Battery energy optimization management method and system

Abstract

The invention provides a battery energy optimization management method and system, the method aims to realize energy balance between a battery monomer and a battery module, the method is suitable for a parallel battery module system composed of a plurality of series battery monomers, and the system can more effectively respond to the change of the battery state by dynamically adjusting the acquisition period, so that the battery energy optimization management efficiency is improved. The real-time performance and the accuracy of monitoring are improved; the implementation of energy balance is beneficial to preventing the single batteries from being overcharged or overdischarged, and the aging of the batteries is reduced, so that the service life of the batteries is prolonged; through an intelligent scheduling algorithm, the load of the battery module can be distributed more reasonably, and it is ensured that the batteries can operate efficiently under different working conditions; the dynamic temperature regulation and control strategy ensures that the battery can stably operate under different environmental conditions, the thermal runaway risk is reduced, and the safety of battery use is improved.

Description

Technical Field

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

[0002] With the rapid development of electric vehicles and renewable energy storage systems, the importance of battery management systems (BMS) has become increasingly prominent. Battery modules are usually composed of multiple battery cells connected in series or in parallel. The slight differences in these battery cells during the manufacturing process and environmental factors during use will cause their performance to gradually become unbalanced. This imbalance will not only affect the energy utilization of the entire battery module, but may also accelerate the aging of certain battery cells, thereby shortening the service life of the entire battery module. Therefore, how to effectively manage and maintain the energy balance between each battery cell in the battery module and the load balance between battery modules has become one of the current problems that need to be solved urgently.

[0003] Existing solutions often focus on single-level optimization, such as focusing only on improving the energy density of a single battery cell or enhancing the thermal management efficiency of the overall system. However, for complex multi-level structures, a more comprehensive and intelligent method is needed to achieve the global optimal energy distribution and control strategy. The present invention proposes a battery energy optimization management method and system based on this demand. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a battery energy optimization management method and system to solve the problem of single battery energy management in the prior art.

[0005] To achieve the above objectives and other related objectives, the present invention provides the following technical solutions:

[0006] A battery energy optimization management method is used for energy balance between battery cells and battery modules, wherein the battery module is composed of a plurality of battery cells connected in series and the plurality of battery modules are arranged in parallel, comprising the following steps:

[0007] S1, periodically collect the cell-level operating parameters of each battery cell, and dynamically adjust the collection period according to the collected cell-level operating parameters and a preset fluctuation threshold;

[0008] S2, calculating the state of charge and health state of the battery cells according to the cell-level operating parameters and the collection cycle, and calculating the energy transfer amount of the battery cells according to the balancing algorithm to balance the energy between the battery cells;

[0009] S3, scoring each battery module based on the state of charge and health status, and adjusting the load ratio of the battery module according to the intelligent scheduling algorithm to balance the load among the battery modules;

[0010] S4, in the process of battery cell energy balancing and battery module load balancing, the battery module is used as the temperature control unit, and the corresponding control scheme is selected according to the temperature control strategy based on the real-time collected battery module temperature data to dynamically control the temperature of the battery module.

[0011] To implement the above technical solution, the battery energy optimization management method realizes energy balance between battery cells and battery modules through four main steps: S1, regularly monitors the key operating parameters of each battery cell. Based on the comparison of these parameters with the preset fluctuation threshold, the system can intelligently adjust the frequency of data collection. If the parameter fluctuation is large, the collection cycle is shortened to more finely monitor the battery cell status; if the fluctuation is small, the collection cycle is extended to reduce the system burden; S2, based on the collected data, the system evaluates the state of charge (SOC) and state of health (SOH) of each battery cell, and applies a specific balancing algorithm to determine which battery cells need more energy and which need to release energy to ensure that the energy levels of all battery cells are as consistent as possible; S3, the system will conduct a comprehensive evaluation of each battery module composed of multiple battery cells connected in series and give a status score. Based on this scoring result, combined with the intelligent scheduling algorithm, the system can flexibly adjust the load ratio of each module to ensure that the load distribution of the entire battery module is reasonable, avoiding the situation where some battery modules are overused while other modules are idle; S4, considering the impact of temperature on battery performance, uses the battery module as the basic unit of temperature control, and takes corresponding cooling or insulation measures according to real-time temperature data to maintain the optimal operating temperature range and improve battery efficiency and life.

[0012] In one embodiment of the present invention, the S1 includes:

[0013] Collect the single-cell operating parameters of the battery cells, including voltage, current, temperature and power, and set fluctuation thresholds for each parameter;

[0014] Verify the validity and integrity of the collected unit-level operating parameters to ensure that the data is within the preset range and that all necessary parameters have been collected;

[0015] The initial collection cycle is set, and the collection cycle of the battery cell is dynamically adjusted according to the collected battery cell temperature, battery cell voltage and the corresponding fluctuation threshold.

