A multi-scene fusion battery health degree calculation method and system

Abstract

The application relates to the field of battery management systems, and particularly discloses a multi-scene fusion battery health degree calculation method and system. now When the battery pack electric quantity is lower than a preset initial calibration threshold value, a standard working condition calibration process is triggered, constant current charging is performed to a full charging state, actual input capacity in the charging process is recorded, and current available capacity C initial is calculated in combination with a voltage platform characteristic curve, wherein C initial is the initial calibration health degree calculated through a formula, C discharge is the battery factory nominal initial capacity; a dynamic use updating step is performed: in the process of continuous charging and discharging of the battery pack, the discharge capacity C discharge and the charging capacity C charge are accumulated in real time through a coulomb integration method, real-time available capacity is calculated in combination with an ampere-hour efficiency correction model, and the current available capacity C now is updated at a preset period. The technical scheme of the application can construct a multi-scene fusion SOH evaluation algorithm for different battery material characteristics, and improve the reliability of the battery management system.

Description

Technical Field

[0001] This invention relates to the field of battery management systems, and in particular to a method and system for calculating battery health across multiple scenarios. Background Technology

[0002] In a battery management system (BMS), battery state of health (SOH) is a core parameter for evaluating battery performance, directly impacting charge / discharge strategies, remaining life prediction, and safety management. Currently, the mainstream ternary lithium batteries and lithium iron phosphate batteries differ significantly in material properties: ternary lithium batteries offer high energy density (200-300 Wh / kg) and low-temperature performance advantages, but have shorter cycle life (800-1500 cycles @ 80% DOD) and are temperature-sensitive (optimal operating temperature 25-45℃), easily experiencing electrolyte decomposition leading to thermal runaway during overcharging; lithium iron phosphate batteries have lower energy density (120-180 Wh / kg), but a cycle life exceeding 3000 cycles @ 80% DOD, and excellent safety (thermal runaway temperature > 500℃). However, their capacity decay is significantly affected by charge / discharge rates, and lithium plating is prone to occur at high temperatures.

[0003] In existing technologies, SOH calculation mainly relies on empirical models based on the number of capacity cycles, which has a dual drawback:

[0004] Insufficient cross-battery type versatility: It fails to distinguish the different degradation characteristics of ternary lithium and lithium iron phosphate batteries. Ternary lithium batteries exhibit a rapid initial capacity decline (approximately 5% degradation in the first 200 cycles) followed by a gradual decrease in the middle and later stages. In contrast, lithium iron phosphate batteries have a more linear degradation curve (approximately 3% degradation per 500 cycles). Traditional single models cannot accurately match the aging patterns of different material systems, resulting in an initial SOH assessment error of up to 15% for ternary lithium batteries and an error exceeding 20% ​​for lithium iron phosphate batteries after long-term use.

[0005] Lack of adaptability to dynamic operating conditions: Without considering the characteristics of battery materials to perceive actual usage scenarios, when the usable capacity of ternary lithium batteries drops sharply at low temperatures (<0℃), traditional models overestimate the state of charge (SOH) because they ignore the impact of temperature on the migration of active materials, which can easily lead to overcharging risks. In fast charging scenarios (>1C), the temporary capacity reduction caused by polarization effect of lithium iron phosphate batteries is misjudged as permanent degradation, resulting in an underestimation of SOH and affecting the accurate prediction of driving range.

[0006] The aforementioned issues make it difficult to formulate targeted balancing strategies for battery management systems (BMS) based on different material systems. Ternary lithium batteries may miss optimal maintenance opportunities due to overestimation of State of Health (SOH), increasing the risk of thermal runaway; while lithium iron phosphate batteries may be prematurely decommissioned due to underestimation of SOH, resulting in resource waste. Therefore, developing a multi-scenario integrated SOH evaluation algorithm tailored to the characteristics of different battery materials has become a key technical bottleneck in improving the reliability of battery management systems. Summary of the Invention

[0007] This invention provides a multi-scenario integrated battery health calculation method, which can construct a multi-scenario integrated SOH evaluation algorithm for different battery material characteristics, thereby improving the reliability of the battery management system.

[0008] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0009] A multi-scenario integrated battery health calculation method includes the following:

[0010] Standard operating condition initial calibration steps: When the battery pack charge is lower than the preset initial calibration threshold, the standard operating condition calibration process is triggered, charging to full charge with a constant current, recording the actual input capacity during charging, and calculating the current available capacity C based on the voltage plateau characteristic curve. now Through formula Calculate the initial calibration health, where C initial This refers to the battery's nominal initial capacity at the time of manufacture.

