Active equalization strategy optimization method and system of battery management system

Abstract

The invention provides an active equalization strategy optimization method and system of a battery management system, and relates to the technical field of battery management.The method includes the steps that voltage, temperature and current data of each single battery are collected, a charging state value is calculated and preprocessed, an equalization strategy is generated through fuzzy logic reasoning, and needed equalization current is deduced; and calculating the required actual equalizing current by combining the influence of the temperature and the charging state value. After the equalization operation is executed, the voltage of the single batteries is monitored in real time to evaluate the equalization effect, and the equalization efficiency of the battery pack is periodically calculated to ensure the effectiveness of the strategy, thereby improving the overall performance and service life of the battery pack. According to the method, fuzzy logic and a real-time monitoring technology are combined, and the balancing strategy of the battery pack can be optimized in a dynamic environment.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and in particular to an active balancing strategy optimization method and system for a battery management system. Background Art

[0002] Battery management systems are a vital component of modern battery technology, especially in the fields of electric vehicles, renewable energy storage, and mobile electronic devices. With the increasing requirements for battery performance and safety, and the rapid development of the electric vehicle market, the technology of battery management systems continues to evolve and innovate. As an energy storage device, the performance of the battery directly affects the operating efficiency and service life of the equipment. The main functions of the battery management system include real-time monitoring of the battery's voltage, temperature, current, and charging status to ensure that the battery operates within a safe range, thereby preventing potential risks such as overcharging, over-discharging, and short circuits. In addition, the battery management system is also responsible for balancing the battery cells to optimize the overall performance of the battery pack and ensure that each single cell operates at the same level, thereby extending the battery's service life and improving its energy utilization.

[0003] In modern battery management systems, the design of active balancing strategies is crucial, especially in terms of battery pack performance and safety. Existing technologies usually use balancing methods based on cell voltage differences, but in battery packs, voltage differences are inevitable due to aging, temperature fluctuations, and inconsistent charging states between cells. In this case, traditional balancing methods are not only inefficient, but also may not be able to effectively achieve energy balance when the cell voltage differences are small, which in turn affects the overall performance and service life of the battery pack. In addition, the lack of comprehensive consideration of multiple parameters such as the internal temperature and SOC of the battery also makes the existing balancing strategies insufficient when dealing with complex working environments.

[0004] The prior art has the following deficiencies:

[0005] First, many traditional balancing strategies rely on static rules or fixed balancing currents. This method is not flexible enough when facing changes in the state of battery cells and cannot adapt to different working conditions and environments in real time. Such a static method may cause some single cells to be overcharged or over-discharged, thereby affecting the performance and safety of the overall battery pack. Secondly, existing balancing strategies often lack comprehensive consideration of battery temperature and charging status. In addition, existing technologies are also insufficient in the evaluation of balancing efficiency. After performing balancing operations, many systems do not perform comprehensive effect monitoring and efficiency calculations, resulting in the inability to detect potential problems in a timely manner, affecting subsequent adjustments to balancing strategies. This design lacks dynamic feedback, which limits the system's adaptive capabilities and cannot effectively respond to changes during battery use.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0007] The object of the present invention is to provide a method and system for optimizing the active balancing strategy of a battery management system to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for optimizing an active balancing strategy of a battery management system, the specific steps comprising:

[0010] Step 1: Collecting battery parameters of each single battery in the battery pack and preprocessing the battery parameter data, wherein the battery parameters include voltage, temperature and charging state value;

[0011] Step 2: Use the preprocessed battery parameters as fuzzy input variables, define fuzzy sets, and create fuzzy rules. Use fuzzy logic reasoning to transform the input variables into fuzzy outputs. Through fuzzy rules, obtain the balancing strategy as the fuzzy control output:

[0012] Step 3: Use fuzzy reasoning method to infer the state of battery cells, obtain the balancing current requirement of battery cells, and calculate the actual balancing current in combination with the current temperature and charging state value of the battery cells;

[0013] Step 4: After performing the balancing operation, monitor the voltage of the battery cells in real time to determine the balancing effect, and calculate the balancing efficiency of the battery pack to regularly evaluate the balancing strategy.

[0014] Furthermore, the specific logic for collecting battery parameters of a single battery is as follows:

[0015] A voltage sensor and a current sensor are arranged in the middle of the top of the battery pack to monitor the voltage and current of the battery pack. A module is arranged in the middle of the side of each battery cell. The module integrates a voltage sensor, a temperature sensor and a current sensor to monitor the voltage, temperature and charging state value of each battery cell. The formula for obtaining the charging state value is:

[0016]

[0017] Among them, SOC(t) represents the current state of charge value, SOC(t 0 ) represents the initial charging state value, C rated is the rated capacity of the battery, I(t) is the current variable from the initial moment to the current moment, the charging state is positive, the discharging state is negative, t 0represents the initial time, and t represents the current time;

[0018] The battery parameters of each battery cell collected are preprocessed. The preprocessing operation includes removing outliers and filling missing values ​​with the numerical mean after removing outliers. The Z-score of each data is calculated. When the absolute value of the Z-score of the data |Z|>3, the data is regarded as an outlier and removed.

