Battery management method and system based on sodium-nickel battery

Abstract

The invention belongs to the technical field of battery management, and discloses a battery management method and system based on a sodium-nickel battery. The method comprises the steps of obtaining working parameters of the sodium-nickel battery pack and judging whether the sodium-nickel battery pack is in a cold state; starting a heater for heating in a cold state, and monitoring and controlling the heating rate; obtaining safety state parameters, and determining to enter an operation state; calculating a charge state value, and controlling a charge-discharge loop; monitoring parameters, and generating fault identification codes and classification and level information; and recording a fault and selecting a processing strategy. Safe and efficient management of the sodium-nickel battery pack can be realized, the service life of the battery is prolonged, and the system reliability is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a battery management method and system based on sodium nickel batteries. BACKGROUND

[0002] With the rapid development of new energy technology, sodium ion batteries, as a new type of energy storage battery, have attracted widespread attention due to their abundant raw material reserves, low cost, environmental protection and safety advantages. Among them, sodium nickel batteries, as an important type of sodium ion batteries, have high energy density, long cycle life, good safety performance and other characteristics, and have shown great application potential in electric vehicles, energy storage power stations and other fields. However, the performance of sodium nickel batteries is closely related to their working temperature, especially in low temperature environment, the battery performance will decrease significantly, therefore, a special battery management system is needed to effectively manage and control it to ensure safe and efficient operation of the battery.

[0003] Currently, there are still some problems in sodium nickel battery management technology. The existing battery management system is not accurate enough in controlling the cold start of sodium nickel batteries, and lacks real-time monitoring and intelligent adjustment mechanism in the heating process, which can easily lead to insufficient or excessive heating, affecting the performance and life of the battery. The existing technology for estimating the state of charge of sodium nickel battery packs is relatively simple, and the accuracy is not high, which makes it difficult to achieve accurate control of the charging and discharging process, and easily causes overcharging or overdischarging, reducing the service life of the battery. The existing fault diagnosis and processing mechanism is not perfect, and lacks fine classification and level division of faults, which cannot take corresponding processing strategies for different types and levels of faults, increasing the safety hazards of the system.

[0004] With the increasing application of sodium nickel batteries in the field of new energy, it is urgent to develop a battery management method that can accurately control the battery heating process, accurately estimate the state of charge of the battery and has a perfect fault management mechanism, in order to improve the efficiency and safety of sodium nickel batteries and prolong their service life. SUMMARY

[0005] The embodiments of the present application provide a battery management method and system based on sodium nickel batteries, which can at least solve some of the problems in the prior art.

[0006] In a first aspect, the embodiments of the present application provide a battery management method based on sodium nickel batteries, comprising: obtaining the working parameters of a sodium nickel battery pack, and determining whether the sodium nickel battery pack is in a cold state according to the temperature parameter in the working parameters; When the sodium nickel battery pack is in a cold state, converting the voltage of the sodium nickel battery pack into the power supply voltage of the battery management system, starting the heater to heat the sodium nickel battery pack, and monitoring the heating rate in real time, and when the heating rate exceeds a preset threshold, controlling the heater to stop heating; Acquire the safety state parameters of the sodium nickel battery pack, and determine to enter the running state when the safety state parameters are all in the normal range; In the running state, calculate the state of charge value of the sodium nickel battery pack according to the working parameters, and control the contactor to disconnect the charging circuit or the discharging circuit according to the state of charge value; Real-time monitoring of the working parameters and the safety state parameters, when an abnormality is detected, a corresponding fault identification code is generated, and the fault classification information and the fault level information are determined according to the fault identification code; The fault identification code, the fault classification information and the fault level information are recorded in the fault record, and the corresponding fault handling strategy is selected according to the fault level information.

[0007] When the sodium nickel battery pack is in a cold state, the voltage of the sodium nickel battery pack is converted into the power supply voltage of the battery management system, the heater is started to heat the sodium nickel battery pack, and the heating rate is monitored in real time, when the heating rate exceeds a preset threshold, the heater is controlled to stop heating, comprising: When the sodium nickel battery pack is in a cold state, determine the upper limit of the heating power and the lower limit of the heating power, convert the voltage of the sodium nickel battery pack into the power supply voltage through the built-in direct current conversion module, and determine whether the sodium nickel battery pack is in a charging state according to the charging and discharging current data in the working parameters; When the sodium nickel battery pack is in a charging state, set the initial heating power according to the upper limit of the heating power, when the sodium nickel battery pack is in a non-charging state, set the initial heating power according to the lower limit of the heating power, and calculate the heating rate according to the change rate of the cell temperature data in the working parameters; Real-time acquisition of the load power and temperature distribution data of the sodium nickel battery pack, calculation of the maximum allowable heating power according to the load power and the temperature distribution data, calculation of the heating power adjustment coefficient according to the deviation of the heating rate and the target heating rate; Determine the target heating power based on the initial heating power and the heating power adjustment coefficient, when the temperature distribution data shows that the cell temperature distribution non-uniformity is greater than a preset uniformity threshold, adjust the target heating power in different temperature regions, and when the heating rate reaches the target heating rate, control the heater to stop heating.

[0008] Real-time acquisition of the load power and temperature distribution data of the sodium nickel battery pack, calculation of the maximum allowable heating power according to the load power and the temperature distribution data, calculation of the heating power adjustment coefficient according to the deviation of the heating rate and the target heating rate, comprising: acquire load power and temperature distribution data of the sodium-nickel battery pack in real time, calculate a power reference value according to the load power and the temperature distribution data, and calculate a battery attenuation degree according to residual capacity data of the sodium-nickel battery pack in a charging and discharging process; determine a power correction interval based on the battery attenuation degree, and dynamically adjust the power reference value in the power correction interval to obtain a maximum allowed heating power; acquire temperature change data of the sodium-nickel battery pack in a preset time period, calculate an actual heating rate according to the temperature change data, and determine a heating rate deviation based on the actual heating rate and a pre-set target heating rate; calculate an initial adjustment coefficient according to the maximum allowed heating power and the heating rate deviation, calculate a temperature change trend according to the temperature change data, and dynamically adjust the initial adjustment coefficient based on the temperature change trend to obtain a corrected initial adjustment coefficient; acquire battery internal resistance data, calculate a resistance correction coefficient according to the battery internal resistance data, and determine a heating power adjustment coefficient based on the corrected initial adjustment coefficient and the resistance correction coefficient.

[0009] acquire safety state parameters of the sodium-nickel battery pack, and determine that the sodium-nickel battery pack enters a running state when the safety state parameters are all in a normal range, including: calculate a parameter change rate according to a change amount of the safety state parameters in a preset time period, and calculate a power fluctuation value according to the parameter change rate; compare the power fluctuation value with a safety fluctuation threshold to obtain a dynamic index, and compare the safety state parameters with a safety range threshold to obtain a static index; determine a safety evaluation criterion based on time sequence distribution characteristics of the dynamic index and the static index, correct the safety evaluation criterion according to coupling correlation degrees between the safety state parameters, and obtain a corrected safety evaluation criterion; calculate a safety state initial value according to the corrected safety evaluation criterion, determine a safety risk level based on trend characteristics of the dynamic index, adjust the safety state initial value according to the safety risk level, and obtain a safety state score; determine that the safety state parameters are all in the normal range when the dynamic index and the static index are both in the normal range and the safety state score is higher than a pre-set safety threshold, and determine that the sodium-nickel battery pack enters the running state.

[0010] in the running state, calculate a state of charge value of the sodium-nickel battery pack according to the working parameters, and control a contactor to disconnect a charging circuit or a discharging circuit according to the state of charge value, including: In the operating state, a real-time current integral value and a real-time voltage integral value of the sodium-nickel battery pack are calculated according to the working parameter; A current accumulation factor is calculated by taking a ratio of the real-time current integral value to a nominal capacity, and a voltage accumulation factor is calculated by taking a ratio of the real-time voltage integral value to a nominal voltage; A charge-discharge state coefficient is determined according to the current accumulation factor and the voltage accumulation factor, and an initial state-of-charge value is calculated according to the working parameter and the charge-discharge state coefficient; A battery polarization characteristic quantity is calculated according to the working parameter, a polarization state sequence is constructed according to a time sequence relationship of the battery polarization characteristic quantity, and the initial state-of-charge value is adaptively corrected based on a change rule feature of the polarization state sequence to obtain a state-of-charge value; When the state-of-charge value is greater than a charging cutoff threshold, a contactor is controlled to disconnect a charging circuit, and when the state-of-charge value is less than a discharging cutoff threshold, the contactor is controlled to disconnect a discharging circuit.