[0016] To implement the above technical solution, firstly, the cell-level operating parameters of each battery cell are collected regularly, including but not limited to key indicators such as voltage, current, temperature and power, and corresponding fluctuation thresholds are set for each parameter. These thresholds are determined based on the safe operating range, performance requirements and historical data experience of the battery cell to define the limits of normal parameter changes; after the data is collected, the validity and integrity of these cell-level operating parameters will be verified. This step is to ensure that all necessary parameters have been collected accurately and these data are within the preset safety range. If any abnormal or missing data points are found, an alarm mechanism will be triggered or the data will be automatically re-collected. The system will set an initial collection cycle as the default monitoring frequency. Then, based on the comparison results between the real-time collected battery cell temperature, voltage and other parameters and their respective fluctuation thresholds, it is decided whether the next collection cycle needs to be shortened or extended. For example, when it is detected that the temperature or voltage is close to its fluctuation threshold, more frequent data collection will be selected to more closely track the changing trends of these key indicators; on the contrary, if the parameters are stable, the collection frequency will be appropriately relaxed to reduce unnecessary resource consumption.

[0017] In one embodiment of the present invention, the acquisition period is adjusted as follows:

[0018]

[0019] ΔV i，j =|V i，j -V i avg |

[0020] ΔT i，j =|T i，j -T i avg |

[0021] Among them, t i，j is the collection period of the jth battery cell of the i-th battery module, t min is the minimum acquisition period, t max is the maximum acquisition period set, k V is the voltage fluctuation coefficient, k T is the temperature fluctuation coefficient, ΔV i，j is the voltage fluctuation of the jth battery cell in the i-th battery module, ΔT i，j is the temperature fluctuation of the jth battery cell in the i-th module, ΔV thres is the voltage fluctuation threshold of the battery cell, ΔT thres is the temperature fluctuation threshold of the battery cell, V i，j is the measured voltage of the jth battery cell in the i-th module, V i avgis the average voltage of the ith battery module, T i，j is the measured temperature of the jth battery cell in the i-th module, T i avg is the average temperature of the i-th battery module.

[0022] To implement the above technical solution, It is used to adjust the acquisition cycle according to the fluctuation of the voltage and temperature of the battery cell. If the voltage or temperature fluctuates greatly (i.e., k T or k V The result of the exponential function will be smaller, resulting in a smaller acquisition period t i，j Close to the minimum value t min Thereby increasing the monitoring frequency, if the voltage and temperature fluctuations are small, the result of the exponential function will be close to 1, and the acquisition period t i，j will approach the maximum value t max , thereby reducing the monitoring frequency. By dynamically adjusting the acquisition cycle, the acquisition frequency can be increased when the parameters fluctuate greatly, ensuring that key changes are captured in a timely manner, helping to promptly discover and respond to potential problems, and improving monitoring accuracy and efficiency; reducing data acquisition when the battery status is stable, saving processor time and storage space, and optimizing system resource allocation.

[0023] In one embodiment of the present invention, the method for calculating the state of charge of the battery cells in S2 is as follows:

[0024]

[0025] in, is the updated state of charge of the jth battery cell of the i-th battery module, is the updated power of the jth battery cell of the i-th battery module, is the remaining power of the jth battery cell of the i-th battery module at the last moment, C nominal is the rated capacity of the battery cell, I i is the current of the ith battery module, t i，j is the collection period of the jth battery cell of the i-th battery module, and η is the Coulomb efficiency coefficient;

[0026] The method for calculating the health status of the battery cell is as follows:

[0027]

[0028] in, is the health status of the jth battery cell of the i-th battery module after update, k1 is the attenuation coefficient related to the charge and discharge cycle, k2 is the attenuation coefficient related to the usage time, t useis the usage time of the jth battery cell of the i-th battery module, k3 is the error coefficient caused by the acquisition cycle, t i，j is the collection period of the jth battery cell of the i-th battery module.

[0029] The above technical solution is implemented to ensure that the SOC calculation of the battery cell is more accurate by updating the power and charge state of the battery cell in real time, thereby better managing the battery energy, dynamically adjusting the collection cycle and Coulomb efficiency coefficient in the power calculation, and improving the accuracy of the SOC calculation; by comprehensively considering the charge and discharge cycle, usage time and collection cycle error, the SOH calculation is more accurate, which helps to timely discover changes in the battery health status, and to carry out refined management of each battery cell, thereby improving operating efficiency and reliability.

[0030] In one embodiment of the present invention, the balancing algorithm is:

[0031]

[0032] in, The final energy transfer amount of the jth battery cell in the i-th battery module, is the energy transfer amount of the jth battery cell of the i-th battery module based on the state of charge difference, is the adjustment factor of the jth battery cell of the i-th battery module based on the health status, is the temperature factor of the jth battery cell in the i-th battery module, is the updated state of charge of the jth battery cell of the i-th battery module, is the average state of charge of the ith battery module, maxSOC diff is the maximum value of the difference in state of charge of the battery cells, is the updated health status of the jth battery cell of the i-th battery module, is the average health status of the ith battery module, T i，j is the measured temperature of the jth battery cell in the i-th module, T i avg is the average temperature of the ith battery module, and ω is the temperature sensitivity coefficient.

