CN118174419A

CN118174419A - Cell calibration method, device, equipment and storage medium

Info

Publication number: CN118174419A
Application number: CN202410582924.0A
Authority: CN
Inventors: 徐楚
Original assignee: Shenzhen Haichen Energy Storage Technology Co ltd
Current assignee: Shenzhen Haichen Energy Storage Technology Co ltd
Priority date: 2024-05-11
Filing date: 2024-05-11
Publication date: 2024-06-11

Abstract

The disclosure provides a method, a device, equipment and a storage medium for calibrating a battery cell, and relates to the technical field of energy storage. Determining a charging parameter of a battery cell to be calibrated according to a preset capacity range of the battery cell to be calibrated; controlling the battery cells to be calibrated to charge by using charging parameters, and collecting voltage values and corresponding accumulated charging electric quantity of a plurality of groups of battery cells to be calibrated to obtain a characteristic parameter array; calibrating a target chemical system to which the cell to be calibrated belongs according to the characteristic parameter array of the cell to be calibrated; determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, controlling the battery cell to be calibrated to stop charging if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the end of charging; and calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity.

Description

Cell calibration method, device, equipment and storage medium

Technical Field

The disclosure relates to the technical field of energy storage, in particular to a battery cell calibration method, a battery cell calibration device, electronic equipment and a computer readable storage medium.

Background

In the energy storage field, the energy storage system includes a Battery cell, an energy management Unit (ENERGY MANAGEMENT Unit, EMU), an energy storage converter (Power Conversion System, PCS), a Battery management system (Battery MANAGEMENT SYSTEM, BMS), and the like. In the operation process of the energy storage system, the BMS acquires the performance parameters of the battery core, the BMS shares the performance parameters of the battery core to the EMU and the PCS, and the EMU transmits control information to the PCS and the BMS according to the optimization and scheduling strategy, so that the battery core is controlled to complete charging and discharging and the like.

In the related art, BMS software is typically developed and calibrated based on specific cell models, requiring manual configuration or adaptation by software update for different cells. However, the above-mentioned cell calibration method has the problems of reduced operation convenience and low cell calibration efficiency.

Disclosure of Invention

The disclosure provides a method, a device, equipment and a storage medium for calibrating a battery cell, which at least overcome the problems of complex operation and low efficiency of a battery cell calibration mode in the related art to a certain extent.

Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.

According to one aspect of the present disclosure, there is provided a cell calibration method, including: determining a charging parameter of the battery cell to be calibrated according to a preset capacity range of the battery cell to be calibrated; controlling the battery cell to be calibrated to charge by the charging parameters, and collecting a plurality of groups of voltage values and corresponding accumulated charging electric quantity of the battery cell to be calibrated to obtain a characteristic parameter array; calibrating a target chemical system to which the battery cell to be calibrated belongs according to the characteristic parameter array of the battery cell to be calibrated; determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, controlling the battery cell to be calibrated to stop charging, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the ending of charging; and calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity.

In one embodiment of the present disclosure, the calibrating the target chemical system to which the to-be-calibrated cell belongs according to the feature parameter array of the to-be-calibrated cell includes: obtaining a charging curve between the voltage value of the battery cell to be calibrated and the accumulated charging electric quantity according to the characteristic parameter array of the battery cell to be calibrated; determining a system calibration characteristic value according to the charging curve; and determining a target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value.

In one embodiment of the present disclosure, the system calibration feature value comprises a first derivative minimum of the charging curve; the determining the target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value comprises the following steps: and if the minimum value of the first derivative of the charging curve is a negative number, determining that the target chemical system to which the cell to be calibrated belongs is a lithium sulfur material system.

In one embodiment of the present disclosure, the system calibration feature values include a second derivative maximum and a second derivative minimum of the charging curve; the determining the target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value comprises the following steps: and if the maximum value of the second derivative and the minimum value of the second derivative are both negative numbers, calibrating the target chemical system to which the cell to be calibrated belongs as a ternary material system.

In one embodiment of the present disclosure, the system calibration feature values include a second derivative maximum and a second derivative minimum of the charging curve; the determining the target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value comprises the following steps: and if the maximum value of the second derivative is positive and the minimum value of the second derivative is negative, calibrating the target chemical system to which the battery cell to be calibrated belongs as a lithium iron phosphate material system.

In one embodiment of the present disclosure, the calibrating the target chemical system to which the to-be-calibrated cell belongs according to the feature parameter array of the to-be-calibrated cell includes: and processing the characteristic parameter array of the battery cell to be calibrated by adopting a pre-trained machine learning model to obtain a target chemical system to which the battery cell to be calibrated belongs, wherein the machine learning model is obtained by training the historical characteristic parameter array and the corresponding chemical system label.

In one embodiment of the present disclosure, the charging parameter includes any one of a charging current, a charging power.

In one embodiment of the present disclosure, the collecting the voltage values of the plurality of groups of the to-be-calibrated battery cells and the corresponding accumulated charge amounts to obtain the characteristic parameter array includes: carrying out ampere-hour integration in the charging process of the battery cell to be calibrated to obtain the charging electric quantity of the battery cell to be calibrated; when the electric quantity charged each time reaches a preset electric quantity change threshold, recording the voltage value of the electric core to be calibrated and the corresponding accumulated electric quantity charged each time to obtain an initial characteristic parameter array, wherein the voltage value of the electric core to be calibrated and the corresponding accumulated electric quantity charged each time are used as a group of characteristic parameter arrays in the initial characteristic parameter array; and screening the characteristic parameters with the voltage values within a first preset voltage range from the initial characteristic parameter array to obtain the characteristic parameter array.

In one embodiment of the present disclosure, the method further comprises: if the data quantity of the characteristic parameter array is larger than a preset data quantity threshold, processing the characteristic parameter array by adopting an interpolation method to obtain a remolded characteristic parameter array with the data quantity being the preset data quantity threshold; and if the data quantity of the characteristic parameter array is smaller than the preset data quantity threshold, processing the characteristic parameter array by adopting an average method to obtain a remodeled characteristic parameter array with the data quantity being the preset data quantity threshold.

In one embodiment of the disclosure, the calibrating the capacity of the to-be-calibrated battery cell according to the accumulated charging capacity includes: determining the calibration coefficient of the battery cell to be calibrated according to a target chemical system to which the battery cell to be calibrated belongs; and determining the capacity of the battery cell to be calibrated according to the accumulated charging capacity and the calibration coefficient.

In one embodiment of the present disclosure, before the determining the charging parameter of the to-be-calibrated battery cell according to the preset capacity range of the to-be-calibrated battery cell, the method further includes: acquiring initial performance data of the battery cell to be calibrated, and if the initial performance data of the battery cell to be calibrated meets the preset calibration condition within the preset sampling time, executing the operation of determining the charging parameters of the battery cell to be calibrated according to the preset capacity range of the battery cell to be calibrated.

In one embodiment of the disclosure, the initial performance data of the to-be-calibrated battery cell includes a temperature, a voltage variation amount of the to-be-calibrated battery cell and a charge and discharge state of the to-be-calibrated battery cell; the initial performance data of the battery cell to be calibrated meets preset calibration conditions, and the method comprises the following steps: the temperature of the battery cell to be calibrated is effective and is within a preset temperature range; the voltage of the battery cell to be calibrated is effective and is within a second preset voltage range; the charge and discharge state of the battery cell to be calibrated is static; and in each sampling interval, the voltage variation of the battery cell to be calibrated is within a preset voltage variation range.

According to another aspect of the present disclosure, there is provided a cell calibration device, including: the charging parameter determining module is used for determining the charging parameter of the battery cell to be calibrated according to the preset capacity range of the battery cell to be calibrated; the characteristic parameter acquisition module is used for controlling the battery cells to be calibrated to be charged by the charging parameters, and acquiring a plurality of groups of voltage values and corresponding accumulated charging electric quantity of the battery cells to be calibrated to obtain a characteristic parameter array; the chemical system calibration module is used for calibrating a target chemical system to which the battery cell to be calibrated belongs according to the characteristic parameter array of the battery cell to be calibrated; the charging capacity determining module is used for determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, controlling the battery cell to be calibrated to stop charging, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the end of charging; and the battery cell capacity calibration module is used for calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity.

According to another aspect of the present disclosure, there is also provided an electronic device including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform any of the above cell calibration methods via execution of the executable instructions.

According to another aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the cell calibration method of any one of the above.

According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the cell calibration method of any one of the above.