[0019] Furthermore, the specific logic for creating fuzzy rules is:

[0020] Set the fuzzy control output to a balanced strategy, the fuzzy set includes low, normal, and high, the voltage range is set to [2.5, 4.2], the unit is V, and the voltage membership function expression is as follows:

[0021] Low Voltage:

[0022]

[0023] Normal voltage:

[0024]

[0025] High Voltage:

[0026]

[0027] Among them, μ Low (V), μ Normal (V), μ High (V) are the membership function expressions of voltage fuzzy sets low, normal and high respectively;

[0028] The temperature range is set to [-20, 60], the unit is ℃, and the membership function expression of temperature is as follows:

[0029] Low temperature:

[0030]

[0031] Normal temperature:

[0032]

[0033] high temperature:

[0034]

[0035] Among them, μ Low (T), μ Normal (T), μ High (T) are the membership function expressions of low, normal and high temperature fuzzy sets respectively;

[0036] Set the SOC range to [0, 100%], and the membership function expression of SOC is as follows:

[0037] Low SOC:

[0038]

[0039] Normal SOC:

[0040]

[0041] High SOC:

[0042]

[0043] Among them, μ Low (SOC), μ Normal (SOC), μ High (SOC) are the membership function expressions of the fuzzy set of low, normal and high state of charge values ​​respectively;

[0044] The fuzzy rules are as follows: Rule 1: If the voltage, temperature and SOC are normal, the balancing strategy is to maintain the current state; Rule 2: If the voltage, temperature and SOC are normal, the balancing strategy is to charge; Rule 3: If the voltage, temperature and SOC are high, the balancing strategy is to discharge; Rule 4: If the voltage, temperature and SOC are normal, the balancing strategy is to reduce charging; Rule 5: If the voltage, temperature and SOC are normal, the balancing strategy is to charge; Rule 6: If the voltage, temperature and SOC are high, the balancing strategy is to discharge; Rule 7: If the voltage, temperature and SOC are normal, the balancing strategy is to reduce charging; Rule 8: If the voltage is normal, the temperature is low, and the SOC is low, the balancing strategy is to discharge; Rule 9: If the voltage is normal, the temperature is low, and the SOC is normal, the balancing strategy is to charge; Rule 10: If the voltage is low, the temperature is normal, and the SOC is normal, the balancing strategy is to charge; Rule 11: If the voltage is low, the temperature is normal, and the SOC is high, the balancing strategy is to discharge; Rule 12: If the voltage is low, the temperature is normal, and the SOC is low, the balancing strategy is to charge; Rule 13: If the voltage is low, the temperature is high, and the SOC is normal, the balancing strategy is to reduce charging; Rule 14: If If the voltage is low, the temperature is high, and the SOC is high, the balancing strategy is to discharge; Rule 15: If the voltage is low, the temperature is high, and the SOC is low, the balancing strategy is to stop charging; Rule 16: If the voltage is low, the temperature is low, and the SOC is normal, the balancing strategy is to charge; Rule 17: If the voltage is low, the temperature is low, and the SOC is low, the balancing strategy is to charge; Rule 18: If the voltage is low, the temperature is low, and the SOC is high, the balancing strategy is to discharge; Rule 19: If the voltage is high, the temperature is normal, and the SOC is normal, the balancing strategy is to maintain the current state; Rule 20: If the voltage is high, the temperature is normal, and the SOC is low, the balancing strategy is to discharge; Rule 21: If the voltage is high, the temperature is normal, and the SOC is high, the balancing strategy is discharge; Rule 22: If the voltage is high, the temperature is high, and the SOC is normal, the balancing strategy is discharge; Rule 23: If the voltage is high, the temperature is high, and the SOC is high, the balancing strategy is discharge; Rule 24: If the voltage is high, the temperature is high, and the SOC is low, the balancing strategy is discharge; Rule 25: If the voltage is high, the temperature is low, and the SOC is high, the balancing strategy is discharge; Rule 26: If the voltage is high, the temperature is low, and the SOC is low, the balancing strategy is charge; Rule 27: If the voltage is high, the temperature is low, and the SOC is normal, the balancing strategy is charge;

[0045] Through fuzzy rules, the rules corresponding to the voltage, current and charging state values ​​at the current moment are found, and their balancing strategies are executed.