[0011] Dynamic usage update steps: During the continuous charging and discharging of the battery pack, the discharge capacity C is accumulated in real time using the coulomb integral method. discharge and charging capacity C charge The real-time available capacity is calculated using the ampere-hour efficiency correction model, and the current available capacity C is updated according to a preset period. now And calculate and update the health status SOH3 dynamically according to the same formula;

[0012] Maintenance and deep calibration procedure: When the vehicle enters maintenance mode or the deep calibration mode is manually triggered, perform a full charge and discharge procedure, discharging to the cutoff voltage with a preset current and recording the discharge capacity C. discharge_full Then charge to full charge using the standard procedure and record the charging capacity C. charge_full The smaller of the two values ​​is taken as the available capacity for depth calibration, using the formula... Calculate, maintain, calibrate, and assess health status;

[0013] Multi-algorithm fusion steps: Establish a SOH fusion calculation model, and take the minimum value of the initial calibration health SOH2, the dynamically updated health SOH3, and the maintenance calibration health SOH4 as the final battery health SOH. final=min(SOH2,SOH3,SOH4); When the deviation between the SOH calculated by any single algorithm and the final value exceeds the preset threshold, the algorithm self-checking mechanism is triggered and the maintenance calibration health value is adopted first until the next standard working condition calibration.

[0014] Furthermore, in the initial calibration step under standard operating conditions, the preset initial calibration threshold is 5%-15% SOC, the constant current is 0.3C-0.7C, and differentiated calibration parameters are set for ternary lithium batteries and lithium iron phosphate batteries:

[0015] For ternary lithium batteries, the calibration temperature range is 20℃-45℃, and the constant current charging termination voltage is 4.20V±0.02V.

[0016] For lithium iron phosphate batteries, the calibration temperature range is 15℃-55℃, and the constant current charging termination voltage is 3.65V±0.03V.

[0017] Furthermore, in the dynamic usage update step, the expression for the ampere-hour efficiency correction model is: η Ah =η0·e -α·|T-25| .(1+β·|I|); where η0 is the standard ampere-hour efficiency, with a default value of 95%, α is the temperature correction coefficient, β is the current correction coefficient, T is the battery temperature, and I is the charge / discharge current; when the cumulative running time reaches the preset cycle or the number of charge / discharge cycles exceeds 200, the capacity update process is triggered.

[0018] Furthermore, in the maintenance and calibration process, the full-charge and discharge process is controlled in stages:

[0019] Discharge phase: Discharge at a current of 0.2C to 2.8V for ternary lithium batteries and 2.5V for lithium iron phosphate batteries, and record the discharge capacity;

[0020] Charging phase: First, charge at a constant current of 0.5C until the charging cutoff voltage, then charge at a constant voltage until the current drops to 0.05C;

[0021] The deep calibration cycle is linked to the vehicle maintenance cycle, or it can be manually triggered when the SOH of any single battery cell is below 90%.

[0022] Furthermore, in the multi-algorithm fusion step, when the deviation between the SOH calculated by a certain algorithm and the final value exceeds 5%, the data backtracking verification of that algorithm is initiated, comparing the historical data of the past 3 calibration cycles;

[0023] If two consecutive verifications fail, the algorithm is locked and a weighted average of other algorithms is temporarily used until the next maintenance calibration.

[0024] Furthermore, it also includes a temperature compensation correction step: establishing a SOH temperature correction coefficient table, and when the battery operating temperature deviates from 25℃, according to the formula: SOH corr =SOH·(1+γ·(T-25)) adjustment, where γ is the temperature coefficient, 0.015 / ℃ for ternary lithium and 0.01 / ℃ for lithium iron phosphate; when the temperature exceeds 60℃ or falls below 0℃, it automatically switches to the maintenance calibration algorithm priority mode.

[0025] Furthermore, it also includes a step for predicting aging trends: establishing a linear regression model based on historical SOH data. pred =k·N+b, where N is the number of cycles, k is the decay rate, and b is the intercept; when the predicted remaining number of cycles is less than 50, a battery replacement warning is triggered, and maintenance calibration data is used to update the prediction model first.

[0026] Furthermore, in the aging trend prediction step: the battery self-discharge rate is defined. Where I leak C represents the measured value of microcurrent leakage. rated This refers to the battery's rated capacity.

[0027] Establish surface temperature gradient index Where T i T j N represents the surface temperature of a single cell. cell The number of individual battery cells; the corrected decay rate k expression is: k ′ = k·(1+0.02·S) leak +0.015·G T )

[0028] When S leak >5% or G T When the temperature exceeds 8℃, a battery consistency warning is triggered and the parameters of the linear regression model are updated.

[0029] Furthermore, in the aging trend prediction step: the charge distribution entropy H is defined. q =-∑(p i lnp i ), where p i The charge density ratio of the i-th electrode reflects the degree of material polarization; the fluctuation value of the magnetic field strength inside the battery, ΔB = σ(B), is collected. t ), where B t Let σ be the real-time magnetic field strength and σ be the standard deviation; establish a nonlinear correction term: SOH pred =k·N+b+λ·H q +μ·ΔB where λ is the charge distribution influence factor, and μ is the magnetic field fluctuation influence factor, determined by fitting historical data; when H qWhen the value is <0.8 and ΔB>0.5mT, it is determined to be in the stage of electrode material degradation, and the calibration frequency is increased.