[0011] The working parameter and the safety state parameter are monitored in real time, and when an abnormality is detected, a corresponding fault identification code is generated, and fault classification information and fault level information are determined according to the fault identification code, including: The working parameter and the safety state parameter are monitored in real time, and a first abnormality marker is obtained by comparing the working parameter with a first monitoring threshold, and a second abnormality marker is obtained by comparing the safety state parameter with a second monitoring threshold; A working parameter abnormality degree is calculated according to a numerical distribution of the first abnormality marker, a safety state parameter abnormality degree is calculated according to a numerical distribution of the second abnormality marker, and a comprehensive abnormality degree is determined based on the working parameter abnormality degree and the safety state parameter abnormality degree; A deviation accumulation amount of the working parameter and the safety state parameter is calculated, the deviation accumulation amount is divided into a plurality of feature intervals, a fault occurrence position is determined according to a parameter distribution density in each feature interval, and a fault identification code is generated; Fault classification information is determined according to the fault identification code, and fault level information is obtained by dividing the fault classification information into fault levels based on the comprehensive abnormality degree.

[0012] A deviation accumulation amount of the working parameter and the safety state parameter is calculated, the deviation accumulation amount is divided into a plurality of feature intervals, a fault occurrence position is determined according to a parameter distribution density in each feature interval, and a fault identification code is generated, including: A standard deviation of the working parameter and the safety state parameter is calculated, and a parameter fluctuation interval is divided according to the standard deviation; The operating parameters and the safety status parameters are compared with the standard parameter values ​​to obtain the parameter deviation. The cumulative amount of the parameter deviation within the parameter fluctuation range is calculated to obtain the cumulative deviation. The cumulative deviation is divided into multiple feature intervals, the number of parameters in each feature interval is calculated, the parameter distribution density is calculated based on the number of parameters, and the parameter clustering region is determined based on the parameter distribution density. The parameter aggregation region is divided into multiple density sub-intervals. The parameter migration direction and migration speed of adjacent density sub-intervals are calculated to obtain the parameter migration trajectory. The parameter aggregation region is then sorted from high to low density value to generate a density decay chain. Based on the decay rate of the density decay chain and the parameter migration trajectory, the spatial and temporal locations of the fault occurrence are determined, and a fault identification code is generated based on the spatial and temporal locations.

[0013] A second aspect of the present invention provides a battery management system based on a sodium-nickel battery, comprising: The first unit is used to acquire the operating parameters of the sodium-nickel battery pack and determine whether the sodium-nickel battery pack is in a cold state based on the temperature parameter in the operating parameters. The second unit is used to convert the voltage of the sodium-nickel battery pack into the power supply voltage of the battery management system when the sodium-nickel battery pack is in a cold state, start the heater to heat the sodium-nickel battery pack, monitor the heating rate in real time, and control the heater to stop heating when the heating rate exceeds a preset threshold. The third unit is used to obtain the safety status parameters of the sodium-nickel battery pack, and determines to enter the operating state when all the safety status parameters are within the normal range. The fourth unit is used to calculate the state of charge (SOC) value of the sodium-nickel battery pack according to the operating parameters under the operating state, and to control the contactor to disconnect the charging circuit or the discharging circuit according to the SOC value. The fifth unit is used to monitor the operating parameters and safety status parameters in real time. When an abnormality is detected, a corresponding fault identification code is generated, and the fault classification information and fault level information are determined based on the fault identification code. The sixth unit is used to record the fault identification code, the fault classification information and the fault level information into the fault record, and select the corresponding fault handling strategy according to the fault level information.

[0014] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0016] The battery management method based on sodium-nickel batteries provided by this invention can identify the cold state of sodium-nickel battery packs in a timely manner and take corresponding heating measures by real-time monitoring of the battery pack's operating parameters and safety status parameters, effectively ensuring the normal start-up and operation of sodium-nickel battery packs in low-temperature environments.

[0017] This invention intelligently controls the charging and discharging circuit based on the state of charge value, avoiding overcharging and over-discharging, extending the service life of the sodium-nickel battery pack, and improving the safety and stability of the battery system.

[0018] This invention establishes a comprehensive fault diagnosis and handling mechanism, which can monitor the battery pack's operating status in real time, quickly determine the fault category and level based on the fault identification code, and automatically select the corresponding fault handling strategy, effectively reducing the risk of battery system failure and improving the reliability and intelligence level of the sodium-nickel battery management system. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of a battery management method based on a sodium-nickel battery according to an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram illustrating the process of a control contactor disconnecting a charging or discharging circuit according to an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0023] Figure 1 This is a schematic flowchart of a battery management method based on a sodium-nickel battery according to an embodiment of the present invention. Figure 1 As shown, the method includes: Obtain the operating parameters of the sodium-nickel battery pack, and determine whether the sodium-nickel battery pack is in a cold state based on the temperature parameter in the operating parameters. When the sodium-nickel battery pack is in a cold state, the voltage of the sodium-nickel battery pack is converted into the power supply voltage of the battery management system, and the heater is started to heat the sodium-nickel battery pack. The heating rate is monitored in real time, and when the heating rate exceeds a preset threshold, the heater is controlled to stop heating. The safety status parameters of the sodium-nickel battery pack are obtained, and the operation state is determined when all the safety status parameters are within the normal range. In the operating state, the state of charge (SOC) value of the sodium-nickel battery pack is calculated based on the operating parameters, and the contactor is controlled to disconnect the charging circuit or the discharging circuit based on the SOC value. The system monitors the operating parameters and safety status parameters in real time. When an anomaly is detected, a corresponding fault identification code is generated, and the fault classification information and fault level information are determined based on the fault identification code. The fault identification code, the fault classification information, and the fault level information are recorded in the fault record, and the corresponding fault handling strategy is selected according to the fault level information.

[0024] This invention achieves safe and efficient operation of the sodium-nickel battery pack by real-time monitoring and intelligent control of its operating parameters.

[0025] The operating parameters of a nickel-sodium battery pack, such as voltage, current, and temperature, can be collected. The temperature parameter is used to determine if the battery pack is in a cold state. The criterion for determining if a nickel-sodium battery pack is in a cold state is that its temperature parameter is below 270℃. This temperature threshold is determined based on the electrochemical characteristics of the nickel-sodium battery pack, as it typically operates normally within the range of 270℃-350℃. Below this temperature range, the conductivity of the internal electrolyte decreases, making normal charging and discharging impossible.

[0026] When the nickel-sodium battery pack is cold, a cold start procedure can be performed. Specifically, a DC-DC converter can be used to convert the voltage of the nickel-sodium battery pack (e.g., 48V) to the power supply voltage of the battery management system (e.g., 12V) to ensure the normal operation of the battery management system. Subsequently, an internal or external heater is activated to heat the nickel-sodium battery pack. The heater uses a ceramic heating element with a power of 2000W, which can effectively raise the temperature of the nickel-sodium battery pack. During the heating process, multiple temperature sensors monitor the temperature changes of various parts of the battery pack in real time and calculate the heating rate. The heating rate is calculated based on the amount of temperature change per unit time, i.e., the degree Celsius increase in temperature per minute. When the heating rate exceeds a preset threshold, for example, when the heating rate exceeds 5°C / minute, the heater will be stopped to prevent a rapid temperature rise from causing a sudden increase in internal pressure in the battery pack and potentially leading to a safety hazard.

[0027] Once the battery pack reaches its operating temperature range, the safety status parameters of the nickel-sodium battery pack can be further obtained. These parameters include, but are not limited to, internal pressure parameters, electrolyte state parameters, and insulation resistance parameters. It can be determined whether these safety status parameters are all within the normal range. When all safety status parameters are within the normal range, the nickel-sodium battery pack is deemed ready for normal operation.