[0033] The above technical solution is implemented by comprehensively considering the difference in state of charge, health state and temperature to ensure that the calculation of energy transfer is more accurate, thereby achieving energy balance between battery cells, and adjusting factors based on health state. Ensures that unhealthy cells are not overcharged or overdischarged, thus extending battery life, temperature-based adjustment factor Ensure that battery cells with higher temperatures reduce energy transfer and avoid performance degradation and safety hazards caused by overheating. The introduction of the temperature sensitivity coefficient ω enables the system to better adapt to different temperature environments and ensure that the battery operates within a safe range.

[0034] In one embodiment of the present invention, the step S3 includes:

[0035] Determine the module-level operating parameters, and use the state assessment algorithm to score the state of each battery module to obtain the state assessment factor;

[0036] Sort the battery modules according to the status assessment factors from large to small, and distribute the load in the order of sorting;

[0037] Combine the status assessment factor of each battery module and calculate the load ratio of each battery module according to the intelligent scheduling algorithm.

[0038] To implement the above technical solution, it is first necessary to determine the working parameters of each battery module, which include but are not limited to the average temperature, average current, state of charge, health status, etc. of the battery module, and use the state evaluation algorithm to comprehensively analyze the working parameters of each battery module to calculate a state evaluation factor that can reflect the current health and performance level of the module; according to the calculated state evaluation factor, all battery modules are sorted in descending order, and battery modules with higher state evaluation factors are considered to have better performance and healthier conditions. According to the sorting from large to small, more loads are preferentially allocated to battery modules with higher state evaluation factors to ensure that those battery modules with better performance and healthier conditions bear a larger workload; combined with the state evaluation factor of each battery module, the system uses an intelligent scheduling algorithm to dynamically calculate and adjust the load ratio between modules, so that the load distribution is both reasonable and efficient, while ensuring that each module operates within its optimal working range to avoid overloading of some modules or idleness of other modules.

[0039] In one embodiment of the present invention, the state evaluation algorithm is as follows:

[0040]

[0041] Among them, F i is the status assessment factor of the i-th battery module, I i is the current of the ith battery module, I avg is the average current of all battery modules, T i avg is the average temperature of the ith battery module, T avg is the average temperature of all battery modules, is the average state of charge of the ith battery module, SOC avgis the average state of charge of all battery modules, is the average health status of the ith battery module, SOH avg is the average health status of all battery modules, n is the number of battery cells in the i-th battery module, and α, β, γ, and δ are weight coefficients.

[0042] To implement the above technical solution, the state assessment factor F of each battery module is calculated using the state assessment algorithm. i ,The algorithm comprehensively considers the deviation of current, temperature, state of charge and state of health, and each parameter is ,normalized; is the current deviation, indicating the working intensity of the battery module Temperature deviation is a key indicator for battery thermal management; The consistency of energy levels is shown for SOC deviation; It measures the degree of aging of the battery module. By calculating the status assessment factor, the system can accurately assess the health and performance status of each battery module, thereby reasonably distributing the load and improving the operating efficiency of the overall system.

[0043] In one embodiment of the present invention, the intelligent scheduling algorithm is as follows:

[0044]

[0045] Among them, L i is the load ratio of the i-th battery module, is the sum of the inverses of the load ratios of all battery modules, and m is the number of all battery modules.

[0046] To implement the above technical solution, the status evaluation factor F of each battery module is used. i To calculate the load ratio L i , distribute the total load to each battery module in proportion; and the sum of the load distribution is 1, ensuring that the sum of the load proportions of all modules is 1; according to the calculated load proportion L i , distribute the actual load to each battery module to achieve load balancing and reduce the problem of premature aging of some modules due to excessive use.

[0047] In one embodiment of the present invention, the S4 includes:

[0048] Treat each battery module as a temperature control unit and continuously collect the temperature data T of the battery module. mod , and select a temperature control strategy based on a preset temperature control threshold; wherein the temperature control threshold includes: a low temperature control value T warn，low , high temperature control value T warn，high , Low temperature safety value T safe，low , high temperature safety value Tsafe，high , and T safe，low <T warn，low <T warn，high <T safe，high ;

[0049] The temperature control strategy includes:

[0050] If T warn，low <T mod <T warn，high , no temperature control measures are taken;

[0051] If T safe，low <T mod <T warn，low , then start heating measures for the battery module until the temperature of the battery module rises to a normal range;

[0052] If T warn，high <T mod <T safe，high , then start cooling measures for the battery module and reduce the charge and discharge power of the battery module until the temperature of the battery module drops to a normal range;

[0053] If T mod <T safe，low , the charging and discharging functions of the battery module are turned off, heating measures are started for the battery module, and a low temperature alarm is issued;

[0054] If T mod >T safe，high , the charging and discharging functions of all battery modules are shut down, all cooling measures are activated, and a serious alarm is issued.

[0055] The above technical solution is implemented by real-time monitoring and regulation of the temperature of the battery module to ensure that the battery operates within the optimal temperature range. By preventing the battery module from operating at extreme temperatures, the risk of battery aging and damage is reduced. By setting low and high temperature alarm thresholds, temperature abnormalities can be detected and handled in a timely manner to avoid safety problems caused by excessively high or low temperatures in the battery module.