[0030] Define electromagnetic interference degree U noise For high-frequency noise voltage, U rated The battery's rated voltage is used; acoustic signals during the charging and discharging process are collected, and the dominant frequency offset Δf = |f| is extracted. t -f0|, where f t f0 is the real-time clock frequency, and f0 is the factory reference clock frequency; a robust correction model is established: SOH corr =SOH pred ·(1-0.1·E emi -0.05·Δf);

[0031] When E emi When the value is >0.2 or Δf>10%, abnormal data points are automatically masked and multi-sensor fusion verification is initiated, prioritizing the adoption of maintenance calibration algorithms for model correction.

[0032] The principle and beneficial effects of this solution are as follows: This invention addresses the differences in electrochemical characteristics of batteries made of different materials such as ternary lithium and lithium iron phosphate, and constructs a multi-scenario integrated SOH evaluation algorithm. Through scenario-specific calibration and dynamic strategy adaptation, the reliability of the battery management system is improved.

[0033] To address the differences between ternary lithium batteries (high energy density, good low-temperature performance but sensitive to high temperatures) and lithium iron phosphate batteries (long cycle life, high temperature resistance but capacity decay is affected by the rate of degradation), differentiated calibration parameters are set: Ternary lithium: calibration temperature range 20℃-45℃, constant current charging termination voltage 4.20V±0.02V, to avoid the risk of electrolyte decomposition at high temperatures; Lithium iron phosphate: calibration temperature range 15℃-55℃, constant current charging termination voltage 3.65V±0.03V, to suit its high temperature resistance and stable voltage platform.

[0034] When the battery pack's charge level falls below a preset threshold (5%-15% SOC), it is charged to the corresponding material's voltage plateau using a constant current. The usable capacity C is then calculated based on the voltage plateau characteristic curve. now This eliminates initial deviations caused by material production differences and storage degradation, and establishes an accurate initial health baseline (SOH2).

[0035] During charging and discharging, the effects of temperature and current are dynamically compensated for by the ampere-hour efficiency correction model to address the differences in energy conversion efficiency of different materials: ternary lithium is more sensitive to temperature, so the temperature coefficient α in the correction model is set to a higher value to strengthen the correction of capacity drop at low temperature or efficiency decay at high temperature; lithium iron phosphate focuses on the effect of current rate, and the polarization effect compensation in fast charging scenarios is adjusted by the current correction coefficient β.

[0036] By accumulating capacity through coulomb integration and combining it with a correction model, the health status SOH3 is updated at a preset period to reflect the capacity changes of batteries with different materials under dynamic operating conditions in real time.

[0037] To address the differences in safe cutoff voltages for different materials (2.8V for ternary lithium and 2.5V for lithium iron phosphate), a dedicated cutoff voltage is set in the deep calibration process: ternary lithium is discharged to 2.8V to prevent lithium metal deposition, while lithium iron phosphate is discharged to 2.5V to release deeper capacity, exposing the irreversible degradation of different materials under extreme conditions (such as the thickening of the SEI film in ternary lithium and the shedding of active material in lithium iron phosphate).

[0038] By obtaining the smaller value of the discharge / charge capacity through full charge and discharge, the deep calibration health (SOH4) is calculated, providing targeted aging detection for batteries of different materials.

[0039] When establishing the fusion model, considering the characteristics of ternary lithium batteries being susceptible to short-term operating condition fluctuations and lithium iron phosphate batteries relying on long-term cycling data, the minimum values ​​of SOH2 (initial calibration), SOH3 (dynamic update), and SOH4 (deep calibration) are taken to ensure that the evaluation results are conservative and reliable: ternary lithium batteries are prone to overestimation due to dynamic updates under high-temperature conditions, so deep calibration SOH4 is used as an anchor point to reduce the risk of thermal runaway; lithium iron phosphate batteries may underestimate dynamic updates in fast-charging scenarios, so initial calibration SOH2 is used to correct short-term deviations.

[0040] When the deviation of a single algorithm exceeds a preset threshold (e.g., 5%), self-verification is triggered and the depth calibration result is adopted first, forming redundant protection for different material properties.

[0041] Dedicated calibration parameters are set for the voltage plateau and temperature sensitivity of ternary lithium and lithium iron phosphate, reducing the initial health error compared to traditional unified calibration methods. The ampere-hour efficiency correction model adjusts the temperature and current coefficients based on material characteristics to address the low-temperature efficiency degradation of ternary lithium and the fast-charging polarization effect of lithium iron phosphate, reflecting changes in the intrinsic properties of the materials in real time.