In the embodiment of the disclosure, according to a preset capacity range of a cell to be calibrated, determining a charging parameter of the cell to be calibrated; controlling the battery cells to be calibrated to charge by using charging parameters, and collecting voltage values and corresponding accumulated charging electric quantity of a plurality of groups of battery cells to be calibrated to obtain a characteristic parameter array; determining a target chemical system to which the cell to be calibrated belongs according to the characteristic parameter array of the cell to be calibrated; determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, controlling the battery cell to be calibrated to stop charging if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the end of charging; and calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity. The battery management system can realize the automatic calibration of the chemical system and the capacity of the single battery cell through BMS intelligent self-adaption, ensures the high efficiency and the accuracy of the battery management system, improves the adaptability of the battery cell, reduces the maintenance cost, promotes the recovery of the battery cell and the adaption of the battery cell, and has strong applicability.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.

Fig. 1 shows a schematic structural diagram of a cell calibration system in an embodiment of the disclosure.

Fig. 2 shows a flowchart of a method for calibrating a battery cell in an embodiment of the disclosure.

FIG. 3 illustrates a flow chart of a target chemistry system calibration method in an embodiment of the present disclosure.

FIG. 4 illustrates a flow chart of another target chemistry system calibration method in an embodiment of the present disclosure.

FIG. 5 illustrates a flow chart of yet another target chemistry system calibration method in an embodiment of the present disclosure.

FIG. 6 illustrates a flow chart of yet another target chemistry system calibration method in an embodiment of the present disclosure.

Fig. 7 shows a flowchart of another cell calibration method in an embodiment of the present disclosure.

FIG. 8 illustrates a machine learning model training method flowchart in an embodiment of the present disclosure.

Fig. 9 shows a flowchart of a method for collecting a feature parameter array in an embodiment of the disclosure.

Fig. 10 shows a flowchart of a method for determining the capacity of a cell to be calibrated in an embodiment of the disclosure.

Fig. 11 shows a flowchart of yet another cell calibration method in an embodiment of the present disclosure.

Fig. 12 shows a schematic diagram of a cell calibration device in an embodiment of the disclosure.

Fig. 13 shows a block diagram of an electronic device in an embodiment of the disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

Because of the strong timeliness and space properties of energy required by people, in order to reasonably utilize the energy and improve the utilization rate of the energy, one energy form needs to be stored by one medium or equipment and then converted into another energy form, and the energy is released in a specific energy form based on future application. At present, the main way of generating green electric energy is to develop green energy sources such as photovoltaic, wind power and the like to replace fossil energy sources.

At present, the generation of green electric energy generally depends on photovoltaic, wind power, water potential and the like, but wind energy, solar energy and the like generally have the problems of strong intermittence and large fluctuation, which can cause unstable power grid, insufficient peak electricity consumption, too much electricity consumption and unstable voltage can cause damage to the electric power, so that the problem of 'wind abandoning and light abandoning' possibly occurs due to insufficient electricity consumption requirement or insufficient power grid acceptance, and the problem needs to be solved by relying on energy storage. The energy is converted into other forms of energy through physical or chemical means and is stored, the energy is converted into electric energy when needed and released, in short, the energy storage is similar to a large-scale 'charge pal', the electric energy is stored when the photovoltaic and wind energy are sufficient, and the stored electric power is released when needed.

Taking electrochemical energy storage as an example, the present disclosure provides an energy storage device, in which a group of chemical batteries are disposed, chemical elements in the batteries are mainly utilized as energy storage media, and a charging and discharging process is accompanied with chemical reaction or change of the energy storage media, which simply means that electric energy generated by wind energy and solar energy is stored in the chemical batteries, and when the use of external electric energy reaches a peak, the stored electric quantity is released for use, or is transferred to a place where the electric quantity is short for reuse.

The present energy storage (i.e. energy storage) application scenario is comparatively extensive, including aspects such as power generation side energy storage, electric wire netting side energy storage and power consumption side energy storage, and the kind of corresponding energy storage equipment includes:

(1) The large energy storage power station applied to the wind power and photovoltaic power station side can assist renewable energy sources to generate electricity to meet grid-connected requirements, and meanwhile, the utilization rate of the renewable energy sources is improved; the energy storage power station is used as a high-quality active/reactive power regulating power supply in a power supply side, so that the load matching of electric energy in time and space is realized, the capacity of absorbing renewable energy sources is enhanced, the instantaneous power change is reduced, the impact on a power grid is reduced, the problem of generating and absorbing new energy sources is solved, and the energy storage power station has great significance in the aspects of standby of a power grid system, relieving peak load power supply pressure and peak regulation and frequency modulation;

(2) The energy storage container applied to the power grid side has the functions of mainly peak regulation, frequency modulation and power grid blocking and peak regulation relieving, and can realize peak clipping and valley filling of the power consumption load, namely the energy storage battery is charged when the power consumption load is low, and the stored electric quantity is released in the peak period of the power consumption load, so that the balance between power production and consumption, such as an energy storage power station system, is realized;

(3) The small energy storage cabinet applied to the electricity utilization side has the main functions of spontaneous electricity utilization, peak Gu Jiacha arbitrage, capacity cost management and power supply reliability improvement. According to the different application scenes, the electricity-side energy storage can be divided into an industrial and commercial energy storage cabinet, household energy storage equipment, an energy storage charging pile and the like, and is generally matched with the distributed photovoltaic. The energy storage can be used by industrial and commercial users for valley peak price difference arbitrage and capacity cost management. In the electric power market implementing peak-valley electricity price, the energy storage system is charged when the electricity price is low, and the energy storage system is discharged when the electricity price is high, so that peak-valley electricity price difference arbitrage is realized, and the electricity cost is reduced. In addition, the energy storage system is suitable for two industrial enterprises with electricity price, can store energy when electricity is used in low valley and discharge the energy when the electricity is used in peak load, so that peak power and the declared maximum demand are reduced, and the purpose of reducing the capacity electricity fee is achieved. The household photovoltaic distribution and storage can improve the spontaneous self-use level of the electric power. Due to high electricity prices and poor power supply stability, the photovoltaic installation requirements of users are pulled. Considering that the photovoltaic power generation is performed in daytime, and the load of a user is generally higher at night, the photovoltaic power can be better utilized through configuration of energy storage, the spontaneous self-use level is improved, and meanwhile the power consumption cost is reduced. In addition, the fields of communication base stations, data centers and the like need to be configured with energy storage for standby power.

Referring to fig. 1, fig. 1 is a schematic structural diagram of an energy storage system 100 according to an embodiment of the disclosure, and the embodiment of fig. 1 of the disclosure is illustrated by taking a power generation/distribution side shared energy storage scenario as an example, and the energy storage system 100 of the disclosure is not limited to the power generation/distribution side energy storage scenario.

The present disclosure provides an energy storage system 100, the energy storage system 100 comprising: the energy storage equipment 110, the high-voltage cable 120, the first electric energy conversion device 130 and the second electric energy conversion device 140, under the power generation condition, the first electric energy conversion device 130 and the second electric energy conversion device 140 are used for converting other forms of energy into electric energy, are connected with the high-voltage cable 120 and are supplied to the power distribution network power utilization side for use, when the power utilization load is low, the first electric energy conversion device 130 and the second electric energy conversion device 140 store multiple generated electric energy into the energy storage equipment 110 when the power generation is excessive, the wind abandoning and the light abandoning rate are reduced, and the power generation and consumption problem of new energy is improved; when the power consumption load is high, the power grid gives an instruction, the electric quantity stored by the energy storage device 110 is cooperated with the high-voltage cable 120 to transmit electric energy to the power consumption side for use in a grid-connected mode, multiple services such as peak regulation, frequency modulation and standby are provided for the operation of the power grid, the peak regulation effect of the power grid is fully exerted, peak clipping and valley filling of the power grid are promoted, and the power supply pressure of the power grid is relieved.

Alternatively, the first electric energy conversion device 130 and the second electric energy conversion device 140 may convert at least one of solar energy, optical energy, wind energy, thermal energy, tidal energy, biomass energy, mechanical energy, and the like into electric energy.

The number of the energy storage devices 110 may be plural, and the plurality of energy storage devices 110 may be connected in series or parallel, and the plurality of energy storage devices 110 may be supported by a separator (not shown) and electrically connected. In the present embodiment, "a plurality of" means two or more. The energy storage device 110 may be further provided with an energy storage box outside for accommodating the energy storage device 110.

