CN116896139A - Novel BMS system based on communication power supply system - Google Patents
Novel BMS system based on communication power supply system Download PDFInfo
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- 238000004891 communication Methods 0.000 title claims abstract description 20
- 238000004458 analytical method Methods 0.000 claims abstract description 30
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims abstract description 26
- 229910052744 lithium Inorganic materials 0.000 claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 17
- 241000282461 Canis lupus Species 0.000 claims abstract description 16
- 238000005457 optimization Methods 0.000 claims abstract description 11
- 238000005070 sampling Methods 0.000 claims abstract description 7
- 238000010219 correlation analysis Methods 0.000 claims description 12
- 238000007599 discharging Methods 0.000 claims description 9
- 238000001514 detection method Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 7
- 238000000513 principal component analysis Methods 0.000 claims description 7
- 238000012544 monitoring process Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 229910044991 metal oxide Inorganic materials 0.000 claims description 3
- 150000004706 metal oxides Chemical class 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 239000004065 semiconductor Substances 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims description 3
- 238000013507 mapping Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000007476 Maximum Likelihood Methods 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/00032—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries characterised by data exchange
- H02J7/00036—Charger exchanging data with battery
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
- H02J7/0036—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits using connection detecting circuits
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Secondary Cells (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention relates to the technical field of power sources, in particular to a novel BMS system based on a communication power source system, which comprises: and a system power-on module: for powering up the system; and a data acquisition module: the system power sampling device is used for collecting battery data of the lithium battery and uploading the battery data to the control analysis module; and a control analysis module: the method comprises the steps of analyzing the battery state through an improved gray wolf optimization algorithm according to received battery data, and recalculating protection parameters of an output battery; and an alarm control module: for updating the system alarm state based on comparing the collected battery data to the alarm threshold. According to the invention, the battery state is analyzed through the BMS system by collecting the data of the power supply system, the protection parameters of the power supply system are dynamically adjusted, so that the battery is more suitable for the operation of the power supply system, and meanwhile, the charge, discharge and balance of the power supply system are controlled through the alarm control module according to the protection parameters and the collected data of the power supply system, so that the frequent collection of the battery by the BMS is reduced.
Description
Technical Field
The invention relates to the technical field of power sources, in particular to a novel BMS system based on a communication power source system.
Background
The BMS system on the market consists of a battery management chip, an embedded microprocessor, embedded software and the like. The BMS realizes the functions of voltage protection, temperature protection, short circuit protection, overcurrent protection, insulation protection and the like of the battery pack through an algorithm according to the state data of the battery cells acquired in real time, and realizes voltage balance management and external data communication among the battery cells. In the prior art, when a BMS system on the market processes data, the number of forwarding times is large, and the real-time performance of the data is reduced, so that the protection real-time performance of a battery is reduced. The following problems still remain:
1. the efficiency is low, and the BMS system only gathers the voltage current data of battery, and data communication is frequent, leads to probably having the risk to the protection of battery to the condition of easily emptying the battery, perhaps appearing overcharging.
2. The compatibility is poor, and for different power supply systems, the BMS needs to modify program parameters to adapt to the power supply system, and cannot be directly substituted for use.
Disclosure of Invention
The present invention aims to solve the drawbacks set forth in the background art above by proposing a new BMS system based on a communication power system.
The technical scheme adopted by the invention is as follows:
provided is a novel BMS system based on a communication power supply system, comprising:
and a system power-on module: for powering up the system;
and a data acquisition module: the system power sampling device is used for collecting battery data of the lithium battery and uploading the battery data to the control analysis module;
and a control analysis module: the method comprises the steps of analyzing the battery state through an improved gray wolf optimization algorithm according to received battery data, and recalculating protection parameters of an output battery;
and an alarm control module: for updating the system alarm state based on comparing the collected battery data to the alarm threshold.
As a preferable technical scheme of the invention: the lithium battery cell state monitoring circuit is characterized by further comprising a protection plate control circuit and a charging current detection circuit, wherein the protection plate control circuit is used for monitoring the lithium battery cell state, and the charging current detection circuit is used for starting a discharging MOS (metal oxide semiconductor) under the condition that the discharging MOS is turned off by the protection plate control circuit.
As a preferable technical scheme of the invention: and the control analysis module polls the system power sampler data in the data acquisition module after the system power sampler is electrified, and the system power sampler uploads the acquired battery data of the lithium battery to the control analysis module.
As a preferable technical scheme of the invention: and after the control analysis module performs normalization processing on the received battery data of the lithium battery, extracting characteristic parameters of the acquired battery data of the lithium battery based on a principal component analysis algorithm.
