CN118024886A - On-line detection control method and system for lithium battery pack for vehicle - Google Patents
On-line detection control method and system for lithium battery pack for vehicle Download PDFInfo
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Abstract
The invention discloses an on-line detection control method and system of a lithium battery pack for a vehicle, and relates to the field of battery management, wherein the method comprises the following steps: acquiring a plurality of battery cells of a lithium battery pack for a target vehicle, and monitoring the real-time running states of the battery cells to acquire a plurality of real-time running state data; performing battery state estimation to obtain a plurality of monomer charge state indexes; acquiring a preset abnormal index threshold value; extracting a first abnormal monomer meeting a first abnormal index threshold and a second abnormal monomer meeting a second abnormal index threshold based on the plurality of monomer state of charge indexes; and performing abnormal battery isolation control on the first abnormal unit and performing performance balance control on the second abnormal unit. The technical problems that the existing detection control of the lithium battery pack for the vehicle is insufficient in monitoring precision, abnormal processing is not timely and accurate enough, and the battery performance is reduced are solved, and the technical effects of improving the monitoring precision, timely and effectively processing the abnormality and improving the battery performance stability are achieved.
Description
Technical Field
The application relates to the field of battery management, in particular to an on-line detection control method and system for a vehicle lithium battery pack.
Background
With the continuous expansion of the electric automobile industry, the performance requirements of the battery system are also increasing. The lithium battery pack for the vehicle is used as a core energy source of the electric automobile, and the safety, stability and service life of the lithium battery pack directly influence the overall performance and user experience of the vehicle. Various performance attenuation and potential safety hazards, such as capacity reduction, internal resistance increase, overheat, overcharge, overdischarge and the like, can occur in the use process of the lithium battery. If the problems cannot be timely and effectively monitored and controlled, the endurance mileage and the service life of the vehicle can be influenced, and serious safety accidents can be caused. In the prior art, the lithium battery is often subjected to cooling treatment after being subjected to operation parameter monitoring so as to ensure safe operation of the lithium battery, however, the lithium battery pack usually comprises a plurality of battery monomers, the operation states of the battery monomers may be different, and the performance of the battery monomers is inconsistent due to simple cooling treatment, so that safety accidents of the electric automobile are easily caused.
In the related art at present, detection control of the lithium battery pack for the vehicle has the technical problems that the monitoring precision is insufficient, the abnormal processing is not timely and accurate enough, and the battery performance is reduced.
Disclosure of Invention
The application provides an on-line detection control method and system for a lithium battery pack of a vehicle, which adopts technical means such as monitoring the running states of a plurality of battery monomers of the lithium battery pack of the vehicle, estimating the running states of the battery according to the running states, accurately acquiring the charge state indexes of the battery monomers, setting the abnormal index thresholds of different levels, carrying out refined abnormal detection and classification on the battery monomers, adopting different control measures on the battery monomers with different abnormal degrees, and the like, thereby achieving the technical effects of improving the monitoring precision, effectively handling the abnormality in time and improving the performance stability of the battery.
The application provides an on-line detection control method of a lithium battery pack for a vehicle, which comprises the following steps:
Acquiring a plurality of battery cells of a lithium battery pack for a target vehicle, and monitoring the real-time running states of the battery cells to acquire a plurality of real-time running state data;
performing battery state estimation according to the plurality of real-time running state data to obtain a plurality of single-body state-of-charge indexes, wherein the plurality of single-body state-of-charge indexes are used for representing the abnormal degree of the states of charge of the plurality of battery single bodies;
Acquiring a preset abnormal index threshold, wherein the preset abnormal index threshold comprises a first abnormal index threshold and a second abnormal index threshold, and the first abnormal index threshold is larger than the second abnormal index threshold;
extracting a first abnormal monomer meeting the first abnormal index threshold and a second abnormal monomer meeting the second abnormal index threshold based on the plurality of monomer state of charge indexes;
And performing abnormal battery isolation control on the first abnormal unit, and performing performance balance control on the second abnormal unit.
In a possible implementation manner, the battery state estimation is performed according to the plurality of real-time operation state data, a plurality of monomer charge state indexes are obtained, and the following processing is performed:
constructing a charge state associated parameter type set;
performing battery state estimation on the same group of battery monomer samples based on a plurality of parameter types in the charge state associated parameter type set to obtain a plurality of estimated sample sets;
performing estimation credibility identification on the plurality of parameter types based on the plurality of estimation sample sets to obtain a plurality of estimation credibility;
Extracting a first parameter type, a second parameter type and a third parameter type with highest credibility based on the plurality of estimated credibility, wherein the first parameter type has a first credibility, the second parameter type has a second credibility and the third parameter type has a third credibility;
And performing battery state estimation on the plurality of real-time running state data based on the first parameter type, the second parameter type and the third parameter type to acquire the plurality of monomer state-of-charge indexes.
In a possible implementation manner, the estimating, credibility and identification are performed on the plurality of parameter types based on the plurality of estimating sample sets, so as to obtain a plurality of estimating credibility, and the following processing is performed:
counting the same estimated sample number of each estimated sample set to obtain a plurality of same estimated sample duty ratio coefficients;
performing estimation deviation recognition on each estimation sample set to obtain a plurality of estimation deviation coefficients;
And carrying out weighted calculation on the plurality of same estimated sample duty ratio coefficients and the plurality of estimated deviation coefficients to obtain the plurality of estimated credibility.