Alternatively, the energy storage device 110 may include, but is not limited to, a battery cell, a battery module, a battery pack, a battery system, and the like. The practical application form of the energy storage device 110 provided in the embodiments of the present disclosure may be, but not limited to, the listed products, and may also be other application forms, and the embodiments of the present disclosure do not strictly limit the application form of the energy storage device 110. The embodiments of the present disclosure will be described with reference to the energy storage device 110 as a single battery. When the energy storage device 110 is a unit cell, the energy storage device 110 may be at least one of a cylindrical cell, a prismatic cell, and the like.

The energy storage system 100 further includes an energy management Unit (ENERGY MANEGEMENT Unit, EMU) for monitoring performance parameters of the energy storage device 110, where the performance parameters may be used as data to be processed, such as performance parameters of a battery module, and storing the monitored performance parameters of the energy storage device 110 to a server, such as a cloud server.

The performance parameters of the energy storage device 110 may include, but are not limited to, a voltage value, a current value, a temperature value, a State of Charge (SOC), a State of Health (SOH), etc. of the energy storage device 110.

The energy storage system 100 may further include, but is not limited to, a liquid cooling integrated cabinet, a Battery management system (Battery MANAGEMENT SYSTEM, BMS), an energy storage converter (Power Conversion System, PCS), fire-fighting equipment, air conditioner, general electric meter, and other entity auxiliary energy storage devices, and the EMU may also collect performance parameters of the entity auxiliary energy storage devices, so that according to the operation state and the requirement of the system, efficient utilization and saving of energy are achieved by controlling and adjusting various devices and parameters.

The BMS may also be called a battery nurse or a battery manager, and mainly comprises a detection module, a control module, a communication module and the like, and has the main functions of monitoring and controlling the State of the battery in real time, including but not limited to monitoring and controlling parameters such as the voltage, the current, the temperature, the State of Charge (SOC), the State of Health (SOH) and the like of the battery. Meanwhile, the battery management system can also perform protection control on the battery, such as overcharge, overdischarge, overcurrent and the like, so that the safety and the service life of the battery are ensured.

The energy storage converter mainly comprises an inverter, a transformer, a controller and the like, and is electric equipment for converting electric energy stored by a battery into alternating current and supplying the alternating current to a power grid or a user side. The main functions of the energy storage converter include converting direct current into alternating current, controlling input or output of electric energy, and ensuring safety and stability of the energy storage system. The performance of the energy storage converter directly affects the operating efficiency and the service life of the energy storage system.

The energy management unit is responsible for the control strategy of the energy storage system, which can influence the decay rate and the cycle life of the batteries in the system, thereby determining the energy storage economy. In addition, the energy management unit can monitor faults in the operation of the energy storage system, and plays roles of protecting equipment in time and guaranteeing safety.

The energy management unit can generate a control strategy of the energy storage system according to the operation parameters of the battery or the real-time operation parameters of the entity auxiliary energy storage equipment, such as the battery management system, the energy storage converter, the air conditioner, the general control ammeter and the like, acquired by the entity auxiliary energy storage equipment, so as to control the operation state of the battery device or the entity auxiliary energy storage equipment and the like.

In the related art, a cell chemical system is a key component constituting a core of a battery, enabling the battery to store and release electric energy. The battery cell consists of a positive electrode, a negative electrode, a diaphragm, electrolyte, a shell and the like, wherein the positive electrode and the negative electrode are the most important components. The positive electrode material is a material for storing positive electrode ions in the battery core, and commonly used positive electrode materials include ternary materials (such as NMC), lithium iron phosphate, lithium sulfur, lithium manganate and the like. The negative electrode is also an important component in the cell, and the negative electrode material is a material for storing negative ions in the cell, and commonly used negative electrode materials include graphite, polymers, and the like.

BMS software is typically developed and cell capacity calibration is performed based on a particular cell model, including the chemical system to which the cell belongs and the cell capacity, where the chemical system to which the cell belongs refers to the type of positive electrode material.

The different battery cells need to be manually configured or adapted by updating the BMS software, i.e. whenever a new type of battery cell is introduced into the battery system or the recovered battery cell is to be reused, the BMS software needs to be adjusted to ensure that the existing system is compatible with the battery cells newly added to the battery system. The method is completed by a professional technician, the battery cell charge and discharge test is carried out, and the BMS software is updated or parameters are recalibrated through off-line operation.

However, while the above approach provides a basic framework for battery management systems, there are significant drawbacks in handling multiple models of cells or recycling cells for reuse. The manual battery cell parameter calibration and BMS software upgrading can greatly reduce the convenience and calibration efficiency of operation, especially in the scene of standard specification battery cell adaptation and recycling battery cells. And the reliance on off-line manual configuration not only increases the time and economic costs, but also limits the adaptability of the battery management system in a dynamic use environment. Therefore, how to improve the adaptability of the battery pack, reduce the maintenance cost, and promote the recycling of the battery cells is a great challenge.

In order to solve at least part of the technical problems, the method for calibrating the battery cell provided by the present disclosure determines a charging parameter of the battery cell to be calibrated according to a preset capacity range of the battery cell to be calibrated; controlling the battery cells to be calibrated to charge by using charging parameters, and collecting voltage values and corresponding accumulated charging electric quantity of a plurality of groups of battery cells to be calibrated to obtain a characteristic parameter array; determining a target chemical system to which the cell to be calibrated belongs according to the characteristic parameter array of the cell to be calibrated; determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, controlling the battery cell to be calibrated to stop charging if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the end of charging; and calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity. The battery management system can realize the automatic calibration of the chemical system and the capacity of the single battery cell through BMS intelligent self-adaption, ensures the high efficiency and the accuracy of the battery management system, improves the adaptability of the battery cell, reduces the maintenance cost, promotes the recovery of the battery cell and the adaption of the battery cell, and has strong applicability.

Under the system architecture, the embodiment of the disclosure provides a cell calibration method, which can be executed by any electronic device with computing processing capability. Such as the BMS described above, etc.

Fig. 2 shows a flowchart of a method for calibrating a battery cell in an embodiment of the disclosure, and as shown in fig. 2, the method for calibrating a battery cell provided in the embodiment of the disclosure includes the following steps:

s202, determining the charging parameters of the battery cell to be calibrated according to the preset capacity range of the battery cell to be calibrated.

In one embodiment, the cell to be calibrated may be a new cell connected to the battery system, or may be a cell recycled after recovery, or may be another situation that needs to recalibrate the chemical system and capacity of the cell, which is not specifically limited in this disclosure.

The chemical system to which the cell to be calibrated belongs can comprise ternary materials, lithium iron phosphate, lithium sulfur, lithium manganate and the like.

The preset capacity range of the battery cell to be calibrated can be set according to actual situations, for example, the preset capacity range is set to 100 Ah-300 Ah.

It should be noted that the charging parameters of the battery cell to be calibrated include any one of charging current and charging power. When the charging parameter of the battery cell to be calibrated is charging current, the charging current is constant in the charging process, and the voltage value of the battery cell to be calibrated is gradually increased; when the charging parameter of the battery cell to be calibrated is charging power, the charging power is kept unchanged in the charging process, the voltage value of the battery cell to be calibrated is gradually increased, and the current of the corresponding battery cell to be calibrated is gradually reduced.

When a proper charging current is selected according to a preset capacity range of the battery cell to be calibrated, a small-rate current value with a low capacity limit value can be selected for charging, so that the characteristic extraction of a charging curve is ensured. For example, 100 Ah-300 Ah of the battery cell to be calibrated can be charged by selecting the multiplying power to be 0.2C (50A of charging current). When a battery cell to be calibrated of a new chemical system is introduced, the charging current corresponding to the battery cell to be calibrated can be pre-configured, a preset current range corresponding to the charging current of the battery cell to be calibrated can be obtained, and the minimum current value is selected from the preset current range to charge.

It should be noted that, the charging power of the battery cell to be calibrated may refer to the situation of the charging current, which is not described herein.

In one embodiment, the preset capacity range and the charging parameters of the battery cells to be calibrated may be preconfigured in the BMS, for example, stored in a charging parameter table of the BMS, and the charging parameters are determined by table lookup.

S204, controlling the battery cells to be calibrated to charge with the charging parameters, and collecting the voltage values and the corresponding accumulated charging electric quantity of the plurality of groups of battery cells to be calibrated to obtain a characteristic parameter array.

When the charging parameters of the battery cells to be calibrated are determined, the BMS controls the connection of the charging loops corresponding to the battery cells to be calibrated, and the charging of the battery cells to be calibrated is controlled according to the charging parameters obtained by looking up the table.