As a preferable technical scheme of the invention: in the principal component analysis algorithm, normal distribution standardization processing is carried out on the preprocessed original battery data to obtain battery data variablesThe jth data representing the ith feature, the main component of the integrated index variable, i.e. the original battery data after dimension reduction, is marked as +.>The relationship between the two is as follows:
wherein, extracting the coefficients corresponding to each itemForming a characteristic parameter matrix。
As a preferable technical scheme of the invention: and in the control analysis module, each characteristic parameter in the characteristic parameter matrix is screened based on a gray correlation degree analysis method.
As a preferable technical scheme of the invention: the gray correlation analysis method is as follows:
wherein ,gray association degree indicating i-th characteristic parameter, < ->Indicating the value of the protection parameter in normal operating conditions, < ->Representing control coefficient->A j-th value representing the i-th feature parameter; and calculating the average value of the association coefficient of each feature, and selecting input feature parameters according to the average value of the association coefficient.
As a preferable technical scheme of the invention: the improved gray wolf optimization algorithm screens battery characteristic parameters based on a gray correlation analysis method to serve as input samples, and battery protection parameters serve as output samples of the model; setting a parameter range, initializing the position of the wolf cluster, performing fitness calculation and iterative optimization, determining whether to output according to whether the iteration number reaches the maximum, and outputting the current value if the iteration number reaches the maximum, otherwise, entering the next cycle; optimal solution of control coefficient found in gray wolf algorithmAnd optimal Nuclear parameters->Substituting the protection parameters into the LSSVM model, and outputting the protection parameters of the battery.
As a preferable technical scheme of the invention: the LSSVM model in the gray correlation analysis method is as follows:
according to the selected characteristic parameters, calculating to obtain characteristic parameter values of various characteristicsAnd feature vector->And will->As input value, the generated protection parameter +.>As the output value, there are the following:
wherein ,is weight vector +.>Transpose of->Is the i-th characteristic parameter->Is a nonlinear mapping function of ∈10->For bias value +.>Fitting errors of the ith characteristic parameters;
wherein , and />By means of the principle function of minimum structural risk->Obtaining:
in-structure risk minimum principle functionThe Lagrangian multiplier L is introduced to obtain:
wherein ,lagrangian multiplier for the ith characteristic parameter, +.>Is a penalty factor;
respectively obtain through the above，/>，/> and />Is a maximum likelihood estimate of (2):
then the LSSVM linear regression equation for how farThe following are provided:
wherein ,as a kernel function->Is a substitution coefficient;
the kernel function selects RBF functions:
wherein ,is a nuclear parameter.
As a preferable technical scheme of the invention: and the alarm threshold value set by the alarm control module is dynamically adjusted according to the battery protection parameters output by the control analysis module, compared with battery data of the lithium battery collected by the system power sampler, the alarm state is updated according to the comparison result, and the charging and discharging states of the lithium battery are controlled according to the alarm state.
Compared with the prior art, the novel BMS system based on the communication power supply system has the beneficial effects that:
according to the invention, the battery state is analyzed through the BMS system by collecting the data of the power supply system, the protection parameters of the power supply system are dynamically adjusted, so that the battery is more suitable for the operation of the power supply system, and meanwhile, the charge, discharge and balance of the power supply system are controlled through the alarm control module according to the protection parameters and the collected data of the power supply system, so that the frequent collection of the battery by the BMS is reduced.
Drawings
Fig. 1 is a system block diagram of a preferred embodiment of the present invention.
The meaning of each label in the figure is: 100. a system power-on module; 200. a data acquisition module; 300. a control analysis module; 400. and an alarm control module.
Detailed Description
It should be noted that, under the condition of no conflict, the embodiments of the present embodiments and features in the embodiments may be combined with each other, and the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a preferred embodiment of the present invention provides a novel BMS system based on a communication power system, including:
system power-on module 100: for powering up the system;
data acquisition module 200: the system power sampling device is used for collecting battery data of the lithium battery through the system power sampling device and uploading the battery data to the control analysis module 300;
control analysis module 300: the method comprises the steps of analyzing the battery state through an improved gray wolf optimization algorithm according to received battery data, and recalculating protection parameters of an output battery;
alarm control module 400: for updating the system alarm state based on comparing the collected battery data to the alarm threshold.
The lithium battery cell state monitoring circuit is characterized by further comprising a protection plate control circuit and a charging current detection circuit, wherein the protection plate control circuit is used for monitoring the lithium battery cell state, and the charging current detection circuit is used for starting a discharging MOS (metal oxide semiconductor) under the condition that the discharging MOS is turned off by the protection plate control circuit.
The control analysis module 300 polls the system power sampler data in the data acquisition module 200 after the system power sampler is powered on, and the system power sampler uploads the acquired battery data of the lithium battery to the control analysis module 300.