In a possible implementation manner, the battery state estimation is performed on the plurality of real-time running state data based on the first parameter type, the second parameter type and the third parameter type, the plurality of monomer state-of-charge indexes are obtained, and the following processing is performed:
Extracting parameters of the plurality of real-time running state data according to the first parameter type, the second parameter type and the third parameter type to obtain first parameter state data, second parameter state data and third parameter state data;
constructing a battery state estimation network, wherein the battery state estimation network comprises a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer and a weighted estimation layer;
And analyzing the first parameter state data, the second parameter state data and the third parameter state data through the battery state estimation network to generate the plurality of monomer state-of-charge indexes.
In a possible implementation, the following process is performed:
The battery state estimation network comprises a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer and a weighting estimation layer, wherein the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer are connected in parallel, and the output ends of the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer are connected with the weighting estimation layer;
the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer are constructed by gradient descent training through historical estimation parameter records;
The weighted estimation layer calculates outputs of the first, second, and third parameter estimation layers based on the first, second, and third trustworthiness.
In a possible implementation manner, performance balance control is performed on the second abnormal monomer, and the following processing is performed:
collecting the connection state of the second abnormal monomer, and obtaining a parallel monomer combination and a serial monomer combination;
Carrying out charge state control influence analysis on the parallel monomer combination and the serial monomer combination, and outputting a parallel influence index and a serial influence index;
Performing equalization control parameter optimization based on a preset equalization control variable, the parallel connection influence index and the serial connection influence index to generate an optimal equalization control parameter;
And performing performance balance control on the second abnormal monomer by using the optimal balance control parameters.
In a possible implementation manner, the performing equalization control parameter optimization based on the predetermined equalization control variable, the parallel impact indicator and the series impact indicator generates an optimal equalization control parameter, and performs the following processing:
constructing a variable-battery state association relationship based on the predetermined equalization control variable;
performing iterative optimization of the battery state rising direction based on the variable-battery state association relation until the battery state is smaller than the second abnormal index threshold value to obtain a plurality of groups of equalization control decisions, wherein the plurality of groups of equalization control decisions comprise equalization control parameters of each monomer in the second abnormal monomers;
And carrying out feedback optimization on the multiple groups of equalization control decisions based on the parallel influence indexes and the serial influence indexes to obtain the optimal equalization control parameters.
The application also provides an on-line detection control system of the lithium battery pack for the vehicle, which comprises the following steps:
The battery cell real-time running state monitoring module is used for acquiring a plurality of battery cells of the lithium battery pack for the target vehicle, and monitoring the real-time running states of the battery cells to acquire a plurality of real-time running state data;
the battery state estimation module is used for carrying out battery state estimation according to the plurality of real-time running state data to obtain a plurality of single charge state indexes, wherein the plurality of single charge state indexes are used for representing the charge state abnormality degree of the plurality of battery single cells;
The system comprises a preset abnormal index threshold value acquisition module, a first abnormal index threshold value acquisition module and a second abnormal index threshold value acquisition module, wherein the preset abnormal index threshold value acquisition module is used for acquiring a preset abnormal index threshold value, the preset abnormal index threshold value comprises a first abnormal index threshold value and a second abnormal index threshold value, and the first abnormal index threshold value is larger than the second abnormal index threshold value;
the abnormal monomer extraction module is used for extracting a first abnormal monomer meeting the first abnormal index threshold and a second abnormal monomer meeting the second abnormal index threshold based on the plurality of monomer charge state indexes;
The abnormal single body control module is used for performing abnormal battery isolation control on the first abnormal single body and performing performance balance control on the second abnormal single body.
According to the on-line detection control method and system for the lithium battery pack for the vehicle, provided by the application, the real-time operation state monitoring is carried out on the battery monomers through obtaining the battery monomers of the lithium battery pack for the vehicle, the real-time operation state data are obtained, the battery state estimation is carried out according to the real-time operation state data, the battery state indexes are obtained, the battery state indexes are used for representing the abnormal degree of the battery monomers, the preset abnormal index threshold is obtained, the preset abnormal index threshold comprises a first abnormal index threshold and a second abnormal index threshold, the first abnormal index threshold is larger than the second abnormal index threshold, the first abnormal monomer meeting the first abnormal index threshold and the second abnormal monomer meeting the second abnormal index threshold are extracted based on the battery state indexes of the battery monomers, finally, the abnormal battery isolation control is carried out on the first abnormal monomer, the second abnormal monomer is carried out, the performance balance control is carried out, the monitoring precision is improved, the abnormality is effectively handled in time, and the technical effect of improving the performance stability of the battery is achieved.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present application, the following description will briefly refer to the accompanying drawings of the embodiments of the present application, in which flowcharts are used to illustrate operations performed by systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
Fig. 1 is a schematic flow chart of an on-line detection control method of a lithium battery pack for a vehicle according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an on-line detection control system for a lithium battery pack for a vehicle according to an embodiment of the present application.
Detailed Description
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, the term "first\second" being referred to merely as distinguishing between similar objects and not representing a particular ordering for the objects. 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 server that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules that may not be expressly listed or inherent to such process, method, article, or apparatus, and unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used herein is for the purpose of describing embodiments of the application only.