[0028] During operation, the state of charge (SOC) of the nickel-sodium battery pack is calculated based on acquired operating parameters. The SOC calculation combines ampere-hour integration with open-circuit voltage correction, determining the SOC by real-time measurement of the battery pack's current integration and periodic open-circuit voltage measurements. The calculated SOC value allows control of the contactor to disconnect the charging or discharging circuit. Specifically, when the SOC reaches 95%, the main contactor disconnects the charging circuit to prevent overcharging; when the SOC drops to 10%, the main contactor disconnects the discharging circuit to prevent over-discharging. This control strategy effectively extends the lifespan of the nickel-sodium battery pack.

[0029] During battery pack operation, operating parameters and safety status parameters are monitored in real time. When an abnormality is detected, a corresponding fault identification code is generated, and the fault classification and fault level information are determined based on the fault identification code. The fault identification code uses an 8-digit hexadecimal number, with the first 2 digits representing the fault classification, the middle 4 digits representing the specific fault point, and the last 2 digits representing the fault level. For example, the fault identification code "01023401" represents a temperature-related fault (01), specifically a level 1 fault (01) of temperature sensor number 2 (0234).

[0030] Fault classification information may include: temperature faults (01), voltage faults (02), current faults (03), pressure faults (04), insulation faults (05), etc. Fault level information includes: Level 1 fault (01, indicating a minor abnormality, can continue to be used but requires attention), Level 2 fault (02, indicating a moderate abnormality, it is recommended to limit use), and Level 3 fault (03, indicating a severe abnormality, must be stopped immediately).

[0031] The fault identification code, fault classification information, and fault level information are recorded in the fault log, which is stored in the non-volatile memory of the battery management system. The fault log includes information such as the fault occurrence time, fault duration, and operating parameters at the time of the fault. Based on the fault level information, a corresponding fault handling strategy can be selected. For a Level 1 fault, a warning message can be issued while allowing the battery pack to continue operating; for a Level 2 fault, the charging and discharging current can be limited to 50% of the normal value, and the fault status can be checked every 30 seconds; for a Level 3 fault, the main contactor can be immediately disconnected, all battery pack operations can be stopped, and an emergency alarm can be issued.

[0032] Taking temperature anomaly as an example, when the temperature at a certain point of the battery pack reaches 370℃ (exceeding the normal upper limit of 350℃) but is below 400℃, a fault code "01023402" can be generated, indicating a level 2 temperature-related fault. The charging and discharging current will then be limited and the cooling system will be activated. When the temperature exceeds 400℃, a fault code "01023403" can be generated, indicating a level 3 temperature-related fault. All contactors will be immediately disconnected and the emergency cooling procedure will be activated.

[0033] Different handling methods can be adopted for different types of faults. For example, for voltage abnormality faults, such as excessively high single-cell voltage, the charging current can be adjusted or the equalization circuit can be activated; for insulation faults, the power supply can be cut off immediately and the user can be notified to check the insulation status; for pressure abnormality faults, the pressure reducing valve can be opened to release excess pressure.

[0034] The above methods enable effective management of nickel-sodium battery packs under different operating conditions, ensuring safe and efficient operation. This approach is particularly suitable for nickel-sodium battery applications in energy storage systems, electric vehicles, and other fields, significantly improving the safety, reliability, and lifespan of nickel-sodium battery packs.

[0035] In one optional embodiment, when the sodium-nickel battery pack is in a cold state, the voltage of the sodium-nickel battery pack is converted to the power supply voltage of the battery management system, and a heater is started to heat the sodium-nickel battery pack. The heating rate is monitored in real time, and when the heating rate exceeds a preset threshold, the heater is controlled to stop heating, including: When the sodium-nickel battery pack is in a cold state, the upper limit and lower limit of the heating power are determined, the voltage of the sodium-nickel battery pack is converted into the supply voltage through the built-in DC-DC conversion module, and the charging and discharging current data in the working parameters are used to determine whether the sodium-nickel battery pack is in a charging state. When the sodium-nickel battery pack is in a charging state, the initial heating power is set according to the upper limit of the heating power. When the sodium-nickel battery pack is in a non-charging state, the initial heating power is set according to the lower limit of the heating power. The heating rate is calculated based on the rate of change of the cell temperature data in the operating parameters. The load power and temperature distribution data of the sodium-nickel battery pack are acquired in real time. The maximum allowable heating power is calculated based on the load power and temperature distribution data. The heating power adjustment coefficient is calculated based on the deviation between the heating rate and the target heating rate. The target heating power is determined based on the initial heating power and the heating power adjustment coefficient. When the temperature distribution data shows that the cell temperature distribution non-uniformity is greater than the preset uniformity threshold, the target heating power is adjusted in different temperature regions. When the heating rate reaches the target heating rate, the heater is controlled to stop heating.

[0036] When starting a nickel-sodium battery pack from a cold state, the upper and lower limits of the heating power can be determined first. The upper limit of the heating power can be set to 5000W, and the lower limit can be set to 2000W. These power limits are determined based on the capacity, internal impedance, and safety requirements of the nickel-sodium battery pack. For example, for a 100kWh nickel-sodium battery pack, the upper limit of the heating power is set to 5% of the rated power of the battery pack.

[0037] The built-in DC-DC converter transforms the voltage of the nickel-sodium battery pack into the power supply voltage for the battery management system. Specifically, when the nickel-sodium battery pack voltage is 350V, the built-in DC-DC converter converts it to a 12V power supply voltage, providing a stable power supply for the battery management system and control circuit. Simultaneously, it can detect the charging and discharging current data in the operating parameters to determine the charging state of the nickel-sodium battery pack. When the detected charging current is greater than 0.5A, the battery pack is determined to be in a charging state.

[0038] When the nickel-sodium battery pack is charging, the initial heating power is set to 4500W based on the upper limit of 5000W; when the nickel-sodium battery pack is not charging, the initial heating power is set to 1800W based on the lower limit of 2000W. The heating rate is calculated based on the rate of change of cell temperature data in the operating parameters. The heating rate calculation is based on the amount of change in cell temperature per unit time, such as the temperature rise in degrees Celsius per minute. Temperature data is collected every 10 seconds, and the current heating rate is obtained by dividing the temperature difference between two adjacent data collections by the time interval.

[0039] The load power can be calculated by monitoring the output current and voltage of the battery pack, while temperature distribution data can be obtained by collecting data from multiple temperature sensors placed at different locations within the battery pack. For example, in a sodium-nickel battery pack consisting of 12 cells, 24 temperature sensors are arranged, with at least two sensors installed per cell. The maximum allowable heating power is calculated based on the load power and temperature distribution data. When the load power is 2000W, the maximum allowable heating power can be set to 3000W to avoid overloading the battery pack.

[0040] The heating power adjustment coefficient is calculated based on the deviation between the actual heating rate and the target heating rate. The target heating rate can be set to 3℃ / minute, which is suitable for the safe heating requirements of sodium-nickel battery packs. When the actual heating rate is 2℃ / minute, which is lower than the target value, the calculated heating power adjustment coefficient is 1.2; when the actual heating rate is 4℃ / minute, which is higher than the target value, the calculated heating power adjustment coefficient is 0.8.

[0041] Based on the initial heating power and the heating power adjustment coefficient, the target heating power is determined. For example, when the initial heating power is 4500W and the heating power adjustment coefficient is 0.8, the target heating power is 3600W. When the temperature distribution data shows that the cell temperature distribution non-uniformity is greater than the preset uniformity threshold, the target heating power is adjusted in different temperature regions. The preset uniformity threshold can be set to 10℃, meaning that when the difference between the highest and lowest temperatures within the battery pack exceeds 10℃, it is considered that the temperature distribution is non-uniform.

[0042] In cases of uneven temperature distribution, heating power is increased in areas with lower temperatures and decreased in areas with higher temperatures. For example, when the temperature in one area of ​​the battery pack is 290°C while the temperature in another area is 275°C, the heating power in the low-temperature area is increased by 15%, and the heating power in the high-temperature area is decreased by 10%, thus achieving temperature balance.

[0043] Once the heating rate reaches the target heating rate of 3℃ / minute and remains stable for a period of time (e.g., 2 minutes), the heater will stop heating. Furthermore, when the battery pack temperature reaches the normal operating temperature threshold (e.g., 300℃), the heater will stop heating regardless of the heating rate to prevent the battery pack from overheating.