[0046] Furthermore, the specific logic for calculating the actual balancing current is:

[0047] The Mamdani fuzzy reasoning method is used to calculate the required balancing current for the single battery that needs to be charged or discharged. The formula is:

[0048]

[0049] in, is the balancing current required by the ith battery cell, k is the proportionality coefficient, V target is the target voltage of the battery cell, V i is the voltage of the i-th battery cell, i is the index of the battery cell, and i∈[1,N], and N is the number of battery cells;

[0050] Combined with the battery temperature, the equalization current is corrected, and the formula for obtaining the temperature correction factor is:

[0051] K T =K T0 +K T1 ×(TT 0 )

[0052] Among them, K T is the temperature correction factor, K T0 , K T1 is a constant related to temperature, T is the current temperature, T 0 is the reference temperature;

[0053] The charging state value is corrected and a correction factor is set. The formula for obtaining the correction factor of the charging state value is:

[0054]

[0055] Among them, K SOC is the correction factor for the state of charge value, SOC nom is the ideal charging state value, SOC max The maximum state of charge of the battery, SOC min is the minimum charging state value;

[0056] Taking into account the influence of temperature and state of charge, the formula for calculating the actual balancing current is:

[0057]

[0058] in, represents the actual balancing current of the ith battery cell;

[0059] According to the calculated actual balancing current, determine the specific charging or discharging current. is positive, charging is performed. If it is negative, discharge occurs.

[0060] Furthermore, the specific logic for calculating the balancing efficiency of the battery pack to periodically evaluate the balancing strategy is as follows:

[0061] After performing the balancing operation, monitor the voltage of the battery cell. If the voltage of the battery cell is within V target If the voltage is within the range of ±0.1V, the balance is considered successful, otherwise return to step 2 to adjust and re-balance;

[0062] The battery pack status is evaluated every 24 hours, and the formula for obtaining input power is:

[0063]

[0064] Among them, E in (t3) is the input power of the battery pack at the end time t3 of the evaluation, P(t2, t3) is the power received by the battery pack from time t2 to t3, t2 is the start time of the evaluation, t3 is the end time of the evaluation, P(t2, t3) = V(t2, t3) × I(t2, t3), V(t2, t3) represents the voltage variable of the battery pack from time t2 to t3, I(t2, t3) represents the current variable of the battery pack from time t2 to t3;

[0065] The formula for calculating the equilibrium efficiency is:

[0066]

[0067] Among them, E eff To evaluate the balancing efficiency of the battery pack, Δt i is the duration of the evaluation process of the ith battery cell, E in It represents the input electrical energy during the evaluation process, in Watts;

[0068] Compare the balancing efficiency of the battery pack with the preset efficiency threshold. If the balancing efficiency of the battery pack is higher than the preset efficiency threshold, no adjustment is required. If the balancing efficiency of the battery pack is lower than the preset efficiency threshold, it indicates that there is a problem with the current balancing strategy and it needs to be readjusted.

[0069] The present invention further provides an active balancing strategy optimization system for a battery management system, wherein the active balancing strategy optimization method for the battery management system is used to implement the active balancing strategy optimization method for the battery management system, comprising:

[0070] A battery parameter acquisition module is used to acquire battery parameters of each single battery in the battery pack and pre-process the battery parameter data, wherein the battery parameters include voltage, temperature and charging state value;

[0071] The fuzzy logic rule definition module is used to take the pre-processed battery parameters as fuzzy input variables, define fuzzy sets, and create fuzzy rules. The input variables are converted into fuzzy outputs using fuzzy logic reasoning. Through the fuzzy rules, the balancing strategy is obtained as the fuzzy control output;

[0072] The actual balancing current calculation module is used to use the fuzzy reasoning method to infer the state of the battery cell, obtain the balancing current demand of the battery cell, and calculate the actual balancing current in combination with the current temperature and charging state value of the battery cell;

[0073] The balancing effect monitoring and evaluation module is used to monitor the voltage of the battery cells in real time after performing the balancing operation to determine the balancing effect, calculate the balancing efficiency of the battery pack and regularly evaluate the balancing strategy.

[0074] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0075] This solution significantly improves the efficiency and flexibility of the battery management system by introducing an active balancing strategy with multi-parameter linkage, combined with fuzzy logic control and real-time data analysis. Using fuzzy logic reasoning technology, this method can flexibly cope with the complexity of batteries under different working conditions. By defining fuzzy sets and creating a series of fuzzy rules, the system can dynamically adjust the balancing strategy based on real-time data to ensure that the battery pack can achieve efficient energy distribution under various conditions. It not only monitors the voltage variation range of battery cells, but also regularly evaluates the balancing efficiency of the battery pack to ensure that potential problems can be discovered and resolved in a timely manner, ensuring the stability and reliability of the overall system. When the balancing efficiency is lower than the preset threshold, the system automatically adjusts the strategy to further optimize battery management. This adaptive feature enables the battery management system to maintain high efficiency under different environments and usage conditions, providing strong support for the application of electric vehicles and other battery-driven devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0077] Figure 2 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0078] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0079] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0080] Example:

[0081] See also Figure 1 , the present invention provides a technical solution:

[0082] A method for optimizing an active balancing strategy of a battery management system, the specific steps comprising:

[0083] Step 1: Collecting battery parameters of each single battery in the battery pack and preprocessing the battery parameter data, wherein the battery parameters include voltage, temperature and charging state value;

[0084] In this embodiment, the specific logic for collecting battery parameters of a single battery is as follows:

[0085] A voltage sensor and a current sensor are arranged in the middle of the top of the battery pack to monitor the voltage and current of the battery pack. A module is arranged in the middle of the side of each battery cell. The module integrates a voltage sensor, a temperature sensor and a current sensor to monitor the voltage, temperature and charging state value of each battery cell. The formula for obtaining the charging state value is:

[0086]

[0087] Among them, SOC(t) represents the current state of charge value, SOC(t 0 ) represents the initial state of charge value, that is, the state of charge value calculated when the battery pack is used for the first time, C rated is the rated capacity of the battery, I(t) is the current variable from the initial moment to the current moment, the charging state is positive, the discharging state is negative, t 0 represents the initial time, and t represents the current time;

[0088] The battery parameters of each battery cell collected are preprocessed. The preprocessing operation includes removing outliers and filling missing values ​​with the numerical mean after removing outliers. The Z-score of each data is calculated. When the absolute value of the Z-score of the data |Z|>3, the data is regarded as an outlier and removed.

[0089] By arranging multiple sensors on the top of the battery pack and on the side of each cell, the system can comprehensively and real-time collect battery parameters, including voltage, temperature and state of charge. This arrangement ensures the comprehensiveness and accuracy of data collection, so that the subsequent balancing strategy can be optimized based on real and reliable data. The adopted SOC calculation formula fully considers the rated capacity and real-time current changes of the battery, and can accurately reflect the charging status of the battery under different working conditions. This dynamic calculation method based on integration can update the charging status of each cell in real time, providing a scientific basis for the balancing strategy and ensuring the effectiveness of the balancing operation. By introducing the Z-score standardization method, outliers in the data can be effectively identified and eliminated. The application of Z-score makes the data preprocessing process have quantitative standards. When the absolute value |Z|>3, the strategy of eliminating outliers ensures the purity of the data set and avoids the negative impact of individual abnormal data on the overall balancing strategy. This preprocessing method can improve the accuracy of the final data analysis and decision-making.

[0090] Step 2: Use the preprocessed battery parameters as fuzzy input variables, define fuzzy sets, and create fuzzy rules. Use fuzzy logic reasoning to transform the input variables into fuzzy outputs. Through fuzzy rules, obtain the balancing strategy as the fuzzy control output;

[0091] In this embodiment, the specific logic for creating fuzzy rules is:

[0092] Set the fuzzy control output to a balanced strategy, the fuzzy set includes low, normal, and high, the voltage range is set to [2.5, 4.2], the unit is V, and the voltage membership function expression is as follows:

[0093] Low Voltage:

[0094]

[0095] Normal voltage:

[0096]

[0097] High Voltage:

[0098]

[0099] Among them, μ Low (V), μ Normal (V), μHigh (V) are the membership function expressions of voltage fuzzy sets low, normal and high respectively;

[0100] The temperature range is set to [-20, 60], the unit is ℃, and the membership function expression of temperature is as follows:

[0101] Low temperature:

[0102]

[0103] Normal temperature:

[0104]

[0105] high temperature:

[0106]

[0107] Among them, μ Low (T), μ Normal (T), μ High (T) are the membership function expressions of low, normal and high temperature fuzzy sets respectively;

[0108] Set the SOC range to [0, 100%], and the membership function expression of SOC is as follows:

[0109] Low SOC:

[0110]

[0111] Normal SOC:

[0112]

[0113] High SOC:

[0114]

[0115] Among them, μ Low (SOC), μ Normal (SOC), μ High (SOC) are the membership function expressions of the fuzzy set of low, normal and high state of charge values ​​respectively;

[0116] The fuzzy rules are as follows: Rule 1: If the voltage, temperature and SOC are normal, the balancing strategy is to maintain the current state; Rule 2: If the voltage, temperature and SOC are normal, the balancing strategy is to charge; Rule 3: If the voltage, temperature and SOC are high, the balancing strategy is to discharge; Rule 4: If the voltage, temperature and SOC are normal, the balancing strategy is to reduce charging; Rule 5: If the voltage, temperature and SOC are normal, the balancing strategy is to charge; Rule 6: If the voltage, temperature and SOC are high, the balancing strategy is to discharge; Rule 7: If the voltage, temperature and SOC are normal, the balancing strategy is to reduce charging; Rule 8: If the voltage is normal, the temperature is low, and the SOC is low, the balancing strategy is to discharge; Rule 9: If the voltage is normal, the temperature is low, and the SOC is normal, the balancing strategy is to charge; Rule 10: If the voltage is low, the temperature is normal, and the SOC is normal, the balancing strategy is to charge; Rule 11: If the voltage is low, the temperature is normal, and the SOC is high, the balancing strategy is to discharge; Rule 12: If the voltage is low, the temperature is normal, and the SOC is low, the balancing strategy is to charge; Rule 13: If the voltage is low, the temperature is high, and the SOC is normal, the balancing strategy is to reduce charging; Rule 14: If If the voltage is low, the temperature is high, and the SOC is high, the balancing strategy is to discharge; Rule 15: If the voltage is low, the temperature is high, and the SOC is low, the balancing strategy is to stop charging; Rule 16: If the voltage is low, the temperature is low, and the SOC is normal, the balancing strategy is to charge; Rule 17: If the voltage is low, the temperature is low, and the SOC is low, the balancing strategy is to charge; Rule 18: If the voltage is low, the temperature is low, and the SOC is high, the balancing strategy is to discharge; Rule 19: If the voltage is high, the temperature is normal, and the SOC is normal, the balancing strategy is to maintain the current state; Rule 20: If the voltage is high, the temperature is normal, and the SOC is low, the balancing strategy is to discharge; Rule 21: If the voltage is high, the temperature is normal, and the SOC is high, the balancing strategy is discharge; Rule 22: If the voltage is high, the temperature is high, and the SOC is normal, the balancing strategy is discharge; Rule 23: If the voltage is high, the temperature is high, and the SOC is high, the balancing strategy is discharge; Rule 24: If the voltage is high, the temperature is high, and the SOC is low, the balancing strategy is discharge; Rule 25: If the voltage is high, the temperature is low, and the SOC is high, the balancing strategy is discharge; Rule 26: If the voltage is high, the temperature is low, and the SOC is low, the balancing strategy is charge; Rule 27: If the voltage is high, the temperature is low, and the SOC is normal, the balancing strategy is charge;