The embodiment of the application provides an on-line detection control method of a lithium battery pack for a vehicle, as shown in fig. 1, comprising the following steps:
step S100, a plurality of battery cells of a lithium battery pack for a target vehicle are obtained, and real-time operation state monitoring is carried out on the battery cells to obtain a plurality of real-time operation state data. Specifically, the target vehicle lithium battery pack refers to a set of lithium batteries specially designed for a specific vehicle (such as an electric vehicle, a hybrid electric vehicle, etc.), and is composed of a plurality of battery cells (i.e., single battery cells) connected in series or in parallel to provide sufficient voltage and capacity to meet the power requirements of the vehicle, wherein the single battery cells are single battery cells in the target vehicle lithium battery pack and have specific voltage and capacity. The method comprises the steps of acquiring key data such as voltage, current, temperature, internal resistance of a battery and the like of each battery cell in a lithium battery pack for a target vehicle in real time by using equipment such as a sensor, an instrument, a data acquisition system and the like, transmitting the acquired real-time and continuous monitoring data to a central control unit or a data processing module through a data bus or a wireless transmission mode, and acquiring a plurality of real-time running state data which reflect various key parameters and state information of the battery cell in the running process.
Step 200, estimating the state of charge of the battery according to the real-time running state data, and obtaining a plurality of monomer state of charge indexes, wherein the monomer state of charge indexes are used for representing the abnormal degree of the state of charge of the battery. Specifically, the collected real-time running state data of the plurality of battery monomers are subjected to preprocessing operations such as filtering and denoising, the accuracy of the data is improved, the state of the battery monomers is estimated based on a battery model or a machine learning algorithm, and indexes related to the battery state are obtained, wherein the indexes comprise the state of charge of the battery monomers (the current residual electric quantity of the battery accounts for the percentage of the total capacity of the battery), the internal resistance of the battery, the self-discharge rate, the temperature and the like, and the indexes jointly form a monomer state of charge index which reflects the current electric quantity, the performance and the health condition of the battery monomers.
In one possible implementation, step S200 further includes step S210 of constructing a state of charge associated parameter type set. That is, parameters closely related to the state of charge of the battery, including the voltage, current, temperature, internal resistance, etc. of the battery are determined in combination with expertise related to the battery field or based on industry experience, etc., and these parameter types are collected together to construct a state of charge related parameter type set, which is the basis for subsequent analysis and estimation. Step S220, performing battery state estimation on the same battery cell sample based on the plurality of parameter types in the state-of-charge related parameter type set, to obtain a plurality of estimated sample sets. The same-family battery cell sample refers to a battery cell having the same or similar design, manufacturing process and use environment as those of a plurality of battery cells of a lithium battery pack for a target vehicle, and state estimation is performed on a group of same-family battery cell samples by using the state-of-charge related parameter type set constructed in the step S210 to obtain a plurality of estimated sample sets, wherein each estimated sample set contains battery state information estimated based on different parameter types. And step S230, carrying out estimation credibility identification on the plurality of parameter types based on the plurality of estimation sample sets to obtain a plurality of estimation credibility. That is, the plurality of estimated sample sets obtained in step S220 are analyzed, the reliability of each parameter type in the battery state estimation is evaluated by comparing the difference between the estimation results of the different parameter types and the actual battery state, and by this process, an estimated reliability is assigned to each parameter type. Step S240, extracting a first parameter type, a second parameter type and a third parameter type with highest reliability based on the plurality of estimated reliabilities, wherein the first parameter type has the first reliability, the second parameter type has the second reliability and the third parameter type has the third reliability. The first three parameter types with the highest credibility are screened out from a plurality of estimated credibility, and the first parameter type, the second parameter type and the third parameter type are obtained. Step S250, performing battery state estimation on the plurality of real-time running state data based on the first parameter type, the second parameter type and the third parameter type, and obtaining the plurality of monomer state of charge indexes. That is, the three most reliable parameter types selected in step S240 are used to perform battery state estimation on the acquired multiple real-time running state data. According to the implementation method, the parameter type with the highest reliability is obtained by carrying out reliability evaluation and screening on the parameter type, and the battery state evaluation is carried out based on the parameter type with the highest reliability, so that more accurate and reliable monomer state-of-charge indexes are obtained, and the technical effect of improving the accuracy and reliability of the battery state estimation is achieved.
In another possible implementation manner, step S230 further includes step S231, performing the same estimation sample number statistics on each estimation sample set, to obtain a plurality of identical estimation sample duty coefficients. Specifically, the estimated sample sets obtained by comparing different parameter types are compared, the number of samples with the same estimated result in each estimated sample set is counted, the number of the same estimated samples reflects the stability and consistency of the parameter types in estimation, the proportion of the same estimated samples to the total number of the respective sample sets is calculated, and a plurality of same estimated sample duty ratio coefficients are obtained, wherein the plurality of same estimated sample duty ratio coefficients quantify the capability of different parameter types in generating the consistent estimated result. Step S232, performing estimation deviation recognition on each estimation sample set to obtain a plurality of estimation deviation coefficients. Specifically, the deviation degree is calculated on every two samples in each estimated sample set, the deviation degree is measured by comparing the difference of the estimated results among different samples, for example, the difference, the variance or the standard deviation among the estimated results of different samples is calculated, further, the average value of the deviation degrees is calculated, and an estimated deviation coefficient is obtained, the estimated deviation coefficient reflects the overall deviation level of the parameter type in estimation, a smaller estimated deviation coefficient means that the deviation of the parameter type in estimation is smaller, and the estimated result is more reliable. Through this step, a plurality of estimated deviation coefficients are obtained, each estimated deviation coefficient corresponding to an estimated deviation degree of a parameter type. And step S233, carrying out weighted calculation on the plurality of identical estimated sample duty ratio coefficients and the plurality of estimated deviation coefficients to obtain the plurality of estimated credibility. Specifically, a weighting mode is set according to actual conditions, and a plurality of estimated credibility of a plurality of parameter types is obtained by carrying out weighting calculation on a plurality of same estimated sample duty ratio coefficients and a plurality of estimated deviation coefficients, wherein the estimated credibility is a quantized index used for measuring the credibility of different parameter types in battery state estimation. According to the implementation method, the performance of different parameter types in battery state estimation is comprehensively and accurately estimated through the statistics of the same estimated sample number and the estimation deviation recognition, and the reliable estimation reliability is obtained, so that the most reliable parameter type can be screened, and the technical effect of providing a reliable basis for battery state estimation is achieved.
