calculating the temperature deviation index, current balance index, and cooling efficiency index include:

[0036] The temperature deviation index is calculated based on the difference between the temperature of each region after adjustment and the target uniform temperature difference. Based on the uniformity of the current of each electrode after adjustment, the ratio of the maximum current deviation rate to the target deviation rate is used as the current balance index.

[0037] The actual heat dissipation is calculated based on the specific heat capacity, flow rate, and temperature difference of the coolant, and the cooling efficiency index is calculated based on the rated parameters of the cooling system.

[0038] This invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation. The steps for constructing the adjustmentability score coefficient include:

[0039] The cooling efficiency index is converted into the same deviation index as the temperature deviation index and the current balance index.

[0040] Based on the requirements of multi-tab battery charging scenarios, the temperature deviation index, current balance index, and cooling efficiency index are prioritized and weighted, and then weighted to obtain the deviation index sum.

[0041] The adjustment score coefficient is calculated based on the deviation index and the thermoelectric risk index.

[0042] This invention provides a method for dynamic adjustment of battery thermoelectric balance based on multi-tab structure cooperation, and the steps for modifying the corresponding adjustment strategy include:

[0043] The correction strength is determined based on the adjustment score coefficient, and the root cause of the unqualified adjustment score coefficient is located by decomposing the temperature deviation index, current balance index and cooling efficiency index.

[0044] Match the multipole adjustment capability to the root cause, and adjust the adjustment strategy according to the correction strength.

[0045] This invention provides a battery thermoelectric balance dynamic adjustment system based on multi-tab structure cooperation, comprising:

[0046] The electrode thermal sensing module is used to collect electrode design parameters, divide the battery into regions based on the electrode design parameters, and collect temperature information, cell voltage and current data of each region in real time.

[0047] The thermoelectric risk analysis module is used to identify different hotspot scenarios based on temperature information, cell voltage and current data, and to construct thermoelectric risk indices for different hotspot scenarios by combining the current state of the battery and the battery aging state.

[0048] The intelligent thermal management module is used to assess different hotspot scenarios and thermal power risk indices, obtain corresponding adjustment instructions, and formulate adjustment strategies based on user habits.

[0049] The adjustment, evaluation, repair, and control module is used to collect real-time data on temperature distribution, current distribution uniformity, and cooling system operation in each area after adjustment. It calculates the temperature deviation index, current balance index, and cooling efficiency index, and evaluates them. If any one of them fails to meet the requirements, it constructs an adjustment score coefficient and corrects the corresponding adjustment strategy.

[0050] This invention provides a method and system for dynamic adjustment of battery thermoelectric balance based on a multi-tab structure. By accurately identifying hotspot scenarios and comprehensively assessing thermoelectric risks, it can promptly detect and address potential safety hazards, thereby improving battery safety. Dynamic adjustment combined with battery aging status effectively slows down the aging process and extends battery life. Personalized adjustment strategies, dynamically adjusted according to user habits, improve the user experience. By comprehensively evaluating the adjustment effect and dynamically correcting the adjustment strategy, it ensures the battery is always in optimal operating condition, improving the overall efficiency of the battery management system. Finally, it enhances the battery management system's ability to monitor and adjust battery status, increasing system reliability. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 This is one of the flowcharts of the battery thermoelectric balance dynamic adjustment method based on multi-tab structure cooperation provided in the embodiments of the present invention;

[0053] Figure 2 This is the second flowchart of the battery thermoelectric balance dynamic adjustment method based on multi-tab structure cooperation provided in the embodiments of the present invention;

[0054] Figure 3 This is a schematic diagram of the dynamic adjustment system for battery thermoelectric balance based on multi-tab structure collaboration provided in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0056] The following is combined Figures 1-3This invention describes a method and system for dynamic adjustment of battery thermoelectric balance based on a multi-tab structure.

[0057] like Figure 1 As shown, the battery thermoelectric balance dynamic adjustment method based on multi-tab structure cooperation provided in this embodiment of the invention includes:

[0058] Collect electrode design parameters, divide the battery into regions based on the electrode design parameters, and collect temperature information, cell voltage and current data of each region in real time.

[0059] The steps for dividing the battery into regions include:

[0060] The number and distribution of electrodes, geometric dimensions, material and process parameters, and electrical performance parameters are collected as electrode design parameters.

