CN118566758B - Method, system and equipment for estimating discharge power peak value of battery pack - Google Patents
Method, system and equipment for estimating discharge power peak value of battery pack Download PDFInfo
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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]
- G01R31/385—Arrangements for measuring battery or accumulator variables
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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
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Abstract
The application discloses a method, a system and equipment for estimating discharge power peak value of a battery pack, and relates to the technical field of batteries, wherein the method comprises the following steps: acquiring discharge data of each battery cell in a target battery pack; determining internal temperature data of the target battery pack according to the discharge data; the internal temperature data includes a center temperature of each of the battery cells; calculating a temperature characteristic value of the battery cell according to the internal temperature data; and acquiring the ambient temperature of the target battery pack, and determining a first discharge power peak value of the target battery pack according to the temperature characteristic value and the ambient temperature. The application has the effect of estimating the discharge power peak value of the battery pack.
Description
Technical Field
The present application relates to the field of battery technologies, and in particular, to a method, a system, and an apparatus for estimating a discharge power peak of a battery pack.
Background
The electric automobile is an important development and upgrading field in the future automobile industry, and along with the rapid development of the electric automobile, a certain-scale retired power battery enters a echelon utilization link in the future, the retired power battery is usually a lithium battery, and the retired power battery usually has a residual capacity of up to 80 percent, has higher utilization value, and can be used in the field with lower requirements on battery performance, such as a low-speed electric automobile or an energy storage system. In addition, if the retired power battery cannot be effectively recycled, lithium resources are wasted.
At present, a lithium battery is mainly adopted as a single battery core in the power battery for the vehicle in the market, but the working performance of the lithium battery is not stable enough due to the self material characteristics of the lithium battery, so that the temperature of the lithium battery is easy to be rapidly increased under the conditions of overcharge, overheat, collision and the like, further the occurrence of a thermal runaway phenomenon is caused, the occurrence time of the thermal runaway is usually short, countermeasures are difficult to be timely made, and serious economic loss or personnel injury are easy to be caused on the thermal runaway site; and the retired power battery has increased internal resistance due to aging, more heat can be generated in the running process, and the problem of thermal runaway is more likely to occur.
In order to reduce the probability of thermal runaway, the gradient utilization of the retired power battery is realized, the discharging process of the retired power battery is researched, and the discharging process of the retired power battery is controlled; in the research process, for a battery pack formed by serial-parallel connection of single battery cells (namely battery cells), the power peak value of the battery pack needs to be estimated, so the application provides a discharge power peak value estimation method of the battery pack.
Disclosure of Invention
In order to estimate the discharge power peak value of a battery pack, the application provides a discharge power peak value estimation method, a discharge power peak value estimation system and discharge power peak value estimation equipment of the battery pack.
In a first aspect, the present application provides a method for estimating a discharge power peak of a battery pack, which adopts the following technical scheme:
a method of estimating a discharge power peak of a battery pack, comprising:
acquiring discharge data of each battery cell in a target battery pack;
determining internal temperature data of the target battery pack according to the discharge data; the internal temperature data includes a center temperature of each of the battery cells;
Calculating a temperature characteristic value of the battery cell according to the internal temperature data;
And acquiring the ambient temperature of the target battery pack, and determining a first discharge power peak value of the target battery pack according to the temperature characteristic value and the ambient temperature.
By adopting the technical scheme, the discharge data of each battery cell in the target battery pack is firstly obtained, then the internal temperature data of the target battery pack is determined according to the discharge data, the internal temperature data comprises the center temperature of each battery cell, then the temperature characteristic value of the battery cell is calculated according to the internal temperature data, then the environment temperature of the target battery pack is obtained, and the first discharge power peak value of the target battery pack is determined according to the temperature characteristic value and the environment temperature; in the method, the first discharge power peak value of the target battery pack is estimated through the discharge data and the ambient temperature of each battery cell in the target battery pack, the operation amount is small, the realization is convenient, the control on the discharge process of the battery pack can be realized according to the first discharge power peak value in the subsequent treatment, the service life of the retired power battery is prolonged, the occurrence probability of the thermal runaway phenomenon is reduced, and the retired power battery is damaged or personnel injury occurs.
