CN116736139B - SOC estimation method of household energy storage system - Google Patents
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Abstract
The method is used for constructing an SOC estimation model in advance based on an ampere-hour integration method, iteratively updating model parameters influenced by temperature in the SOC estimation model at each moment, adding a micro-current acquisition device for acquiring current on a current core side, combining the current on a BMS working side to obtain more accurate system current at each moment, and substituting the system current at a plurality of continuous moments into the SOC estimation model with continuously updated model parameters to obtain more accurate SOC estimation values at each moment, so that the method is particularly suitable for a household storage scene with complex charge and discharge scenes, and can provide a solution capable of actually falling to the ground for the household storage scene which cannot be fully filled.
Description
Technical Field
The application relates to the technical field of household storage, in particular to an SOC estimation method of a household energy storage system.
Background
The large-scale energy storage technology is a key technology for realizing popularization and application of new energy, and because the power generated by new energy sources such as wind power, photovoltaic and the like has fluctuation and poor adjustable performance, the energy balance on the power supply and demand side can be effectively adjusted by configuring the energy storage device, the intermittence and instability of the output of the new energy sources are overcome, the running efficiency of power equipment is improved, the safe and reliable running of a system is ensured, and the energy storage device is a key supporting technology of a new energy system.
The lithium iron phosphate battery has the characteristics of excellent charge and discharge performance, good safety, long cycle life and the like, and the energy storage device based on the lithium iron phosphate battery is widely used in the energy storage fields of renewable energy micro-grid systems, electric automobiles, communication base stations and the like. The energy storage device based on the lithium iron phosphate battery is required to be provided with a corresponding battery management system (Battery Mmanagement System, BMS) for intelligent management and maintenance of the lithium iron phosphate battery, and in the application process of the battery management system, real-time accurate estimation of the state of charge (SOC) of the battery is important and difficult, and is an important parameter for scientifically and reasonably utilizing the energy of the battery.
Because the computing resources in the BMS equipped with the energy storage device are limited, a more accurate SOC estimated value is difficult to obtain according to the relation between capacity and voltage, conventional SOC estimation often needs to rely on actual charge and discharge capacity calculation, and actual electric quantity statistics is carried out according to ampere-hour integration at charge and discharge moments, so that the residual capacity is continuously calculated to obtain the SOC estimated value.
Along with the burst of large-scale energy storage, household energy storage devices (referred to as household energy storage for short) are also becoming popular, and compared with power station energy storage devices, the household energy storage application scenes and charging and discharging processes are more complex. The charging and discharging strategy of the household storage is not only used, but can be used for charging and discharging adjustment due to the control of the inverter, and is influenced by load, charging and discharging strategy and a variable power generation end (most of the charging and discharging strategies are photovoltaic), so that the charging and discharging of the battery are very irregular, the 100% discharging depth cannot be achieved under most conditions, and even 50% of the charging and discharging depth cannot be guaranteed to be full in a condition of configuring a large-capacity battery. Therefore, under a user storage scene, the SOC estimated value obtained by using the traditional method often has larger error, and the intelligent management and maintenance of the battery are affected.
Disclosure of Invention
The applicant provides an SOC estimation method for a household energy storage system, aiming at the problems and the technical requirements, and the technical scheme of the application is as follows:
the utility model provides a SOC estimation method of domestic energy storage system, this domestic energy storage system includes electric core and battery management system BMS, and this domestic energy storage system still includes little electric current collection system, little electric current collection system installs at the input/output side of electric core and carries out communication connection with BMS, and the SOC estimation method of BMS execution includes:
collecting BMS working side current, and obtaining cell side current collected by a microcurrent collecting device;
determining a system current I (t) at any current moment t according to the collected BMS working side current and the obtained battery cell side current, wherein t is a parameter;
substituting the system current I (t) at the current time t and the system currents at a plurality of historical times before the current time t into the constructed SOC estimation model to obtain an SOC estimation value at the current time t;
the SOC estimation model is constructed in advance based on an ampere-hour integration method, and model parameters in the SOC estimation model are affected by temperature and are updated iteratively at each moment.