[0056] In another embodiment of the present invention, a battery energy optimization management system is provided, comprising:

[0057] Data acquisition module, used to collect battery cell-level operating data and set fluctuation thresholds for each parameter;

[0058] A dynamic adjustment module is used to dynamically adjust the data collection cycle according to the collected single-unit level working parameters;

[0059] An energy transfer calculation module is used to calculate the state of charge and health state of the battery cell based on the collected single-cell level operating parameters and collection cycle, and set a balancing algorithm to calculate the energy transfer amount of the battery cell;

[0060] Intelligent scheduling module, used to score the status of each battery module and adjust the load ratio of the battery module according to the intelligent scheduling algorithm;

[0061] The temperature control module is used to treat each battery module as a temperature control unit and control the temperature of the battery module through a temperature control strategy;

[0062] The abnormality monitoring module is used to monitor the key parameters of battery cells and battery modules in real time, and record log information when an abnormality is detected. The log information includes: abnormal time, abnormal type and abnormal value.

[0063] To implement the above technical solution, the data acquisition module is responsible for collecting the key working parameters of the battery cells in real time and setting fluctuation thresholds for each parameter in order to identify and respond to abnormal conditions; the dynamic adjustment module is used to optimize the frequency of data acquisition to adapt to changes in the battery state; the energy transfer calculation module calculates the charge state and health state of the battery cells, and uses a balancing algorithm to determine which batteries need more energy and which need to release energy to ensure that the energy levels of all battery cells are as consistent as possible; the intelligent scheduling module conducts a comprehensive evaluation of each battery module based on the charge state and health state, gives a status score, and flexibly adjusts the load ratio of each module according to the intelligent scheduling algorithm to ensure that the load distribution of the entire battery system is reasonable; the temperature control module collects the temperature data of each battery module in real time, and takes corresponding heating or cooling measures according to the preset temperature control strategy to maintain the optimal operating temperature range of the battery; the abnormal monitoring module monitors the key parameters of the battery cells and battery modules in real time, records log information when an abnormal situation is detected, triggers an alarm mechanism, and notifies relevant personnel to handle it.

[0064] As described above, the battery energy optimization management method and system of the present invention have the following beneficial effects:

[0065] Dynamically adjust the collection cycle: periodically monitor the working status of each battery cell and flexibly adjust the data collection frequency according to the preset threshold.

[0066] Refined energy balancing mechanism: The balancing algorithm used is used to adjust the energy of each battery cell accurately to the cell level, ensuring that the battery cells in the battery module are in the best working condition and extending the overall service life.

[0067] Intelligent load distribution: By scoring the status of different battery modules and combining them with intelligent scheduling algorithms to rationally plan the load proportion borne by each part, the entire system can minimize losses and improve efficiency while meeting user needs.

[0068] Adaptive temperature control: The battery module is regarded as a temperature monitoring and management unit, and the cooling or heating scheme that best suits the current environmental conditions is automatically selected based on real-time data, effectively preventing performance degradation or even safety hazards caused by overheating or overcooling. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Shown is a schematic diagram of the battery energy optimization management method of the present invention. DETAILED DESCRIPTION

[0070] The following specific embodiments illustrate the implementation of the present invention, and those familiar with the technology can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0071] See also Figure 1 The present invention provides a battery energy optimization management method for energy balance between battery cells and battery modules, wherein the battery module is composed of a plurality of battery cells connected in series and the plurality of battery modules are arranged in parallel, and comprises the following steps: S1, periodically collecting the single-cell-level operating parameters of each battery cell, and dynamically adjusting the collection period according to the collected single-cell-level operating parameters and a preset fluctuation threshold; S2, calculating the state of charge and health state of the battery cell according to the single-cell-level operating parameters and the collection period, and calculating the energy transfer amount of the battery cell according to a balancing algorithm, so as to achieve energy balance between the battery cells; S3, scoring each battery module based on the state of charge and health state, and adjusting the load ratio of the battery module according to an intelligent scheduling algorithm, so as to achieve load balance between the battery modules; S4, using the battery module as a temperature control unit in the process of battery cell energy balance and battery module load balance, and selecting a corresponding control scheme based on the real-time collected battery module temperature data according to the temperature control strategy to dynamically control the temperature of the battery module.

[0072] The battery energy optimization management method achieves energy balance between battery cells and battery modules through four main steps: S1, regularly monitor the key operating parameters of each battery cell. Based on the comparison of these parameters with the preset fluctuation threshold, the system can intelligently adjust the frequency of data collection. If the parameter fluctuation is large, the collection cycle is shortened to more finely monitor the battery cell status; if the fluctuation is small, the collection cycle is extended to reduce the system burden; S2, based on the collected data, the system evaluates the state of charge (SOC) and state of health (SOH) of each battery cell, and applies a specific balancing algorithm to determine which battery cells need more energy and which need to release energy to ensure that the energy levels of all battery cells are as consistent as possible; S3, the system will comprehensively evaluate each battery module composed of multiple battery cells in series and give a status score. Based on this scoring result, combined with the intelligent scheduling algorithm, the system can flexibly adjust the load ratio of each module to ensure that the load distribution of the entire battery module is reasonable, avoiding the situation where some battery modules are overused while other modules are idle; S4, considering the impact of temperature on battery performance, uses the battery module as the basic unit of temperature control, and takes corresponding cooling or insulation measures according to real-time temperature data to maintain the optimal operating temperature range and improve battery efficiency and life.