Optionally, the step of determining the internal temperature data of the target battery pack according to the discharge data includes:
acquiring a historical discharge data set; the historical discharge data set comprises historical discharge data and historical center temperature data corresponding to the historical discharge data;
Training and optimizing a pre-constructed center temperature generation model according to the historical discharge data set to obtain an optimized center temperature generation model;
and respectively inputting the discharge data of each battery cell into the optimized center temperature generation model to obtain the center temperature of each battery cell.
By adopting the technical scheme, in order to obtain the central temperature of each single battery, a historical discharge data set is firstly obtained, the historical discharge data set comprises historical discharge data and historical central temperature data corresponding to the historical discharge data, then a pre-built central temperature generation model is trained and optimized according to the historical discharge data set to obtain an optimized central temperature generation model, and then the discharge data of each single battery are respectively input into the optimized central temperature generation model to obtain the central temperature of each single battery.
Optionally, the step of training and optimizing the pre-constructed central temperature generation model according to the historical discharge data set to obtain an optimized central temperature generation model includes: the pre-built central temperature generation model is a pre-built vector machine regression model;
Training the pre-constructed vector machine regression model according to the historical discharge data set to obtain a trained vector machine regression model;
And optimizing the trained vector machine regression model according to a five-fold cross validation mode to obtain an optimized vector machine regression model.
By adopting the technical scheme, in order to obtain the optimized vector machine regression model, the pre-constructed vector machine regression model is trained according to the historical discharge data set to obtain the trained vector machine regression model, and then the trained vector machine regression model is optimized according to the five-fold cross validation mode to obtain the optimized vector machine regression model.
Optionally, the temperature characteristic value includes a center temperature maximum value and a center temperature deviation maximum value.
Optionally, the step of determining the first discharge power peak of the target battery pack according to the temperature characteristic value and the ambient temperature includes:
Acquiring a historical temperature dataset; the historical temperature data set comprises historical temperature characteristic data, environment temperature data corresponding to the historical temperature characteristic data and historical discharge power data corresponding to the historical temperature characteristic data;
establishing a fuzzy control rule according to the historical temperature dataset, and establishing a fuzzy control model according to the fuzzy control rule;
optimizing the fuzzy control model to obtain an optimized fuzzy control model;
and inputting the temperature characteristic value and the ambient temperature into the optimized fuzzy control model to obtain a first discharge power peak value of the target battery pack.
By adopting the technical scheme, in order to generate the first discharge power peak value of the target battery pack, a historical temperature data set is firstly obtained, the historical temperature data set comprises historical temperature characteristic data, environment temperature data corresponding to the historical temperature characteristic data and historical discharge power data corresponding to the historical temperature characteristic data, then a fuzzy control rule is established according to the historical temperature data set, a fuzzy control model is constructed according to the fuzzy control rule, then the fuzzy control model is optimized, an optimized fuzzy control model is obtained, and finally the temperature characteristic value and the environment temperature are input into the optimized fuzzy control model, so that the first discharge power peak value of the target battery pack is obtained.
Optionally, after the step of obtaining the ambient temperature of the target battery pack and determining the first discharge power peak value of the target battery pack according to the temperature characteristic value and the ambient temperature, the method further includes:
And obtaining a cooling mode of the target battery pack, and calculating a second discharge power peak value according to the first discharge power peak value and a power peak value optimization formula corresponding to the cooling mode.
By adopting the technical scheme, in order to optimize the first discharge power peak value, the cooling mode of the target battery pack is acquired first, and then the second discharge power peak value is calculated according to the first discharge power peak value and the power peak value optimizing formula corresponding to the cooling mode.
Optionally, the step of calculating a second discharge power peak according to the power peak optimization formula corresponding to the first discharge power peak and the cooling mode includes: the cooling mode comprises natural air cooling, forced air cooling and liquid air cooling;
when the cooling mode of the target battery pack is natural air cooling, calculating a second discharge power peak Pom according to the first discharge power peak Pm and a corresponding preset first power peak optimization formula Pom =pm (1+a0); wherein A0 is a heat dissipation efficiency coefficient corresponding to natural air cooling;
when the cooling mode of the target battery pack is forced air cooling, calculating a second discharge power peak Pom according to the first discharge power peak Pm and a corresponding preset second power peak optimization formula Pom =pm (1+a1); wherein A1 is a heat dissipation efficiency coefficient corresponding to forced air cooling;
when the cooling mode of the target battery pack is liquid air cooling, calculating a second discharge power peak Pom according to the first discharge power peak Pm and a corresponding preset third power peak optimization formula Pom =pm (1+a2); wherein A2 is a heat dissipation efficiency coefficient corresponding to liquid air cooling.