It should be noted that, the characteristic parameter array of the to-be-calibrated battery cell is used for recording the corresponding relationship between the real-time voltage of the to-be-calibrated battery cell and the accumulated charging electric quantity in the first preset voltage range in the charging process.

The first preset voltage range is preset in the BMS, and the first preset voltage range may be determined according to actual requirements, for example, the first preset voltage range may be 3000 v-3500 v.

In the charging process of the battery cells to be calibrated, the voltage values and the corresponding accumulated charging electric quantity of a plurality of groups of battery cells to be calibrated can be collected. In the actual acquisition process, the voltage variation of the battery cell to be calibrated can be fixed, and when the voltage value of the battery cell to be calibrated increases by the voltage variation, the accumulated charge quantity of the battery cell to be calibrated is obtained by ampere-hour integration, and the voltage value of the battery cell to be calibrated and the corresponding accumulated charge quantity are recorded each time to obtain a characteristic parameter array; the charging electric quantity variation of the battery cell to be calibrated can be fixed, and the voltage value of the battery cell to be calibrated and the corresponding accumulated charging electric quantity are recorded once to obtain the characteristic parameter array each time the charging electric quantity of the battery cell to be calibrated reaches the charging electric quantity variation.

For example, if the voltage variation is 10mV, the ampere-hour integration is started when the voltage value of the to-be-calibrated battery is 3000mV, the accumulated charge amount obtained by the ampere-hour integration is recorded as Q0 when the voltage value of the to-be-calibrated battery is 3000mV, the accumulated charge amount obtained by the ampere-hour integration is recorded as Q1 when the voltage value of the to-be-calibrated battery is 3010mV, Q1) is recorded as a set of characteristic parameters, the accumulated charge amount obtained by the ampere-hour integration is recorded as Q2 when the voltage value of the to-be-calibrated battery is 3020mV, the { (3020, Q2) is recorded as a second set of characteristic parameters, and so on until the voltage value of the to-be-calibrated battery reaches 3500mV, the accumulated charge amount obtained by the ampere-hour integration is recorded as Qm { (3500, qm) }, wherein m is the acquisition times, the characteristic extraction is completed, and the characteristic parameter sets { (3000, q0) (3010, qm) … … (3500, qm) }.

S206, calibrating a target chemical system to which the cell to be calibrated belongs according to the characteristic parameter array of the cell to be calibrated.

In one embodiment, the chemical system to which the cell belongs may include ternary material systems, lithium iron phosphate material systems, lithium sulfur material systems, lithium manganate material systems, and the like. Each chemical system corresponds to different characteristic parameters of the battery cell to be calibrated, and then the target chemical system to which the battery cell to be calibrated belongs can be determined according to the characteristic parameters of the battery cell to be calibrated.

And S208, determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, controlling the battery cell to be calibrated to stop charging if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the ending of charging.

In one embodiment, determining the charging termination voltage of the cell to be calibrated based on the target chemical system in S208 includes: and determining the charge termination voltage corresponding to the target chemical system according to a preset corresponding relation, wherein the preset corresponding relation is used for representing the corresponding relation between the target chemical system and the charge termination voltage.

The preset correspondence may be pre-configured in the BMS, and one target chemical system may correspond to a fixed value of the charging termination voltage, or may correspond to a range of the charging termination voltage.

The charge termination voltage may be configured to be 4.2V for a ternary material system cell and 3.65V for a lithium iron phosphate material system cell.

It should be noted that the specific manner of charging termination voltage of the battery cells of the above material system is merely provided as an example for illustrating the embodiments of the present disclosure, and should not be construed as limiting the scope of the present disclosure.

When the voltage value of the battery cell to be calibrated is larger than or equal to the charging termination voltage, the battery cell to be calibrated is judged to be charged, the BMS disconnects the battery cell to be calibrated from the charging loop, so that the battery cell to be calibrated stops charging, and the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the ending of charging can be calculated through ampere-hour integration.

S210, calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity.

In one embodiment, after the accumulated charge capacity of the to-be-calibrated battery cell is obtained, the capacity of the to-be-calibrated battery cell can be calibrated based on the accumulated charge capacity, and as an initial calibration capacity, a State of Health (SOH) estimation algorithm is transferred to for subsequent capacity learning to perform real-time estimation.

In the embodiment of the disclosure, according to a preset capacity range of a cell to be calibrated, determining a charging parameter of the cell to be calibrated; controlling the battery cells to be calibrated to charge by using charging parameters, and collecting voltage values and corresponding accumulated charging electric quantity of a plurality of groups of battery cells to be calibrated to obtain a characteristic parameter array; calibrating a target chemical system to which the cell to be calibrated belongs according to the characteristic parameter array of the cell to be calibrated; determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, controlling the battery cell to be calibrated to stop charging if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the end of charging; and calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity. The battery management system can realize the automatic calibration of the chemical system and the capacity of the single battery cell through BMS intelligent self-adaption, ensures the high efficiency and the accuracy of the battery management system, improves the adaptability of the battery cell, reduces the maintenance cost, promotes the recovery of the battery cell and the adaption of the battery cell, and has strong applicability.

FIG. 3 illustrates a flow chart of a target chemistry system calibration method in an embodiment of the present disclosure. Based on the embodiment of fig. 2, S206 is further refined into S302 to S306, so as to define a chemical system for calibrating the cell to be calibrated by extracting the system calibration characteristic value of the charging curve. As shown in fig. 3, in one embodiment, S206 is configured to calibrate a target chemical system to which the cell to be calibrated belongs according to the feature parameter array of the cell to be calibrated, including:

S302, obtaining a charging curve between a voltage value and an accumulated charging electric quantity of the battery cell to be calibrated according to a characteristic parameter array of the battery cell to be calibrated;

S304, determining a system calibration characteristic value according to the charging curve;

s306, determining a target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value.

After the characteristic parameter array of the battery cell to be calibrated is obtained, the characteristic parameters can be fitted by taking the accumulated charging electric quantity Q as an abscissa and taking the voltage value V of the battery cell to be calibrated as an ordinate, so that a charging curve (namely V-Q curve) of the battery cell to be calibrated is obtained.

The system calibration characteristic value is used for representing a charging curve or data points with representative properties of the charging curve, the determination and extraction of the system calibration characteristic value are required to be determined based on different chemical systems, the chemical system ranges of the battery cells are different, and the extracted system calibration characteristic value is different.

The system calibration feature values determined from the charging curve may include, but are not limited to, a first derivative minimum, a second derivative maximum, a second derivative minimum, etc. of the charging curve. The minimum value of the first derivative of the charging curve can be obtained by performing first-order derivation on the V-Q curve, so that a change rate curve of the voltage value of the battery cell to be calibrated and the accumulated charging electric quantity, namely a dV/dQ curve, and the minimum value of the first derivative can be determined according to the dV/dQ curve.

The maximum value and the minimum value of the second derivative of the charging curve can be obtained by performing second derivative on the V-Q curveCurve according to/>The curve may determine a second derivative maximum and a second derivative minimum.

In one embodiment, the corresponding relation between the system calibration characteristic values corresponding to different chemical systems may be stored in advance, and after the system calibration characteristic value of the to-be-calibrated battery cell is determined, the target chemical system to which the to-be-calibrated battery cell corresponding to the system calibration characteristic value belongs is determined according to the corresponding relation.

It should be noted that, the voltage value V of the to-be-calibrated battery cell may be used as an abscissa, and the accumulated charge quantity Q may be used as an ordinate to fit the characteristic parameters to obtain a charge curve (i.e. Q-V curve) of the to-be-calibrated battery cell, and on this basis, extraction of the system calibration characteristic value and determination of the target chemical system to which the to-be-calibrated battery cell belongs may be performed.

In the embodiment of the disclosure, the charging curve of the battery cell to be calibrated is obtained by fitting the characteristic parameter array of the battery cell to be calibrated, and the system calibration characteristic value is extracted, so that the target chemical system of the battery cell to be calibrated is calibrated, the target chemical system of the battery cell to be calibrated can be calibrated quickly and efficiently, the BMS can realize the automatic calibration of the chemical system of the single battery cell in an intelligent self-adaptive manner, the maintenance cost is reduced, the battery cell recovery and the battery cell adaptation are promoted, and the applicability is strong.