In another possible implementation manner, step S250 further includes step S251, performing parameter extraction on the plurality of real-time running state data according to the first parameter type, the second parameter type and the third parameter type, to obtain first parameter state data, second parameter state data and third parameter state data. Specifically, based on the design and operation principle of the battery system, the corresponding relation between each parameter type and a specific data source in the real-time operation state data is determined, and then the numerical value or the data sequence corresponding to each parameter type is extracted from the real-time operation state data to obtain first parameter state data, second parameter state data and third parameter state data, wherein the first parameter state data, the second parameter state data and the third parameter state data respectively represent the real-time performance of the battery monomer under different parameter types. Step S252, a battery state estimation network is constructed, wherein the battery state estimation network comprises a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer and a weighted estimation layer. Specifically, the battery state estimation network is a multi-layer deep learning model, and includes a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer, and a weighted estimation layer, where each layer is responsible for processing and analyzing state data of a corresponding parameter type to extract information related to a battery state. The first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer respectively correspond to state data of the first parameter type, the second parameter type and the third parameter type, and feature extraction and conversion are carried out on the state data through different neural network structures (such as a convolution layer, a full connection layer and the like) to generate feature vectors related to battery states; the weighting estimation layer is responsible for carrying out weighting fusion on the feature vectors output by the first three parameter estimation layers, and the weighting estimation layer can automatically adjust the weight of each parameter type by learning the contribution degree of different parameter types in battery state estimation, so that a more accurate battery state estimation result is obtained. Step S253, analyzing, by the battery state estimation network, the first parameter state data, the second parameter state data, and the third parameter state data, to generate the plurality of monomer state of charge indexes. Specifically, the extracted first parameter state data, second parameter state data and third parameter state data are input into a constructed battery state estimation network, and the battery state estimation network processes and analyzes the state data layer by layer in a forward propagation mode to finally generate a plurality of monomer state of charge indexes. According to the implementation mode, the state of the battery monomer is accurately estimated through the deep learning model, and the technical effects of improving the reliability and stability of estimation are achieved.
In another possible implementation manner, step S252 further includes step S2521, where the battery state estimation network includes a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer, and a weighting estimation layer, and the first parameter estimation layer, the second parameter estimation layer, and the third parameter estimation layer are connected in parallel, and outputs of the first parameter estimation layer, the second parameter estimation layer, and the third parameter estimation layer are connected to the weighting estimation layer. Specifically, the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer are connected in parallel, so that respective data can be processed simultaneously, the calculation efficiency is improved, the output ends of the three parameter estimation layers are connected with the weighting estimation layer, and the weighting estimation layer can receive the output from each parameter estimation layer and perform subsequent weighting and integration operations. Step S2522, the first parameter estimation layer, the second parameter estimation layer, and the third parameter estimation layer perform gradient descent training construction through historical estimation parameter records. Specifically, the history estimation parameter record includes the result of previous battery state estimation and corresponding real-time running state data, and is used for constructing a training data set and training a battery state estimation network. In the training process, parameters of a battery state estimation network are optimized through a gradient descent algorithm, the battery state estimation network adjusts internal parameters of the battery state estimation network according to the relation between input and output in training data so as to minimize prediction errors, and through repeated iterative training, the battery state estimation network can learn how to extract information related to the battery state from real-time running state data and accurately estimate the battery state. In step S2523, the weighted estimation layer calculates the outputs of the first, second and third parameter estimation layers based on the first, second and third credibilities. Specifically, the reliability of each parameter type is taken as a weight, the contribution degree of the output of the corresponding parameter estimation layer in the final estimation result is adjusted, the higher the reliability of the parameter type is, the larger the weight in the weighted calculation is, the weighted estimation layer multiplies the output of each parameter estimation layer with the corresponding reliability weight, and the results are summed to obtain the final battery state estimation result. By taking the credibility of each parameter type as a weight, the implementation method comprehensively considers the contributions of different parameter types in battery state estimation, and achieves the technical effect of improving the estimation accuracy and reliability.
Step S300, a preset abnormal index threshold is obtained, wherein the preset abnormal index threshold comprises a first abnormal index threshold and a second abnormal index threshold, and the first abnormal index threshold is larger than the second abnormal index threshold. Specifically, a predetermined abnormality index threshold is set according to design requirements, historical operation data or expert experience of a lithium battery pack for a target vehicle, and the predetermined abnormality index threshold is preset and is used for judging whether an abnormal index limit occurs in a battery cell or not. The first abnormal index threshold is a relatively high threshold, and is used for judging whether the battery state is in a serious abnormal range, when the single charge state index exceeds the first abnormal index threshold, the battery single body has serious performance reduction, potential safety hazard or impending failure, and corresponding measures such as shutdown inspection, battery replacement and the like need to be immediately taken; the second abnormality index threshold is a relatively low threshold for early warning of potential problems that may exist in the battery cells, and when the state of charge index of the cell exceeds the second abnormality index threshold but does not reach the first abnormality index threshold, it means that the battery performance is gradually decreasing or some potential safety hazard exists, but does not reach the level of emergency treatment, and some preventive measures, such as adjusting the battery usage strategy, enhancing monitoring, etc., may be taken to delay further decrease in the battery performance or avoid deterioration of the potential problems.