[0044] During the heating process, parameters such as battery pack voltage, current, and temperature are continuously monitored. If any abnormality is detected (e.g., a sudden and rapid temperature rise exceeding 5°C / minute or a temperature exceeding 320°C in a certain area), heating is immediately stopped and the system enters protection mode to ensure the safe operation of the battery pack. This precise heating management method effectively improves the efficiency of sodium-nickel battery packs and extends their lifespan.

[0045] In one optional implementation, the load power and temperature distribution data of the sodium-nickel battery pack are acquired in real time; the maximum allowable heating power is calculated based on the load power and temperature distribution data; and a heating power adjustment coefficient is calculated based on the deviation between the heating rate and the target heating rate, including: The load power and temperature distribution data of the sodium-nickel battery pack are acquired in real time. A power reference value is calculated based on the load power and temperature distribution data. The battery degradation rate is calculated based on the remaining capacity data of the sodium-nickel battery pack during the charging and discharging process. The power correction range is determined based on the battery degradation, and the power reference value is dynamically adjusted within the power correction range to obtain the maximum allowable heating power. The temperature change data of the sodium-nickel battery pack is collected within a preset time period. The actual heating rate is calculated based on the temperature change data, and the heating rate deviation is determined based on the actual heating rate and the preset target heating rate. The initial adjustment coefficient is calculated based on the maximum allowable heating power and the heating rate deviation. The temperature change trend is calculated based on the temperature change data. The initial adjustment coefficient is dynamically adjusted based on the temperature change trend to obtain the corrected initial adjustment coefficient. Obtain battery internal resistance data, calculate resistance correction coefficient based on battery internal resistance data, and determine heating power adjustment coefficient based on the corrected initial adjustment coefficient and the resistance correction coefficient.

[0046] This invention can adjust the heating power in real time to ensure that the battery is heated efficiently within a safe range.

[0047] First, the load power and temperature distribution data of the nickel-sodium battery pack can be collected in real time. The load power data is calculated by sampling the output current and voltage of the battery pack, with a sampling period of 100 milliseconds. The temperature distribution data is collected by temperature sensors placed at different locations on the battery pack. Multiple temperature sensors can be evenly distributed at the top, middle, and bottom of the battery pack, with a sampling interval of 1 second. The collected load power is compared with a preset safe power threshold to determine the baseline power value. For example, when the detected battery pack load power is 5 kW and the temperature distribution is 38°C at the top, 36°C in the middle, and 34°C at the bottom, the power baseline value can be determined to be 3.5 kW according to a preset algorithm.

[0048] The remaining capacity of the battery pack is calculated in real time using the coulomb counting method, and the battery degradation rate is obtained by comparing it with the initial capacity. In practice, the used capacity can be determined by monitoring the cumulative value of the battery's charge and discharge current, and then the degradation rate can be calculated by comparing it with the rated capacity. For example, if the initial battery capacity is 100 amps, and the remaining capacity is measured to be 85 amps after multiple charge and discharge cycles, then the battery degradation rate is 15%.

[0049] Based on the calculated battery degradation rate, a power correction range can be determined, which typically narrows as the battery degradation rate increases. For example, for a battery with a degradation rate of 15%, the power correction range can be set to 85% to 105% of the base power; if the degradation rate reaches 30%, the range can be adjusted to 70% to 90% of the base power. Within the determined correction range, the base power value is dynamically adjusted according to the current battery state to obtain the maximum allowable heating power. For example, when the base power value is 3.5 kW and the degradation rate is 15%, the correction range is 2.975 kW to 3.675 kW, and the maximum allowable heating power within this range can be determined to be 3.2 kW based on the current temperature distribution.

[0050] Temperature change data of the battery pack is collected within a preset time period (e.g., 5 consecutive minutes), and the actual heating rate is obtained by calculating the rate of temperature change per unit time. The preset target heating rate can be determined according to the battery characteristics, such as a temperature increase of 1.5℃ per minute. The heating rate deviation is determined by comparing the difference between the actual heating rate and the target heating rate. For example, when the target heating rate is 1.5℃ per minute, and the actual measured heating rate is 1.2℃ per minute, the heating rate deviation is 0.3℃ per minute.

[0051] Based on the maximum allowable heating power and the heating rate deviation, the initial adjustment coefficient is calculated. When the heating rate deviation is positive (i.e., the actual heating rate is lower than the target rate), the adjustment coefficient is greater than 1; when the deviation is negative, the adjustment coefficient is less than 1. For example, when the maximum allowable heating power is 3.2 kW and the heating rate deviation is 0.3℃ per minute (the actual rate is lower than the target rate), the initial adjustment coefficient can be calculated to be 1.15.

[0052] Further analysis of temperature change data is needed to calculate temperature change trends, including the rate of temperature change and its direction at multiple consecutive time points, to predict future temperature changes. If the temperature change trend shows that the rate of temperature increase is accelerating, the initial adjustment coefficient should be appropriately reduced, even if the current heating rate is lower than the target value, to prevent temperature overshoot. For example, when the rate of temperature change is detected to gradually increase from 0.8°C / min to 1.2°C / min over the past 5 minutes, although it is still lower than the target rate of 1.5°C / min, the initial adjustment coefficient is revised from 1.15 to 1.08 considering the accelerating temperature rise trend.

[0053] The battery's internal resistance can be measured at a specific current using the voltage drop method. A resistance correction factor can then be calculated based on this data; the higher the internal resistance, the smaller the correction factor, thus limiting the heating power. For example, if the measured battery internal resistance is 15 milliohms, which is higher than the standard value of 12 milliohms, the calculated correction factor is 0.92.

[0054] Finally, the corrected initial adjustment coefficient is multiplied by the resistance correction coefficient to obtain the heating power adjustment coefficient. In the example above, the final heating power adjustment coefficient is 1.08 × 0.92 = 0.994. This adjustment coefficient will be applied to the current heating power to achieve dynamic adjustment. For example, if the current heating power is 2.8 kW, after applying this adjustment coefficient, it will be adjusted to 2.8 × 0.994 = 2.78 kW.

[0055] By continuously executing the above steps, closed-loop control of the heating power of the nickel-sodium battery pack is achieved. By collecting load power and temperature data in real time, and combining this with battery degradation, temperature change trends, and internal resistance changes, the heating power is dynamically calculated and adjusted to ensure efficient heating of the battery under safe constraints. This method is particularly suitable for temperature-sensitive high-energy-density batteries like nickel-sodium batteries, effectively extending battery life and improving system safety.

[0056] In one optional implementation, the safety status parameters of the sodium-nickel battery pack are obtained, and when all the safety status parameters are within the normal range, the system is determined to enter the operating state, including: The change rate of the safety status parameter is calculated by measuring the change in the safety status parameter within a preset time period, and the power fluctuation value is calculated based on the change rate of the parameter. The power fluctuation value is compared with the safety fluctuation threshold to obtain a dynamic index, and the safety status parameter is compared with the safety range threshold to obtain a static index. The safety assessment criteria are determined based on the time-series distribution characteristics of the dynamic and static indicators, and then modified according to the coupling correlation between the safety status parameters to obtain the modified safety assessment criteria. The initial safety status value is calculated based on the revised safety assessment criteria. The safety risk level is determined based on the changing trend characteristics of the dynamic indicators. The initial safety status value is adjusted according to the safety risk level to obtain the safety status score. When both the dynamic and static indicators are within the normal range and the safety status score is higher than the preset safety threshold, it is determined that all safety status parameters are within the normal range, and the sodium-nickel battery pack enters the operating state.

[0057] Obtaining the safety status parameters of a nickel-sodium battery pack typically includes electrochemical parameters such as voltage, current, temperature, and internal resistance. These parameters can be collected in real time through the battery pack's built-in management system, with a sampling frequency set to once every 500ms to ensure the continuity and integrity of data acquisition. For example, for a certain model of nickel-sodium battery pack, its normal voltage range is 2.5V to 4.2V, and its normal temperature range is -20℃ to 60℃.

[0058] After obtaining the safety status parameters, the rate of change of these parameters can be calculated by measuring their changes over a preset time period. A preset time period of 10 seconds can be set to ensure that the parameters' immediate changes are reflected without misjudgment due to noise interference. For example, if the battery voltage drops from 3.8V to 3.6V within 10 seconds, the rate of change is -0.02V / second. The power fluctuation value can be calculated by multiplying the voltage change rate by the current change rate. For instance, if the current increases from 2A to 3A within the same time period, the rate of change is 0.1A / second, and the power fluctuation value can be calculated as 0.18W / second.