[0117] Through fuzzy rules, the rules corresponding to the voltage, current and charging state values ​​at the current moment are found, and their balancing strategies are executed.

[0118] By defining fuzzy sets (low, normal, high) and corresponding membership functions, the uncertainty and ambiguity of input variables (voltage, temperature, SOC) can be flexibly handled. This method can fully take into account the complexity of battery status. Unlike traditional threshold judgment, fuzzy logic can be closer to the actual operating status, thereby achieving a more accurate balancing strategy. By creating multiple fuzzy rules, the system can automatically generate corresponding balancing strategies based on different combinations of current battery status. This rule-based decision-making mechanism improves the response speed and adaptability of the system, can quickly respond to various complex situations, and ensure that the battery pack operates in a safe and efficient state. Converting complex input signals into clear output strategies makes the balancing control process more intelligent. With the help of fuzzy control output, the system can implement corresponding charging, discharging or maintaining the current state strategies under different battery states to ensure that each single battery operates in the best state. Through clear fuzzy rules and membership function expressions, the decision-making process of the system becomes more transparent.

[0119] Step 3: Use fuzzy reasoning method to infer the state of battery cells, obtain the balancing current requirement of battery cells, and calculate the actual balancing current in combination with the current temperature and charging state value of the battery cells;

[0120] In this embodiment, the specific logic for calculating the actual balancing current is:

[0121] The Mamdani fuzzy reasoning method is used to calculate the required balancing current for the single battery that needs to be charged or discharged. The formula is:

[0122]

[0123] in, is the balancing current required by the ith battery cell, k is the proportionality coefficient, V target is the target voltage of the battery cell, V i is the voltage of the i-th battery cell, i is the index of the battery cell, and i∈[1,N], and N is the number of battery cells; the target voltage of the battery cell of the lithium-ion battery can be set to 4.2V, and the target voltage of the battery cell of the lead-acid battery can be set to 2.4V;

[0124] Combined with the battery temperature, the equalization current is corrected, and the formula for obtaining the temperature correction factor is:

[0125] K T =K T0 +K T1 ×(TT 0 )

[0126] Among them, K T is the temperature correction factor, KT0 , K T1 is a constant related to temperature, T is the current temperature, T 0 is the reference temperature, set here K T0 =1, while for lithium-ion batteries K T1 =0.02, lead-acid battery K T1 =0.009;

[0127] The charging state value is corrected and a correction factor is set. The formula for obtaining the correction factor of the charging state value is:

[0128]

[0129] Among them, K SOC is the correction factor for the state of charge value, SOC nom is the ideal charging state value, SOC max The maximum state of charge of the battery, SOC min is the minimum charging state value;

[0130] For a lithium-ion battery, the ideal state of charge value can be set to 80%, the maximum state of charge value can be set to 100%, and the minimum state of charge value can be set to 20%. For a lead-acid battery, the ideal state of charge value can be set to 80%, the maximum state of charge value can be set to 100%, and the minimum state of charge value can be set to 40%.

[0131] Taking into account the influence of temperature and state of charge, the formula for calculating the actual balancing current is:

[0132]

[0133] in, represents the actual balancing current of the ith battery cell;

[0134] According to the calculated actual balancing current, determine the specific charging or discharging current. is positive, charging is performed. If it is negative, discharge occurs.