Step S400, based on the plurality of monomer charge state indexes, extracting a first abnormal monomer meeting the first abnormal index threshold and a second abnormal monomer meeting the second abnormal index threshold. Specifically, the state of charge index of each single battery is checked one by one, compared with a first abnormal index threshold, and if the state of charge index of a single battery exceeds the first abnormal index threshold, the single battery is marked as a first abnormal single battery, and the first abnormal single battery is a single battery with serious performance problem or potential safety hazard and needs immediate attention and treatment. Similarly, the state of charge index of each single battery is checked one by one and compared with a second abnormal index threshold, if the state of charge index of a single battery exceeds the second abnormal index threshold but does not reach the first abnormal index threshold, the single battery is marked as a second abnormal single battery, and the second abnormal single battery is a single battery which has no serious problem but has potential risk and needs to pay attention to and take preventive measures.
And S500, performing abnormal battery isolation control on the first abnormal single body, and performing performance balance control on the second abnormal single body. Specifically, for the battery marked as the first abnormal unit, since the charge state of the battery is seriously deviated from the normal range, there may be serious performance problem or potential safety hazard, and thus isolation control is immediately performed, for example, by controlling a related circuit or a mechanical device, the connection with the first abnormal unit is disconnected, the first abnormal unit is prevented from continuing to participate in the charge-discharge process of the lithium battery pack for the target vehicle, or the first abnormal unit is physically isolated from the lithium battery pack for the target vehicle, so that the first abnormal unit is prevented from causing adverse effects on other normal unit batteries. The battery marked as the second abnormal monomer is subjected to performance balance control although the charge state of the battery does not reach the serious abnormal degree, potential risks exist, the performance of the second abnormal monomer is controlled by adjusting the working condition or parameters of the lithium battery pack for the target vehicle, the performance of each battery monomer is kept as consistent as possible, the performance of the second abnormal monomer is improved, and the problem deterioration is avoided. According to the embodiment of the application, the real-time operation state monitoring is carried out on a plurality of battery monomers of the lithium battery pack for the target vehicle, the battery state estimation is carried out according to the real-time operation state data, the charge state indexes of the battery monomers are accurately obtained, the abnormal index thresholds of different levels are set, the battery monomers are subjected to refined abnormal detection and classification, and different control measures and other technical means are adopted for the battery monomers with different abnormal degrees, so that the technical effects of improving the monitoring precision, timely and effectively processing the abnormality and further improving the performance stability of the battery are achieved.
In one possible implementation manner, performance balance control is performed on the second abnormal monomer, and step S500 further includes step S510, collecting a connection state of the second abnormal monomer, and obtaining a parallel monomer combination and a serial monomer combination. Specifically, parameters such as voltage and current of each battery cell in the lithium battery pack for the target vehicle are acquired by using a sensor or a circuit detection means, based on the acquired parameters, the connection mode of the second abnormal cell in the lithium battery pack for the target vehicle is analyzed, which cells are connected in parallel with the second abnormal cell (namely sharing the same voltage) and which cells are connected in series with the second abnormal cell (namely forming a current path), and according to the analysis result, the parallel-connected battery cells are divided into parallel cell combinations, and the series-connected battery cells are divided into series cell combinations. And step S520, carrying out charge state control influence analysis on the parallel monomer combination and the serial monomer combination, and outputting a parallel influence index and a serial influence index. Specifically, the state of charge of each battery cell in the parallel monomer combination and the serial monomer combination is measured, the influence of the state of charge difference between the battery cells in the parallel monomer combination on balance control, such as energy transfer efficiency, balance speed and the like, is analyzed, the influence of the state of charge difference of the battery cells in the serial monomer combination on overall voltage stability and energy output is also analyzed, and based on the influence analysis result, a parallel influence index and a serial influence index are calculated, wherein the influence indexes can be numerical values or grades and are used for quantifying the influence degree of the state of charge control under different connection modes. And step S530, carrying out equalization control parameter optimization based on a preset equalization control variable, the parallel connection influence index and the series connection influence index, and generating an optimal equalization control parameter. Specifically, according to the type and performance requirements of the battery cells, preset equalization control variables, such as an equalization current range, equalization time and the like, an optimization algorithm, such as a genetic algorithm, particle swarm optimization and the like, is used for searching optimal equalization control parameters, parallel influence indexes and serial influence indexes are used as inputs of the optimization algorithm, parameter optimization is performed in combination with the preset equalization control variables, and equalization control parameter combinations which enable the performance equalization effect to reach the optimal are found through repeated iterative computation. And S540, performing performance balance control on the second abnormal monomer by using the optimal balance control parameters. I.e. the one that is the one. And (3) applying the optimal balance control parameters generated in the step (S530) to a battery management system, adjusting the charge-discharge strategy, the optimal temperature management strategy and the like of the battery cells according to the optimal balance control parameters, improving the performance of the second abnormal cell and realizing the performance balance between the second abnormal cell and other battery cells through the balance control process. The realization mode carries out equalization control parameter optimization based on various indexes and variables, improves the accuracy and efficiency of equalization control, and achieves the technical effects of optimizing the battery performance and prolonging the service life of the battery.
