Battery energy optimization management method and system

Abstract

The present invention provides a battery energy optimization management method and system, which aims to achieve energy balance between battery cells and battery modules. The method is applicable to a parallel battery module system composed of multiple battery cells connected in series. By dynamically adjusting the acquisition cycle, the system can more effectively respond to changes in battery status and improve the real-time and accuracy of monitoring; the implementation of energy balance helps to prevent overcharging or over-discharging of battery cells, reduce battery aging, and thus extend the service life of the battery; through an 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 dynamic temperature 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.

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 typically consist of multiple battery cells connected in series or parallel. Minor differences in the manufacturing process and environmental factors during use can lead to gradual performance imbalances. This imbalance not only affects the energy utilization of the entire battery module but can also accelerate the aging of certain battery cells, thereby shortening the service life of the entire battery module. Therefore, effectively managing and maintaining energy balance between battery cells within a battery module and load balancing across modules has become a pressing issue.

[0003] Existing solutions often focus on single-level optimization, such as increasing 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 approach is needed to achieve a globally optimal energy distribution and control strategy. This present invention addresses this need by proposing a battery energy optimization management method and system. 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-mentioned and other related purposes, the present invention provides the following technical solutions:

[0006] A battery energy optimization management method for balancing energy between battery cells and between battery modules, wherein the battery module is composed of multiple battery cells connected in series and multiple 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 of the battery cells based on the cell-level operating parameters and the acquisition cycle, and calculating the energy transfer amount of the battery cells according to the balancing algorithm to achieve energy balance 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 achieve load balance 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 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.

[0011] To implement the above technical solution, the battery energy optimization management method achieves 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 comprehensively evaluate each battery module composed of multiple battery cells connected in series and give a status score. Based on this scoring result, combined with an intelligent scheduling algorithm, the system can flexibly adjust the load ratio of each module to ensure a reasonable load distribution for the entire battery module, avoiding situations where some battery modules are overused while others are idle; S4, taking into account 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 based on 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 single-cell operating parameters of battery cells, including voltage, current, temperature, and power, and set fluctuation thresholds for each parameter;

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

[0015] Set the initial collection cycle and dynamically adjust the battery cell collection cycle according to the collected battery cell temperature, battery cell voltage and the corresponding fluctuation threshold.

[0016] To implement the above technical solution, each battery cell's cell-level operating parameters, including but not limited to key indicators such as voltage, current, temperature, and charge, are regularly collected. Fluctuation thresholds are set for each parameter. These thresholds are determined based on the battery cell's safe operating range, performance requirements, and historical data experience to define the limits of normal parameter fluctuations. After data collection, the validity and integrity of these cell-level operating parameters are verified. This step ensures that all necessary parameters have been accurately collected and are within pre-set safety limits. If any abnormal or missing data points are detected, an alarm mechanism is triggered or data is automatically recollected. The system sets an initial collection cycle as the default monitoring frequency. Then, based on the comparison of real-time collected battery cell parameters such as temperature and voltage with their respective fluctuation thresholds, a decision is made as to whether to shorten or lengthen the next collection cycle. For example, if the temperature or voltage is detected approaching its fluctuation threshold, data collection is performed more frequently to more closely track the changing trends of these key indicators. Conversely, if the parameters are stable, the collection frequency is 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 set 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 i-th 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 battery cell voltage and temperature. If the voltage or temperature fluctuation is large (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 Thus, the monitoring frequency is increased. 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 By dynamically adjusting the acquisition cycle, the acquisition frequency can be increased when parameter fluctuations are large, ensuring timely capture of key changes. This helps to promptly identify and respond to potential problems, improving monitoring accuracy and efficiency. When the battery status is stable, data acquisition is reduced, 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 in the i-th battery module, is the updated power of the jth battery cell in the i-th battery module, is the remaining capacity 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 i-th 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 health status calculation method of the battery cell is as follows:

[0027]

[0028] in, is the updated health status of the jth battery cell of the i-th battery module, 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 in the i-th battery module.