The beneficial technical effects of this application are:
the utility model discloses a SOC estimation method of household energy storage system, this SOC estimation method builds in advance and obtains the SOC estimation model based on ampere-hour integration method, and the model parameter that receives the temperature influence in the SOC estimation model carries out iterative update at every moment, increase little electric current collection device simultaneously and be used for gathering electric core side current, combine BMS working side current to obtain the more accurate system current of each moment, because the accuracy of system current has been improved, and take into account the change update of model parameter, consequently, substituting the system current of continuous a plurality of moments into the SOC estimation model that model parameter constantly updated, can obtain the more accurate SOC estimation value of each moment, especially be applicable to in the complicated user's of charge-discharge scene stores up the scene in order to store up this and can not fully put the scene and provide the solution that can actually fall to the ground.
In addition, based on the newly added micro-current acquisition device for the traditional household energy storage system, the metering of the self-consumption power consumption of the BMS can be realized.
Drawings
Fig. 1 is a flowchart of an SOC estimation method according to an embodiment of the present application.
Detailed Description
The following describes the embodiments of the present application further with reference to the accompanying drawings.
The application discloses SOC estimation method of domestic energy storage system, this domestic energy storage system is except including conventional electric core and battery management system BMS, still includes little electric current collection system, and this little electric current collection system installs the input/output side of electric core and with BMS carries out communication connection. The SOC estimation method performed by the BMS includes the following steps, please refer to the flowchart shown in fig. 1:
step 1, collecting current I of working side of BMS BMS And acquiring the side current I of the battery cell acquired by the micro-current acquisition device cell 。
In one embodiment, the micro-current collecting device comprises an MCU and a current transformer, the current transformer is arranged on the input and output sides of the battery core, the current transformer collects the current of the input and output sides of the battery core, the current is reduced according to a preset proportion and then is provided for the MCU as the collected initial micro-current, the MCU and the BMS communicate in a serial mode, the MCU sends the collected initial micro-current to the BMS through the serial port, and the BMS re-amplifies the received initial micro-current according to the preset proportion and then serves as the current I of the battery core collected by the micro-current collecting device cell . In one embodiment, the MCU and BMS communicate in that 485 fashion.
Because the micro-current acquisition device is arranged on the input and output sides of the battery cell, the acquired current of the input and output sides of the battery cell is higher than the current I of the working side of the BMS BMS The current of the input and output sides of the battery cell is reduced in an equal proportion due to mutual inductance, so that the acquisition accuracy is more accurate.
And 2, determining a system current I (t) at any current moment t according to the acquired BMS working side current and the acquired battery cell side current, wherein t is a parameter.
And when the household energy storage system is in a charging and discharging stage, determining the charging and discharging current at the current moment t as the system current I (t) according to the collected BMS working side current and the obtained battery cell side current. Cell side current I due to charge-discharge stage cell There is a large interval, and because of the conversion accuracy problem, in order to ensure accuracy, in one embodiment, three cases are: (1) And when the current on the battery cell side is smaller than a first current threshold value, taking the current on the battery cell side as the charge and discharge current at the current time t. (2) When the current of the battery cell side reaches the first current threshold value and is smaller than a second current threshold value, the battery cell side is connected with the battery cellThe current of the BMS working side and the current of the battery cell side are weighted to obtain the charge and discharge current at the current time t, and the weights of the current time t and the battery cell side can be predefined during the weighted calculation, for example, the weighted calculation is simply performed through averaging. (3) And when the battery cell side current reaches the second current threshold, taking the BMS working side current as a charging and discharging current at the current time t. The first current threshold and the second current threshold are preset, for example, the first current threshold may be set to 0.5A, and the second current threshold may be set to 1.0A.
In addition, as the microcurrent acquisition device is arranged on the input and output sides of the battery cell, the acquired battery cell side current I cell All current including household energy storage system, cell side current I cell With the BMS working side current I BMS The current difference value is the current generated by BMS self-power consumption, so the self-power consumption of the BMS in the charging and discharging stage can be calculated by the current difference value between the current of the battery cell side and the current of the BMS working side.
When the household energy storage system is not in the charge-discharge stage, the collected battery cell side current I cell The current generated by BMS self-consumption is completely used for directly determining that the collected battery cell side current is the BMS self-consumption current at the current time t and is used as the system current I (t) at the current time t.
And 3, substituting the system current I (t) at the current time t and the system currents at a plurality of historical times before the current time t into the constructed SOC estimation model to obtain an SOC estimation value at the current time t. The SOC estimation model is constructed in advance based on an ampere-hour integration method, and model parameters in the SOC estimation model are affected by temperature and are updated iteratively at each moment.