By adopting the above technical scheme, in order to perform targeted optimization on the first discharge power peak according to three cooling modes of natural air cooling, forced air cooling and liquid air cooling, when the cooling mode of the target battery pack is natural air cooling, calculating a second discharge power peak Pom according to the first discharge power peak Pm and a corresponding preset first power peak optimization formula Pom =pm (1+a0), wherein A0 is a heat dissipation efficiency coefficient corresponding to natural air cooling; when the cooling mode of the target battery pack is forced air cooling, calculating a second discharge power peak Pom according to a first discharge power peak Pm and a corresponding preset second power peak optimization formula Pom =pm (1+a1), wherein A1 is a heat dissipation efficiency coefficient corresponding to forced air cooling; when the cooling mode of the target battery pack is liquid air cooling, calculating a second discharge power peak Pom according to a first discharge power peak Pm and a corresponding preset third power peak optimization formula Pom =pm (1+a2), wherein A2 is a heat dissipation efficiency coefficient corresponding to liquid air cooling.
In a second aspect, the present application further provides a system for estimating a discharge power peak value of a battery pack, which adopts the following technical scheme:
a discharge power peak estimation system of a battery pack, comprising:
The discharging data acquisition module is used for acquiring the discharging data of each battery cell in the target battery pack;
A center temperature generation module for determining internal temperature data of the target battery pack according to the discharge data; the internal temperature data includes a center temperature of each of the battery cells;
the temperature characteristic value generation module is used for calculating the temperature characteristic value of the battery cell according to the internal temperature data;
and the power peak generating module is used for acquiring the ambient temperature of the target battery pack and determining a first discharge power peak value of the target battery pack according to the temperature characteristic value and the ambient temperature.
The system further comprises:
And the power peak value optimizing module is used for acquiring the cooling mode of the target battery pack and calculating a second discharging power peak value according to the first discharging power peak value and a power peak value optimizing formula corresponding to the cooling mode.
In a third aspect, the present application further provides a computer device, which adopts the following technical scheme:
A computer device comprising a memory and a processor, the memory having stored thereon a computer program executable on the processor, the processor implementing the method of the first aspect when executing the computer program.
In summary, the application at least comprises the following beneficial technical effects: firstly, acquiring discharge data of each battery cell in a target battery pack, then determining internal temperature data of the target battery pack according to the discharge data, wherein the internal temperature data comprises center temperatures of each battery cell, then calculating temperature characteristic values of the battery cells according to the internal temperature data, then acquiring environment temperature of the target battery pack, and determining a first discharge power peak value of the target battery pack according to the temperature characteristic values and the environment temperature; in the method, the first discharge power peak value of the target battery pack is estimated through the discharge data and the ambient temperature of each battery cell in the target battery pack, the operation amount is small, the realization is convenient, the control on the discharge process of the battery pack can be realized according to the first discharge power peak value in the subsequent treatment, the service life of the retired power battery is prolonged, the occurrence probability of the thermal runaway phenomenon is reduced, and the retired power battery is damaged or personnel injury occurs.
Drawings
In order to more clearly illustrate the embodiments of the present application or the prior art, a brief description of the drawings is provided below in which like elements or parts are generally identified by like reference numerals throughout the several views of the drawings that are required for use in the detailed description or the prior art. In the drawings, elements or portions thereof are not necessarily drawn to scale.
FIG. 1 is a schematic overall flow diagram of an embodiment of the present application.
Fig. 2 is a schematic diagram illustrating a connection relationship between a battery management system and a battery cell according to an embodiment of the application.
Fig. 3 is a schematic diagram showing comparison between a measured value and an estimated value of a center temperature of a single battery according to an embodiment of the present application.
Fig. 4 is a schematic diagram of the system of the present application.