In another possible implementation, step S530 further includes step S531 of constructing a variable-battery state association relationship based on the predetermined equalization control variable. Specifically, an association relationship between a variable and a battery state is built according to a preset equalization control variable, a mathematical or logic model between the variable and the battery state is determined by analyzing the rule of influence of different equalization control variables on the battery state, and in this way, a variable-battery state association relationship is built. And step S532, performing iterative optimization of the battery state rising direction based on the variable-battery state association relation until the battery state is smaller than the second abnormal index threshold value to obtain a plurality of groups of equalization control decisions, wherein the plurality of groups of equalization control decisions comprise equalization control parameters of each monomer in the second abnormal monomers. Specifically, the objective of iterative optimization is to find multiple sets of equalization control decisions, so that the monomer charge state index of the second abnormal monomer is gradually reduced and is finally smaller than the second abnormal index threshold, an optimization algorithm (such as gradient descent, particle swarm optimization and the like) is used for searching and iterating in multiple possible equalization control decision spaces, multiple sets of equalization control decisions meeting the requirements are obtained through multiple iterations and optimizations, and each set of equalization control decisions comprises equalization control parameters aiming at each monomer in the second abnormal monomer. And step S533, performing feedback optimization on the multiple groups of equalization control decisions based on the parallel influence indexes and the serial influence indexes to obtain the optimal equalization control parameters. Specifically, after a plurality of groups of equalization control decisions are obtained, the influence of the parallel influence index and the series influence index is further considered, the equalization control decisions which possibly cause performance degradation are adjusted or optimized by evaluating the influence of different equalization control decisions on the overall performance of the lithium battery pack for the target vehicle, finally, an optimal group of equalization control decisions is selected, and the optimal equalization control parameters corresponding to the optimal equalization control decisions are obtained, wherein the optimal equalization control parameters meet the performance improvement of the second abnormal monomer and simultaneously give consideration to the performance equalization of the lithium battery pack for the whole target vehicle. The implementation mode adopts a two-stage optimizing strategy, wherein a plurality of groups of possible balanced control decisions are found through a variable-battery state association relationship in the first stage, the possible balanced control decisions are further optimized through parallel connection and series connection influence indexes in the second stage, an optimal solution is finally determined, the searching range is reduced through the two-stage optimizing strategy, the optimizing efficiency is improved, meanwhile, multiple factors are comprehensively considered for optimizing, the obtained optimal balanced control parameters are ensured to meet the performance improvement requirement of a single second abnormal monomer, the performance balance of the lithium battery pack for the whole target vehicle can be realized, the local optimal solution is avoided, the global optimal is ensured, and the technical effect that the optimizing process is more comprehensive and accurate is achieved.
In the above, an on-line detection control method of a lithium battery pack for a vehicle according to an embodiment of the present invention is described in detail with reference to fig. 1. Next, an on-line detection control system of a lithium battery pack for a vehicle according to an embodiment of the present invention will be described with reference to fig. 2.
The on-line detection control system for the vehicle lithium battery pack is used for solving the technical problems that the detection control of the existing vehicle lithium battery pack is insufficient in monitoring precision, abnormal treatment is not timely and accurate enough, and further the performance of the battery is reduced, and achieves the technical effects of improving the monitoring precision, timely and effectively treating the abnormality and improving the performance stability of the battery. An on-line detection control system of a lithium battery pack for a vehicle includes: the system comprises a battery cell real-time running state monitoring module 10, a battery state estimating module 20, a preset abnormal index threshold value acquiring module 30, an abnormal cell extracting module 40 and an abnormal cell control module 50.
The battery cell real-time running state monitoring module 10 is used for acquiring a plurality of battery cells of a lithium battery pack for a target vehicle, and monitoring the battery cells in real time to acquire a plurality of real-time running state data;
the battery state estimation module 20 is configured to perform battery state estimation according to the plurality of real-time operation state data, and obtain a plurality of monomer state of charge indexes, where the plurality of monomer state of charge indexes are used to represent abnormal degrees of states of charge of the plurality of battery monomers;
The predetermined abnormal indicator threshold obtaining module 30 is configured to obtain a predetermined abnormal indicator threshold, where the predetermined abnormal indicator threshold includes a first abnormal indicator threshold and a second abnormal indicator threshold, and the first abnormal indicator threshold is greater than the second abnormal indicator threshold;
The abnormal monomer extraction module 40 is configured to extract, based on the plurality of monomer state of charge indexes, a first abnormal monomer that satisfies the first abnormal index threshold and a second abnormal monomer that satisfies the second abnormal index threshold;
The abnormal cell control module 50 is configured to perform abnormal battery isolation control on the first abnormal cell and perform performance balance control on the second abnormal cell.
Next, the specific configuration of the battery state estimation module 20 will be described in detail. As described above, the battery state estimation module 20 may further perform battery state estimation according to the plurality of real-time operation state data to obtain a plurality of unit charge state indexes, and further include: the charge state associated parameter type set construction unit is used for constructing a charge state associated parameter type set; the estimated sample set acquisition unit is used for estimating the battery state of the same group of battery monomer samples based on a plurality of parameter types in the charge state associated parameter type sets to obtain a plurality of estimated sample sets; the estimation credibility identification unit is used for carrying out estimation credibility identification on the plurality of parameter types based on the plurality of estimation sample sets to obtain a plurality of estimation credibility; the parameter type extraction unit is used for extracting a first parameter type, a second parameter type and a third parameter type with highest credibility based on the plurality of estimated credibility, wherein the first parameter type has the first credibility, the second parameter type has the second credibility and the third parameter type has the third credibility; the battery state estimation unit is used for carrying out battery state estimation on the plurality of real-time running state data based on the first parameter type, the second parameter type and the third parameter type, and acquiring the plurality of monomer charge state indexes.