[0059] Dynamic indicators are obtained by comparing power fluctuation values ​​with a safe fluctuation threshold, which can be set to 0.5 W / s. If the power fluctuation value is below this threshold, the dynamic indicator is considered normal; if it exceeds this threshold, the dynamic indicator is considered abnormal. Simultaneously, static indicators are obtained by comparing safe status parameters with safe range thresholds. For example, for the temperature parameter, if the measured value is 45℃, it falls within the safe range of -20℃ to 60℃, and the static indicator for this parameter is considered normal.

[0060] Safety assessment criteria are determined based on the time-series distribution characteristics of dynamic and static indicators. The time-series distribution characteristics refer to the distribution pattern of each indicator within a continuous time window. For example, if dynamic indicators are within the normal range for 90% of the time within 30 minutes, and slightly exceed the range for 10% of the time, while static indicators remain within the normal range throughout, then the safety assessment criterion can be determined as "slight fluctuations - sustainable operation." The safety assessment criteria are then revised based on the coupling correlation between safety state parameters. The coupling correlation is obtained through correlation analysis between parameters. For example, a rise in battery temperature often leads to a decrease in internal resistance; this correlation can be quantified using a coupling coefficient of 0.8. When a 3°C temperature increase is detected accompanied by a 2mΩ decrease in internal resistance, the coupling correlation can be used to determine that this is a normal physical phenomenon rather than a safety hazard, thus revising the assessment criterion from "requiring attention" to "normal operation."

[0061] The initial safety status value is calculated based on the revised safety assessment criteria. This initial value can be set as a base score of 100 points, with deductions based on the deviation of each parameter. For example, if the voltage deviates from the normal median by 10%, 5 points are deducted; if the temperature is close to the upper limit but not exceeded, 3 points are deducted, resulting in an initial safety status value of 92 points. Next, the safety risk level is determined based on the changing trend characteristics of dynamic indicators. These trends can be obtained by fitting historical data of the dynamic indicators to determine whether they are upward, downward, or fluctuating. A stable or slightly fluctuating trend indicates a low safety risk level; a significant upward trend indicates a medium safety risk level; and a sharp upward trend indicates a high safety risk level. For low-risk levels, the initial safety status value remains unchanged; for medium-risk levels, it is multiplied by 0.9; and for high-risk levels, it is multiplied by 0.7. Assuming a slight fluctuating trend is detected in the dynamic indicators, the safety risk level is low, and the safety status score remains at 92 points.

[0062] When both dynamic and static indicators are within the normal range and the safety status score is higher than the preset safety threshold, it is determined that the safety status parameters are all within the normal range, and the sodium-nickel battery pack enters the operating state. The preset safety threshold can be set to 85 points. In this example, the safety status score is 92 points, which is higher than the threshold value. Therefore, the sodium-nickel battery pack can enter the normal operating state.

[0063] In practical applications, this method can effectively address the safety assessment needs under complex operating conditions. For example, in the event of a sudden increase in ambient temperature, relying solely on static thresholds might misjudge it as an abnormal state. However, this method, by considering dynamic change characteristics and the coupling relationship between parameters, can accurately identify this as a normal fluctuation caused by environmental factors, avoiding unnecessary system downtime. Another scenario is when battery pack aging causes a slow increase in internal resistance. Although the parameters remain within the static safety range, dynamic characteristics may reveal potential risks, allowing for early adjustment of operating strategies and extending battery pack lifespan.

[0064] Through the above methods, the safe operating status of the sodium-nickel battery pack can be more accurately determined, which not only avoids potential safety hazards but also maximizes the normal operating efficiency of the battery pack.

[0065] In one optional implementation, during the operating state, the state of charge (SOC) value of the sodium-nickel battery pack is calculated based on the operating parameters, and the contactor is controlled to disconnect the charging circuit or the discharging circuit based on the SOC value, including: Under the operating conditions, the real-time integral values ​​of the sodium-nickel battery pack and the real-time integral values ​​of the voltage are calculated based on the operating parameters. The current accumulation factor is calculated by the ratio of the real-time integrated current value to the nominal capacity, and the voltage accumulation factor is calculated by the ratio of the real-time integrated voltage value to the nominal voltage. The charge / discharge state coefficients are determined based on the current accumulation factor and the voltage accumulation factor, and the initial value of the state of charge is calculated based on the operating parameters and the charge / discharge state coefficients. The battery polarization characteristic quantities are calculated based on the operating parameters. The battery polarization characteristic quantities are then used to construct a polarization state sequence according to the time sequence. The initial value of the state of charge is adaptively corrected based on the changing characteristics of the polarization state sequence to obtain the state of charge value. When the state of charge value is greater than the charging cutoff threshold, the contactor is controlled to disconnect the charging circuit; when the state of charge value is less than the discharging cutoff threshold, the contactor is controlled to disconnect the discharging circuit.

[0066] During operation, the sodium-nickel battery pack calculates the state of charge (SOC) value using operating parameters and controls the contactor to disconnect the charging or discharging circuit based on the SOC value.

[0067] Figure 2 This is a schematic diagram illustrating the process of a control contactor disconnecting a charging or discharging circuit according to an embodiment of the present invention. Figure 2 As shown, the real-time integral values ​​of the sodium-nickel battery pack and the real-time integral values ​​of the voltage can be calculated based on the collected operating parameters. Specifically, the current integral value is obtained by integrating the charging and discharging current of the battery pack over time, and the voltage integral value is obtained by integrating the terminal voltage of the battery pack over time. For example, during a charging process, the current value is collected once per second. Assuming that the current values ​​collected in a certain 5 seconds are 10A, 10.2A, 10.5A, 10.3A, and 10.1A, then the current integral value for these 5 seconds is (10 + 10.2 + 10.5 + 10.3 + 10.1) × 1s = 51.1 A•s. Similarly, if the voltage values ​​collected in these 5 seconds are 121V, 121.5V, 122V, 122.3V, and 122.5V, then the voltage integral value is (121 + 121.5 + 122 + 122.3 + 122.5) × 1s = 609.3 V•s.

[0068] The current accumulation factor is calculated by comparing the real-time integral value of the current with the nominal capacity. For example, if the nominal capacity is 100Ah and the current integral value is 20Ah, then the current accumulation factor is 20÷100=0.2. Similarly, the voltage accumulation factor is calculated by comparing the real-time integral value of the voltage with the nominal voltage. For example, if the nominal voltage is 120V and the voltage integral value is 7200V•h, then the voltage accumulation factor is 7200÷(120×3600)=0.0167.

[0069] The charge / discharge state coefficient is determined based on the current accumulation factor and the voltage accumulation factor. In the charging state, the charge / discharge state coefficient can be set to 1; in the discharging state, it can be set to -1; and in the resting state, it can be set to 0. The initial state of charge (SOC) is calculated based on the operating parameters and the charge / discharge state coefficient. For example, in the charging state, if the current accumulation factor is 0.2 and the initial SOC is 0.3, then the initial SOC is 0.3 + 0.2 × 1 = 0.5.

[0070] Battery polarization characteristics are calculated based on operating parameters, including concentration polarization, electrochemical polarization, and ohmic polarization. These characteristics are extracted by analyzing the battery's voltage response curves under different states of charge. For example, during discharge, the battery terminal voltage decreases with increasing depth of discharge; the polarization characteristics can be obtained by analyzing the rate of voltage change. Suppose that during a discharge process, the battery voltage drops from 122V to 118V, and the discharge capacity decreases from 80Ah to 60Ah. The rate of voltage change is (122-118)÷(80-60) = 0.2V / Ah, which can be considered one of the polarization characteristics.

[0071] A polarization state sequence is constructed by arranging battery polarization characteristics according to their temporal relationships. Based on the changing patterns of this sequence, the initial state of charge (SOC) value is adaptively corrected to obtain the SOC value. For example, if the polarization state sequence analysis reveals that the battery is in a high polarization state, indicating that the actual usable capacity is lower than the theoretically calculated value, the initial SOC value will be corrected downwards. Conversely, if the polarization state is low, the SOC value will be corrected upwards. In a specific case, if the initial SOC value is 0.5, and polarization characteristic analysis reveals a high degree of battery polarization, the SOC value will be corrected to 0.48.