[0061] Number and distribution of tabs: Determine the number of tabs (e.g., 3-5 tabs for mobile phone batteries, 8-12 tabs for power batteries) and the spatial distribution (symmetrical, matrix, or ring distribution). Obtain the coordinate position of the tabs (accuracy ±0.05mm) through CAD drawings or 3D modeling.

[0062] Electrode geometry: Collect electrode width (0.5-1mm for mobile phone batteries, 1-3mm for power batteries), thickness (50-200μm), length (customized according to battery thickness, error ≤0.1mm), and the welding area dimensions between the electrode and the electrode sheet.

[0063] Materials and process parameters: Record the tab material (copper-aluminum composite foil, pure copper / aluminum foil, silver plating thickness), surface treatment process (electroplated nickel / silver, anti-oxidation coating), and welding method between the tab and the electrode sheet (energy parameters of laser welding and ultrasonic welding).

[0064] Electrical performance parameters: The contact resistance between the tab and the electrode plate (target value ≤1mΩ) and the internal resistance of the tab itself (internal resistance per 10mm length) were measured using the four-probe method. Ensure that the parameters meet the low internal resistance requirement.

[0065] Based on the battery capacity and target charge / discharge rate, the number of tabs is determined to meet the current density requirements, and the number of zones is determined by uniformly cutting the positive and negative electrode plates based on the number of tabs.

[0066] Based on the fixed distance between the sub-electrode and the center of the electrode tab, radial partitions centered on the electrode tab are formed to determine the partition boundaries.

[0067] Voltage sampling points are deployed at the non-welded end of each sub-electrode based on the number of partitions and partition boundaries, and temperature sensors are embedded in partitions.

[0068] Based on temperature information, cell voltage and current data, different hotspot scenarios are identified. Combined with the current state of the battery and the battery aging state, a thermoelectric risk index is constructed for different hotspot scenarios.

[0069] like Figure 2 As shown, the steps for identifying different hotspot scenarios include:

[0070] Temperature information, cell voltage and current data of each area are collected synchronously at a preset frequency, and high-frequency interference is eliminated and abnormal data is removed.

[0071] Temperature characteristics are obtained by extracting the absolute value of the monopole temperature and the temperature difference between the monopole from the preprocessed temperature information and calculating the rate of temperature change.

[0072] Voltage characteristics are obtained by analyzing the regional voltage drop and polarization voltage in the pre-processed cell voltage, and current characteristics are obtained by analyzing the current distribution deviation and current change rate in the pre-processed current data.

[0073] Based on temperature characteristics and the thermal safety requirements of multi-tab batteries, and to meet the constraints of temperature compliance and duration, a three-level hot spot threshold is set.

[0074] Level 3 hotspot thresholds may include: Potential hotspot scenarios: When the temperature of a single electrode reaches 40℃ to 45℃ (excluding 45℃), and the temperature difference between the electrode and the adjacent electrode does not exceed 5℃, and this lasts for 2 seconds or more, it is determined to be a potential hotspot, triggering an early warning level response, and only data marking and trend tracking are performed.

[0075] Intervention-required hotspot scenarios: An intervention-level response is triggered when any of the following conditions are met and last for 1 second or more: ① Tab temperature ≥ 45℃. ② Temperature difference between the tab and adjacent tabs > 5℃. ③ Tab temperature change rate ≥ 2℃ / second (i.e., temperature rise exceeding 2℃ per second). In this case, preliminary cooling or current regulation measures need to be initiated.

[0076] Emergency protection scenario: When the electrode temperature is ≥50℃, or the average temperature difference between the electrode and the cell is >10℃, or the temperature change rate is ≥5℃ / second, or a sudden drop in cell voltage occurs (the drop is ≥5% of the rated voltage), it is determined to be an emergency protection scenario, triggering the highest priority protection level response, and immediately implementing global current reduction and full load cooling.

[0077] Different hotspot scenarios are obtained by cross-validating the three-level hotspot thresholds based on voltage and current characteristics.

[0078] Hotspot scenarios may include: high-rate overcurrent hotspots: operating conditions: charge / discharge rate exceeding 3C.

[0079] Electrothermal characteristic matching: The temperature of a certain electrode area is ≥45℃, and the current deviation rate of the electrode is >15% (i.e., the current density is significantly higher than that of other electrodes).