[0135] By adopting the Mamdan i fuzzy reasoning method, the state of each battery cell can be deeply analyzed. The required balancing current is calculated based on the difference between the actual voltage of the battery and the target voltage. The advantage of this method is that it can combine fuzzy logic with quantitative calculation to provide more accurate balancing current requirements. This process effectively improves the scientific nature of the balancing strategy. By introducing a temperature correction factor, the effect of battery temperature on the balancing current is taken into account to ensure the effectiveness of the balancing strategy under different environmental conditions. K TThe calculation method enables the system to dynamically adjust the balancing current according to the current temperature change to prevent battery performance degradation or safety hazards caused by excessively high or low temperatures. SOC , the system can flexibly adjust the balancing current according to the actual charging state of the battery. This correction factor ensures that the balancing needs of the battery in different charging stages can be effectively responded to, avoiding low charging efficiency or battery damage caused by improper charging state. This step comprehensively considers the voltage, temperature and charging state value of the battery cell to form a balancing current calculation model affected by multiple factors. This comprehensive analysis can help the system make more reasonable and optimized charging or discharging decisions in a complex operating environment and improve the overall performance of the battery pack.

[0136] Step 4: After performing the balancing operation, monitor the voltage of the battery cells in real time to determine the balancing effect, and calculate the balancing efficiency of the battery pack to regularly evaluate the balancing strategy.

[0137] In this embodiment, the specific logic for calculating the balancing efficiency of the battery pack to periodically evaluate the balancing strategy is:

[0138] After performing the balancing operation, monitor the voltage of the battery cell. If the voltage of the battery cell is within V target If the voltage is within the range of ±0.1V, the balance is considered successful, otherwise return to step 2 to adjust and re-balance;

[0139] The battery pack status is evaluated every 24 hours, and the formula for obtaining input power is:

[0140]

[0141] Among them, E in (t3) is the input power of the battery pack at the end time t3 of the evaluation, P(t2, t3) is the power received by the battery pack from time t2 to t3, t2 is the start time of the evaluation, t3 is the end time of the evaluation, P(t2, t3) = V(t2, t3) × I(t2, t3), V(t2, t3) represents the voltage variable of the battery pack from time t2 to t3, I(t2, t3) represents the current variable of the battery pack from time t2 to t3;

[0142] The formula for calculating the equilibrium efficiency is:

[0143]

[0144] Among them, E eff To evaluate the balancing efficiency of the battery pack, Δt i is the duration of the evaluation process of the ith battery cell, E in It represents the input electrical energy during the evaluation process, in Watts;

[0145] Compare the balancing efficiency of the battery pack with the preset efficiency threshold. If the balancing efficiency of the battery pack is higher than the preset efficiency threshold, no adjustment is required. If the balancing efficiency of the battery pack is lower than the preset efficiency threshold, it indicates that there is a problem with the current balancing strategy and it needs to be readjusted.

[0146] By monitoring the voltage of the battery cells in real time, the effect of the balancing operation can be judged in time. target The balancing is considered successful if the voltage is less than ±0.1V. This mechanism can quickly feedback the effectiveness of the balancing operation, thereby improving the response speed and accuracy of the system. This real-time monitoring and feedback can effectively avoid performance degradation or safety risks caused by voltage imbalance of battery cells. The status of the battery pack is evaluated every 24 hours, the input power is obtained and the balancing efficiency is calculated, so that the entire system can make decisions based on actual data rather than assumptions. This data-driven approach enables the battery management system to continuously optimize the balancing strategy during long-term use to ensure the health and performance of the battery pack. The numerator represents the sum of the equivalent current output by all battery cells during the balancing process multiplied by their duration. This part actually reflects the energy provided or received by the battery cell during the balancing operation, which can effectively capture the contribution of the battery cell in the balancing process. The denominator is the input power of the battery pack during the evaluation process, which represents the total energy received by the battery pack in the same time period. This part provides a benchmark for the balancing efficiency, allowing us to evaluate the actual balancing effect achieved under a given input energy. The value range of balancing efficiency is between 0 and 1. The closer the value is to 1, the more effective the balancing operation is and the better the balancing purpose can be achieved. The value close to 0 indicates that the balancing effect is not good and the strategy may need to be adjusted. Therefore, the preset efficiency threshold is set to 0.8 to calculate the balancing efficiency, making the evaluation of the balancing effect quantitative. This method can clearly reflect the actual effect of the balancing operation, which helps to identify potential problems and take corresponding adjustment measures.

[0147] See also Figure 2 The present invention further provides an active balancing strategy optimization system for a battery management system, wherein the active balancing strategy optimization method for the battery management system is used to implement the active balancing strategy optimization method for the battery management system, comprising:

[0148] A battery parameter acquisition module is used to acquire battery parameters of each single battery in the battery pack and pre-process the battery parameter data, wherein the battery parameters include voltage, temperature and charging state value;

[0149] The fuzzy logic rule definition module is used to take the pre-processed battery parameters as fuzzy input variables, define fuzzy sets, and create fuzzy rules. The input variables are converted into fuzzy outputs using fuzzy logic reasoning. Through the fuzzy rules, the balancing strategy is obtained as the fuzzy control output;

[0150] The actual balancing current calculation module is used to use the fuzzy reasoning method to infer the state of the battery cell, obtain the balancing current demand of the battery cell, and calculate the actual balancing current in combination with the current temperature and charging state value of the battery cell;

[0151] The balancing effect monitoring and evaluation module is used to monitor the voltage of the battery cells in real time after performing the balancing operation to determine the balancing effect, calculate the balancing efficiency of the battery pack and regularly evaluate the balancing strategy.