[0072] When the state of charge (SOC) value is greater than the charging cutoff threshold, the contactor is controlled to disconnect the charging circuit; when the SOC value is less than the discharging cutoff threshold, the contactor is controlled to disconnect the discharging circuit. For example, the charging cutoff threshold is set to 0.95, and the discharging cutoff threshold is set to 0.1. During a charging process, when the calculated SOC value reaches 0.96, the contactor will immediately disconnect the charging circuit to stop the charging process; during a discharging process, when the SOC value drops to 0.09, the contactor will disconnect the discharging circuit to stop the discharging process.

[0073] To improve system reliability, multiple protection mechanisms can be implemented. For example, in addition to state-of-charge (SOC) control, upper voltage limit control (e.g., 125V) and lower voltage limit control (e.g., 105V) can be set. When the battery pack voltage exceeds these limits, the contactor will be controlled to disconnect the corresponding circuit regardless of the SOC value. Furthermore, temperature protection is also necessary. When the battery temperature exceeds 60°C or falls below -20°C, the charging and discharging process can be interrupted to ensure battery safety.

[0074] Through the above methods, the state of charge of sodium-nickel battery packs can be accurately assessed, and the charging and discharging process can be effectively controlled, thereby ensuring the safe operation of the battery pack and extending its service life.

[0075] In one optional implementation, the operating parameters and the safety status parameters are monitored in real time. When an anomaly is detected, a corresponding fault identification code is generated. Based on the fault identification code, fault classification information and fault level information are determined, including: The working parameters and the safety status parameters are monitored in real time. The working parameters are compared with a first monitoring threshold to obtain a first anomaly marker, and the safety status parameters are compared with a second monitoring threshold to obtain a second anomaly marker. The degree of abnormality of the working parameters is calculated based on the numerical distribution of the first abnormality marker, the degree of abnormality of the safety status parameters is calculated based on the numerical distribution of the second abnormality marker, and the comprehensive degree of abnormality is determined based on the degree of abnormality of the working parameters and the degree of abnormality of the safety status parameters. Calculate the cumulative deviation between the operating parameters and the safety status parameters, divide the cumulative deviation into multiple feature intervals, determine the fault location based on the parameter distribution density within each feature interval, and generate a fault identification code. Based on the fault identification code, the fault classification information is determined, and the fault level is divided into fault levels based on the comprehensive anomaly degree to obtain fault level information.

[0076] In this embodiment, to achieve real-time monitoring and fault diagnosis of the sodium-nickel battery's operating status, operating parameters and safety status parameters can be monitored in real time. When an anomaly is detected, a corresponding fault identification code is generated. Specifically, the operating parameters are compared with a preset first monitoring threshold. For example, for the battery charging current parameter, the normal operating range is set to 20±2A. When the measured value exceeds this range, it is marked as abnormal. A first abnormality mark is generated, with 0 for the normal state and integer values ​​from 1 to 5 for the abnormal state, depending on the degree of deviation. Similarly, the safety status parameters are compared with a second monitoring threshold. For example, the normal range for the battery cell temperature is 45±5℃. If it exceeds this range, a second abnormality mark is generated, and the marking method is the same as the first abnormality mark.

[0077] After obtaining the anomaly markers, the degree of anomaly in the working parameters is calculated. Specifically, this involves statistically analyzing the percentage of non-zero values ​​among the first anomaly markers within a certain time window (e.g., 10 minutes), and then weighting the calculation based on the magnitude of the anomalies. For example, if 600 data points are collected within 10 minutes, and 60 of them are marked as anomalies, with most being at level 2, then the degree of anomaly in the working parameters is calculated to be approximately 20%. Similarly, the degree of anomaly in the safety status parameters is calculated based on the numerical distribution of the second anomaly markers. If, within the same time period, there are 45 anomalies in the safety status parameters, mostly minor anomalies at level 1, then the degree of anomaly in the safety status parameters is approximately 15%.

[0078] The overall degree of anomaly is determined by combining the degree of anomaly of the working parameters and the degree of anomaly of the safety status parameters. In this example, a weighted average method can be used, assigning a weight of 0.6 to the degree of anomaly of the working parameters and a weight of 0.4 to the degree of anomaly of the safety status parameters, resulting in an overall degree of anomaly of 0.6 × 20% + 0.4 × 15% = 18%.

[0079] The cumulative deviation of operating parameters and safety status parameters is calculated. For each monitored parameter, the difference between its actual value and the normal value is calculated and accumulated over time. For example, if the battery internal resistance is consistently higher than the normal range and the deviation gradually increases, the cumulative deviation within 10 minutes can reach 30mΩ. The cumulative deviation is divided into multiple characteristic intervals. For example, for internal resistance deviation, it can be divided into four intervals: [0-10mΩ], [10-20mΩ], [20-40mΩ], and [above 40mΩ]; for temperature deviation, it can be divided into four intervals: [0-50], [50-100], [100-200], and [above 200] (unit: degrees Celsius per minute).

[0080] By analyzing the parameter distribution density within each characteristic range, the location of the fault can be determined. If the battery internal resistance deviation is mainly distributed in the [20-40mΩ] range, while the temperature deviation is mainly concentrated in the [50-100] range, combined with the analysis of other parameters, the specific battery cell can be located as cells 3 to 7. Based on this, a fault identification code is generated, such as "NaNi-0307-IR", where "NaNi" represents a sodium-nickel battery, "0307" represents cells 3 to 7, and "IR" indicates abnormal internal resistance.

[0081] The fault classification information is determined based on the fault identification code. In this example, the fault classification corresponding to "NaNi-0307-IR" is "abnormal internal resistance of battery cell". Further, based on the previously calculated overall abnormality level (18%), the fault is graded as follows: 0-10% is Level 1 (minor), 10%-30% is Level 2 (moderate), 30%-60% is Level 3 (serious), and above 60% is Level 4 (dangerous). In this example, the fault level is Level 2 (moderate), and the complete fault level information is generated as "Level 2 - Abnormal internal resistance of battery cell".

[0082] In practical applications, multiple monitoring threshold combinations can be configured to adapt to different operating conditions. For example, under fast charging conditions, the charging current threshold can be appropriately adjusted to 25±3A, and the temperature threshold can be adjusted to 50±8℃. The characteristic range of the cumulative deviation can also be customized according to the battery type and operating environment to improve the accuracy of fault diagnosis.

[0083] The above method enables timely detection of operational anomalies in sodium-nickel batteries, accurate location of faulty battery cells, and reasonable fault classification. This provides maintenance personnel with a scientific basis for decision-making, significantly improving battery system maintenance efficiency and reducing system downtime losses due to faults. This method is particularly suitable for energy storage systems and electric vehicles with high reliability requirements, effectively extending battery life and improving overall operational efficiency.

[0084] In one optional implementation, the cumulative deviation between the operating parameters and the safety status parameters is calculated, the cumulative deviation is divided into multiple characteristic intervals, the fault location is determined based on the parameter distribution density within each characteristic interval, and a fault identification code is generated, including: Calculate the standard deviation of the operating parameters and the safety status parameters, and divide the parameter fluctuation range according to the standard deviation; The operating parameters and the safety status parameters are compared with the standard parameter values ​​to obtain the parameter deviation. The cumulative amount of the parameter deviation within the parameter fluctuation range is calculated to obtain the cumulative deviation. The cumulative deviation is divided into multiple feature intervals, the number of parameters in each feature interval is calculated, the parameter distribution density is calculated based on the number of parameters, and the parameter clustering region is determined based on the parameter distribution density. The parameter aggregation region is divided into multiple density sub-intervals. The parameter migration direction and migration speed of adjacent density sub-intervals are calculated to obtain the parameter migration trajectory. The parameter aggregation region is then sorted from high to low density value to generate a density decay chain. Based on the decay rate of the density decay chain and the parameter migration trajectory, the spatial and temporal locations of the fault occurrence are determined, and a fault identification code is generated based on the spatial and temporal locations.

[0085] First, the operating parameters and safety status parameters of the nickel-sodium battery are collected in real time. For example, for a series-connected nickel-sodium battery module, operating parameters such as charging voltage of 340V, cell #5 temperature of 48℃, and charging current of 18A can be collected, as well as safety status parameters such as total battery pack voltage difference of 15mV and insulation resistance of 800MΩ.