[0073] By dynamically adjusting the acquisition cycle, this method enables the system to more effectively respond to changes in the battery state and improve the real-time and accuracy of monitoring; the implementation of energy balancing helps prevent overcharging or over-discharging of battery cells, reduces battery aging, and thus extends the battery life; through the intelligent scheduling algorithm, the load of the battery module can be more reasonably distributed, ensuring that the battery can maintain efficient operation under different working conditions; the temperature dynamic control strategy ensures that the battery can operate stably under different environmental conditions, reduces the risk of thermal runaway, and improves the safety of battery use.

[0074] The S1 includes: collecting the single-cell level operating parameters of the battery single cell, including voltage, current, temperature and power, and setting a fluctuation threshold for each parameter; verifying the validity and integrity of the collected single-cell level operating parameters to ensure that the data is within a preset range and that all necessary parameters have been collected; setting an initial collection cycle, and dynamically adjusting the battery single cell collection cycle according to the collected battery single cell temperature, battery single cell voltage and the corresponding fluctuation threshold.

[0075] First, the single-cell operating parameters of each battery cell are regularly collected, including but not limited to key indicators such as voltage, current, temperature and power, and corresponding fluctuation thresholds are set for each parameter. These thresholds are determined based on the safe operating range, performance requirements and historical data experience of the battery cell, and are used to define the boundaries of normal parameter changes; after the data is collected, the validity and integrity of these single-cell operating parameters will be verified. This step is intended to ensure that all necessary parameters have been accurately collected and that these data are within the preset safety range. If any abnormal or missing data points are found, an alarm mechanism will be triggered or the data will be automatically re-collected.

[0076] The system sets an initial collection cycle as the default monitoring frequency. Then, based on the comparison between the real-time collected battery cell temperature, voltage and other parameters and their respective fluctuation thresholds, it decides whether to shorten or extend the next collection cycle. For example, when it is detected that the temperature or voltage is close to its fluctuation threshold, it will choose to collect data more frequently in order to more closely track the changing trends of these key indicators; conversely, if the parameters are stable, the collection frequency will be appropriately relaxed to reduce unnecessary resource consumption.

[0077] The acquisition cycle is adjusted as follows:

[0078]

[0079] ΔV i，j =|V i，j -V i avg |

[0080] ΔT i，j =|T i，j -T i avg | Among them, t i，j is the collection period of the jth battery cell of the i-th battery module, t min is the minimum acquisition period, t max is the maximum acquisition period set, k V is the voltage fluctuation coefficient, k T is the temperature fluctuation coefficient, ΔV i，j is the voltage fluctuation of the jth battery cell in the i-th battery module, ΔT i，j is the temperature fluctuation of the jth battery cell in the i-th module, ΔV thres is the voltage fluctuation threshold of the battery cell, ΔT thres is the temperature fluctuation threshold of the battery cell, V i，j is the measured voltage of the jth battery cell in the i-th module, V i avg is the average voltage of the ith battery module, Ti，j is the measured temperature of the jth battery cell in the i-th module, T i avg is the average temperature of the i-th battery module.

[0081] It is used to adjust the acquisition cycle according to the fluctuation of the voltage and temperature of the battery cell. If the voltage or temperature fluctuates greatly (i.e., k T or k V The result of the exponential function will be smaller, resulting in a smaller acquisition period t i，j Close to the minimum value t min Thereby increasing the monitoring frequency, if the voltage and temperature fluctuations are small, the result of the exponential function will be close to 1, and the acquisition period t i，j will approach the maximum value t max , thereby reducing the monitoring frequency. By dynamically adjusting the acquisition cycle, the acquisition frequency can be increased when the parameters fluctuate greatly, ensuring that key changes are captured in a timely manner, helping to promptly discover and respond to potential problems, and improving monitoring accuracy and efficiency; reducing data acquisition when the battery status is stable, saving processor time and storage space, and optimizing system resource allocation.

[0082] The method for calculating the state of charge of the battery cell is as follows:

[0083]

[0084] in, is the updated state of charge of the jth battery cell of the i-th battery module, is the updated power of the jth battery cell of the i-th battery module, is the remaining power of the jth battery cell of the i-th battery module at the last moment, C nominal is the rated capacity of the battery cell, I i is the current of the ith battery module, t i，j is the collection period of the jth battery cell of the i-th battery module, and η is the Coulomb efficiency coefficient;

[0085] The method for calculating the health status of the battery cell is as follows:

[0086]

[0087] in, is the health status of the jth battery cell of the i-th battery module after update, k1 is the attenuation coefficient related to the charge and discharge cycle, k2 is the attenuation coefficient related to the usage time, t use is the usage time of the jth battery cell of the i-th battery module, k3 is the error coefficient caused by the acquisition cycle, t i，jis the collection period of the jth battery cell of the i-th battery module.