[0080] The core cause is that at high rates, the current flows concentrated through some of the tabs, resulting in excessively high local current density and concentrated Joule heat.

[0081] Aging internal resistance hotspot: Operating conditions: The battery cycle count exceeds 500 times and the internal resistance growth rate is >20%.

[0082] Electrothermal characteristic matching: The tab temperature is 40~45℃, and the polarization voltage in the corresponding region exceeds 20% of the average polarization voltage.

[0083] The core cause is that battery aging leads to increased local resistance, which in turn increases heat generation when current passes through (exacerbating the Joule heating effect).

[0084] Simultaneous charging and use with hotspot overlay: Operating conditions: The battery is charging and the device load power exceeds [a certain threshold]. (e.g., running high-power applications such as games and videos).

[0085] Electrothermal characteristic matching: tab temperature ≥ 43℃, and current change rate ≥ 1A / s (severe current fluctuation).

[0086] The core cause is that the charging current and the load current are superimposed, which leads to a sudden increase in the current load inside the battery and a rapid accumulation of heat.

[0087] Hotspot for poor heat dissipation: Operating conditions: Ambient temperature exceeds 35℃, and the heat dissipation coefficient is low. (For example, the phone is covered with a thick case, or the heat dissipation channel of the power battery is blocked).

[0088] Electrothermal characteristic matching: The temperature of all electrodes is ≥44℃, but the temperature difference between electrodes is ≤3℃ (the temperature rises uniformly).

[0089] The core cause is that high external temperatures or obstructed heat dissipation paths lead to a decrease in the overall heat dissipation efficiency of the battery, preventing heat from being dissipated in a timely manner.

[0090] The steps for constructing thermal power risk indices for different hotspot scenarios include:

[0091] Based on the current state of the battery, the charging status and high-temperature environment are determined, and the high-temperature thermoelectric risk index is calculated by extracting the real-time charging current, rated current, ambient temperature, and the battery's maximum tolerance temperature under the current state. The formula is expressed as follows:

[0092]

[0093] In the formula, It is a normalized charging rate. It is the normalized ambient temperature. It is the average temperature of the electrode. , , These are weighting coefficients. It is the real-time charging current. It is the rated current. It is the ambient temperature. This is a reference value for room temperature. It is the highest temperature that the battery can withstand.

[0094] The battery aging state is obtained based on the cumulative charge-discharge cycle count, and the aging thermoelectric risk index is calculated by extracting the internal resistance growth rate, capacity decay rate, and tab temperature standard deviation. The formula is expressed as:

[0095]

[0096] In the formula, It is the internal resistance growth rate. It is the capacity decay rate. It is the standard deviation of the electrode temperature. , , These are weighting coefficients. It is the increase in the inner resistance of the electrode. It is the initial internal resistance. It is the cell capacity decay rate. It is the initial capacity. It is the aging thermoelectric risk index.

[0097] The thermoelectric risk index is obtained by weighting and summing the high-temperature thermoelectric risk index and the aging thermoelectric risk index.

[0098] We assess different hotspot scenarios and thermal power risk indices, obtain corresponding adjustment instructions, and formulate adjustment strategies based on user habits.

[0099] The steps to obtain the corresponding adjustment instructions include:

[0100] The identified hotspot scenarios are linked to the corresponding thermal power risk indices, and association rules are formulated.

[0101] High-rate overcurrent hotspots: Only associated with the high-rate, high-temperature thermoelectric risk index (reflecting the risk of imbalance at high rates / temperatures). Aging internal resistance hotspots: Only associated with the aging thermoelectric risk index (reflecting the risk of imbalance caused by aging). Hotspots resulting from simultaneous charging and use: Associated with the high-rate, high-temperature thermoelectric risk index. Hotspots with poor heat dissipation: Associated with the high-rate, high-temperature thermoelectric risk index (high-temperature environment + poor heat dissipation, primarily affecting the ambient temperature component of the high-rate, high-temperature thermoelectric risk index).

[0102] The risk level is determined by combining the thermal power risk index associated with different hotspot scenarios with preset thresholds.

[0103] High-rate overcurrent / simultaneous charging and use / poor heat dissipation hotspots (related) Low risk: ≤0.6, corresponding to the following scenario characteristics: tab temperature ≤42℃, current deviation ≤10%, ambient temperature ≤32℃, and no obvious hot spots.