The calculation formula of the ampere-hour integration method can be expressed asSOC 0 The initial SOC value at time t=0, k is the cell charge/discharge efficiency, i s Is instantaneous current, C N Is the rated capacity of the battery cell. Instantaneous current i in the above formula s Can be determined by step 2The system current I (t) at each instant.
The charge-discharge efficiency k of the battery cell represents the conversion efficiency in the battery cell, and the battery cell has equivalent internal resistance which can be changed in practice and is the instantaneous current i s Meanwhile, the internal resistance of the battery cell also changes, so that certain loss is generated, and the loss is the reason that the charge and discharge efficiency k of the battery cell is not 100%. In the conventional method, the absolute value of the charge-discharge efficiency k of the battery cell is generally preset as an empirical value, and the positive and negative signs are adjusted according to the charge-discharge stage. However, in practice, the absolute value of the charging and discharging efficiency k of the battery core is not fixed, and the charging and discharging efficiency k of the battery core is improved due to the fact that the actual capacity of the battery core has a larger relation with the working temperature of the household energy storage system and the initial working temperature and the working temperature in the actual charging and discharging stage are continuously improved. The application improves the ampere-hour integration method by constructing an expression of the charge and discharge efficiency k of the battery cell to construct an SOC estimation model. Since the cell charge-discharge efficiency k is generated from the loss of the cell internal resistance, the inside of the cell comprises ohmic resistance and polarization internal resistance, and the cell can be obtained according to an open circuit voltage method:
U d (t)＝U oc (t)-R 0 (t)I(t)-U AR (t)
wherein U is d (t) is the voltage at the output end of the battery cell at the time t, U oc (t) is the open circuit voltage at time t, R 0 (t) the ohmic internal resistance at time t, U AR (t) represents a polarization voltage generated by the internal polarization resistance at time t, then the core charge-discharge efficiency of the time t is actuallyIt can also be seen that k (t) varies with the system current and the internal resistance of the cell.
Taking into account the polarisation voltage U AR (t) is related to the rate of change of current, thus characterizing the polarization voltage as a system current at several times in historya i (t) represents a correlation corresponding to I (t-I) at time tThe numbers i and n are parameters, respectively. The larger the value of the parameter n is, the higher the estimation accuracy of the SOC estimation model is, but the larger the relative calculation scale is.
Then, according to the above formula of open circuit voltage method, the charge and discharge efficiency k (t) of the battery cell without considering the temperature influence can be obtained based on phi (t) = [ I (t), I (t-1), … I (t-n)]Sum matrix a (t) = [ a ] 0 (t),a 1 (t)…a n (t)]The expression of (2) includes substituting the expression of the polarization voltage into the expression of the open circuit voltage method:
with a correlation coefficient a corresponding to the system current I (t) at the present time t 0 (t)＝R 0 (t) can be combined to give:
according toObtain->
Further, since the internal resistance exists in the battery cell, other dissipation of actual charge and discharge mainly affects the heat loss of the electronic element and the internal resistance loss in the battery cell, and the temperature also affects the charge and discharge efficiency of the battery cell, so the expression of k (T) obtained by the above embodiment is the charge and discharge efficiency of the battery cell without considering the temperature effect, and on the basis, the model parameter B (T) = [ beta ] affected by the working temperature of the household energy storage system is introduced 0 (t),β 1 (t)…β n (t)]Obtaining the charge and discharge efficiency k of the battery cell considering the temperature influence E (T) =b (T) ×k (T). Based on Φ (t) = [ I (t), I (t-1), … I (t-n)]Sum matrix a (t) = [ a ] 0 (t),a 1 (t)…a n (t)]Expression of (2)The formula is that:
in the above formula, U oc (t) can be considered as a constant which does not vary with time, the input matrix Φ (t) = [ I (t), I (t-1), … I (t-n) at each instant t]Can also be directly measured, B (T) = [ beta ] 0 (t),β 1 (t)…β n (t)]Matrix a (t) = [ a ] 0 (t),a 1 (t)…a n (t)]Is changed along with the influence of temperature, so that the charge and discharge efficiency k of the battery cell is caused when the influence of temperature is considered E (T) changes, thus updating B (T) = [ beta ] by iteration 0 (t),β 1 (t)…β n (t)]Matrix a (t) = [ a ] 0 (t),a 1 (t)…a n (t)]The charge and discharge efficiency k of the battery cell can be dynamically updated when the temperature influence is considered E (t). Although the parameter B (T) = [ beta ] 0 (t),β 1 (t)…β n (t)]The temperature is actually influenced by the working temperature of the household energy storage system, but the temperature at different moments is also different, so the equivalent considered parameter B (T) = [ beta ] 0 (t),β 1 (t)…β n (t)]Is influenced by time.