Fig. 5 is a block diagram of the computer device of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the application discloses a discharge power peak estimation method of a battery pack.
Referring to fig. 1, a method for estimating a discharge power peak of a battery pack includes:
Step S11, obtaining discharge data of each battery cell in the target battery pack.
It should be noted that, in this embodiment, the discharge data of each battery cell must include the surface temperature of each battery cell, and the discharge data of each battery cell may further include at least one of a discharge current, a discharge voltage, and an SOC value, and in the implementation process, only the surface temperature of each battery cell may be collected, and in step S12, the internal temperature data of the target battery pack may be determined by the surface temperature of each battery cell; referring to fig. 2, the battery management system is respectively connected with each battery cell (i.e., the electric core), and the battery management system can collect discharge data of each battery cell in real time, pre-process the collected discharge data, and is also responsible for performing monitoring operations such as charge and discharge control, thermal management, and the like.
Step S12, determining internal temperature data of the target battery pack according to the discharge data.
Wherein the internal temperature data includes a center temperature of each battery cell.
In this embodiment, referring to fig. 3, the center temperature of each battery cell determined in step S12 is a center temperature estimated value, the center temperature measured value is acquired by a temperature sensor disposed on each battery cell, and it is known by comparing the center temperature estimated value and the center temperature measured value that the center temperature of the battery cell obtained in step S12 is substantially identical to the measured value of the battery cell.
Step S13, calculating the temperature characteristic value of the battery cell according to the internal temperature data.
The temperature characteristic value of the battery cell comprises a central temperature maximum value and a central temperature deviation maximum value.
The maximum value of the center temperature is the maximum value of the center temperatures of all the battery cells, and the maximum value of the center temperature deviation is the maximum value of the center temperature deviation values of all the battery cells.
It will be appreciated that referring to fig. 2, if there are N cells in the target battery pack, the center temperature of all the cells in the target battery pack may be expressed as:
{Ti(1)、Ti(2)、Ti(3)、…、Ti(N)}
Wherein Ti (1) is the center temperature of the No. 1 battery cell, ti (2) is the center temperature of the No. 2 battery cell, ti (3) is the center temperature of the No. 3 battery cell, and Ti (N) is the center temperature of the N battery cell;
the maximum value Tim of the center temperature of the battery cells in the target battery pack is as follows:
Tim=MAX{Ti(1)、Ti(2)、Ti(3)、…、Ti(N)}；
the calculation formula of the center temperature average value Tia of the target battery pack is:
Tia=(Ti(1)+Ti(2)+Ti(3)+…+Ti(N))/N
The calculation formula of the center temperature deviation value delta Ti of each battery cell in the target battery pack is as follows:
ΔTi(1)=Ti(1)-Tia；
ΔTi(2)=Ti(2)-Tia；
ΔTi(3)=Ti(3)-Tia；
……
ΔTi(N)=Ti(N)-Tia；
Wherein Δti (1) is the center temperature deviation value of the No.1 battery cell, Δti (2) is the center temperature deviation value of the No.2 battery cell, Δti (3) is the center temperature deviation value of the No. 3 battery cell, and Δti (N) is the center temperature deviation value of the No. 4 battery cell;
The maximum value ΔTim of the target battery pack center temperature deviation is:
ΔTim=MAX{ΔTi(1)、ΔTi(2)、ΔTi(3)…、ΔTi(N)}。
Step S14, the ambient temperature of the target battery pack is obtained, and the first discharge power peak value of the target battery pack is determined according to the temperature characteristic value and the ambient temperature.
In the above embodiment, the discharge data of each battery cell in the target battery pack is obtained first, then the internal temperature data of the target battery pack is determined according to the discharge data, the internal temperature data includes the center temperature of each battery cell, then the temperature characteristic value of the battery cell is calculated according to the internal temperature data, then the ambient temperature of the target battery pack is obtained, and the first discharge power peak value of the target battery pack is determined according to the temperature characteristic value and the ambient temperature; in the method, the first discharge power peak value of the target battery pack is estimated through the discharge data and the ambient temperature of each battery cell in the target battery pack, the operation amount is small, the realization is convenient, the control on the discharge process of the battery pack can be realized according to the first discharge power peak value in the subsequent treatment, the service life of the retired power battery is prolonged, the occurrence probability of the thermal runaway phenomenon is reduced, and the retired power battery is damaged or personnel injury occurs.