[0104] Medium risk: 0.6 < ≤0.8 corresponds to the following scenario characteristics: tab temperature 42~45℃, current deviation 10%~15%, ambient temperature 32~35℃, and potential hotspots appear.

[0105] High risk: >0.8, corresponding scenario characteristics: tab temperature >45℃, current deviation >15%, ambient temperature >35℃, hotspots requiring intervention appear.

[0106] Aging internal resistance hotspots (related) Low risk: ≤0.5 corresponds to the following scenario characteristics: internal resistance growth rate ≤15%, capacity decay rate ≤10%, tab temperature standard deviation ≤3℃, and minimal aging impact.

[0107] Medium risk: 0.5 < ≤0.7 corresponds to the following scenario characteristics: internal resistance growth rate of 15%~20%, capacity decay rate of 10%~20%, tab temperature standard deviation of 3~5℃, and potential hot spots related to aging.

[0108] High risk: >0.7, corresponding scenario characteristics: internal resistance growth rate >20%, capacity decay rate >20%, tab temperature standard deviation >5℃, indicating aging-related hotspots requiring intervention.

[0109] If in any scenario the electrode temperature is ≥50℃ or the temperature of a single electrode is 10℃ higher than that of the adjacent electrode, it will be directly determined as an emergency protection level, and the highest priority command will be triggered first, without relying on index evaluation.

[0110] Based on the risk level and different hot-spot scenario types, and combined with the adjustment capabilities of the multipole structure, targeted adjustment commands are output.

[0111] Low-risk scenarios: minor intervention, maintaining performance, high-rate overcurrent / using while charging / poor heat dissipation ( ≤0.6):

[0112] Instruction 1: Maintain the current charge / discharge rate and fine-tune the current distribution through the multi-tab (reduce the proportion of current in the high-load tab by 5%~8%).

[0113] Command 2: Activate the low-power mode of the cooling system (maintain liquid cooling flow rate at 60%~70% of the rated value, or reduce air cooling speed by 20%).

[0114] Aging internal resistance ( ≤0.5): Instruction 1: Slightly limit the current in the high internal resistance tab region (identified by voltage drop) (current percentage reduced by 3%~5%).

[0115] Instruction 2: No additional cooling is required; maintain only basic heat dissipation (avoid excessive cooling and wasting energy).

[0116] Medium-risk scenarios: Moderate intervention, risk control, high-rate overcurrent / simultaneous charging and use / poor heat dissipation (0.6 < ≤0.8):

[0117] Command 1: Dynamic current shunting (by switching the MOSFET, the proportion of high-load tab current is reduced by 10%~15%, and the current is shunted to the low-temperature tab).

[0118] Command 2: Targeted cooling enhancement (increase the liquid cooling flow rate in the corresponding hot spot tab area by 30%~50%, or activate the low power mode of the semiconductor cooling chip).

[0119] Instruction 3: For scenarios involving simultaneous charging and use, an additional "load-charging current balancing" is added (for every increase in load power...). The charging current decreased by 0.5A.

[0120] Aging internal resistance (0.5 < ≤0.7): Instruction 1: Precisely limit the current in the high internal resistance tab area (reduce the current ratio by 12%~18%) to avoid aggravating heat generation due to internal resistance.

[0121] Instruction 2: Enhance cooling in high internal resistance areas (increase the flow rate of the corresponding tab-side cooling channel by 40%, or reduce the temperature of the heat sink in the mounting area by 3~5℃).

[0122] High-risk scenarios: strong intervention, prevention of runaway, high-rate overcurrent / simultaneous charging and use / poor heat dissipation ( >0.8):

[0123] Command 1: Reduce charging rate + dynamic current shunting (the charging rate is reduced to 70%~80% of the original rate, and the proportion of high-load tab current is reduced by 20%~25%).

[0124] Command 2: Full load cooling (liquid cooling flow rate increased to 110%~120% of the rated value, semiconductor cooling chip starts high power mode, temperature drops by 5~7℃ within 10 seconds).

[0125] Command 3: In scenarios where charging and using simultaneously, an additional "load priority adjustment" is triggered (pausing unnecessary loads, such as background applications, and controlling the load power). within).

[0126] Aging internal resistance ( >0.7): Instruction 1: Deep current limiting in high internal resistance region (current percentage reduced by 25%~30%), while limiting the total charge and discharge current of the battery to 80% of the rated value.