FIG. 4 illustrates a flow chart of another target chemistry system calibration method in an embodiment of the present disclosure. Based on the embodiment of fig. 3, S306 is further refined to S3062, so as to define the case that the system calibration feature value is the minimum value of the first derivative of the charging curve. As shown in FIG. 4, in one embodiment, the system calibration feature value comprises a first derivative minimum of the charging curve; s306 determines a target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value, and comprises the following steps:

s3062, if the minimum value of the first derivative of the charging curve is a negative number, determining that the target chemical system to which the cell to be calibrated belongs is a lithium sulfur material system.

It should be noted that, the specific implementation manner of S302 to S304 in the embodiment is the same as the specific implementation manner of S302 to S304 in the foregoing embodiment, and will not be repeated here.

For lithium ion batteries, in the first derivative dV/dQ curve of the charge curve, the peak may reflect the phase change of the active material during intercalation and deintercalation. In the disclosure, when the minimum value of the first derivative of the charging curve of the cell to be calibrated is a negative number, the voltage value of the cell to be calibrated is a non-monotonic curve, and the target chemical system to which the cell to be calibrated belongs is calibrated as a lithium sulfur material system.

FIG. 5 illustrates a flow chart of yet another target chemistry system calibration method in an embodiment of the present disclosure. Based on the embodiment of fig. 3, S306 is further refined to S3064, so as to define the case that the system calibration feature value is the maximum value and the minimum value of the second derivative of the charging curve. As shown in FIG. 5, in one embodiment, the system calibration feature values include a second derivative maximum and a second derivative minimum of the charging curve; s306 determines a target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value, and comprises the following steps:

S3064, if the maximum value and the minimum value of the second derivative are negative numbers, the target chemical system to which the cell to be calibrated belongs is calibrated to be a ternary material system.

FIG. 6 illustrates a flow chart of yet another target chemistry system calibration method in an embodiment of the present disclosure. Based on the embodiment of fig. 3, S306 is further refined to S3066 to define another case where the system calibration feature value is the maximum value and the minimum value of the second derivative of the charging curve. In one embodiment, the system calibration feature values include a second derivative maximum and a second derivative minimum of the charging curve; s306 determines a target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value, and comprises the following steps:

s3066, if the maximum value of the second derivative is positive and the minimum value of the second derivative is negative, the target chemical system to which the cell to be calibrated belongs is calibrated to be a lithium iron phosphate material system.

In the embodiment of the disclosure, the battery cell of the lithium sulfur material system can be calibrated rapidly by selecting a proper system calibration characteristic value, the calibration precision and the calibration efficiency are improved, the capacity of the battery cell can be calibrated in a self-adaptive mode for BMS software, and the high efficiency and the accuracy of a battery management system are ensured.

It should be noted that the selection of the system calibration feature values and the manner of determining the cell chemical system according to the system calibration feature values are merely examples provided for illustrating the embodiments of the present disclosure, and should not be construed as limiting the protection scope of the present disclosure. And (3) for other chemical systems of the battery cell, properly adjusting the calibration characteristic value of the system and the corresponding calibration condition.

Fig. 7 shows a flowchart of another cell calibration method in an embodiment of the present disclosure. S206 is further refined to S2062 on the basis of the embodiment of fig. 2 to define the case of calibrating the cell chemistry system by means of a pre-trained machine learning model. As shown in fig. 7, in one embodiment, the calibrating the target chemical system to which the cell to be calibrated belongs according to the feature parameter array of the cell to be calibrated in S206 includes:

S2062, processing the characteristic parameter array of the battery cell to be calibrated by adopting a pre-trained machine learning model to obtain a target chemical system to which the battery cell to be calibrated belongs, wherein the machine learning model is obtained by training the historical characteristic parameter array and the corresponding chemical system label.

In one embodiment, the machine learning model may be a machine learning model such as an artificial neural network (ARTIFICIAL NEURAL NETWORK, ANN), a support vector machine (Support Vector Machine, SVM), a relevance vector machine (RELEVANCE VECTOR MACHINE, RVM), a k-Nearest Neighbor (kNN), a random forest regression (Random Forest Regression, RFR), and the like.

And inputting the characteristic parameter array of the battery cell to be calibrated into a pre-trained machine learning model as an input quantity, and taking a target chemical system to which the battery cell to be calibrated belongs as an output quantity. It should be noted that, the machine learning model is a model after training, and the training of the machine learning model is obtained based on the historical characteristic parameter array of the battery cell and the corresponding chemical system label.

In the embodiment of the disclosure, a target chemical system to which the battery cell to be calibrated belongs is obtained through a pre-trained machine learning model and a characteristic parameter array of the battery cell to be calibrated, so that the prediction precision and accuracy of the battery cell chemical system are improved, the prediction time of the battery cell chemical system is shortened, and the calibration efficiency of the battery cell chemical system is improved.

FIG. 8 illustrates a machine learning model training method flowchart in an embodiment of the present disclosure. As shown in fig. 8, in one embodiment, the machine learning model is trained by:

S802, acquiring a history characteristic parameter array and a corresponding chemical system label;

S804, processing the historical characteristic parameter array through a machine learning model to be trained to obtain a chemical system predicted value of the battery cell;

s806, determining a current loss function value according to the chemical system predicted value and the chemical system label of the battery cell;

s808, judging whether a preset training stop condition is met, and if so, executing S810; if not, executing S812;

s810, obtaining a pre-trained machine learning model;

and S812, adjusting model parameters of the machine learning model to be trained, returning to S804 for retraining until the preset training stop condition is met.

In one embodiment, the historical feature parameter array and the corresponding chemical system label may be acquired during use of the cell.

The satisfaction of the preset training stop condition in S808 may include: if the current loss function value is smaller than or equal to the preset loss function threshold and the training frequency reaches the preset frequency threshold, or if the current loss function value is smaller than or equal to the preset loss function threshold and the training time length reaches the preset time length threshold, judging that the preset training stop condition is met.

The loss function of the machine learning model may be a mean square error loss function, a root mean square error loss function or an average absolute error loss function, and may be determined according to practical situations.

It should be noted that the foregoing preset loss function threshold, the preset number of times threshold, and the preset duration threshold may be set according to actual situations, for example, the preset loss function threshold may be 0.2, the preset number of times threshold may be 1000, the preset duration threshold may be 12 hours, and the disclosure is not limited specifically.

In one embodiment, when the current loss function value of the chemical system prediction value to which the chemical system tag and the battery cell belong is greater than a preset loss function threshold, the difference between the chemical system prediction values to which the chemical system tag and the battery cell belong is indicated to be greater, and the machine learning model cannot accurately determine the chemical system to which the battery cell belongs, so that model parameters of the machine learning model need to be adjusted, for example, the model parameters of the machine learning model may include weight coefficients of each functional layer, and the machine learning model after parameter adjustment is retrained according to a historical feature parameter array, and the steps are repeated until a preset training stop condition is met, thereby obtaining a pre-trained machine learning model; therefore, the prediction accuracy of the chemical system of the battery cell is improved, and the predicted value of the chemical system of the battery cell is closer to the actual value.

Fig. 9 shows a flowchart of a method for collecting a feature parameter array in an embodiment of the disclosure. Based on the embodiment of fig. 2, S204 is further refined into S902 to S906, so as to define the collection mode of the feature parameter array. As shown in fig. 9, in one embodiment, the step S204 of collecting the voltage values of the plurality of groups of to-be-calibrated cells and the corresponding accumulated charge amounts to obtain the feature parameter array includes:

s902, carrying out ampere-hour integration in the charging process of the battery cell to be calibrated to obtain the charging electric quantity of the battery cell to be calibrated;

S904, when the electric quantity of each time reaches a preset electric quantity change threshold, recording a voltage value of the electric core to be calibrated and a corresponding accumulated electric quantity of the electric core to be calibrated to obtain an initial characteristic parameter array, wherein the voltage value of the electric core to be calibrated and the corresponding accumulated electric quantity of each time recorded are used as a group of characteristic parameter arrays in the initial characteristic parameter array;

S906, screening characteristic parameters with voltage values within a first preset voltage range from the initial characteristic parameter array to obtain the characteristic parameter array.

In one embodiment, the characteristic parameter array of the to-be-calibrated battery cell is used for recording the corresponding relation between the real-time voltage of the to-be-calibrated battery cell and the accumulated charging electric quantity in the first preset voltage range in the charging process, and the target chemical system of the battery cell is calibrated through the small voltage range, so that the charging end voltage of the battery cell is determined according to the target chemical system of the battery cell, and the processing efficiency of the battery cell calibration is improved.