[0029] The above technical solution is implemented to ensure more accurate SOC calculation of battery cells by updating the power level and state of charge of battery cells in real time, thereby better managing battery energy and dynamically adjusting the acquisition cycle and Coulomb efficiency coefficient in power calculation, thereby improving the accuracy of SOC calculation; by comprehensively considering the charge and discharge cycle, usage time and acquisition cycle error, SOH calculation is more accurate, which helps to timely detect changes in battery health status, carry out refined management of each battery cell, and improve operational 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 in the i-th battery module, is the average state of charge of the i-th 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 i-th 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, and ω is the temperature sensitivity coefficient.

[0033] To implement the above technical solution, by comprehensively considering the difference in state of charge, health status and temperature, the calculation of energy transfer is ensured to be 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 discharged, thereby extending battery life, based on temperature adjustment factors Ensure that battery cells with higher temperatures reduce energy transfer to 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 module-level operating parameters and use a state assessment algorithm to score each battery module to obtain a state assessment factor;

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

[0037] Combined with the status assessment factor of each battery module and based on the intelligent scheduling algorithm, the load ratio of each battery module is calculated.

[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. 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 in descending order, and battery modules with higher state assessment factors are considered to have better performance and healthier conditions. According to the sorting from largest to smallest, 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.

[0039] In one embodiment of the present invention, the state assessment 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 i-th battery module, I avg is the average current of all battery modules, T i avg is the average temperature of the i-th battery module, T avg is the average temperature of all battery modules, is the average state of charge of the i-th battery module, SOC avgis the average state of charge of all battery modules, is the average health status of the i-th 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 algorithm is used to calculate the state assessment factor F of each battery module. i ,The algorithm takes into account the deviations of current, temperature, state of charge and health state, and each parameter is normalized; is the current deviation, which indicates the working intensity of the battery module Temperature deviation is a key indicator of 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 evaluate 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 inverse of the load ratio 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 , allocating 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 step 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 charge and discharge 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 will be turned off, all cooling measures will be activated, and a serious alarm will be issued.

[0055] The above technical solution is implemented by real-time monitoring and regulation of the battery module temperature 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 anomalies can be detected and handled in a timely manner to avoid safety problems caused by excessively high or low battery module temperatures.

[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] Dynamic adjustment module, used to dynamically adjust the data collection period according to the collected single-unit level working parameters;

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

[0060] The intelligent scheduling module 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;

[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 the 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 operating 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 status; 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 require more energy and which batteries need to release energy to ensure that the energy levels of all battery cells are as consistent as possible; the intelligent scheduling module comprehensively evaluates 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 the 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: By periodically monitoring the working status of each battery cell and flexibly adjusting the data collection frequency according to the preset threshold.

[0066] Refined energy balancing mechanism: The balancing algorithm used adjusts the energy of each battery cell accurately to the cell level, ensuring that all 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 treated as a temperature monitoring and management unit, and the cooling or heating solution 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. Those skilled in the art 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 unless there is a conflict.