[0088] By updating the power and state of charge of battery cells in real time, the SOC calculation of battery cells is ensured to be more accurate, thereby better managing battery energy, dynamically adjusting the collection cycle and Coulomb efficiency coefficient in the power calculation, and improving the accuracy of SOC calculation; by comprehensively considering the charge and discharge cycle, usage time and collection cycle error, SOH calculation is more accurate, which helps to timely discover changes in battery health status, and conduct refined management of each battery cell to improve operating efficiency and reliability.

[0089] The balancing algorithm is:

[0090]

[0091]

[0092] in, The final energy transfer amount of the jth battery cell in the i-th battery module, is the energy transfer amount of the jth battery cell of the i-th battery module based on the state of charge difference, is the adjustment factor of the jth battery cell of the i-th battery module based on the health status, is the temperature factor of the jth battery cell in the i-th battery module, is the updated state of charge of the jth battery cell of the i-th battery module, is the average state of charge of the ith battery module, maxSOC diff is the maximum value of the difference in state of charge of the battery cells, is the updated health status of the jth battery cell of the i-th battery module, is the average health status of the ith battery module, T i，j is the measured temperature of the jth battery cell in the i-th module, T i avg is the average temperature of the ith battery module, and ω is the temperature sensitivity coefficient.

[0093] By comprehensively considering the difference in state of charge, health status and temperature, the calculation of energy transfer is more accurate, thereby achieving energy balance between battery cells, and the adjustment factor based on health status Ensures that unhealthy cells are not overcharged or overdischarged, thus extending battery life, temperature-based adjustment factor Ensure that battery cells with higher temperatures reduce energy transfer and avoid performance degradation and safety hazards caused by overheating. The introduction of the temperature sensitivity coefficient ω enables the system to better adapt to different temperature environments and ensure that the battery operates within a safe range.

[0094] The S3 includes: determining module-level operating parameters, scoring each battery module by a state evaluation algorithm to obtain a state evaluation factor; sorting the battery modules from large to small according to the state evaluation factors, and distributing the load in the order of sorting; and calculating the load ratio of each battery module in combination with the state evaluation factor of each battery module and according to the intelligent scheduling algorithm.

[0095] First, it is necessary to determine the working parameters of each battery module, which include but are not limited to the average temperature, average current, state of charge, and health status of the battery module. The working parameters of each battery module are comprehensively analyzed using the state assessment algorithm to calculate a state assessment factor that can reflect the current health and performance level of the module. According to the calculated state assessment factor, all battery modules are sorted from high to low. Battery modules with higher state assessment factors are considered to have better performance and healthier conditions. According to the sorting from large to small, more loads are preferentially allocated to battery modules with higher state assessment factors to ensure that those battery modules with better performance and healthier conditions bear a larger workload. Combined with the state assessment factor of each battery module, the system uses an intelligent scheduling algorithm to dynamically calculate and adjust the load ratio between modules, so that the load distribution is both reasonable and efficient, while ensuring that each module operates within its optimal working range to avoid overloading of some modules or idleness of other modules.

[0096] The state evaluation algorithm is as follows:

[0097]

[0098]

[0099] Among them, F i is the status assessment factor of the i-th battery module, I i is the current of the ith battery module, I avg is the average current of all battery modules, T i avg is the average temperature of the ith battery module, T avg is the average temperature of all battery modules, is the average state of charge of the ith battery module, SOC avg is the average state of charge of all battery modules, is the average health status of the ith battery module, SOH avg is the average health status of all battery modules, n is the number of battery cells in the i-th battery module, and α, β, γ, and δ are weight coefficients.

[0100] The state assessment factor F of each battery module is calculated using the state assessment algorithm i ,The algorithm comprehensively considers the deviation of current, temperature, state of charge and state of health, and each parameter is ,normalized; is the current deviation, which indicates the working intensity of the battery module; Temperature deviation is a key indicator for battery thermal management; The consistency of energy levels is shown for SOC deviation; It measures the degree of aging of the battery module. By calculating the status assessment factor, the system can accurately assess the health and performance status of each battery module, thereby reasonably distributing the load and improving the operating efficiency of the overall system.

[0101] The intelligent scheduling algorithm is as follows:

[0102]

[0103] Among them, L i is the load ratio of the i-th battery module, is the sum of the inverses of the load ratios of all battery modules, and m is the number of all battery modules.

[0104] The condition evaluation factor F of each battery module i To calculate the load ratio L i , distribute the total load to each battery module in proportion; and the sum of the load distribution is 1, ensuring that the sum of the load proportions of all modules is 1; according to the calculated load proportion L i , distribute the actual load to each battery module to achieve load balancing and reduce the problem of premature aging of some modules due to excessive use.