[0127] Instruction 2: Full-area cooling optimization (prioritize heat dissipation in high internal resistance tab areas, increasing the proportion of cooling resources allocated to 60%~70%).

[0128] Command 3: Activate the aging compensation algorithm (dynamically adjust the voltage protection threshold of this area according to the internal resistance growth ratio to avoid false triggering of protection).

[0129] Emergency Protection Scenario: Highest Priority, Safety First. General Instruction 1: Global Current Reduction (Charging / Discharging Current reduced to below 0.5C, current evenly distributed across multiple tabs, with no biased load). General Instruction 2: Cooling System Full Load Operation (Liquid Cooling / Air Cooling / Semiconductor Cooling Chips all operate at maximum power, prioritizing tab temperature reduction). General Instruction 3: If tab temperature does not drop below 45℃ within 10 seconds, trigger "Charging Pause / Discharging Current Limit" until temperature returns to a safe range.

[0130] The steps to develop a regulatory strategy include:

[0131] Charging behavior data is obtained based on users' time preferences and charging scenarios, while usage habit data is obtained from users' load preferences and temperature sensitivity.

[0132] Time period preference: Collect the distribution of users' charging start time over the past 30 days (such as the proportion of charging at night from 23:00 to 7:00, and the proportion of fast charging before commuting) to identify high-frequency charging periods (such as "long charging at night" and "fast charging in the morning").

[0133] Scene tags: By linking GPS with usage logs, the charging scene is marked (such as "charging at home", "charging in the car", "charging at a public fast charging station"), and the ambient temperature of the corresponding scene is recorded (such as 25℃ at home, 40℃ in the car in summer).

[0134] Load preferences: Statistics on high-frequency scenarios where users charge and use simultaneously (such as gaming, video, and navigation) and load power (such as load power during gaming). ).

[0135] Temperature sensitivity: By analyzing users' historical feedback on device heating (such as manually reducing brightness or closing applications) or records of automatic frequency reduction by the device, the user's tolerance to temperature is quantified (such as "prioritizing performance at high temperatures" or "prioritizing feel at low temperatures").

[0136] User habits are derived by collecting charging behavior data and usage habit data from areas where users are frequently active.

[0137] Develop adjustment strategies by matching user habits with adjustment instructions.

[0138] Strategy adaptation based on charging time period: Nighttime long charging scenario:

[0139] Adjustment command adaptation: widen the tab temperature warning threshold from 40℃ to 43℃ (reduce the start-up and shutdown noise of the cooling system), and prioritize the use of low noise mode for the cooling system (reduced liquid cooling flow by 20% and fan speed by 60%).

[0140] Example: When users are accustomed to charging between 11 PM and 7 AM, only low-noise cooling will be activated to avoid affecting their sleep.

[0141] Morning fast charging scenario: Adjustment command adaptation: Start the tab pre-cooling 5 minutes in advance (down to 30℃), allowing Current limiting is triggered only when the value is 0.85 (0.05 higher than the standard threshold), prioritizing charging speed.

[0142] Load-based strategy adaptation: Use while charging under high load.

[0143] Adjustment command adaptation: When the load power When the load increases, the "load priority" mode is automatically triggered—the charging current is dynamically adjusted according to the load power (for every increase in load power). The charging current is reduced by 0.8A, while the electrode cooling resources are shifted to the electrode area corresponding to the load-related chip (flow rate increases by 30%).

[0144] Example: The user charges while playing a game, even if... =0.6 (low risk), and also actively reduced the charging current from 5A to 3.5A to avoid the superposition of current and heat generation.

[0145] Low load charging while using:

[0146] Adjustment command adaptation: Maintain standard adjustment logic, only when... Current limiting is triggered when the value is greater than 0.7, prioritizing charging efficiency.

[0147] Temperature tolerance-based strategy adaptation: High-tolerance users:

[0148] Adjustment command adaptation: Increase the tab temperature intervention threshold by 2-3℃ (if intervention hotspots need to be adjusted from 45℃ to 47-48℃), increase the cooling system start-up delay by 5-10 seconds, and reduce the intervention on performance.

[0149] For users with low tolerance: Adjustment command adaptation: Reduce the tab temperature warning threshold by 2-3℃ (e.g., adjust the potential hot spot from 40℃ to 37-38℃), start the cooling system in advance, and prioritize ensuring that the hand temperature is <40℃.