[0042] Deeply calibrated material-specific cutoff voltages reduce the overcharge risk of ternary lithium batteries and improve the deep discharge capacity detection capability of lithium iron phosphate batteries, adapting to the safe operating range of different materials. The minimum value fusion strategy naturally adapts to differences in material characteristics. For example, dynamic updates of ternary lithium batteries under high-temperature conditions may overestimate; triggering protection with a low value obtained through deep calibration improves the accuracy of thermal runaway warnings. For lithium iron phosphate batteries undergoing long-term shallow charging and discharging, initial calibration corrects production deviations, preventing premature retirement. For the different aging rates of ternary lithium batteries (shorter cycle life) and lithium iron phosphate batteries (longer cycle life), the deep calibration cycle is linked to material characteristics (e.g., every 500 cycles for ternary lithium batteries, every 1000 cycles for lithium iron phosphate batteries), improving maintenance efficiency.

[0043] The multi-scenario fusion algorithm supports battery packs using different battery materials (such as ternary lithium + lithium iron phosphate hybrid energy storage systems). Through material-specific calibration and fusion decision-making, it improves the reliability of overall battery health assessment by 25%, adapting to complex application scenarios such as new energy vehicles and energy storage power stations. The self-verification mechanism addresses typical faults related to material characteristics (such as temperature sensor drift in ternary lithium batteries and increased internal resistance in lithium iron phosphate batteries). By backtracking historical calibration data, it temporarily switches to a deep calibration priority strategy to ensure reliable results are still output even under abnormal operating conditions caused by material characteristics. Attached Figure Description

[0044] Figure 1 This is a flowchart of the initial calibration steps under standard operating conditions in a multi-scenario battery health calculation method;

[0045] Figure 2 This is a flowchart illustrating the dynamic use of update steps in a multi-scenario integrated battery health calculation method.

[0046] Figure 3 This is a flowchart of the maintenance depth calibration steps in a multi-scenario battery health calculation method;

[0047] Figure 4 This is a flowchart of the multi-algorithm fusion steps in a multi-scenario battery health calculation method;

[0048] Figure 5 This is a flowchart of the temperature compensation correction step in a multi-scenario battery health calculation method.

[0049] Figure 6 This is a flowchart of the aging trend prediction step in a multi-scenario battery health calculation method. Detailed Implementation

[0050] The following detailed description illustrates the specific implementation method:

[0051] A multi-scenario integrated battery health calculation method (e.g.) Figure 1-6 (As shown), including the following steps:

[0052] Standard operating condition initial calibration steps: When the battery pack charge is lower than the preset initial calibration threshold, the standard operating condition calibration process is triggered, charging to full charge with a constant current, recording the actual input capacity during charging, and calculating the current available capacity C based on the voltage plateau characteristic curve. now Through formula Calculate the initial calibration health, where C initial This refers to the battery's nominal initial capacity at the time of manufacture.

[0053] The preset initial calibration threshold is 5%-15% SOC, the constant current is 0.3C-0.7C, and differentiated calibration parameters are set for ternary lithium batteries and lithium iron phosphate batteries:

[0054] For ternary lithium batteries, the calibration temperature range is 20℃-45℃, and the constant current charging termination voltage is 4.20V±0.02V.

[0055] For lithium iron phosphate batteries, the calibration temperature range is 15℃-55℃, and the constant current charging termination voltage is 3.65V±0.03V.

[0056] Dynamic usage update steps: During the continuous charging and discharging of the battery pack, the discharge capacity C is accumulated in real time using the coulomb integral method. discharge and charging capacity C charge The real-time available capacity is calculated using the ampere-hour efficiency correction model, and the current available capacity C is updated according to a preset period. now And calculate and update the health status SOH3 dynamically using the same formula.

[0057] The expression for the ampere-hour efficiency correction model is: η Ah =η0·e -α·|T-25| ·(1+β·|I|); where η0 is the standard ampere-hour efficiency, with a default value of 95%, α is the temperature correction coefficient, β is the current correction coefficient, T is the battery temperature, and I is the charge / discharge current; when the cumulative running time reaches the preset cycle or the number of charge / discharge cycles exceeds 200, the capacity update process is triggered.

[0058] Maintenance and deep calibration procedure: When the vehicle enters maintenance mode or the deep calibration mode is manually triggered, perform a full charge and discharge procedure, discharging to the cutoff voltage with a preset current and recording the discharge capacity C. discharge_full Then charge to full charge using the standard procedure and record the charging capacity C. charge_full The smaller of the two values ​​is taken as the available capacity for depth calibration, using the formula... Calculate, maintain, calibrate, and assess health.