[0086] After collecting the parameters, the standard deviations of the operating parameters and safety state parameters can be calculated. Specifically, the standard deviation for 100 consecutively collected sets of battery cell temperature data is 1.5℃, and the standard deviation for 100 consecutively collected sets of battery cell voltage data is 8mV. Based on the calculated standard deviations, the parameter fluctuation ranges can be divided. For example, the fluctuation range for battery cell temperature can be divided into [46.5℃, 49.5℃], and the fluctuation range for battery cell voltage can be divided into [3.392V, 3.408V].

[0087] Next, the collected operating parameters and safety status parameters are compared with the preset standard parameter values ​​to obtain the parameter deviation. For example, the standard parameter value for the temperature of battery cell #5 is 45℃, while the actual collected temperature is 48℃, so the parameter deviation is 3℃; the standard parameter value for the voltage of battery cell #5 is 3.40V, while the actual collected voltage is 3.38V, so the parameter deviation is -20mV.

[0088] Based on the calculated parameter deviations, the distribution of these deviation values ​​within the parameter fluctuation range is statistically analyzed, and the cumulative deviation is calculated. For example, in 1000 continuously monitored time points, the temperature deviation of battery cell #5 occurred 850 times in the range of [2℃, 4℃], and the voltage deviation of battery cell #5 occurred 720 times in the range of [-25mV, -15mV]. These cumulative occurrences are the cumulative deviation.

[0089] The cumulative deviation is divided into multiple characteristic intervals. For example, the cumulative deviation of battery cell temperature is divided into four characteristic intervals: [0℃, 1℃], [1℃, 2℃], [2℃, 4℃], and [4℃, 6℃]. The cumulative deviation of battery cell voltage is divided into four characteristic intervals: [-30mV, -20mV], [-20mV, -10mV], [-10mV, 0mV], and [0mV, 10mV].

[0090] Calculate the number of parameters in each characteristic interval. For example, in the four characteristic intervals of battery cell temperature, the number of parameters are 50, 100, 850, and 0, respectively; in the four characteristic intervals of battery cell voltage, the number of parameters are 720, 180, 100, and 0, respectively.

[0091] The parameter distribution density is calculated based on the number of parameters and the width of each characteristic interval. For example, the parameter distribution density of a battery cell temperature in the characteristic interval [2℃, 4℃] is 850 / 2 = 425 per ℃; the parameter distribution density of a battery cell voltage in the characteristic interval [-30mV, -20mV] is 720 / 10 = 72 per mV.

[0092] By analyzing the parameter distribution density, the parameter clustering regions are determined. In this example, the parameter clustering region for battery cell temperature is [2℃, 4℃], and the parameter clustering region for battery cell voltage is [-30mV, -20mV].

[0093] The parameter aggregation region is further divided into multiple density sub-intervals. For example, the parameter aggregation region of battery cell temperature [2℃, 4℃] is divided into four density sub-intervals: [2℃, 2.5℃], [2.5℃, 3℃], [3℃, 3.5℃], and [3.5℃, 4℃]. The parameter aggregation region of battery cell voltage [-30mV, -20mV] is divided into four density sub-intervals: [-30mV, -27.5mV], [-27.5mV, -25mV], [-25mV, -22.5mV], and [-22.5mV, -20mV].

[0094] The direction and velocity of parameter migration between adjacent density sub-intervals are calculated. For example, through data analysis at consecutive time points, it was found that the cell temperature migrates from [2.5℃, 3℃] to [3℃, 3.5℃] at a velocity of 0.05℃ / minute; the cell voltage migrates from [-27.5mV, -25mV] to [-25mV, -22.5mV] at a velocity of 0.3mV / minute. These migration directions and velocities constitute the parameter migration trajectory.

[0095] The parameter cluster regions are sorted from high to low density values ​​to generate density decay chains. For example, the density decay chain for battery cell temperature is: [3℃, 3.5℃]→[2.5℃, 3℃]→[3.5℃, 4℃]→[2℃, 2.5℃]; the density decay chain for battery cell voltage is: [-25mV, -22.5mV]→[-27.5mV, -25mV]→[-22.5mV, -20mV]→[-30mV, -27.5mV].

[0096] Based on the decay rate and parameter migration trajectory of the density decay chain, the spatial and temporal locations of the faults are determined. For example, analysis shows that the density of battery cell #5 is highest in the temperature range of [3℃, 3.5℃], and the parameters migrate from the low temperature region to the high temperature region with a slower decay rate, indicating that the fault occurs in battery cell #5. Analysis also shows that the density of battery cell #5 is highest in the voltage range of [-25mV, -22.5mV], and the parameters are continuously negatively biased with a faster decay rate, indicating that this cell has a capacity decay problem.

[0097] Based on the above analysis, the spatial location of the fault was determined to be battery cell #5, and the temporal location was approximately 30 minutes after monitoring began. A fault identification code was generated based on this information, for example, "NaNi-C05-30-T48-V338", where "NaNi" indicates a sodium-nickel battery, "C05" indicates cell number 5, "30" indicates the fault occurred 30 minutes ago, "T48" indicates the temperature parameter is 48℃, and "V338" indicates the voltage parameter is 3.38V. This fault identification code can be used for rapid location and handling of faults in the battery system.

[0098] The battery management system based on sodium-nickel batteries in this embodiment of the invention includes: The first unit is used to acquire the operating parameters of the sodium-nickel battery pack and determine whether the sodium-nickel battery pack is in a cold state based on the temperature parameter in the operating parameters. The second unit is used to convert the voltage of the sodium-nickel battery pack into the power supply voltage of the battery management system when the sodium-nickel battery pack is in a cold state, start the heater to heat the sodium-nickel battery pack, monitor the heating rate in real time, and control the heater to stop heating when the heating rate exceeds a preset threshold. The third unit is used to obtain the safety status parameters of the sodium-nickel battery pack, and determines to enter the operating state when all the safety status parameters are within the normal range. The fourth unit is used to calculate the state of charge (SOC) value of the sodium-nickel battery pack according to the operating parameters under the operating state, and to control the contactor to disconnect the charging circuit or the discharging circuit according to the SOC value. The fifth unit is used to monitor the operating parameters and safety status parameters in real time. When an abnormality is detected, a corresponding fault identification code is generated, and the fault classification information and fault level information are determined based on the fault identification code. The sixth unit is used to record the fault identification code, the fault classification information and the fault level information into the fault record, and select the corresponding fault handling strategy according to the fault level information.

[0099] A third aspect of the present invention provides an electronic device, comprising: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0100] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0101] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A battery management method based on sodium-nickel batteries, characterized in that, include: Obtain the operating parameters of the sodium-nickel battery pack, and determine whether the sodium-nickel battery pack is in a cold state based on the temperature parameter in the operating parameters. When the sodium-nickel battery pack is in a cold state, the voltage of the sodium-nickel battery pack is converted into the power supply voltage of the battery management system, and the heater is started to heat the sodium-nickel battery pack. The heating rate is monitored in real time, and when the heating rate exceeds a preset threshold, the heater is controlled to stop heating. The safety status parameters of the sodium-nickel battery pack are obtained, and the operation state is determined when all the safety status parameters are within the normal range. In the operating state, the state of charge (SOC) value of the sodium-nickel battery pack is calculated based on the operating parameters, and the contactor is controlled to disconnect the charging circuit or the discharging circuit based on the SOC value. The system monitors the operating parameters and safety status parameters in real time. When an anomaly is detected, a corresponding fault identification code is generated, and the fault classification information and fault level information are determined based on the fault identification code. The fault identification code, the fault classification information, and the fault level information are recorded in the fault record, and the corresponding fault handling strategy is selected according to the fault level information.