[0150] Real-time data collection of temperature distribution, current distribution uniformity, and cooling system operation in each region after adjustment; calculation of temperature deviation index, current balance index, and cooling efficiency index; evaluation; and construction of adjustment score coefficients to correct the corresponding adjustment strategy if any one of them fails to meet the requirements.

[0151] The steps for calculating the temperature deviation index, current balance index, and cooling efficiency index include:

[0152] The temperature deviation index is calculated based on the difference between the temperature of each region after adjustment and the target uniform temperature difference. Based on the uniformity of the current of each electrode after adjustment, the ratio of the maximum current deviation rate to the target deviation rate is used as the current balance index.

[0153] The actual heat dissipation is calculated based on the specific heat capacity, flow rate, and temperature difference of the coolant, and the cooling efficiency index is calculated based on the rated parameters of the cooling system.

[0154] The steps to construct the moderating score coefficient include:

[0155] The cooling efficiency index is converted into the same deviation index as the temperature deviation index and the current balance index.

[0156] Based on the requirements of multi-tab battery charging scenarios, the temperature deviation index, current balance index, and cooling efficiency index are prioritized and weighted, and then weighted to obtain the deviation index sum.

[0157] Based on the deviation index and the thermoelectric risk index, the adjustability score coefficient is calculated, expressed by the following formula:

[0158]

[0159] In the formula, It is the adjustment score coefficient. It is the deviation index and, It is a thermoelectric risk index. , It is the adjustment weight coefficient.

[0160] The steps for modifying the corresponding adjustment strategy include:

[0161] The correction strength is determined based on the adjustment score coefficient, and the root cause of the unqualified adjustment score coefficient is located by decomposing the temperature deviation index, current balance index and cooling efficiency index.

[0162] Match the multipole adjustment capability to the root cause, and adjust the adjustment strategy according to the correction strength.

[0163] Corrections for non-compliance with temperature deviation index include: local overheating: reduce the proportion of overheated tab current (minor correction: 3%~5% reduction; severe correction: 8%~12% reduction).

[0164] Targeted Enhanced Cooling: Liquid cooling flow rate in the corresponding tab area is increased by 5%~12% or semiconductor cooling power is increased by 5%~12%.

[0165] Large overall temperature difference: Optimize the flow distribution of the liquid cooling pipeline, tilting it 5%~12% towards the high-temperature tab side.

[0166] Activate the multi-electrode "current sharing mode" to force the current ratio difference between each electrode to be ≤8%.

[0167] Corrections for non-compliant current balance index can include: current concentration: adjusting the MOSFET shunt threshold; reducing the upper limit of high-load tab current by 5% to 12%.

[0168] Internal resistance compensation: For tabs with high internal resistance, the current ratio is reduced by 3% to 8% and preheated to 35°C.

[0169] Dynamic imbalance: Increase the BMS shunt response frequency to 200Hz (from 100Hz).

[0170] Corrections for substandard cooling efficiency indices may include: Liquid cooling system: Increase pump speed by 3% to 12% (e.g., from 3000 rpm to 3300 rpm). Clean the liquid cooling lines; if the flow rate is still insufficient, start the backup pump (if available).

[0171] Semiconductor cooling: Increase the supply voltage by 3%~8% (e.g., from 5V to 5.4V). Force the activation of the heatsink cleaning program (e.g., using a vibrating motor to clean the heatsink).

[0172] like Figure 3 As shown, based on the same general inventive concept, this invention also protects a battery thermoelectric balance dynamic adjustment system based on a multi-tab structure, the adjustment system comprising:

[0173] The electrode thermal sensing module is used to collect electrode design parameters, divide the battery into regions based on the electrode design parameters, and collect temperature information, cell voltage and current data of each region in real time.

[0174] The thermoelectric risk analysis module is used to identify different hotspot scenarios based on temperature information, cell voltage and current data, and to construct thermoelectric risk indices for different hotspot scenarios by combining the current state of the battery and the battery aging state.

[0175] The intelligent thermal management module is used to assess different hotspot scenarios and thermal power risk indices, obtain corresponding adjustment instructions, and formulate adjustment strategies based on user habits.