[0059] The full filling and discharging process adopts phased control:

[0060] Discharge phase: Discharge at a current of 0.2C to 2.8V for ternary lithium batteries and 2.5V for lithium iron phosphate batteries, and record the discharge capacity;

[0061] Charging phase: First, charge at a constant current of 0.5C until the charging cutoff voltage, then charge at a constant voltage until the current drops to 0.05C;

[0062] The deep calibration cycle is linked to the vehicle maintenance cycle, or it can be manually triggered when the SOH of any single battery cell is below 90%.

[0063] Multi-algorithm fusion steps: Establish a SOH fusion calculation model, and take the minimum value of the initial calibration health SOH2, the dynamically updated health SOH3, and the maintenance calibration health SOH4 as the final battery health SOH. final =min(SOH2,SOH3,SOH4); When the deviation between the SOH calculated by any single algorithm and the final value exceeds the preset threshold, the algorithm self-checking mechanism is triggered and the maintenance calibration health value is adopted first until the next standard working condition calibration.

[0064] When the deviation between the SOH calculated by a certain algorithm and the final value exceeds 5%, the data backtracking verification of the algorithm is initiated, and the historical data of the past 3 calibration cycles is compared.

[0065] If two consecutive verifications fail, the algorithm is locked and a weighted average of other algorithms is temporarily used until the next maintenance calibration.

[0066] Temperature compensation correction steps: Establish a SOH temperature correction coefficient table. When the battery operating temperature deviates from 25℃, according to the formula: SOH corr =SOH·(1+γ·(T-25)) adjustment, where γ is the temperature coefficient, 0.015 / ℃ for ternary lithium and 0.01 / ℃ for lithium iron phosphate; when the temperature exceeds 60℃ or falls below 0℃, it automatically switches to the maintenance calibration algorithm priority mode.

[0067] Steps for predicting aging trends: Establish a linear regression model based on historical SOH data. pred =k·N+b, where N is the number of cycles, k is the decay rate, and b is the intercept; when the predicted remaining number of cycles is less than 50, a battery replacement warning is triggered, and maintenance calibration data is used to update the prediction model first.

[0068] Define battery self-discharge rate Among them I leak C represents the measured value of microcurrent leakage. rated This refers to the battery's rated capacity.

[0069] Establish surface temperature gradient index Where T i T j N represents the surface temperature of a single cell. cell The number of individual battery cells; the corrected decay rate k expression is: k ′ = k·(1+0.02·S) leak +0.015·G T )

[0070] When S leak >5% or G T When the temperature exceeds 8℃, a battery consistency warning is triggered and the parameters of the linear regression model are updated.

[0071] Define charge distribution entropy H q =-∑(p i lnp i ), where p i The charge density ratio of the i-th electrode reflects the degree of material polarization; the fluctuation value of the magnetic field strength inside the battery, ΔB = σ(B), is collected. t ), where B t Let σ be the real-time magnetic field strength and σ be the standard deviation; establish a nonlinear correction term: SOH pred =k·N+b+λ·H q +μ·ΔB

[0072] Where λ is the charge distribution influence factor and μ is the magnetic field fluctuation influence factor, determined by fitting historical data; when H q When the value is <0.8 and ΔB>0.5mT, it is determined to be in the stage of electrode material degradation, and the calibration frequency is increased.

[0073] Define electromagnetic interference degree Among them U noise For high-frequency noise voltage, U rated The battery's rated voltage is used; acoustic signals during the charging and discharging process are collected, and the dominant frequency offset Δf = |f| is extracted. t -f0|, where f t f0 is the factory default clock frequency, f0 is the real-time clock frequency; a robust correction model is established: SOH corr =SOH pred ·(1-0.1·E emi -0.05·Δf);

[0074] When E emi When the value is >0.2 or Δf >10%, abnormal data points are automatically masked and multi-sensor fusion verification is initiated, prioritizing the adoption of maintenance calibration algorithms for model correction.

[0075] In practical use: Taking a ternary lithium battery pack for electric vehicles (rated capacity 50Ah, nominal voltage 3.7V) as an example, when the battery pack's SOC drops to 10% (preset initial calibration threshold), the BMS triggers the standard operating condition calibration process:

[0076] Place the battery pack in a constant temperature chamber (temperature 25℃, within the calibrated temperature range of 20℃-45℃) and connect it to the charger.

[0077] Charged at a constant current of 0.5C (25A) to 4.20V±0.02V (termination voltage of ternary lithium constant current charging), entering the constant voltage charging stage until the charging current drops to 0.05C (2.5A), and the actual input capacity is recorded as 48.5Ah.

[0078] According to the formula Initial calibration complete.

[0079] For lithium iron phosphate battery packs (rated capacity 100Ah, nominal voltage 3.2V), calibration is triggered when the SOC drops to 15%.

[0080] At a temperature of 35°C (within the calibration temperature range of 15°C-55°C), charge to 3.65V±0.03V with a current of 0.6C (60A).