[0071] See also Figure 1 The present invention provides a battery energy optimization management method for energy balancing between battery cells and between battery modules, wherein the battery module is composed of multiple battery cells connected in series and the multiple battery modules are arranged in parallel, comprising the following steps: S1, periodically collecting the cell-level operating parameters of each battery cell, and dynamically adjusting the collection period according to the collected cell-level operating parameters and a preset fluctuation threshold; S2, calculating the charge state and health state of the battery cell according to the cell-level operating parameters and the collection period, and calculating the energy transfer amount of the battery cell according to a balancing algorithm to achieve energy balance between the battery cells; S3, scoring each battery module based on the charge state and health state, and adjusting the load ratio of the battery module according to an intelligent scheduling algorithm 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 balancing and battery module load balancing, 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] This battery energy optimization management method achieves 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 comprehensively evaluate each battery module composed of multiple battery cells connected in series and give a status score. Based on this scoring result, combined with an intelligent scheduling algorithm, the system can flexibly adjust the load ratio of each module to ensure a reasonable load distribution for the entire battery module, avoiding situations where some battery modules are overused while others are idle; S4, taking into account 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 based on 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 battery status 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 dynamic temperature 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 single-cell operating parameters of the battery 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 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 cell collection cycle based on the collected battery cell temperature, battery 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. 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 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 collected accurately 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. It then determines whether to shorten or lengthen the next collection cycle based on the comparison of real-time data collected from battery cell temperature, voltage, and other parameters with their respective fluctuation thresholds. For example, if the temperature or voltage approaches its fluctuation threshold, data collection will be increased 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 period 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 set minimum acquisition period, t max is the set maximum acquisition period, 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 i-th 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 battery cell voltage and temperature. If the voltage or temperature fluctuation is large (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 Thus, the monitoring frequency is increased. 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 By dynamically adjusting the acquisition cycle, the acquisition frequency can be increased when parameter fluctuations are large, ensuring timely capture of key changes. This helps to promptly identify and respond to potential problems, improving monitoring accuracy and efficiency. When the battery status is stable, data acquisition is reduced, saving processor time and storage space, and optimizing system resource allocation.

[0082] The state of charge of the battery cell is calculated as follows:

[0083]

[0084] in, is the updated state of charge of the jth battery cell in the i-th battery module, is the updated power of the jth battery cell in the i-th battery module, is the remaining capacity 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 i-th 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 health status calculation method of the battery cell is as follows:

[0086]

[0087] in, is the updated health status of the jth battery cell of the i-th battery module, 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 in the i-th battery module.

[0088] By updating the battery capacity 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. The acquisition cycle and coulomb efficiency coefficient in the power calculation are dynamically adjusted to improve the accuracy of SOC calculation. By comprehensively considering the charge and discharge cycle, usage time and acquisition cycle error, the SOH calculation is more accurate, which helps to timely detect changes in battery health status, carry out refined management of each battery cell, and improve operational 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 in the i-th battery module, is the average state of charge of the i-th 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 i-th 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, 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 discharged, thereby extending battery life, based on temperature adjustment factors Ensure that battery cells with higher temperatures reduce energy transfer to 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 the status of each battery module using a status assessment algorithm to obtain a status assessment factor; sorting the battery modules from large to small according to the status assessment factor, and distributing the load in the order of sorting; and calculating the load ratio of each battery module based on the status assessment factor of each battery module and 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 in descending order, and battery modules with higher state assessment factors are considered to have better performance and healthier conditions. According to the sorting from largest to smallest, 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 i-th battery module, I avg is the average current of all battery modules, T i avg is the average temperature of the i-th battery module, T avg is the average temperature of all battery modules, is the average state of charge of the i-th battery module, SOC avg is the average state of charge of all battery modules, is the average health status of the i-th 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] Calculate the state assessment factor F of each battery module using the state assessment algorithm i ,The algorithm takes into account the deviations of current, temperature, state of charge and health state, and each parameter is normalized; is the current deviation, which indicates the working intensity of the battery module; Temperature deviation is a key indicator of 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 evaluate 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 inverse of the load ratio of all battery modules, and m is the number of all battery modules.

[0104] The status 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 , allocating 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] The step 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 , then shut down the charge and discharge function of the battery module, start heating measures for the battery module, and issue a low temperature alarm; if T mod <T safe，high , the charging and discharging functions of all battery modules will be turned off, all cooling measures will be activated, and a serious alarm will be issued.

[0107] By real-time monitoring and regulation of the battery module temperature, the battery is ensured to operate 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 anomalies are detected and handled in a timely manner to avoid safety issues caused by excessively high or low battery module temperatures.