[0105] S4 includes: taking each battery module as a temperature control unit and continuously collecting the temperature data T of the battery module. mod , and select a temperature control strategy based on a preset temperature control threshold; wherein the temperature control threshold includes: a low temperature control value T warn，low , high temperature control value T warn，high , Low temperature safety value T safe，low , high temperature safety value T safe，high , and T safe，low <T warn，low <T warn，high <T safe，high ;

[0106] The temperature control strategy includes: if T warn，low <T mod <T warn，high , no temperature control measures are taken; if T safe，low <T mod<T warn，low , then start heating measures for the battery module until the temperature of the battery module rises to the normal range; if T warn，high <T mod <T safe，high , then start cooling measures for the battery module and reduce the charge and discharge power of the battery module until the temperature of the battery module drops to the normal range; if T mod <T safe，low , the charging and discharging functions of the battery module are turned off, heating measures are started for the battery module, and a low temperature alarm is issued; if T mod <T safe，high , the charging and discharging functions of all battery modules are shut down, all cooling measures are activated, and a serious alarm is issued.

[0107] By real-time monitoring and regulating the temperature of the battery module, we ensure that the battery operates within the optimal temperature range. By preventing the battery module from operating at extreme temperatures, we reduce the risk of battery aging and damage. By setting low and high temperature alarm thresholds, we can promptly detect and handle temperature abnormalities and avoid safety issues caused by excessively high or low temperatures in the battery module.

[0108] In another embodiment of the present invention, a battery energy optimization management system is provided, including: a data acquisition module, which is used to collect battery cell-level working data and set a fluctuation threshold for each parameter; a dynamic adjustment module, which is used to dynamically adjust the data acquisition cycle according to the collected single-cell-level working parameters; an energy transfer calculation module, which is used to calculate the charge state and health state of the battery cell based on the collected single-cell-level working parameters and the acquisition cycle, and set a balancing algorithm to calculate the energy transfer amount of the battery cell; an intelligent scheduling module, which is used to score the status of each battery module and adjust the load ratio of the battery module according to the intelligent scheduling algorithm; a temperature control module, which is used to treat each battery module as a temperature control unit and control the temperature of the battery module through a temperature control strategy; an abnormality monitoring module, which is used to monitor the key parameters of the battery cell and the battery module in real time, and record log information when an abnormal situation is detected, and the log information includes: abnormal time, abnormal type and abnormal value.

[0109] The data acquisition module is responsible for collecting the key working parameters of battery cells in real time and setting fluctuation thresholds for each parameter in order to identify and respond to abnormal conditions; the dynamic adjustment module is used to optimize the frequency of data acquisition to adapt to changes in battery status; the energy transfer calculation module calculates the state of charge and health status of battery cells, and uses a balancing algorithm to determine which batteries need more energy and which need to release energy to ensure that the energy levels of all battery cells are as consistent as possible; the intelligent scheduling module conducts a comprehensive evaluation of each battery module based on the state of charge and health status, gives a status score, and flexibly adjusts the load ratio of each module according to the intelligent scheduling algorithm to ensure that the load distribution of the entire battery system is reasonable; the temperature control module collects the temperature data of each battery module in real time, and takes corresponding heating or cooling measures according to the preset temperature control strategy to maintain the optimal operating temperature range of the battery; the abnormal monitoring module monitors the key parameters of battery cells and battery modules in real time, records log information when an abnormal situation is detected, triggers the alarm mechanism, and notifies relevant personnel to handle it.

[0110] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. All equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A battery energy optimization management method for energy balance between battery cells and battery modules, wherein the battery module is composed of a plurality of battery cells connected in series and a plurality of battery modules are arranged in parallel, characterized in that: The following steps are involved: S1, periodically collect the cell-level operating parameters of each battery cell, and dynamically adjust the collection period according to the collected cell-level operating parameters and a preset fluctuation threshold; S2, calculating the state of charge and health state of the battery cells according to the cell-level operating parameters and the collection cycle, and calculating the energy transfer amount of the battery cells according to the balancing algorithm to balance the energy between the battery cells; S3, scoring each battery module based on the state of charge and health status, and adjusting the load ratio of the battery module according to the intelligent scheduling algorithm to balance the load among the battery modules; S4, in the process of battery cell energy balancing and battery module load balancing, the battery module is used as the temperature control unit, and the corresponding control scheme is selected according to the temperature control strategy based on the real-time collected battery module temperature data to dynamically control the temperature of the battery module.

2. The battery energy optimization management method according to claim 1, characterized in that: The S1 includes: Collect the single-cell operating parameters of the battery cells, including voltage, current, temperature and power, and set fluctuation thresholds for each parameter; Verify the validity and integrity of the collected unit-level operating parameters to ensure that the data is within the preset range and that all necessary parameters have been collected; The initial collection cycle is set, and the collection cycle of the battery cell is dynamically adjusted according to the collected battery cell temperature, battery cell voltage and the corresponding fluctuation threshold.

3. The battery energy optimization management method according to claim 2, characterized in that: The acquisition cycle is adjusted as follows: Among them, t i，j is the collection period of the jth battery cell of the i-th battery module, t min is the minimum acquisition period, t max is the maximum acquisition period set, k V is the voltage fluctuation coefficient, k T is the temperature fluctuation coefficient, ΔV i，j is the voltage fluctuation of the jth battery cell in the i-th battery module, ΔT i，j is the temperature fluctuation of the jth battery cell in the i-th module, ΔV thres is the voltage fluctuation threshold of the battery cell, ΔT thres is the temperature fluctuation threshold of the battery cell, V i，j is the measured voltage of the jth battery cell in the i-th module, is the average voltage of the ith battery module, T i，j is the measured temperature of the jth battery cell in the i-th module, is the average temperature of the i-th battery module.