[0081] After constant voltage charging is completed, the actual input capacity is 93Ah, and the calculated SOH2 = 93%.

[0082] By charging to the electrochemical plateau voltage with a constant current, polarization interference is eliminated, and usable capacity is directly obtained. Compared with the traditional method that relies on the factory nominal value, the initial calibration error is reduced by 40% (the voltage plateau characteristics reflect the effective utilization rate of active materials, avoiding baseline deviations caused by production differences and storage degradation). Ternary lithium batteries have a lower upper limit for calibration temperature (45℃) to prevent the risk of electrolyte decomposition at high temperatures; lithium iron phosphate batteries allow a higher temperature range (55℃) to match their thermal stability, making the calibration parameters of batteries of different materials closer to the actual operating range (setting boundary conditions based on the thermodynamic properties of materials improves calibration safety). It is suitable for scenarios such as new vehicle manufacturing and battery pack maintenance, ensuring a uniform baseline for the capacity of individual cells, reducing the initial inconsistency of the entire battery pack, and eliminating capacity dispersion during manufacturing and transportation through a standardized calibration process.

[0083] In the daily operation of electric buses, a ternary lithium battery pack (rated capacity 200Ah) is continuously charged and discharged. The BMS updates the State of Health (SOH) according to the following steps: Data acquisition: The charging and discharging current I and the battery temperature T are collected at a frequency of 1Hz. When the discharge current I = 0.8C (160A) and the temperature T = 35℃, the following is calculated using the ampere-hour efficiency correction model: η Ah =95%·e -0.01·|35-25| • (1 + 0.05 • 0.8) = 88.7% Capacity Update: After 400 hours of cumulative operation (preset cycle), the discharge capacity recorded by coulomb integration is 165 Ah. The corrected usable capacity is... calculate

[0084] Through the temperature exponential decay term e -α|T-25|The system incorporates a linear correction term for current (1+β|I|) to quantify polarization effects and energy loss, addressing the issue of efficiency fluctuations not considered in traditional Coulomb integration methods. This reduces dynamic capacity update errors by 30% (based on fitting the relationship between ampere-hour efficiency and temperature-current using the Butler-Volmer equation, improving real-time assessment accuracy). Updates are triggered at preset intervals (300-500 hours or 200 cycles) to avoid interference from single-cycle fluctuations, such as sudden capacity drops at low temperatures. Periodic calibration filters out random errors, ensuring SOH update stability (sliding window data processing suppresses short-term noise). Suitable for vehicle-mounted scenarios with frequent start-stop cycles, it reflects the actual usable battery capacity under different currents and temperatures, providing accurate input for energy management systems. Through multi-physical parameter fusion modeling, it covers over 80% of actual operating conditions.

[0085] A lithium iron phosphate battery pack (rated capacity 500Ah) at an energy storage power station is undergoing its annual maintenance and deep calibration is being performed: Discharge phase: Discharged to 2.5V at 0.2C (100A), recording a discharge capacity of 450Ah. Charging phase: First charged to 3.65V at a constant current of 0.5C (250A), then charged at a constant voltage until the current drops to 0.05C (25A), recording a charging capacity of 460Ah. Health calculation: Taking the smaller value of the discharge and charging capacities, 450Ah, the health is calculated...

[0086] Staged full-charge and discharge cycles (constant current + constant voltage) cover the battery's operating voltage limits, exposing the passivation of active materials caused by long-term shallow charging and discharging. Compared to routine dynamic updates, this can detect 10%-15% more irreversible capacity decay. Through capacity release under extreme conditions, chronic failure of electrode materials can be identified. Linked with the vehicle / energy storage system maintenance cycle (e.g., annually), it avoids the impact of frequent deep calibration on battery life, while ensuring timely detection of aging batteries, reducing maintenance costs by 20%. Manual calibration is triggered when a single battery cell's SOH falls below 90%, identifying lagging cells. For example, cells in an energy storage system with an SOH difference exceeding 5% can be replaced earlier, improving the cycle life of the entire battery pack.

[0087] An electric forklift battery pack (ternary lithium, rated capacity 100Ah) exhibited an algorithm deviation: initial calibration SOH2 = 95%, dynamic update SOH3 = 88%, maintenance calibration SOH4 = 85%, and final SOH... final =85%. If SO3 and SO3 final When the deviation reached 7% (>5% threshold), the BMS reviewed the data from the previous three calibrations and found that the temperature sensor drift caused abnormal ampere-hour efficiency correction. Therefore, the SOH3 algorithm was locked and 0.4·SOH2+0.6·SOH4=89% was temporarily adopted until the next maintenance.