2. The method according to claim 1, characterized in that, When the nickel-sodium battery pack is cold, the voltage of the nickel-sodium battery pack is converted to the power supply voltage of the battery management system, and the heater is started to heat the nickel-sodium battery pack. The heating rate is monitored in real time, and when the heating rate exceeds a preset threshold, the heater is controlled to stop heating, including: When the sodium-nickel battery pack is in a cold state, the upper limit and lower limit of the heating power are determined, the voltage of the sodium-nickel battery pack is converted into the supply voltage through the built-in DC-DC conversion module, and the charging and discharging current data in the working parameters are used to determine whether the sodium-nickel battery pack is in a charging state. When the sodium-nickel battery pack is in a charging state, the initial heating power is set according to the upper limit of the heating power. When the sodium-nickel battery pack is in a non-charging state, the initial heating power is set according to the lower limit of the heating power. The heating rate is calculated based on the rate of change of the cell temperature data in the operating parameters. The load power and temperature distribution data of the sodium-nickel battery pack are acquired in real time. The maximum allowable heating power is calculated based on the load power and temperature distribution data. The heating power adjustment coefficient is calculated based on the deviation between the heating rate and the target heating rate. The target heating power is determined based on the initial heating power and the heating power adjustment coefficient. When the temperature distribution data shows that the cell temperature distribution non-uniformity is greater than the preset uniformity threshold, the target heating power is adjusted in different temperature regions. When the heating rate reaches the target heating rate, the heater is controlled to stop heating.

3. The method according to claim 2, characterized in that, Real-time acquisition of load power and temperature distribution data of the sodium-nickel battery pack; calculation of the maximum allowable heating power based on the load power and temperature distribution data; and calculation of the heating power adjustment coefficient based on the deviation between the heating rate and the target heating rate, including: The load power and temperature distribution data of the sodium-nickel battery pack are acquired in real time. A power reference value is calculated based on the load power and temperature distribution data. The battery degradation rate is calculated based on the remaining capacity data of the sodium-nickel battery pack during the charging and discharging process. The power correction range is determined based on the battery degradation, and the power reference value is dynamically adjusted within the power correction range to obtain the maximum allowable heating power. The temperature change data of the sodium-nickel battery pack is collected within a preset time period. The actual heating rate is calculated based on the temperature change data, and the heating rate deviation is determined based on the actual heating rate and the preset target heating rate. The initial adjustment coefficient is calculated based on the maximum allowable heating power and the heating rate deviation. The temperature change trend is calculated based on the temperature change data. The initial adjustment coefficient is dynamically adjusted based on the temperature change trend to obtain the corrected initial adjustment coefficient. Obtain battery internal resistance data, calculate resistance correction coefficient based on battery internal resistance data, and determine heating power adjustment coefficient based on the corrected initial adjustment coefficient and the resistance correction coefficient.

4. The method according to claim 1, characterized in that, Obtain the safety status parameters of the sodium-nickel battery pack, and determine the operating state when all safety status parameters are within the normal range, including: The change rate of the safety status parameter is calculated by measuring the change in the safety status parameter within a preset time period, and the power fluctuation value is calculated based on the change rate of the parameter. The power fluctuation value is compared with the safety fluctuation threshold to obtain a dynamic index, and the safety status parameter is compared with the safety range threshold to obtain a static index. The safety assessment criteria are determined based on the time-series distribution characteristics of the dynamic and static indicators, and the safety assessment criteria are modified according to the coupling correlation between the safety status parameters to obtain the modified safety assessment criteria. The initial safety status value is calculated based on the revised safety assessment criteria. The safety risk level is determined based on the changing trend characteristics of the dynamic indicators. The initial safety status value is adjusted according to the safety risk level to obtain the safety status score. When both the dynamic and static indicators are within the normal range and the safety status score is higher than the preset safety threshold, it is determined that all safety status parameters are within the normal range, and the sodium-nickel battery pack enters the operating state.

5. The method according to claim 1, characterized in that, In the operating state, the state of charge (SOC) value of the sodium-nickel battery pack is calculated based on the operating parameters, and the contactor is controlled to disconnect the charging circuit or the discharging circuit based on the SOC value, including: Under the operating conditions, the real-time integral values ​​of the sodium-nickel battery pack and the real-time integral values ​​of the voltage are calculated based on the operating parameters. The current accumulation factor is calculated by the ratio of the real-time integrated current value to the nominal capacity, and the voltage accumulation factor is calculated by the ratio of the real-time integrated voltage value to the nominal voltage. The charge / discharge state coefficients are determined based on the current accumulation factor and the voltage accumulation factor, and the initial value of the state of charge is calculated based on the operating parameters and the charge / discharge state coefficients. The battery polarization characteristic quantities are calculated based on the operating parameters. The battery polarization characteristic quantities are then used to construct a polarization state sequence according to the time sequence. The initial value of the state of charge is adaptively corrected based on the changing characteristics of the polarization state sequence to obtain the state of charge value. When the state of charge value is greater than the charging cutoff threshold, the contactor is controlled to disconnect the charging circuit; when the state of charge value is less than the discharging cutoff threshold, the contactor is controlled to disconnect the discharging circuit.

6. The method according to claim 1, characterized in that, The system monitors the operating parameters and safety status parameters in real time. When an anomaly is detected, a corresponding fault identification code is generated. Based on the fault identification code, fault classification information and fault level information are determined, including: The working parameters and the safety status parameters are monitored in real time. The working parameters are compared with a first monitoring threshold to obtain a first anomaly marker, and the safety status parameters are compared with a second monitoring threshold to obtain a second anomaly marker. The degree of abnormality of the working parameters is calculated based on the numerical distribution of the first abnormality marker, the degree of abnormality of the safety status parameters is calculated based on the numerical distribution of the second abnormality marker, and the comprehensive degree of abnormality is determined based on the degree of abnormality of the working parameters and the degree of abnormality of the safety status parameters. Calculate the cumulative deviation between the operating parameters and the safety status parameters, divide the cumulative deviation into multiple feature intervals, determine the fault location based on the parameter distribution density within each feature interval, and generate a fault identification code. Based on the fault identification code, the fault classification information is determined, and the fault level is divided into fault levels based on the comprehensive anomaly degree to obtain fault level information.

7. The method according to claim 6, characterized in that, Calculate the cumulative deviation between the operating parameters and the safety status parameters, divide the cumulative deviation into multiple characteristic intervals, determine the fault location based on the parameter distribution density within each characteristic interval, and generate a fault identification code, including: Calculate the standard deviation of the operating parameters and the safety status parameters, and divide the parameter fluctuation range according to the standard deviation; The operating parameters and the safety status parameters are compared with the standard parameter values ​​to obtain the parameter deviation. The cumulative amount of the parameter deviation within the parameter fluctuation range is calculated to obtain the cumulative deviation. The cumulative deviation is divided into multiple feature intervals, the number of parameters in each feature interval is calculated, the parameter distribution density is calculated based on the number of parameters, and the parameter clustering region is determined based on the parameter distribution density. The parameter aggregation region is divided into multiple density sub-intervals. The parameter migration direction and migration speed of adjacent density sub-intervals are calculated to obtain the parameter migration trajectory. The parameter aggregation region is then sorted according to the density value from high to low to generate a density decay chain. Based on the decay rate of the density decay chain and the parameter migration trajectory, the spatial and temporal locations of the fault occurrence are determined, and a fault identification code is generated based on the spatial and temporal locations.

8. A battery management system based on a sodium-nickel battery, used to implement the method as described in any one of claims 1-7, characterized in that, include: The first unit is used to acquire the operating parameters of the sodium-nickel battery pack and determine whether the sodium-nickel battery pack is in a cold state based on the temperature parameter in the operating parameters. The second unit is used to convert the voltage of the sodium-nickel battery pack into the power supply voltage of the battery management system when the sodium-nickel battery pack is in a cold state, start the heater to heat the sodium-nickel battery pack, monitor the heating rate in real time, and control the heater to stop heating when the heating rate exceeds a preset threshold. The third unit is used to obtain the safety status parameters of the sodium-nickel battery pack, and determines to enter the operating state when all the safety status parameters are within the normal range. The fourth unit is used to calculate the state of charge (SOC) value of the sodium-nickel battery pack according to the operating parameters under the operating state, and to control the contactor to disconnect the charging circuit or the discharging circuit according to the SOC value. The fifth unit is used to monitor the operating parameters and safety status parameters in real time. When an abnormality is detected, a corresponding fault identification code is generated, and the fault classification information and fault level information are determined based on the fault identification code. The sixth unit is used to record the fault identification code, the fault classification information and the fault level information into the fault record, and select the corresponding fault handling strategy according to the fault level information.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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