[0152] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0153] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0154] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for optimizing active balancing strategy of a battery management system, characterized in that: The specific steps include: Step 1: Collecting battery parameters of each single battery in the battery pack and preprocessing the battery parameter data, wherein the battery parameters include voltage, temperature and charging state value; Step 2: Use the preprocessed battery parameters as fuzzy input variables, define fuzzy sets, and create fuzzy rules. Use fuzzy logic reasoning to transform the input variables into fuzzy outputs. Through fuzzy rules, obtain the balancing strategy as the fuzzy control output; Step 3: Use fuzzy reasoning method to infer the state of battery cells, obtain the balancing current requirement of battery cells, and calculate the actual balancing current in combination with the current temperature and charging state value of the battery cells; Step 4: After performing the balancing operation, monitor the voltage of the battery cells in real time to determine the balancing effect, and calculate the balancing efficiency of the battery pack to regularly evaluate the balancing strategy.

2. The active balancing strategy optimization method of a battery management system according to claim 1, characterized in that: The specific logic for collecting battery parameters of single cells is as follows: A voltage sensor and a current sensor are arranged in the middle of the top of the battery pack to monitor the voltage and current of the battery pack. A module is arranged in the middle of the side of each battery cell. The module integrates a voltage sensor, a temperature sensor and a current sensor to monitor the voltage, temperature and charging state value of each battery cell. The formula for obtaining the charging state value is: Among them, SOC(t) represents the current state of charge value, SOC(t0) represents the initial state of charge value, C rated is the rated capacity of the battery, I(t) is the current variable from the initial moment to the current moment, the charging state is positive, the discharging state is negative, t0 represents the initial moment, and t represents the current moment; The battery parameters of each battery cell collected are preprocessed. The preprocessing operation includes removing outliers and filling missing values ​​with the numerical mean after removing outliers. The Z-score of each data is calculated. When the absolute value of the Z-score of the data |Z|>3, the data is regarded as an outlier and removed.

3. The active balancing strategy optimization method of a battery management system according to claim 2 is characterized in that: The specific logic for creating fuzzy rules is: Set the fuzzy control output to a balanced strategy, the fuzzy set includes low, normal, and high, the voltage range is set to [2.5, 4.2], the unit is V, and the voltage membership function expression is as follows: Low Voltage: Normal voltage: High Voltage: Among them, μ Low (V), μ Normal (V), μ High (V) are the membership function expressions of voltage fuzzy sets low, normal and high respectively; The temperature range is set to [-20, 60], the unit is ℃, and the membership function expression of temperature is as follows: Low temperature: Normal temperature: high temperature: Among them, μ Low (T), μ Normal (T), μ High (T) are the membership function expressions of low, normal and high temperature fuzzy sets respectively; Set the SOC range to [0,100%], and the membership function expression of SOC is as follows: Low SOC: Normal SOC: High SOC: Among them, μ Low (SOC), μ Normal (SOC), μ High (SOC) are the membership function expressions of the fuzzy set of low, normal and high state of charge values ​​respectively; The fuzzy rules are as follows: Rule 1: If the voltage, temperature and SOC are normal, the balancing strategy is to maintain the current state; Rule 2: If the voltage, temperature and SOC are normal, the balancing strategy is to charge; Rule 3: If the voltage, temperature and SOC are high, the balancing strategy is to discharge; Rule 4: If the voltage, temperature and SOC are normal, the balancing strategy is to reduce charging; Rule 5: If the voltage, temperature and SOC are normal, the balancing strategy is to charge; Rule 6: If the voltage, temperature and SOC are high, the balancing strategy is to discharge; Rule 7: If the voltage, temperature and SOC are normal, the balancing strategy is to reduce charging; Rule 8: If the voltage is normal, the temperature is low, and the SOC is low, the balancing strategy is to discharge; Rule 9: If the voltage is normal, the temperature is low, and the SOC is normal, the balancing strategy is to charge; Rule 10: If the voltage is low, the temperature is normal, and the SOC is normal, the balancing strategy is to charge; Rule 11: If the voltage is low, the temperature is normal, and the SOC is high, the balancing strategy is to discharge; Rule 12: If the voltage is low, the temperature is normal, and the SOC is low, the balancing strategy is to charge; Rule 13: If the voltage is low, the temperature is high, and the SOC is normal, the balancing strategy is to reduce charging; Rule 14: If If the voltage is low, the temperature is high, and the SOC is high, the balancing strategy is to discharge; Rule 15: If the voltage is low, the temperature is high, and the SOC is low, the balancing strategy is to stop charging; Rule 16: If the voltage is low, the temperature is low, and the SOC is normal, the balancing strategy is to charge; Rule 17: If the voltage is low, the temperature is low, and the SOC is low, the balancing strategy is to charge; Rule 18: If the voltage is low, the temperature is low, and the SOC is high, the balancing strategy is to discharge; Rule 19: If the voltage is high, the temperature is normal, and the SOC is normal, the balancing strategy is to maintain the current state; Rule 20: If the voltage is high, the temperature is normal, and the SOC is low, the balancing strategy is to discharge; Rule 21: If the voltage is high, the temperature is normal, and the SOC is high, the balancing strategy is discharge; Rule 22: If the voltage is high, the temperature is high, and the SOC is normal, the balancing strategy is discharge; Rule 23: If the voltage is high, the temperature is high, and the SOC is high, the balancing strategy is discharge; Rule 24: If the voltage is high, the temperature is high, and the SOC is low, the balancing strategy is discharge; Rule 25: If the voltage is high, the temperature is low, and the SOC is high, the balancing strategy is discharge; Rule 26: If the voltage is high, the temperature is low, and the SOC is low, the balancing strategy is charge; Rule 27: If the voltage is high, the temperature is low, and the SOC is normal, the balancing strategy is charge; Through fuzzy rules, the rules corresponding to the voltage, current and charging state values ​​at the current moment are found, and their balancing strategies are executed.