[0088] The minimum value strategy avoids overestimation of State of Harm (SOH). For example, when dynamic updates misjudge due to current fluctuations, the lower value from maintenance calibration is used as the standard, reducing the risk of overcharging by 60%. Data backtracking verification (comparing historical data from three cycles) identifies sensor anomalies, such as efficiency correction deviations caused by decreased accuracy of the temperature sensor. Algorithms lock in and use weighted averaging to maintain system stability. Combining initial calibration (static capacity), dynamic updates (real-time operating conditions), and maintenance calibration (deep testing) covers the entire battery lifecycle from "new" to "old" to "aged," solving the adaptability problem of a single algorithm at different stages.

[0089] Starting temperature compensation for electric vehicles (ternary lithium battery, measured SOH 80%, ambient temperature -10℃) in northern winter: Correction calculation: based on the formula SOH corr =80%·(1-0.015·35)=75.8% (γ=-0.015 / ℃ is the temperature coefficient of ternary lithium). Extreme handling: If the battery temperature rises to 65℃, automatically switch to maintenance calibration priority mode, suspend dynamic updates, and rely on deep calibration data.

[0090] The temperature coefficient γ is fitted based on the Arrhenius equation, quantifying the effect of temperature on the lithium-ion diffusion rate and improving the accuracy of SOH correction at low temperatures. This involves thermodynamic modeling of temperature-capacity decay to compensate for changes in the migration ability of active materials. When the temperature exceeds the limit (>60℃ or <0℃), the dynamic model error is amplified, switching to a maintenance calibration priority mode to reduce the risk of misjudgment at high temperatures. The compensated SOH is synchronized to the battery thermal management system, for example, automatically activating the heating element at low temperatures (target temperature 25℃) and triggering liquid cooling at high temperatures, forming a closed loop of "state assessment - cooling / heating".

[0091] A backup battery pack (lithium iron phosphate, rated capacity 200Ah) for a certain communication base station was found to have: micro-current leakage I leak =4μA, calculate Surface temperature gradient Corrected decay rate k ′ =k·(1+0.02×2+0.015×0.4375)=1.047k.

[0092] For ternary lithium batteries after high-power discharge, the charge density distribution of the electrode plates is collected, and the charge distribution entropy H is calculated. q =0.6 (reflecting intensified polarization), magnetic field fluctuation ΔB = 0.7mT, corrected SOH pred =kN+b-0.4×0.6-0.3×0.7=kN+b-0.45, indicating the electrode is in the deterioration stage, and the calibration frequency is shortened from 3 months to 2 weeks.

[0093] The battery pack detected U under high-frequency interference from the motor. noise =180mV (rated voltage 3.7V), Eemi =0.048; the main frequency offset of the charging / discharging acoustic signal Δf = 12%, calculate SOH. corr =SOH pred • (1 - 0.1 × 0.048 - 0.05 × 0.12 = 0.9892SOHpred, triggering multi-sensor verification to eliminate interference.)

[0094] Microcurrent leakage S leak Temperature gradient G reflects the degree of electrolyte decomposition. T It reflects uneven heat dissipation and quantifies the superimposed effect of the two on the aging rate through a linear correction term, capturing more early degradation signals than traditional models.

[0095] Charge distribution entropy H q The polarization state of the material is characterized (the lower the value, the more severe the polarization). The magnetic field fluctuation ΔB reflects dendrite growth or structural deformation. The nonlinear correction term solves the problem of microstructural changes that cannot be identified by capacitance / internal resistance, thus improving the accuracy of the degradation stage judgment.

[0096] Electromagnetic Interference Level E emi The signal noise and mechanical anomaly are quantified by the main frequency offset Δf, respectively. The robust model reduces the misjudgment of SOH by high-frequency interference and structural changes, and reduces the evaluation error in the strong electromagnetic environment of vehicle.

[0097] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A multi-scenario integrated battery health calculation method, characterized in that, Includes the following: Standard operating condition initial calibration procedure: When the battery pack charge is lower than the preset initial calibration threshold, the standard operating condition calibration process is triggered, charging to full charge with a constant current, recording the actual input capacity during charging, and calculating the current available capacity based on the voltage plateau characteristic curve. Through formula Calculate the initial calibration health, where This refers to the battery's nominal initial capacity at the time of manufacture. Dynamic usage update steps: During the continuous charging and discharging of the battery pack, the discharge capacity is accumulated in real time using the coulomb integral method. and charging capacity The real-time available capacity is calculated using an ampere-hour efficiency correction model, and the current available capacity is updated according to a preset period. And calculate and dynamically update health using the same formula. ; The expression for the ampere-hour efficiency correction model is as follows: ;in, For standard ampere-hour efficiency, the default value is 95%. This is the temperature correction factor. Here, T is the current correction factor, T is the battery temperature, and I is the charging / discharging current. Maintenance and deep calibration procedure: When the vehicle enters maintenance mode or the deep calibration mode is manually triggered, perform a full charge and discharge procedure, discharging to the cutoff voltage with a preset current and recording the discharge capacity. Then charge to full charge using the standard procedure and record the charging capacity. The smaller of the two values ​​is taken as the available capacity for depth calibration, using the formula... Calculate, maintain, calibrate, and assess health status; Multi-algorithm fusion steps: Establish a SOH fusion calculation model, and calculate the initial calibration health status. Dynamically update health status Maintenance and calibration of health status The minimum value is taken as the final battery health score. When the deviation between the SOH calculated by any single algorithm and the final value exceeds a preset threshold, the algorithm self-verification mechanism is triggered and the maintenance calibration health value is adopted first until the next standard operating condition calibration.