As a further embodiment of the estimation method, the step of determining the internal temperature data of the target battery pack from the discharge data includes:
step S21, a history discharge dataset is acquired.
The historical discharge data set comprises historical discharge data and historical center temperature data corresponding to the historical discharge data;
And S22, training and optimizing a pre-constructed center temperature generation model according to the historical discharge data set to obtain an optimized center temperature generation model.
Step S23, the discharge data of each battery cell is respectively input into the optimized center temperature generation model, and the center temperature of each battery cell is obtained.
In the above embodiment, in order to obtain the center temperature of each battery cell, a historical discharge data set is first obtained, where the historical discharge data set includes historical discharge data and historical center temperature data corresponding to the historical discharge data, then a pre-constructed center temperature generation model is trained and optimized according to the historical discharge data set to obtain an optimized center temperature generation model, and then the discharge data of each battery cell is respectively input into the optimized center temperature generation model to obtain the center temperature of each battery cell.
As a further embodiment of the estimation method, the step of training and optimizing the pre-constructed center temperature generation model according to the historical discharge data set to obtain an optimized center temperature generation model includes: the pre-built central temperature generation model is a pre-built vector machine regression model;
And step S31, training a pre-constructed vector machine regression model according to the historical discharge data set to obtain a trained vector machine regression model.
It should be noted that the pre-constructed vector machine regression model may be a pre-constructed correlation vector machine regression model or a support vector machine regression model.
And step S32, optimizing the trained vector machine regression model according to a five-fold cross validation mode to obtain an optimized vector machine regression model.
In the above embodiment, in order to obtain an optimized vector machine regression model, a pre-constructed vector machine regression model is trained according to a historical discharge data set to obtain a trained vector machine regression model, and then the trained vector machine regression model is optimized according to a five-fold cross-validation mode to obtain an optimized vector machine regression model.
As a further embodiment of the estimation method, the step of determining the first discharge power peak of the target battery pack according to the temperature characteristic value and the ambient temperature includes:
Step S41, acquiring a historical temperature dataset; the historical temperature data set comprises historical temperature characteristic data, environment temperature data corresponding to the historical temperature characteristic data and historical discharge power data corresponding to the historical temperature characteristic data.
Step S42, establishing a fuzzy control rule according to the historical temperature data set, and constructing a fuzzy control model according to the fuzzy control rule.
And step S43, optimizing the fuzzy control model to obtain an optimized fuzzy control model.
And S44, inputting the temperature characteristic value and the ambient temperature into the optimized fuzzy control model to obtain a first discharge power peak value of the target battery pack.
In the above embodiment, in order to generate the first discharge power peak value of the target battery pack, a historical temperature dataset is first obtained, the historical temperature dataset includes historical temperature feature data, ambient temperature data corresponding to the historical temperature feature data, and historical discharge power data corresponding to the historical temperature feature data, then a fuzzy control rule is established according to the historical temperature dataset, a fuzzy control model is constructed according to the fuzzy control rule, then the fuzzy control model is optimized to obtain an optimized fuzzy control model, and finally the temperature feature value and the ambient temperature are input into the optimized fuzzy control model, so that the first discharge power peak value of the target battery pack is obtained.
As a further embodiment of the estimation method, after the step of obtaining the ambient temperature of the target battery pack and determining the first discharge power peak value of the target battery pack according to the temperature characteristic value and the ambient temperature, the method further includes:
And obtaining a cooling mode of the target battery pack, and calculating a second discharge power peak value according to the first discharge power peak value and a power peak value optimization formula corresponding to the cooling mode.
In the above embodiment, in order to optimize the first discharge power peak, the cooling mode of the target battery pack is acquired first, and then the second discharge power peak is calculated according to the power peak optimization formula corresponding to the first discharge power peak and the cooling mode.