After the control analysis module 300 performs normalization processing on the received battery data of the lithium battery, the collected battery data of the lithium battery is extracted with characteristic parameters based on a principal component analysis algorithm.
In the principal component analysis algorithm, normal distribution standardization processing is carried out on the preprocessed original battery data to obtain battery data variablesThe jth data representing the ith feature, the main component of the integrated index variable, i.e. the original battery data after dimension reduction, is marked as +.>The relationship between the two is as follows:
wherein, extracting the coefficients corresponding to each itemForming a characteristic parameter matrix。
In the control analysis module 300, each feature parameter in the feature parameter matrix is screened based on a gray correlation analysis method.
The gray correlation analysis method is as follows:
wherein ,gray association degree indicating i-th characteristic parameter, < ->Indicating the value of the protection parameter in normal operating conditions, < ->Representing control coefficient->A j-th value representing the i-th feature parameter; and calculating the average value of the association coefficient of each feature, and selecting input feature parameters according to the average value of the association coefficient.
The improved gray wolf optimization algorithm screens battery characteristic parameters based on a gray correlation analysis method to serve as input samples, and battery protection parameters serve as output samples of the model; setting a parameter range, initializing the position of the wolf cluster, performing fitness calculation and iterative optimization, determining whether to output according to whether the iteration number reaches the maximum, and outputting the current value if the iteration number reaches the maximum, otherwise, entering the next cycle; optimal solution of control coefficient found in gray wolf algorithmAnd optimal Nuclear parameters->Substituting the protection parameters into the LSSVM model, and outputting the protection parameters of the battery.
The LSSVM model in the gray correlation analysis method is as follows:
according to the selected characteristic parameters, calculating to obtain characteristic parameter values of various characteristicsAnd feature vector->And will->As input value, the generated protection parameter +.>As the output value, there are the following:
wherein ,is weight vector +.>Transpose of->Is the i-th characteristic parameter->Is a nonlinear mapping function of ∈10->For bias value +.>Fitting errors of the ith characteristic parameters;
wherein , and />By means of the principle function of minimum structural risk->Obtaining:
in-structure risk minimum principle functionThe Lagrangian multiplier L is introduced to obtain:
wherein ,lagrangian multiplier for the ith characteristic parameter, +.>Is a penalty factor;
respectively obtain through the above，/>，/> and />Is a maximum likelihood estimate of (2):
then the LSSVM linear regression equation for how farThe following are provided:
wherein, as a kernel function,is a substitution coefficient;
the kernel function selects RBF functions:
wherein ,is a nuclear parameter.
The alarm threshold set by the alarm control module 400 is dynamically adjusted according to the battery protection parameters output by the control analysis module 300, compared with the battery data of the lithium battery collected by the system power sampler, the alarm state is updated according to the comparison result, and the charging and discharging states of the lithium battery are controlled according to the alarm state.
In this embodiment, after the system power-up module 100 is powered up, the system is initialized, and after the system initialization is completed, the system power sampler is powered up, the control analysis module 300 polls the battery data sampled by the system power sampler in the data acquisition module 200, the system power sampler uploads the sampled data such as system power, input voltage, input current, output voltage, output current and the like to the control analysis module 300, the control analysis module 300 analyzes the battery state according to each received data, and the received system power, input voltage, input current, output voltage and output current can be respectively set as the first to fifth types of characteristics, namelyCalculating characteristic parameters of each type of characteristic through a principal component analysis algorithm, taking system power as a first type of characteristic as an example, and setting sampling data of a system power sampler as +.>The units are W, and the data is subjected to dimension reduction to obtain corresponding +.>Value, obtained:
calculating to obtain corresponding characteristic parameter matrixAnd screening characteristic parameters based on a gray correlation analysis method:
and calculating the correlation coefficient mean value of each feature, and selecting the feature parameter of the battery data closest to the first 90% of the correlation coefficient mean value as the input feature parameter according to the correlation coefficient mean value.
Outputting the protection parameters of the battery according to an improved gray wolf optimization algorithm, and taking the battery protection parameters as an output sample of the model; respectively selecting the first 30%,60% and 90% of sample data in the battery data as training set samples of the algorithm, and the rest of the sample data in the battery data as test set samples of the algorithm for verifying the accuracy of the algorithm; setting parameters in LSSVM model and />Is in the range +.>The population scale is set to 20, the population dimension is set to 2, and the maximum iteration number is set to 100; initializing the position of the wolf group, performing fitness calculation and iterative optimization, determining whether to output according to whether the iterative times reach the maximum, and outputting the current value if the iterative times reach the maximum, otherwise, entering the next cycle; optimal solution searched in the gray wolf algorithm +.> and />Substituting the protection parameters into the LSSVM model, and outputting the protection parameters of the battery.