2. The battery health calculation method based on multi-scenario fusion according to claim 1, characterized in that, In the standard operating condition initial calibration procedure, the preset initial calibration threshold is 5%-15% SOC, the constant current is 0.3C-0.7C, and differentiated calibration parameters are set for ternary lithium batteries and lithium iron phosphate batteries: For ternary lithium batteries, the calibration temperature range is 20℃-45℃, and the constant current charging termination voltage is 4.20V±0.02V. For lithium iron phosphate batteries, the calibration temperature range is 15℃-55℃, and the constant current charging termination voltage is 3.65V±0.03V.

3. The battery health calculation method based on multi-scenario fusion according to claim 2, characterized in that: When the cumulative running time reaches the preset period or the number of charge-discharge cycles exceeds 200, the capacity update process is triggered.

4. The battery health calculation method based on multi-scenario fusion according to claim 3, characterized in that, In the maintenance and calibration process, the full charge and discharge process is controlled in stages: Discharge phase: Discharge at a current of 0.2C to 2.8V for ternary lithium batteries and 2.5V for lithium iron phosphate batteries, and record the discharge capacity; Charging phase: First, charge at a constant current of 0.5C until the charging cutoff voltage, then charge at a constant voltage until the current drops to 0.05C; The deep calibration cycle is linked to the vehicle maintenance cycle, or can be manually triggered when the SOH of any single battery cell is below 90%.

5. A multi-scenario fusion battery health calculation method according to claim 4, characterized in that, In the multi-algorithm fusion step, when the deviation between the SOH calculated by a certain algorithm and the final value exceeds 5%, the data backtracking verification of that algorithm is initiated, and the historical data of the past 3 calibration cycles are compared. If two consecutive verifications fail, the algorithm is locked and a weighted average of other algorithms is temporarily used until the next maintenance calibration.

6. The battery health calculation method based on multi-scenario fusion according to claim 5, characterized in that, It also includes a temperature compensation correction step: establishing a SOH temperature correction coefficient table, and when the battery operating temperature deviates from 25℃, according to the formula: Adjustments, among which The temperature coefficient is 0.015 / ℃ for ternary lithium and 0.01 / ℃ for lithium iron phosphate; when the temperature exceeds 60℃ or falls below 0℃, it automatically switches to the maintenance calibration algorithm priority mode.

7. The battery health calculation method based on multi-scenario fusion according to claim 6, characterized in that, It also includes an aging trend prediction step: establishing a linear regression model based on historical SOH data. Where N is the number of cycles, k is the decay rate, and b is the intercept; when the predicted remaining number of cycles is less than 50, a battery replacement warning is triggered, and maintenance calibration data is used to update the prediction model.

8. The battery health calculation method based on multi-scenario fusion according to claim 7, characterized in that, In the aging trend prediction step: the battery self-discharge rate is defined. ,in This is the measured value of micro-current leakage. This refers to the battery's rated capacity. Establish surface temperature gradient index ,in This refers to the surface temperature of a single cell. The number of individual battery cells; the corrected decay rate k expression is: ; when or At that time, a battery consistency warning is triggered and the parameters of the linear regression model are updated.

9. The battery health calculation method based on multi-scenario fusion according to claim 8, characterized in that, In the aging trend prediction step: the charge distribution entropy is defined. ,in For the first The charge density ratio of each electrode reflects the degree of material polarization; the fluctuation value of the internal magnetic field strength of the battery is collected. ,in For real-time magnetic field strength, For the standard deviation; establish a nonlinear correction term: ; in, The charge distribution influencing factor, The magnetic field fluctuation influence factor was determined by fitting historical data; when and When mT is reached, it is determined to be the stage of electrode material degradation and the calibration frequency is increased. Define electromagnetic interference degree ,in It is a high-frequency noise voltage. The battery's rated voltage; acoustic signals during charging and discharging are collected and the dominant frequency offset is extracted. ,in For real-time clock frequency, The factory reference clock frequency is used; a robust correction model is established: ; when or At that time, abnormal data points are automatically blocked and multi-sensor fusion verification is initiated, with maintenance calibration algorithms being adopted first for model correction.

10. A multi-scenario integrated battery health calculation system, characterized in that, The method described in any one of claims 1-9 was adopted.

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