[0176] The adjustment, evaluation, repair, and control module is used to collect real-time data on temperature distribution, current distribution uniformity, and cooling system operation in each area after adjustment. It calculates the temperature deviation index, current balance index, and cooling efficiency index, and evaluates them. If any one of them fails to meet the requirements, it constructs an adjustment score coefficient and corrects the corresponding adjustment strategy.

[0177] This embodiment provides a method and system for dynamic adjustment of battery thermoelectric balance based on a multi-tab structure. By collecting tab design parameters, determining the number of zones based on battery capacity and target charge / discharge rate, forming radial zones centered on the tabs to define zone boundaries, and rationally deploying voltage sampling points and embedded temperature sensors, it can achieve precise regional division of the battery, providing a solid foundation for subsequent temperature, voltage, and current data acquisition and management. Temperature information, cell voltage, and current data of each zone are synchronously collected and preprocessed at a preset frequency, extracting temperature, voltage, and current characteristics. Three-level hotspot thresholds are set based on the thermal safety requirements of multi-tab batteries and cross-validated to accurately identify different hotspot scenarios, providing a reliable basis for subsequent risk assessment and adjustment. By binding hotspot scenarios with thermoelectric risk indices to establish correlation rules, determining risk levels, and outputting targeted adjustment commands based on the adjustment capabilities of the multi-tab structure, and formulating adjustment strategies based on user habits, precise adjustments can be made according to actual conditions and user needs, improving user experience and battery efficiency. The system comprehensively calculates the temperature deviation index, current balance index, and cooling efficiency index to evaluate the adjustment effect. When any one of these indicators fails to meet the requirements, an adjustment score coefficient is constructed to pinpoint the root cause. The adjustment strategy is then modified based on the multi-tab adjustment capability, enabling timely detection and optimization of problems during the adjustment process, ensuring that the battery is always in a good thermoelectric balance state.

[0178] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus necessary general-purpose hardware platforms, and of course, it can also be implemented using hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as... Disks, optical discs, etc., including a number of instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in various embodiments or some portions of embodiments.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A battery thermal balance dynamic adjustment method based on multi-tab structure cooperation, suitable for mobile phone extreme charging scenarios, characterized in that, The method comprises the following steps: Collecting tab design parameters, dividing the battery into regions based on the tab design parameters, and collecting temperature information, cell voltage, and current data of each region in real time; Identifying different hot spot scenarios based on the temperature information, cell voltage, and current data, combining the current state of the battery and the battery aging state to construct a thermal-electric risk index under different hot spot scenarios; The step of identifying different hot spot scenarios comprises: Synchronously collecting the temperature information, cell voltage, and current data of each region at a predetermined frequency, eliminating high-frequency interference, and removing abnormal data; Extracting the absolute value of single-tab temperature and the temperature difference between tabs from the pre-processed temperature information to calculate the temperature variation rate and obtain the temperature characteristics; Analyzing the regional voltage drop and polarization voltage in the pre-processed cell voltage to obtain the voltage characteristics, and analyzing the current allocation deviation and current variation rate in the pre-processed current data to obtain the current characteristics; Based on the temperature characteristics, combined with the thermal safety requirements of multi-tab batteries, and meeting the temperature compliance and duration constraints, set three levels of hot spot thresholds; According to the voltage characteristics and the current characteristics, cross-verify the three levels of hot spot thresholds to obtain different hot spot scenarios; The step of constructing a thermal-electric risk index under different hot spot scenarios comprises: According to the current state of the battery, determine the charging state and high temperature environment, and extract the real-time charging current, rated current, environmental temperature, and maximum tolerance temperature of the battery to calculate the high-multiple high-temperature thermal-electric risk index; According to the cumulative charge and discharge cycle number of the battery, obtain the battery aging state, and extract the internal resistance growth rate, capacity attenuation rate, and tab temperature standard deviation to calculate the aging thermal-electric risk index; Weighted sum of the high-multiple high-temperature thermal-electric risk index and the aging thermal-electric risk index to obtain the thermal-electric risk index; Evaluate different hot spot scenarios and thermal-electric risk indexes to obtain corresponding adjustment instructions and develop adjustment strategies combined with user habits; Real-time collection of the temperature distribution, current allocation uniformity, and cooling system operation data of each region after adjustment, calculation of the temperature deviation index, current balance index, and cooling efficiency index, and evaluation, if any of them is unqualified, construct an adjustment score coefficient, modify the corresponding adjustment strategy; The steps of calculating the temperature deviation index, the current balance index, and the cooling efficiency index comprise: According to the difference between the temperature of each region after adjustment and the target uniform temperature difference, calculate the temperature deviation index, and according to the uniformity of the current of each tab after adjustment, use the ratio of the maximum current deviation rate to the target deviation rate as the current balance index; According to the specific heat capacity, flow rate, and temperature difference of the cooling liquid, calculate the actual heat dissipation, and based on the rated parameters of the cooling system, calculate the cooling efficiency index.