The alarm control module 400 adjusts the alarm threshold according to the output protection parameters of the battery, updates the alarm state according to the comparison result, and controls the charge, discharge and balance of the lithium battery through the protection board control circuit and the charge current detection circuit according to the alarm state.
After the protection parameters of the battery are output, in order to ensure the performance of the evaluation model, each sample in the test set is used for predicting by using a trained LSSVM model, and the predicted result is compared with the actual result.
In this embodiment, the protection parameter of the output battery is x, and the prediction result thereofCan be expressed as:
wherein and />Is a parameter of the LSSVM model, +.>Is to add the input vector to the kernel function>Mapping to a feature vector obtained in a high-dimensional space. />Is a sign function, representing a two-class classification of the result.
Where appropriate performance metrics are used to evaluate the performance of the model, in this embodiment using Root Mean Square Error (RMSE),
wherein Is the number of samples of the test set, +.>Is the actual result, +.>Is the model predictive result.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (10)
1. Novel BMS system based on communication power supply system, its characterized in that includes:
system power-up module (100): for powering up the system;
a data acquisition module (200): the system comprises a control analysis module (300) and a power sampling module, wherein the control analysis module is used for collecting battery data of a lithium battery through the system power sampling device and uploading the battery data to the control analysis module (300);
control analysis module (300): the method comprises the steps of analyzing the battery state through an improved gray wolf optimization algorithm according to received battery data, and recalculating protection parameters of an output battery;
alarm control module (400): for updating the system alarm state based on comparing the collected battery data to the alarm threshold.
2. The novel BMS system based on a communication power system according to claim 1, wherein: the lithium battery cell state monitoring circuit is characterized by further comprising a protection plate control circuit and a charging current detection circuit, wherein the protection plate control circuit is used for monitoring the lithium battery cell state, and the charging current detection circuit is used for starting a discharging MOS (metal oxide semiconductor) under the condition that the discharging MOS is turned off by the protection plate control circuit.
3. The novel BMS system based on a communication power system according to claim 1, wherein: the control analysis module (300) polls system power sampler data after the system power sampler is powered on, and the system power sampler uploads collected battery data of the lithium battery to the control analysis module (300).
4. The novel BMS system based on a communication power system according to claim 3, wherein: and after the control analysis module (300) performs normalization processing on the received battery data of the lithium battery, extracting characteristic parameters of the acquired battery data of the lithium battery based on a principal component analysis algorithm.
5. The novel BMS system based on a communication power system according to claim 4, wherein: in the principal component analysis algorithm, normal distribution standardization processing is carried out on the preprocessed original battery data to obtain battery data variables，/>The jth data representing the ith feature, the comprehensive index variable, namely the main component of the original battery data after dimension reduction, is marked as +.>The relationship between the two is as follows:
wherein, extracting the coefficients corresponding to each itemForming a characteristic parameter matrix。
6. The novel BMS system based on a communication power system according to claim 5, wherein: and in the control analysis module (300), each characteristic parameter in the characteristic parameter matrix is screened based on a gray correlation analysis method.
7. The novel BMS system based on a communication power system according to claim 6, wherein: the gray correlation analysis method is as follows:
wherein ,gray association degree indicating i-th characteristic parameter, < ->Indicating the value of the protection parameter in normal operating conditions, < ->Representing control coefficient->A j-th value representing the i-th feature parameter; and calculating the average value of the association coefficient of each feature, and selecting input feature parameters according to the average value of the association coefficient.
8. The novel BMS system based on a communication power system according to claim 7, wherein: the improved gray wolf optimization algorithm screens battery characteristic parameters based on a gray correlation analysis method to serve as input samples, and battery protection parameters serve as output samples of the model; setting a parameter range, initializing the position of the wolf group, performing fitness calculation and iterative optimizationWhether to output is determined according to whether the iteration number reaches the maximum, the current value is output when the iteration number reaches the maximum, and otherwise, the next cycle is started; optimal solution of control coefficient found in gray wolf algorithmAnd optimal Nuclear parameters->Substituting the protection parameters into the LSSVM model, and outputting the protection parameters of the battery.
9. The novel BMS system based on a communication power system according to claim 8, wherein: the LSSVM model in the gray correlation analysis method is as follows:
wherein ,as a kernel function->For substitution coefficient->Lagrangian multiplier for the ith characteristic parameter, +.>Is a bias value;
the kernel function selects RBF functions:
wherein ,is a nuclear parameter.
10. The novel BMS system based on a communication power system according to claim 1, wherein: the alarm threshold value set by the alarm control module (400) is dynamically adjusted according to the battery protection parameters output by the control analysis module (300), compared with the battery data of the lithium battery collected by the system power sampler, the alarm state is updated according to the comparison result, and the charging and discharging states of the lithium battery are controlled according to the alarm state.
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