In this embodiment, the change amount of the charging electric quantity of the to-be-calibrated battery cell can be fixed, and the voltage value of the to-be-calibrated battery cell and the corresponding accumulated charging electric quantity are recorded each time to obtain an initial characteristic parameter array, where the initial characteristic parameter array is also used as a basis for obtaining the accumulated charging capacity by carrying out subsequent ampere-hour integration.

The preset power change threshold may be determined according to practical situations, and the disclosure is not limited specifically. For example, for a cell with a capacity low of 0.5% soc,100Ah to 300Ah, the preset power change threshold may be set to 0.5Ah.

For example, when the preset electric quantity change threshold is set to 0.5Ah, the to-be-calibrated battery cell performs ampere-hour integration in the charging process, voltage values of the to-be-calibrated battery cell corresponding to ampere-hour integration of 0 and 0.5Ah … … and 0.5n Ah are recorded respectively, namely V0 and V1 … … Vn, the obtained initial characteristic parameter array is { (V0, 0) (V1, 0.5) … … (Vn, 0.5 n) }, wherein n is a positive integer, and the data quantity of the initial characteristic parameter array is represented. And screening characteristic parameters of the voltage value of the battery cell to be calibrated in a first preset voltage range from the initial characteristic parameter array to obtain the characteristic parameter array, wherein for example, if V1-Vn are in the first preset voltage range, the characteristic parameter array is { (V1, 0.5) … … (Vn, 0.5 n) }.

In the embodiment of the disclosure, when the electric quantity charged each time reaches the preset electric quantity change threshold, the voltage value of the electric core to be calibrated and the corresponding accumulated electric quantity charged are recorded, so that the characteristic parameter array can be conveniently and rapidly obtained, and a data base is provided for the calibration of the electric core.

In one embodiment, the method for calibrating the capacity of the battery cell provided by the embodiment of the disclosure further includes: if the data quantity of the characteristic parameter array is larger than a preset data quantity threshold value, processing the characteristic parameter array by adopting an interpolation method to obtain a remodeled characteristic parameter array with the data quantity being the preset data quantity threshold value; if the data quantity of the characteristic parameter array is smaller than the preset data quantity threshold value, an average method is adopted to process the characteristic parameter array, and the remodeled characteristic parameter array with the data quantity being the preset data quantity threshold value is obtained.

The preset data amount threshold may be set according to actual requirements, for example, the preset data amount threshold is configured to be 20, that is, the remodeled feature parameter array includes 20 pairs of feature parameters.

Assuming that a preset data quantity threshold of the characteristic parameter array is defined as Nd, the data quantity of the recorded characteristic parameter array is Np, and when Nd is larger than Np, an interpolation method (such as a linear interpolation method) can be adopted to remodel the characteristic parameter array, and the data quantity of the remodeled characteristic parameter array is Nd; when Nd is smaller than Np, an average method (such as a moving average method) can be adopted to remodel the characteristic parameter array, and the data volume of the remodeled characteristic parameter array is Nd.

For example, when the threshold of the characteristic parameter array is configured to be 20 and the data volume of the recorded characteristic parameter array is 11, the situation that Nd > Np is satisfied, and the characteristic parameter array is remodelled by adopting an interpolation method. If the recorded characteristic parameter array is expressed as { (V1, 0.5) (V2, 1) … … (V11, 5.5) }, the first inserted characteristic parameter may be expressed as [ ]0.75), The average value of two adjacent characteristic parameters is used as an insertion value, 10 groups of insertion characteristic parameters are obtained, the insertion characteristic parameters are sequentially inserted into an original characteristic parameter array according to the sequence, and the remolded characteristic parameter array is expressed as { (V1, 0.5) (/ >0.75) (V2, 1) … … (V11, 5.5) }. It should be noted that the number of the inserted feature parameters may be adjusted according to practical situations, for example, discarding one of the feature parameters, adding one of the feature parameters, and the like.

For example, when the threshold of the characteristic parameter array is configured to be 20 and the data volume of the recorded characteristic parameter array is 30, the situation that Nd < Np is satisfied, and the characteristic parameter array is remodeled by adopting an average method. If the recorded feature parameter array is expressed as { (V1, 0.5) (V2, 1) (V3, 1.5) … … (V30, 15) }, then the average of the two adjacent sets of feature parameters can be used as a trend term to obtain the remodeled feature parameter array.

Because the capacity of the battery cell to be calibrated is uncertain, the data volume in the acquired characteristic parameter array is uncertain in the charging process, and the dimension of the characteristic parameter array is remodeled according to the data volume of the characteristic parameter array, so that the characteristic parameter array can be ensured to have enough data volume for fitting a charging curve or training and predicting a machine learning model, and the problem of large calculation amount caused by overlarge data volume of the characteristic parameter array can be prevented, thereby improving the efficiency of battery cell capacity calibration.

Fig. 10 shows a flow chart of a method for calibrating the capacity of a cell to be calibrated in an embodiment of the disclosure. Based on the embodiment of fig. 2, S210 is further refined to S1002 to S1004 to calibrate the capacity of the cell to be calibrated. In one embodiment, as shown in fig. 10, the step S210 of calibrating the capacity of the cell to be calibrated according to the accumulated charge capacity includes:

s1002, determining the calibration coefficient of the cell to be calibrated according to a target chemical system to which the cell to be calibrated belongs;

S1004, determining the capacity of the battery cell to be calibrated according to the accumulated charge capacity and the calibration coefficient.

In one embodiment, the capacity of the cell to be calibrated can be obtained by accumulating the ratio of the charging capacity to the calibration coefficient; the product of the charging capacity and the calibration coefficient can be accumulated to obtain the capacity of the battery cell to be calibrated.

The capacity of the battery cell to be calibrated is obtained by integrating the ratio of the charging capacity to the calibration coefficient.

When the target chemical system of the cell to be calibrated is a ternary material system, determining the calibration coefficient of the cell to be calibrated as 1; when the target chemical system of the cell to be calibrated is a lithium iron phosphate material system, the calibration coefficient of the cell to be calibrated is determined to be 0.98 because the cell of the lithium iron phosphate material system has a part of capacity before charging is started.

It should be noted that, for the battery cells of other chemical systems, there is a difference in the existing capacity before starting charging, different calibration coefficients can be determined according to the battery cells of different chemical systems, and the values of the calibration coefficients can be determined through experiments or experience, which is not particularly limited in the disclosure.

In the embodiment of the disclosure, different calibration coefficients are determined for different chemical systems, so that the existing capacity of the battery cell before charging is started is fully considered, and the accuracy and precision of battery cell capacity calibration are improved.

Fig. 11 shows a flowchart of yet another cell calibration method in an embodiment of the present disclosure. Based on the embodiment of fig. 2, S201 is added to define the starting conditions of the cell capacity calibration before S202. As shown in fig. 11, in one embodiment, before determining the charging parameter of the to-be-calibrated battery cell according to the preset capacity range of the to-be-calibrated battery cell in S202, the battery cell capacity calibration method provided by the embodiment of the present disclosure further includes:

s201, acquiring initial performance data of the battery cell to be calibrated, and if the initial performance data of the battery cell to be calibrated meets the preset calibration condition within the preset sampling time, executing S202 to determine the charging parameters of the battery cell to be calibrated according to the preset capacity range of the battery cell to be calibrated.

It should be noted that, the specific implementation manner of S202 to S210 in the embodiment of the present disclosure is the same as the specific implementation manner of S202 to S210 in the foregoing embodiment, and will not be repeated here.

In one embodiment, the initial performance data of the cell to be calibrated comprises the temperature, voltage variation and charge and discharge states of the cell to be calibrated; the initial performance data of the battery cell to be calibrated meets the preset calibration conditions, and the method comprises the following steps: the temperature of the battery cell to be calibrated is effective and is within a preset temperature range; the voltage of the battery cell to be calibrated is effective and is within a second preset voltage range; the charge and discharge state of the cell to be calibrated is static; and in each sampling interval, the voltage variation of the battery cell to be calibrated is within a preset voltage variation range.

The fact that the temperature of the battery cell to be calibrated is effective means that the temperature sampling hardware is free of problems and the temperature sampling value is assigned to be an effective value. The preset temperature range may be 15-30 ℃.

The voltage of the cell to be calibrated is valid, namely the voltage sampling hardware is not abnormal, and the voltage sampling value is assigned to be a valid value. The second preset voltage range may be less than or equal to 3000mV.

The state of charge of the cell to be calibrated is a state in which the cell is not charged or discharged.