Then k is taken up E Substituting the expression of (t) into an ampere-hour integration method, and constructing an SOC estimation model as follows:
wherein, any C (j) is the capacity of the moment j calculated based on the system current I (j) of any moment j, and may be calculated by a conventional method, which is not described in detail in this embodiment. C (0) is a preset initial capacity. In the above-described SOC estimation model, the SOC 0 Is a constant, the input matrix Φ (t) = [ I (t), I (t-1), … I (t-n) at each time t]After measurement, the correspondingCan be calculated.
C in the SOC estimation model N And may also be determined. In the case of rated capacity C of the cell N Maintaining a preset rated capacity C N0 Unchanged, the rated capacity C is preset directly N0 Rated capacity C as cell real time N Substituting into the SOC estimation model. Alternatively, consider the rated capacity C of the cell N Along with the working temperature change of the household energy storage system, firstly determining the working temperature T of the household energy storage system at the current time T, and then determining the rated capacity C corresponding to the working temperature T at the current time T N And substituted into the SOC estimation model. Rated capacity C corresponding to different operating temperatures T N A corresponding relation curve can be established in advance, and the rated capacity C corresponding to the working temperature T at the current moment T can be determined by inquiring N . When the working temperature T of the household energy storage system at the current time T is determined, based on the universal function of the BMS, the battery core temperature can be used for collecting the ambient temperature and the battery core temperature, and the battery core temperature at the current time T is used as the working temperature T of the household energy storage system because the battery core temperature is collected to be the temperature of the battery core tab side and is closer to the temperature of the battery core.
Thus, for the model parameter a (t) = [ a ] influenced by temperature in the SOC estimation model 0 (t),a 1 (t)…a n (t)]And B (T) = [ beta ] 0 (t),β 1 (t)…β n (t)]And carrying out iterative updating at each moment, substituting the system current I (t) at the current moment t and the system currents at a plurality of historical moments before the current moment t into the constructed SOC estimation model, and calculating to obtain the SOC estimation value SOC (t) at the current moment t.
In one embodiment, the SOC estimation values at each time instant are solved based on the SOC estimation model by an improved least squares method, comprising the steps of:
(1) Matrix B (T) = [ β ] at initialization time t=0 0 (t),β 1 (t)…β n (t)]Matrix a (t) = [ a ] 0 (t),a 1 (t)…a n (t)]A covariance matrix P (0) and a forgetting factor γ.
(2) Determining an input matrix Φ (t) =for the SOC estimation model at time t[I(t),I(t-1),…I(t-n)]Output matrix Y (t) = [ U ] oc (SOC(t))-U d (t)]，U oc (SOC (t)) represents an open circuit voltage corresponding to the SOC estimation value SOC (t) at time t.
(3) Calculating the gain at time tΦ T (t) is the transpose of the matrix Φ (t), and P (t) is the covariance of the current time t.
(4) Iterative updating to determine the model parameter B (T) = [ beta ] at the current time T 0 (t),β 1 (t)…β n (t)]Sum matrix a (t) = [ a ] 0 (t),a 1 (t)…a n (t)]The method comprises the following steps of:
wherein B (T')= [ β 0 (t-1),β 1 ((t-1))…β n (t-1)]。
(5) Substituting acquired phi (T) = [ I (T), I (T-1) and … I (T-n) ] into the model parameters B (T) and A (T) of the current time T, and solving the SOC estimation model to obtain an SOC estimation value of the current time T.
(6) Updating covariance matrix of current time tAnd entering the next round of iterative computation of the SOC estimation value at the moment t+1.
According to the above process, the corresponding model parameters can be continuously estimated according to the data generated in practice, and the accurate SOC estimation value can be obtained by combining the system current acquired in real time.
What has been described above is only a preferred embodiment of the present application, which is not limited to the above examples. It is to be understood that other modifications and variations which may be directly derived or contemplated by those skilled in the art without departing from the spirit and concepts of the present application are to be considered as being included within the scope of the present application.