The estimating, credible and identifying the parameter types based on the estimated sample sets to obtain a plurality of estimated credibles, and the estimating, credible and identifying unit may further include: the same estimated sample duty ratio coefficient obtaining subunit is used for carrying out statistics on the number of the same estimated samples on each estimated sample set to obtain a plurality of same estimated sample duty ratio coefficients; the estimated deviation coefficient acquisition subunit is used for carrying out estimated deviation identification on each estimated sample set to obtain a plurality of estimated deviation coefficients; and the weighting calculation subunit is used for carrying out weighting calculation on the plurality of identical estimated sample duty ratio coefficients and the plurality of estimated deviation coefficients to obtain the plurality of estimated credibility.
Wherein, the battery state estimation unit may further perform battery state estimation on the plurality of real-time running state data based on the first parameter type, the second parameter type and the third parameter type to obtain the plurality of monomer state-of-charge indexes, and the battery state estimation unit may further include: the parameter state data acquisition subunit is used for extracting parameters of the plurality of real-time running state data according to the first parameter type, the second parameter type and the third parameter type to acquire first parameter state data, second parameter state data and third parameter state data; the battery state estimation network construction subunit is used for constructing a battery state estimation network, wherein the battery state estimation network comprises a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer and a weighted estimation layer; the network analysis subunit is configured to analyze the first parameter state data, the second parameter state data, and the third parameter state data through the battery state estimation network, and generate the plurality of monomer state-of-charge indexes.
Wherein the battery state estimation network construction subunit may further include: the estimation layer construction micro unit is used for the battery state estimation network and comprises a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer and a weighting estimation layer, wherein the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer are connected in parallel, and the output ends of the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer are connected with the weighting estimation layer; the estimation layer construction micro unit is used for performing gradient descent training construction on the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer through historical estimation parameter records, and the weighted estimation layer calculates the output of the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer based on the first credibility, the second credibility and the third credibility.
Next, the specific configuration of the abnormal unit control module 50 will be described in detail. As described above, performing performance balancing control on the second abnormal monomer, the abnormal monomer control module 50 may further include: the monomer connection state acquisition unit is used for acquiring the connection state of the second abnormal monomer and acquiring a parallel monomer combination and a serial monomer combination; the charge state control influence analysis unit is used for carrying out charge state control influence analysis on the parallel monomer combination and the serial monomer combination and outputting a parallel influence index and a serial influence index; the equalization control parameter optimizing unit is used for optimizing the equalization control parameters based on a preset equalization control variable, the parallel connection influence index and the serial connection influence index to generate optimal equalization control parameters; and the monomer performance balance control unit is used for performing performance balance control on the second abnormal monomer according to the optimal balance control parameter.
Wherein the equalization control parameter optimizing unit performs equalization control parameter optimizing based on a predetermined equalization control variable, the parallel influence index and the series influence index to generate an optimal equalization control parameter, and the equalization control parameter optimizing unit may further include: the association relation construction subunit is used for constructing a variable-battery state association relation based on the preset equalization control variable; the multi-group equalization control decision obtaining subunit is used for performing iterative optimization on the rising direction of the battery state based on the variable-battery state association relation until the battery state is smaller than the second abnormal index threshold value to obtain multi-group equalization control decisions, wherein the multi-group equalization control decisions comprise equalization control parameters of each monomer in the second abnormal monomers; and the optimal equalization control parameter acquisition subunit is used for carrying out feedback optimization on the multiple groups of equalization control decisions based on the parallel influence index and the serial influence index to obtain the optimal equalization control parameters.
The on-line detection control system for the vehicle lithium battery pack provided by the embodiment of the invention can execute the on-line detection control method for the vehicle lithium battery pack provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Although the present application makes various references to certain modules in a system according to embodiments of the present application, any number of different modules may be used and run on a user terminal and/or server, including units and modules that are merely partitioned by functional logic, but are not limited to the above-described partitioning, so long as the corresponding functionality is enabled; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present application.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application. In some cases, the acts or steps recited in the present application may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Claims (8)
1. An on-line detection control method of a lithium battery pack for a vehicle is characterized by comprising the following steps:
Acquiring a plurality of battery cells of a lithium battery pack for a target vehicle, and monitoring the real-time running states of the battery cells to acquire a plurality of real-time running state data;
performing battery state estimation according to the plurality of real-time running state data to obtain a plurality of single-body state-of-charge indexes, wherein the plurality of single-body state-of-charge indexes are used for representing the abnormal degree of the states of charge of the plurality of battery single bodies;
Acquiring a preset abnormal index threshold, wherein the preset abnormal index threshold comprises a first abnormal index threshold and a second abnormal index threshold, and the first abnormal index threshold is larger than the second abnormal index threshold;
extracting a first abnormal monomer meeting the first abnormal index threshold and a second abnormal monomer meeting the second abnormal index threshold based on the plurality of monomer state of charge indexes;
And performing abnormal battery isolation control on the first abnormal unit, and performing performance balance control on the second abnormal unit.