As a further embodiment of the estimation method, the step of calculating the second discharge power peak according to the power peak optimization formula corresponding to the first discharge power peak and the cooling mode includes: the cooling mode comprises natural air cooling, forced air cooling and liquid air cooling;
When the cooling mode of the target battery pack is natural air cooling, calculating a second discharge power peak Pom according to the first discharge power peak Pm and a corresponding preset first power peak optimization formula Pom =pm (1+a0).
Wherein A0 is a heat dissipation efficiency coefficient corresponding to natural air cooling.
When the cooling mode of the target battery pack is forced air cooling, the second discharge power peak Pom is calculated according to the first discharge power peak Pm and a corresponding preset second power peak optimization formula Pom =pm (1+a1).
Wherein A1 is a heat dissipation efficiency coefficient corresponding to forced air cooling.
When the cooling mode of the target battery pack is liquid air cooling, calculating a second discharge power peak Pom according to a first discharge power peak Pm and a corresponding preset third power peak optimization formula Pom =pm (1+a2).
Wherein A2 is a heat dissipation efficiency coefficient corresponding to liquid air cooling.
In the above embodiment, in order to perform targeted optimization on the first discharge power peak according to three cooling modes of natural air cooling, forced air cooling and liquid air cooling, when the cooling mode of the target battery pack is natural air cooling, the second discharge power peak Pom is calculated according to the first discharge power peak Pm and a corresponding preset first power peak optimization formula Pom =pm (1+a0), and A0 is a heat dissipation efficiency coefficient corresponding to natural air cooling; when the cooling mode of the target battery pack is forced air cooling, calculating a second discharge power peak Pom according to a first discharge power peak Pm and a corresponding preset second power peak optimization formula Pom =pm (1+a1), wherein A1 is a heat dissipation efficiency coefficient corresponding to forced air cooling; when the cooling mode of the target battery pack is liquid air cooling, calculating a second discharge power peak Pom according to a first discharge power peak Pm and a corresponding preset third power peak optimization formula Pom =pm (1+a2), wherein A2 is a heat dissipation efficiency coefficient corresponding to liquid air cooling.
The embodiment of the application also discloses a system for estimating the discharge power peak value of the battery pack.
Referring to fig. 4, a discharge power peak estimation system of a battery pack includes:
The discharging data acquisition module is used for acquiring the discharging data of each battery cell in the target battery pack;
A center temperature generation module for determining internal temperature data of the target battery pack according to the discharge data; the internal temperature data includes a center temperature of each battery cell;
The temperature characteristic value generation module is used for calculating the temperature characteristic value of the battery monomer according to the internal temperature data;
And the power peak generating module is used for acquiring the ambient temperature of the target battery pack and determining a first discharge power peak value of the target battery pack according to the temperature characteristic value and the ambient temperature.
A discharge power peak estimation system of a battery pack, further comprising:
and the power peak value optimizing module is used for acquiring the cooling mode of the target battery pack and calculating a second discharge power peak value according to the first discharge power peak value and a power peak value optimizing formula corresponding to the cooling mode.
The system for estimating the discharge power peak value of the battery pack can realize any one of the methods for estimating the discharge power peak value of the battery pack, and the specific working process of the system for estimating the discharge power peak value of the battery pack can refer to the corresponding process in the method for estimating the discharge power peak value of the battery pack.
The embodiment of the application also discloses computer equipment.
Referring to fig. 5, a computer device includes a memory and a processor, the memory stores a computer program executable on the processor, and the processor implements any one of the above methods for estimating a discharge power peak value of a battery pack when executing the computer program.
The foregoing description of the preferred embodiments of the application is not intended to limit the scope of the application in any way, including the abstract and drawings, in which case any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.
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| CN111913109B (en) * | 2019-05-09 | 2023-04-25 | 上海汽车集团股份有限公司 | Method and device for predicting peak power of battery |
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| CN111458650B (en) * | 2020-01-15 | 2022-11-15 | 合肥国轩高科动力能源有限公司 | Method for estimating peak power of lithium ion power battery system |
| CN111983473B (en) * | 2020-08-24 | 2023-03-10 | 哈尔滨理工大学 | Lithium ion power battery safety degree evaluation method and device based on support vector machine |
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| CN115993539B (en) * | 2023-03-22 | 2023-06-06 | 国民技术股份有限公司 | Method and device for predicting SOP of battery based on real-time temperature |
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