Claims (8)
1. The utility model provides a SOC estimation method of domestic energy storage system, domestic energy storage system includes electric core and battery management system BMS, its characterized in that, domestic energy storage system still includes little electric current collection system, little electric current collection system installs the input/output side of electric core and with BMS carries out communication connection, the SOC estimation method of BMS execution includes:
collecting BMS working side current, and obtaining the battery cell side current collected by the micro-current collecting device;
determining any current moment according to the acquired BMS working side current and the acquired battery cell side currentSystem current +.>，/>Is a parameter; when the household energy storage system is in a charging and discharging stage, determining the current moment according to the acquired BMS working side current and the acquired battery cell side current>Is used as the system current +.>Comprising: when the current on the battery cell side is smaller than a first current threshold value, the current on the battery cell side is used as the current moment +.>Charging and discharging current of (a); when the battery cell side current reaches the first current threshold and is smaller than a second current threshold, weighting calculation is carried out on the BMS working side current and the battery cell side current to obtain the current moment +.>Charging and discharging current of (a); when the battery cell side current reaches the second current threshold value, taking the BMS working side current as the current moment +.>Charging and discharging current of (a); when the household energy storage system is not in a charging and discharging stage, determining that the acquired battery cell side current is the current moment +.>Is self-consuming and acts as current moment +.>System current +.>；
Will be at the current timeSystem current +.>Current moment +.>Substituting the system current of a plurality of previous historical moments into the constructed SOC estimation model to obtain the current moment +.>SOC estimation value of (2);
the SOC estimation model is constructed in advance based on an ampere-hour integration method, and model parameters in the SOC estimation model are affected by temperature and are updated iteratively at each moment.
2. The SOC estimation method of claim 1, wherein the constructed SOC isAny current moment indicated by the SOC estimation modelSOC estimation value +.>The method comprises the following steps:
；
wherein,indicating time->Initial SOC value at time, +.>Is the current time +.>Open circuit voltage of>Is rated capacity, parameter->Time->Is the current time +.>First->System current at each history time, +.>Is a positive integer parameter, arbitrary +>Based on ∈10 at any time>System current +.>Calculated time ∈ ->Capacity of->For initial capacity, the iteratively updated model parameters in the SOC estimation model comprise +.>Matrix->，/>Indicating the time of day electric +.>When and->Corresponding correlation coefficients.
3. The SOC estimation method of claim 2, wherein a rated capacity in the SOC estimation modelAs the operating temperature of the household energy storage system changes, the SOC estimation method further includes:
collecting the current timeAs the cell temperature of the domestic energy storage system at the present moment +.>Operating temperature of>；
Determining the current timeOperating temperature of>Corresponding rated capacity->And substituting into the SOC estimation model to calculate the current time +.>SOC estimation value of (c).
4. The SOC estimation method of claim 2, wherein the current time is obtainedThe SOC estimation value of (2) includes:
determining the SOC estimation model at a timeInput matrix of->Output matrix，/>Is time->Output voltage, ">Representation and time->SOC estimation value +.>A corresponding open circuit voltage;
calculating the time of dayGain of->，/>Is a matrix->Transposed matrix of>For the current time +.>Is a covariance matrix of (a);
iterative update to determine the current timeModel parameters of>Sum matrixThe method comprises the following steps of:
；
at the current momentModel parameters of>And->Solving the SOC estimation model to obtain the current moment +.>SOC estimation value of (2);
updating the current timeCovariance matrix>And proceeds to the next round of iteration,is amnesia factor, is->。
5. The SOC estimation method according to claim 1, characterized in that the SOC estimation method further comprises: and when the household energy storage system is in a charging and discharging stage, calculating the self-consumption power consumption of the BMS in the charging and discharging stage according to a current difference value between the battery cell side current and the BMS working side current.
6. The SOC estimation method as claimed in claim 1, wherein the micro current collection device includes an MCU and a current transformer, the current transformer is installed at an input/output side of the battery cell, the current transformer collects current of the input/output side of the battery cell, the current transformer is reduced according to a predetermined ratio and provides the reduced current to the MCU as an initial micro current, the MCU and the BMS communicate in a serial manner, the MCU sends the initial micro current to the BMS through a serial port, and the BMS amplifies the initial micro current according to the predetermined ratio and serves as current of the battery cell side collected by the micro current collection device.