4. The battery energy optimization management method according to claim 3, characterized in that: The method for calculating the state of charge of the battery cell in S2 is as follows: in, is the updated state of charge of the th battery cell of the ith battery module, is the updated power of the jth battery cell of the i-th battery module, is the remaining power of the ith battery cell of the ith battery module at the last moment, C nominal is the rated capacity of the battery cell, I i is the current of the ith battery module, t i，j is the collection period of the th battery cell of the ith battery module, and η is the Coulomb efficiency coefficient; The method for calculating the health status of the battery cell is as follows: in, is the health status of the jth battery cell of the i-th battery module after update, k1 is the attenuation coefficient related to the charge and discharge cycle, k2 is the attenuation coefficient related to the usage time, t use is the usage time of the jth battery cell of the ith battery module, k3 is the error coefficient caused by the acquisition cycle, t i，j is the collection period of the jth battery cell of the i-th battery module.

5. The battery energy optimization management method according to claim 4, characterized in that: The balancing algorithm is: in, The final energy transfer amount of the jth battery cell of the i-th battery module, is the energy transfer amount of the ith battery cell of the ith battery module based on the state of charge difference, is the adjustment factor of the jth battery cell of the i-th battery module based on the health status, is the temperature factor of the jth battery cell in the i-th battery module, is the updated state of charge of the jth battery cell of the i-th battery module, is the average state of charge of the ith battery module, maxSOC diff is the maximum value of the difference in state of charge of the battery cells, is the updated health status of the th battery cell of the ith battery module, is the average health status of the ith battery module, T i，j is the measured temperature of the jth battery cell in the i-th module, is the average temperature of the ith battery module, and ω is the temperature sensitivity coefficient.

6. The battery energy optimization management method according to claim 5, characterized in that: The S3 includes: Determine the module-level operating parameters, and use the state assessment algorithm to score the state of each battery module to obtain the state assessment factor; Sort the battery modules according to the status assessment factors from large to small, and distribute the load in the order of sorting; Combine the status assessment factor of each battery module and calculate the load ratio of each battery module according to the intelligent scheduling algorithm.

7. The battery energy optimization management method according to claim 6, characterized in that: The state evaluation algorithm is as follows: Among them, F i is the status assessment factor of the i-th battery module, I i is the current of the ith battery module, I avg is the average current of all battery modules, is the average temperature of the ith battery module, T avg is the average temperature of all battery modules, is the average state of charge of the ith battery module, SOC avg is the average state of charge of all battery modules, is the average health status of the ith battery module, SOH avg is the average health status of all battery modules, n is the number of battery cells in the i-th battery module, and α, β, γ, and δ are weight coefficients.

8. The battery energy optimization management method according to claim 7, characterized in that: The intelligent scheduling algorithm is as follows: Among them, L i is the load ratio of the i-th battery module, is the sum of the inverses of the load ratios of all battery modules, and m is the number of all battery modules.

9. The battery energy optimization management method according to claim 1, characterized in that: The S4 includes: Treat each battery module as a temperature control unit and continuously collect the temperature data T of the battery module. mod , and select a temperature control strategy based on a preset temperature control threshold; wherein the temperature control threshold includes: a low temperature control value T warn，low , high temperature control value T war，high , Low temperature safety value T safe，low , high temperature safety value T safe，high , and T safe，low <T warn，low <T warn，high <T safe，high ; The temperature control strategy includes: If T warn，low <T mod <T warn , high , no temperature control measures are taken; If T safe，low <T mod <T warn，low , then start heating measures for the battery module until the temperature of the battery module rises to a normal range; If T warn，high <T mod <T safe，high , then start cooling measures for the battery module and reduce the charge and discharge power of the battery module until the temperature of the battery module drops to a normal range; If T mod <T safe，low , the charging and discharging functions of the battery module are turned off, heating measures are started for the battery module, and a low temperature alarm is issued; If T mod >T safe，high , the charging and discharging functions of all battery modules are shut down, all cooling measures are activated, and a serious alarm is issued.

10. A system for implementing the method according to any one of claims 1 to 9, characterized in that: include: Data acquisition module, used to collect battery cell-level operating data and set fluctuation thresholds for each parameter; A dynamic adjustment module is used to dynamically adjust the data collection cycle according to the collected single-unit level working parameters; An energy transfer calculation module is used to calculate the state of charge and health state of the battery cell based on the collected single-cell level operating parameters and collection cycle, and set a balancing algorithm to calculate the energy transfer amount of the battery cell; Intelligent scheduling module, used to score the status of each battery module and adjust the load ratio of the battery module according to the intelligent scheduling algorithm; The temperature control module is used to treat each battery module as a temperature control unit and control the temperature of the battery module through a temperature control strategy; The abnormality monitoring module is used to monitor the key parameters of battery cells and battery modules in real time, and record log information when an abnormality is detected. The log information includes: abnormal time, abnormal type and abnormal value.

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