4. The active balancing strategy optimization method of a battery management system according to claim 3 is characterized in that: The specific logic for calculating the actual balancing current is: The Mamdani fuzzy reasoning method is used to calculate the required balancing current for the single battery that needs to be charged or discharged. The formula is: in, is the balancing current required by the ith battery cell, k is the proportionality coefficient, V target is the target voltage of the battery cell, V i is the voltage of the i-th battery cell, i is the index of the battery cell, and i∈[1,N], and N is the number of battery cells; Combined with the battery temperature, the equalization current is corrected, and the formula for obtaining the temperature correction factor is: K T =K T0 +K T1 ×(T-T0) Among them, K T is the temperature correction factor, K T0 , K T1 is a constant related to temperature, T is the current temperature, and T0 is the reference temperature; The charging state value is corrected and a correction factor is set. The formula for obtaining the correction factor of the charging state value is: Among them, K SOC is the correction factor for the state of charge value, SOC nom is the ideal charging state value, SOC max The maximum state of charge of the battery, SOC min is the minimum charging state value; Taking into account the influence of temperature and state of charge, the formula for calculating the actual balancing current is: in, represents the actual balancing current of the ith battery cell; According to the calculated actual balancing current, determine the specific charging or discharging current. is positive, charging is performed. If it is negative, discharge occurs.

5. The method for optimizing active balancing strategy of a battery management system according to claim 4, characterized in that: The specific logic for calculating the balancing efficiency of the battery pack to periodically evaluate the balancing strategy is: After performing the balancing operation, monitor the voltage of the battery cell. If the voltage of the battery cell is within V target If the voltage is within the range of ±0.1v, the balance is considered successful, otherwise return to step 2 to adjust and re-balance; The battery pack status is evaluated every 24 hours, and the formula for obtaining input power is: Among them, E in (t3) is the input power of the battery pack at the end time t3 of the evaluation, P(t2, t3) is the power received by the battery pack from time t2 to t3, t2 is the start time of the evaluation, t3 is the end time of the evaluation, P(t2, t3) = V(t2, t3) × I(t2, t3), V(t2, t3) represents the voltage variable of the battery pack from time t2 to t3, I(t2, t3) represents the current variable of the battery pack from time t2 to t3; The formula for calculating the equilibrium efficiency is: Among them, E eff To evaluate the balancing efficiency of the battery pack, △t i is the duration of the evaluation process of the ith battery cell, E in It represents the input electrical energy during the evaluation process, in Watts; Compare the balancing efficiency of the battery pack with the preset efficiency threshold. If the balancing efficiency of the battery pack is higher than the preset efficiency threshold, no adjustment is required. If the balancing efficiency of the battery pack is lower than the preset efficiency threshold, it indicates that there is a problem with the current balancing strategy and it needs to be readjusted.

6. An active balancing strategy optimization system for a battery management system, characterized in that: The active balancing strategy optimization system of a battery management system is used to implement the active balancing strategy optimization method of a battery management system according to any one of claims 1 to 5, comprising: A battery parameter acquisition module is used to acquire battery parameters of each single battery in the battery pack and pre-process the battery parameter data, wherein the battery parameters include voltage, temperature and charging state value; The fuzzy logic rule definition module is used to take the pre-processed battery parameters as fuzzy input variables, define fuzzy sets, and create fuzzy rules. The input variables are converted into fuzzy outputs using fuzzy logic reasoning. Through the fuzzy rules, the balancing strategy is obtained as the fuzzy control output; The actual balancing current calculation module is used to use the fuzzy reasoning method to infer the state of the battery cell, obtain the balancing current demand of the battery cell, and calculate the actual balancing current in combination with the current temperature and charging state value of the battery cell; The balancing effect monitoring and evaluation module is used to monitor the voltage of the battery cells in real time after performing the balancing operation to determine the balancing effect, calculate the balancing efficiency of the battery pack and regularly evaluate the balancing strategy.

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