[0108] In another embodiment of the present invention, a battery energy optimization management system is provided, including: a data acquisition module for collecting battery cell-level operating data and setting a fluctuation threshold for each parameter; a dynamic adjustment module for dynamically adjusting the data acquisition cycle according to the collected cell-level operating parameters; an energy transfer calculation module for calculating the charge state and health state of the battery cell based on the collected cell-level operating parameters and the acquisition cycle, and setting a balancing algorithm to calculate the energy transfer amount of the battery cell; an intelligent scheduling module for scoring the status of each battery module and adjusting the load ratio of the battery module according to the intelligent scheduling algorithm; a temperature control module for treating each battery module as a temperature control unit and controlling the temperature of the battery module through a temperature control strategy; an abnormality monitoring module for real-time monitoring of key parameters of battery cells and battery modules, and recording log information when an abnormality is detected, wherein the log information includes: abnormal time, abnormal type and abnormal value.

[0109] The data acquisition module is responsible for collecting the key operating 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 the battery cells, and uses a balancing algorithm to determine which batteries require 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 comprehensively evaluates 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. Any equivalent modifications or variations made by persons skilled in the art without departing from the spirit and technical concepts disclosed herein shall be encompassed 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 multiple battery cells connected in series and multiple 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 of the battery cells based on the cell-level operating parameters and the acquisition cycle, and calculating the energy transfer amount of the battery cells according to the balancing algorithm to achieve energy balance 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 achieve load balance among the battery modules; S4, using the battery module as a temperature control unit during battery cell energy balancing and battery module load balancing, 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; Collect single-cell operating parameters of battery cells, including voltage, current, temperature, and power, and set fluctuation thresholds for each parameter; Verify the validity and integrity of collected unit-level operating parameters to ensure that the data is within the preset range and that all necessary parameters have been collected; Set the initial collection cycle and dynamically adjust the battery cell collection cycle based on the collected battery cell temperature, battery cell voltage and the corresponding fluctuation threshold; ΔV i，j =|V i，j -V i avg | 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 set minimum acquisition period, t max is the set maximum acquisition period, 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 i-th 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; 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 jth battery cell in the i-th battery module, is the updated power of the jth battery cell in the i-th battery module, is the remaining capacity 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 i-th 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; The health status calculation method of the battery cell is as follows: in, is the updated health status of the jth battery cell of the i-th battery module, 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，j is the collection period of the jth battery cell of the i-th battery module; The balancing algorithm is: 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 in the i-th battery module, is the average state of charge of the i-th 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 i-th 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, and ω is the temperature sensitivity coefficient.

2. The battery energy optimization management method according to claim 1, characterized in that: The S3 includes: Determine module-level operating parameters and use a state assessment algorithm to score each battery module to obtain a state assessment factor; Sort the battery modules according to their status assessment factors from large to small, and distribute the load in the order of sorting; Combined with the status assessment factor of each battery module and based on the intelligent scheduling algorithm, the load ratio of each battery module is calculated.

3. The battery energy optimization management method according to claim 2, 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 i-th battery module, I avg is the average current of all battery modules, T i avg is the average temperature of the i-th battery module, T avg is the average temperature of all battery modules, is the average state of charge of the i-th battery module, SOC avg is the average state of charge of all battery modules, is the average health status of the i-th 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.

4. The battery energy optimization management method according to claim 3, 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 inverse of the load ratio of all battery modules, and m is the number of all battery modules.

5. 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 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 ; 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 charge and discharge 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 will be turned off, all cooling measures will be activated, and a serious alarm will be issued.

6. A system for implementing the method according to any one of claims 1 to 5, characterized in that: include: Data acquisition module, used to collect battery cell-level operating data and set fluctuation thresholds for each parameter; Dynamic adjustment module, used to dynamically adjust the data collection period according to the collected single-unit level working parameters; Energy transfer calculation module, which is used to calculate the state of charge and health of the battery cells based on the collected single-cell operating parameters and collection period, and set the balancing algorithm to calculate the energy transfer amount of the battery cells; The intelligent scheduling module 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; 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 the 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.

Images