The sampling interval may be configured to collect the voltage value of the cell to be calibrated once every 1s, and the sampling interval may also be referred to as a step size. The voltage variation of the battery cell to be calibrated is the difference value of the voltage values obtained by two adjacent times, the preset voltage variation range can be set to be between-5 mV and 5mV, namely, the voltage variation of the battery cell to be calibrated does not exceed 5mV in each sampling interval. The preset sampling duration may be 5min.

It should be noted that, the values of the preset sampling period, the preset temperature range, the second preset voltage range, the preset voltage variation range, and the like are merely examples provided for illustrating the embodiments of the present disclosure, and should not be considered as limiting the protection scope of the present disclosure. The values of the respective amounts may be determined according to actual situations, and the present disclosure is not particularly limited.

In one embodiment, when the cell to be calibrated is kept stationary in a normal temperature environment, the voltage is effective and lower than 3V, and the cell to be calibrated is kept stable for a preset sampling period, the cell to be calibrated is judged to reach a preset calibration condition, and a calibration indication position is triggered, so that BMS software enters a calibration state. The calibration indication bit is used for indicating the BMS software to enter a calibration state.

It should be noted that, besides the above performance parameters of the battery core to be calibrated, such as temperature, voltage variation, charge and discharge states, etc., which meet preset conditions, other performance parameters for determining whether the battery core can be calibrated can also be used as a basis for determining whether the BMS software enters the calibration state, and the disclosure is not limited specifically.

In the embodiment of the disclosure, by acquiring the initial performance data of the battery cell to be calibrated, when the initial performance data meets the preset calibration condition, the calibration indication position is triggered to indicate the BMS software to enter the calibration state, so that the battery cell is ensured to stand under the normal temperature environment, the influence of factors such as temperature fluctuation on the battery cell state is avoided, and the effectiveness and reliability of the battery cell capacity calibration are improved.

Based on the same inventive concept, the embodiment of the disclosure also provides a cell calibration device, as described in the following embodiment. Since the principle of solving the problem of the embodiment of the device is similar to that of the embodiment of the method, the implementation of the embodiment of the device can be referred to the implementation of the embodiment of the method, and the repetition is omitted.

Fig. 12 shows a schematic diagram of a cell calibration device according to an embodiment of the disclosure, and as shown in fig. 12, the device includes a charging parameter determination module 1210, a characteristic parameter acquisition module 1220, a chemical system calibration module 1230, a charging capacity determination module 1240, and a cell capacity calibration module 1250.

The charging parameter determining module 1210 is configured to determine a charging parameter of the to-be-calibrated battery cell according to a preset capacity range of the to-be-calibrated battery cell;

the characteristic parameter acquisition module 1220 is used for controlling the battery cells to be calibrated to be charged by the charging parameters, and acquiring the voltage values and the corresponding accumulated charging electric quantity of the plurality of groups of battery cells to be calibrated to obtain a characteristic parameter array;

the chemical system calibration module 1230 is configured to determine, according to the characteristic parameter array of the to-be-calibrated battery cell, a target chemical system to which the to-be-calibrated battery cell belongs;

The charging capacity determining module 1240 is configured to determine a charging termination voltage of the to-be-calibrated battery cell based on the target chemical system, and if the voltage value of the to-be-calibrated battery cell reaches the charging termination voltage, control the to-be-calibrated battery cell to stop charging, and calculate an accumulated charging capacity of the to-be-calibrated battery cell from starting charging to ending charging;

and the battery cell capacity calibration module 1250 is used for calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity.

It should be noted that, the above-mentioned charging parameter determining module 1210, the characteristic parameter collecting module 1220, the chemical system calibration module 1230, the charging capacity determining module 1240 and the cell capacity calibration module 1250 correspond to S202 to S210 in the method embodiment, and the above-mentioned modules are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the disclosure of the above-mentioned method embodiment. It should be noted that the modules described above may be implemented as part of an apparatus in a computer system, such as a set of computer-executable instructions.

In one embodiment, the chemical system calibration module 1230 is configured to obtain a charging curve between a voltage value of the to-be-calibrated battery cell and an accumulated charging electric quantity according to a characteristic parameter array of the to-be-calibrated battery cell; determining a system calibration characteristic value according to the charging curve; and determining a target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value.

In one embodiment, the system calibration feature value comprises a first derivative minimum of the charging curve; the chemical system calibration module 1230 is configured to determine that the target chemical system to which the cell to be calibrated belongs is a lithium sulfur material system if the minimum value of the first derivative of the charging curve is a negative value.

In one embodiment, the system calibration feature values include a second derivative maximum and a second derivative minimum of the charging curve; the chemical system calibration module 1230 is configured to calibrate the target chemical system to which the cell to be calibrated belongs to as a ternary material system if the maximum value of the second derivative and the minimum value of the second derivative are both negative numbers.

In one embodiment, the system calibration feature values include a second derivative maximum and a second derivative minimum of the charging curve; the chemical system calibration module 1230 is configured to calibrate the target chemical system to which the cell to be calibrated belongs to as a lithium iron phosphate material system if the maximum value of the second derivative is positive and the minimum value of the second derivative is negative.

In one embodiment, the chemical system calibration module 1230 is configured to process the feature parameter array of the to-be-calibrated battery cell by using a pre-trained machine learning model to obtain a target chemical system to which the to-be-calibrated battery cell belongs, where the machine learning model is obtained by training the historical feature parameter array and a corresponding chemical system label.

The charging parameter includes any one of a charging current and a charging power.

In one embodiment, the characteristic parameter collection module 1220 is configured to perform ampere-hour integration during the charging process of the to-be-calibrated battery cell, so as to obtain the charging electric quantity of the to-be-calibrated battery cell; when the electric quantity charged each time reaches a preset electric quantity change threshold, recording a voltage value of the electric core to be calibrated and a corresponding accumulated electric quantity charged each time to obtain an initial characteristic parameter array, wherein the voltage value of the electric core to be calibrated and the corresponding accumulated electric quantity charged each time are used as a group of characteristic parameter arrays in the initial characteristic parameter array; and screening the characteristic parameters with the voltage values within a first preset voltage range from the initial characteristic parameter array to obtain the characteristic parameter array.

In one embodiment, the feature parameter collection module 1220 is further configured to, if the data size of the feature parameter array is greater than the preset data size threshold, process the feature parameter array by adopting an interpolation method to obtain a remodeled feature parameter array with the data size being the preset data size threshold; if the data quantity of the characteristic parameter array is smaller than the preset data quantity threshold value, an average method is adopted to process the characteristic parameter array, and the remodeled characteristic parameter array with the data quantity being the preset data quantity threshold value is obtained.

In one embodiment, the cell capacity calibration module 1250 is configured to determine a calibration coefficient of the cell to be calibrated according to a target chemical system to which the cell to be calibrated belongs; and determining the capacity of the battery cell to be calibrated according to the accumulated charging capacity and the calibration coefficient.

In one embodiment, the device further includes a calibration state determining module, not shown in the drawings, wherein the calibration state determining module is configured to obtain initial performance data of the to-be-calibrated battery cell before determining the charging parameter of the to-be-calibrated battery cell according to the preset capacity range of the to-be-calibrated battery cell, and if the initial performance data of the to-be-calibrated battery cell satisfies the preset calibration condition within the preset sampling duration, execute an operation of determining the charging parameter of the to-be-calibrated battery cell according to the preset capacity range of the to-be-calibrated battery cell.

It should be noted that, the initial performance data of the battery cell to be calibrated includes the temperature, voltage variation and charge and discharge states of the battery cell to be calibrated; the initial performance data of the battery cell to be calibrated meets the preset calibration conditions, and the method comprises the following steps: the temperature of the battery cell to be calibrated is effective and is within a preset temperature range; the voltage of the battery cell to be calibrated is effective and is within a second preset voltage range; the charge and discharge state of the cell to be calibrated is static; and in each sampling interval, the voltage variation of the battery cell to be calibrated is within a preset voltage variation range.

Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

An electronic device 1300 according to such an embodiment of the present disclosure is described below with reference to fig. 13. The electronic device 1300 shown in fig. 13 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.

As shown in fig. 13, the electronic device 1300 is embodied in the form of a general purpose computing device. The components of the electronic device 1300 may include, but are not limited to: the at least one processing unit 1310, the at least one memory unit 1320, and a bus 1330 connecting the different system components (including the memory unit 1320 and the processing unit 1310).