2. The method for on-line detection control of a lithium battery pack for a vehicle according to claim 1, wherein the performing battery state estimation according to the plurality of real-time operation state data to obtain a plurality of monomer state-of-charge indexes comprises:
constructing a charge state associated parameter type set;
performing battery state estimation on the same group of battery monomer samples based on a plurality of parameter types in the charge state associated parameter type set to obtain a plurality of estimated sample sets;
performing estimation credibility identification on the plurality of parameter types based on the plurality of estimation sample sets to obtain a plurality of estimation credibility;
Extracting a first parameter type, a second parameter type and a third parameter type with highest credibility based on the plurality of estimated credibility, wherein the first parameter type has a first credibility, the second parameter type has a second credibility and the third parameter type has a third credibility;
And performing battery state estimation on the plurality of real-time running state data based on the first parameter type, the second parameter type and the third parameter type to acquire the plurality of monomer state-of-charge indexes.
3. The method for controlling on-line detection of a lithium battery pack for a vehicle according to claim 2, wherein the performing the estimation reliability recognition on the plurality of parameter types based on the plurality of estimation sample sets to obtain a plurality of estimation credibility includes:
counting the same estimated sample number of each estimated sample set to obtain a plurality of same estimated sample duty ratio coefficients;
performing estimation deviation recognition on each estimation sample set to obtain a plurality of estimation deviation coefficients;
And carrying out weighted calculation on the plurality of same estimated sample duty ratio coefficients and the plurality of estimated deviation coefficients to obtain the plurality of estimated credibility.
4. The method for online detection control of a lithium battery pack for a vehicle according to claim 2, wherein the performing battery state estimation on the plurality of real-time operation state data based on the first parameter type, the second parameter type, and the third parameter type, and obtaining the plurality of monomer state-of-charge indexes, includes:
Extracting parameters of the plurality of real-time running state data according to the first parameter type, the second parameter type and the third parameter type to obtain first parameter state data, second parameter state data and third parameter state data;
constructing a battery state estimation network, wherein the battery state estimation network comprises a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer and a weighted estimation layer;
And analyzing the first parameter state data, the second parameter state data and the third parameter state data through the battery state estimation network to generate the plurality of monomer state-of-charge indexes.
5. The on-line detection control method of a lithium battery pack for a vehicle according to claim 4, wherein the battery state estimation network includes a first parameter estimation layer, a second parameter estimation layer, a third parameter estimation layer, and a weight estimation layer, and the first parameter estimation layer, the second parameter estimation layer, and the third parameter estimation layer are connected in parallel, and output ends of the first parameter estimation layer, the second parameter estimation layer, and the third parameter estimation layer are connected with the weight estimation layer;
the first parameter estimation layer, the second parameter estimation layer and the third parameter estimation layer are constructed by gradient descent training through historical estimation parameter records;
The weighted estimation layer calculates outputs of the first, second, and third parameter estimation layers based on the first, second, and third trustworthiness.
6. The on-line detection control method of a lithium battery pack for a vehicle according to claim 1, wherein performing performance balance control on the second abnormal cell comprises:
collecting the connection state of the second abnormal monomer, and obtaining a parallel monomer combination and a serial monomer combination;
Carrying out charge state control influence analysis on the parallel monomer combination and the serial monomer combination, and outputting a parallel influence index and a serial influence index;
Performing equalization control parameter optimization based on a preset equalization control variable, the parallel connection influence index and the serial connection influence index to generate an optimal equalization control parameter;
And performing performance balance control on the second abnormal monomer by using the optimal balance control parameters.
7. The method of on-line detection control of a lithium battery pack for a vehicle according to claim 6, wherein the performing equalization control parameter optimization based on a predetermined equalization control variable, the parallel impact indicator, and the series impact indicator, generating an optimal equalization control parameter, comprises:
constructing a variable-battery state association relationship based on the predetermined equalization control variable;
performing iterative optimization of the battery state rising direction based on the variable-battery state association relation until the battery state is smaller than the second abnormal index threshold value to obtain a plurality of groups of equalization control decisions, wherein the plurality of groups of equalization control decisions comprise equalization control parameters of each monomer in the second abnormal monomers;
And carrying out feedback optimization on the multiple groups of equalization control decisions based on the parallel influence indexes and the serial influence indexes to obtain the optimal equalization control parameters.
8. An on-line detection control system for a lithium battery pack for a vehicle, the system being for implementing the on-line detection control method for a lithium battery pack for a vehicle according to any one of claims 1 to 7, the system comprising:
The battery cell real-time running state monitoring module is used for acquiring a plurality of battery cells of the lithium battery pack for the target vehicle, and monitoring the real-time running states of the battery cells to acquire a plurality of real-time running state data;
the battery state estimation module is used for carrying out battery state estimation according to the plurality of real-time running state data to obtain a plurality of single charge state indexes, wherein the plurality of single charge state indexes are used for representing the charge state abnormality degree of the plurality of battery single cells;
The system comprises a preset abnormal index threshold value acquisition module, a first abnormal index threshold value acquisition module and a second abnormal index threshold value acquisition module, wherein the preset abnormal index threshold value acquisition module is used for acquiring a preset abnormal index threshold value, the preset abnormal index threshold value comprises a first abnormal index threshold value and a second abnormal index threshold value, and the first abnormal index threshold value is larger than the second abnormal index threshold value;
the abnormal monomer extraction module is used for extracting a first abnormal monomer meeting the first abnormal index threshold and a second abnormal monomer meeting the second abnormal index threshold based on the plurality of monomer charge state indexes;
The abnormal single body control module is used for performing abnormal battery isolation control on the first abnormal single body and performing performance balance control on the second abnormal single body.
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