2. The battery thermal level balance dynamic adjustment method based on the multi-tab structure coordination according to claim 1, characterized in that, The step of dividing the battery into regions comprises: Collecting the number and distribution of tabs, geometric size, material and process parameters, and electrical performance parameters as the tab design parameters; Based on the battery capacity and the target charge and discharge rate, the number of tabs meets the current density requirement, and based on the number of tabs, the positive and negative plates are evenly cut to determine the number of partitions; Forming a radial partition with the tab as the center based on a fixed distance between the sub-pole piece and the center of the tab to determine the partition boundary; Deploying a voltage sampling point at the non-welding end of each sub-pole piece in combination with the number of partitions and the partition boundary, and partitioning the temperature sensor.

3. The battery thermal level balancing dynamic adjustment method based on the multi-tab structure coordination according to claim 1, characterized in that, The step of obtaining the corresponding adjustment instruction includes: Binding the identified hotspot scene with the corresponding thermoelectric risk index, and formulating the correlation rule; According to the thermoelectric risk index associated with different hotspot scenes, and in combination with the preset threshold, the risk level is determined; Based on the risk level and different hotspot scene types, in combination with the adjustment capability of the multi-tab structure, the corresponding adjustment instruction is output.

4. The battery thermal level balancing dynamic adjustment method based on the multi-tab structure coordination according to claim 1, characterized in that, The step of formulating the adjustment strategy includes: According to the charging behavior data obtained from the user's time period preference and charging scene, and the usage habit data obtained from the user's load preference and temperature sensitivity, the user habit is obtained from the charging behavior data and the usage habit data collected in the long-term activity area of the user; The user habit is matched with the adjustment instruction to formulate the adjustment strategy. The step of constructing the adjustment score coefficient includes:

5. The battery thermal level balancing dynamic adjustment method based on the multi-tab structure coordination according to claim 1, characterized in that, Convert the cooling efficiency index into the same deviation index as the temperature deviation index and the current balance index; According to the demand of the multi-tab battery pole charging scene, the temperature deviation index, the current balance index and the cooling efficiency index are prioritized and weighted to obtain the deviation index sum; According to the deviation index sum and the thermoelectric risk index, the adjustment score coefficient is calculated. The step of modifying the corresponding adjustment strategy includes:

6. The battery thermal level balancing dynamic adjustment method based on the multi-tab structure coordination according to claim 1, characterized in that, Based on the adjustment score coefficient, the correction degree is determined, and by disassembling the temperature deviation index, the current balance index and the cooling efficiency index, the root cause of the unqualified adjustment score coefficient is located; According to the root cause matching the multi-tab adjustment capability, the adjustment strategy is modified according to the correction degree. The adjustment system includes:

7. A battery thermal balance dynamic adjustment system based on multi-tab structure coordination, which adopts the battery thermal balance dynamic adjustment method based on multi-tab structure coordination according to any one of claims 1 to 6, characterized in that, Pole area electrothermal sensing module, for collecting tab design parameters, dividing the battery area based on the tab design parameters, and collecting temperature information, cell voltage and current data of each area in real time; Thermoelectric risk analysis module, for identifying different hotspot scenes based on the temperature information, the cell voltage and the current data, constructing the thermoelectric risk index under different hotspot scenes in combination with the current battery state and the battery aging state; Hot strategy intelligent adjustment module, for evaluating different hotspot scenes and thermoelectric risk indexes, obtaining corresponding adjustment instructions, and formulating adjustment strategies in combination with user habits; Adjustment and evaluation control module, for real-time collection of temperature distribution, current distribution uniformity and cooling system operation data after adjustment, calculation of temperature deviation index, current balance index and cooling efficiency index, and evaluation, if any item is unqualified, the adjustment score coefficient is constructed, and the corresponding adjustment strategy is modified. ​

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