Wherein the storage unit stores program code that is executable by the processing unit 1310 such that the processing unit 1310 performs steps according to various exemplary embodiments of the present disclosure described in the above section of the "exemplary method" of the present specification. For example, the processing unit 1310 may perform the following steps of the method embodiment described above: determining the charging parameters of the battery cell to be calibrated according to the preset capacity range of the battery cell to be calibrated; controlling the battery cells to be calibrated to charge by using charging parameters, and collecting voltage values and corresponding accumulated charging electric quantity of a plurality of groups of battery cells to be calibrated to obtain a characteristic parameter array; determining a target chemical system to which the cell to be calibrated belongs according to the characteristic parameter array of the cell to be calibrated; determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, controlling the battery cell to be calibrated to stop charging if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the end of charging; and calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity.

The storage unit 1320 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 13201 and/or cache memory 13202, and may further include Read Only Memory (ROM) 13203.

The storage unit 1320 may also include a program/utility 13204 having a set (at least one) of program modules 13205, such program modules 13205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.

Bus 1330 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.

The electronic device 1300 may also communicate with one or more external devices 1340 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 1300, and/or any device (e.g., router, modem, etc.) that enables the electronic device 1300 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1350. Also, the electronic device 1300 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, for example, the Internet, through a network adapter 1360. As shown, the network adapter 1360 communicates with other modules of the electronic device 1300 over the bus 1330. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 1300, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

In particular, according to embodiments of the present disclosure, the process described above with reference to the flowcharts may be implemented as a computer program product comprising: and the computer program realizes the battery cell capacity calibration method when being executed by the processor.

In an exemplary embodiment of the present disclosure, a computer-readable storage medium, which may be a readable signal medium or a readable storage medium, is also provided. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the terminal device.

More specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

In this disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.

Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order, or that all illustrated steps be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.

From the description of the above embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. The battery cell calibration method is characterized by comprising the following steps of:

Determining a charging parameter of the battery cell to be calibrated according to a preset capacity range of the battery cell to be calibrated;

controlling the battery cell to be calibrated to charge by the charging parameters, and collecting a plurality of groups of voltage values and corresponding accumulated charging electric quantity of the battery cell to be calibrated to obtain a characteristic parameter array;

Calibrating a target chemical system to which the battery cell to be calibrated belongs according to the characteristic parameter array of the battery cell to be calibrated;

determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, controlling the battery cell to be calibrated to stop charging, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the ending of charging;

And calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity.

2. The method for calibrating a battery cell according to claim 1, wherein calibrating the target chemical system to which the battery cell to be calibrated belongs according to the characteristic parameter array of the battery cell to be calibrated comprises:

Obtaining a charging curve between the voltage value of the battery cell to be calibrated and the accumulated charging electric quantity according to the characteristic parameter array of the battery cell to be calibrated;

Determining a system calibration characteristic value according to the charging curve;

And determining a target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value.

3. The cell calibration method according to claim 2, wherein the system calibration feature value comprises a first derivative minimum of the charging curve;

the determining the target chemical system to which the cell to be calibrated belongs according to the system calibration characteristic value comprises the following steps:

and if the minimum value of the first derivative of the charging curve is a negative number, determining that the target chemical system to which the cell to be calibrated belongs is a lithium sulfur material system.

4. The cell calibration method according to claim 2, wherein the system calibration feature values include a second derivative maximum value and a second derivative minimum value of the charging curve;

And if the maximum value of the second derivative and the minimum value of the second derivative are both negative numbers, calibrating the target chemical system to which the cell to be calibrated belongs as a ternary material system.

5. The cell calibration method according to claim 2, wherein the system calibration feature values include a second derivative maximum value and a second derivative minimum value of the charging curve;

And if the maximum value of the second derivative is positive and the minimum value of the second derivative is negative, calibrating the target chemical system to which the battery cell to be calibrated belongs as a lithium iron phosphate material system.

6. The method for calibrating a battery cell according to claim 1, wherein calibrating the target chemical system to which the battery cell to be calibrated belongs according to the characteristic parameter array of the battery cell to be calibrated comprises:

and processing the characteristic parameter array of the battery cell to be calibrated by adopting a pre-trained machine learning model to obtain a target chemical system to which the battery cell to be calibrated belongs, wherein the machine learning model is obtained by training the historical characteristic parameter array and the corresponding chemical system label.

7. The method of claim 1, wherein the charging parameter comprises any one of a charging current and a charging power.

8. The method for calibrating a battery cell according to claim 1, wherein the step of acquiring the voltage values and the corresponding accumulated charge amounts of the battery cells to be calibrated to obtain the characteristic parameter array includes:

Carrying out ampere-hour integration in the charging process of the battery cell to be calibrated to obtain the charging electric quantity of the battery cell to be calibrated;

When the electric quantity charged each time reaches a preset electric quantity change threshold, recording the voltage value of the electric core to be calibrated and the corresponding accumulated electric quantity charged each time to obtain an initial characteristic parameter array, wherein the voltage value of the electric core to be calibrated and the corresponding accumulated electric quantity charged each time are used as a group of characteristic parameter arrays in the initial characteristic parameter array;

And screening the characteristic parameters with the voltage values within a first preset voltage range from the initial characteristic parameter array to obtain the characteristic parameter array.

9. The method of cell calibration of claim 8, further comprising:

if the data quantity of the characteristic parameter array is larger than a preset data quantity threshold, processing the characteristic parameter array by adopting an interpolation method to obtain a remolded characteristic parameter array with the data quantity being the preset data quantity threshold;

And if the data quantity of the characteristic parameter array is smaller than the preset data quantity threshold, processing the characteristic parameter array by adopting an average method to obtain a remodeled characteristic parameter array with the data quantity being the preset data quantity threshold.

10. The method for calibrating a battery cell according to claim 1, wherein the calibrating the capacity of the battery cell to be calibrated according to the accumulated charge capacity comprises:

determining the calibration coefficient of the battery cell to be calibrated according to a target chemical system to which the battery cell to be calibrated belongs;

And determining the capacity of the battery cell to be calibrated according to the accumulated charging capacity and the calibration coefficient.

11. The method for calibrating a battery cell according to any of claims 1-10, wherein before the determining the charging parameter of the battery cell to be calibrated according to the preset capacity range of the battery cell to be calibrated, the method further comprises:

acquiring initial performance data of the battery cell to be calibrated;

And if the initial performance data of the battery cell to be calibrated meets the preset calibration condition within the preset sampling duration, executing the operation of determining the charging parameters of the battery cell to be calibrated according to the preset capacity range of the battery cell to be calibrated.

12. The method for calibrating a battery cell according to claim 11, wherein the initial performance data of the battery cell to be calibrated comprises a temperature, a voltage variation amount of the battery cell to be calibrated and a charge and discharge state of the battery cell to be calibrated;

the initial performance data of the battery cell to be calibrated meets preset calibration conditions, and the method comprises the following steps:

the temperature of the battery cell to be calibrated is effective and is within a preset temperature range;

The voltage of the battery cell to be calibrated is effective and is within a second preset voltage range;

the charge and discharge state of the battery cell to be calibrated is static; and is also provided with

And in each sampling interval, the voltage variation of the battery cell to be calibrated is within a preset voltage variation range.

13. The utility model provides a electric core calibration device which characterized in that includes:

the charging parameter determining module is used for determining the charging parameter of the battery cell to be calibrated according to the preset capacity range of the battery cell to be calibrated;

the characteristic parameter acquisition module is used for controlling the battery cells to be calibrated to be charged by the charging parameters, and acquiring a plurality of groups of voltage values and corresponding accumulated charging electric quantity of the battery cells to be calibrated to obtain a characteristic parameter array;

The chemical system calibration module is used for calibrating a target chemical system to which the battery cell to be calibrated belongs according to the characteristic parameter array of the battery cell to be calibrated;

The charging capacity determining module is used for determining the charging termination voltage of the battery cell to be calibrated based on the target chemical system, if the voltage value of the battery cell to be calibrated reaches the charging termination voltage, controlling the battery cell to be calibrated to stop charging, and calculating the accumulated charging capacity of the battery cell to be calibrated from the beginning of charging to the end of charging;

And the battery cell capacity calibration module is used for calibrating the capacity of the battery cell to be calibrated according to the accumulated charging capacity.

14. An electronic device, comprising:

a processor; and

A memory for storing executable instructions of the processor;

Wherein the processor is configured to perform the cell calibration method of any one of claims 1-12 via execution of the executable instructions.

15. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the cell calibration method according to any one of claims 1-12.