7. The SOC estimation method of claim 2, wherein the method of constructing the SOC estimation model includes:
the polarization voltage is represented by the system current at a plurality of time points, and the charge and discharge efficiency of the battery cell without considering the temperature influence is obtained according to an open circuit voltage methodBased on->Sum matrixIs an expression of (2);
introducing a temperature of operation subject to the domestic energy storage systemModel parameters of influence->Obtaining the temperature considered->Cell charge-discharge efficiency at influence +.>Based onSum matrix->And (3) substituting the expression into an ampere-hour integration method, and constructing to obtain the SOC estimation model.
8. The SOC estimation method of claim 7, wherein a cell charge-discharge efficiency irrespective of temperature influence is determinedThe expression of (2) includes:
determining time according to open circuit voltage methodOutput voltage +.>，/>Indicating time->Ohmic internal resistance of->Indicating time->A polarization voltage generated by the polarization internal resistance of the battery;
characterization of polarization voltage with System Current at several moments of the history；
To be used forFinishing and determining->；
According toObtain->。
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140122569A (en) * | 2013-04-10 | 2014-10-20 | 비나텍주식회사 | SOC management apparatus of ultra-capacitor pack for hybrid energy storage devices, and control method of the same |
| CN107390127A (en) * | 2017-07-11 | 2017-11-24 | 欣旺达电动汽车电池有限公司 | A kind of SOC estimation method |
| CN107576919A (en) * | 2017-10-20 | 2018-01-12 | 广东石油化工学院 | Power battery charged state estimating system and method based on ARMAX models |
| CN110596593A (en) * | 2019-08-26 | 2019-12-20 | 浙江大学 | Lithium ion battery SOC estimation method based on intelligent adaptive extended Kalman filtering |
| WO2021136419A1 (en) * | 2019-12-30 | 2021-07-08 | 杭州海康机器人技术有限公司 | Soc estimation accuracy determination method, device, and storage medium |
| WO2021217698A1 (en) * | 2020-04-29 | 2021-11-04 | 上海理工大学 | Diagnosis method for distinguishing micro-short-circuit fault of battery from small-capacity fault of battery |
| CN115866452A (en) * | 2022-12-01 | 2023-03-28 | 江苏云储数字能源科技有限公司 | Cloud BMS system based on wireless communication and cloud-up method |
| CN115935206A (en) * | 2022-12-07 | 2023-04-07 | 昆明理工大学 | SOC estimation method and device of energy storage system, equipment and storage medium |
| CN116401585A (en) * | 2023-04-19 | 2023-07-07 | 江苏果下科技有限公司 | Energy storage battery failure risk assessment method based on big data |
-
2023
- 2023-07-13 CN CN202310862965.0A patent/CN116736139B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140122569A (en) * | 2013-04-10 | 2014-10-20 | 비나텍주식회사 | SOC management apparatus of ultra-capacitor pack for hybrid energy storage devices, and control method of the same |
| CN107390127A (en) * | 2017-07-11 | 2017-11-24 | 欣旺达电动汽车电池有限公司 | A kind of SOC estimation method |
| CN107576919A (en) * | 2017-10-20 | 2018-01-12 | 广东石油化工学院 | Power battery charged state estimating system and method based on ARMAX models |
| CN110596593A (en) * | 2019-08-26 | 2019-12-20 | 浙江大学 | Lithium ion battery SOC estimation method based on intelligent adaptive extended Kalman filtering |
| WO2021136419A1 (en) * | 2019-12-30 | 2021-07-08 | 杭州海康机器人技术有限公司 | Soc estimation accuracy determination method, device, and storage medium |
| WO2021217698A1 (en) * | 2020-04-29 | 2021-11-04 | 上海理工大学 | Diagnosis method for distinguishing micro-short-circuit fault of battery from small-capacity fault of battery |
| CN115866452A (en) * | 2022-12-01 | 2023-03-28 | 江苏云储数字能源科技有限公司 | Cloud BMS system based on wireless communication and cloud-up method |
| CN115935206A (en) * | 2022-12-07 | 2023-04-07 | 昆明理工大学 | SOC estimation method and device of energy storage system, equipment and storage medium |
| CN116401585A (en) * | 2023-04-19 | 2023-07-07 | 江苏果下科技有限公司 | Energy storage battery failure risk assessment method based on big data |
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