CN117761541A - Battery energy state detection method for battery management system - Google Patents
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Abstract
The invention discloses a battery energy state detection method for a battery management system, which particularly relates to the technical field of battery management and comprises the following steps: obtaining a battery fixed performance loss index and a battery operation performance loss index based on the battery state information; obtaining a battery energy state evaluation coefficient based on the combined analysis of the battery fixed performance loss index and the battery operation performance loss index; correcting the battery energy state evaluation coefficient based on the charging behavior influence factor, the discharging behavior influence factor and the operation environment influence parameter to obtain a corrected battery energy state evaluation coefficient; and comparing the corrected battery energy state evaluation coefficient with a preset value tha, and when the battery energy state evaluation coefficient is lower than the preset value tha, giving a warning for abnormal battery conditions to a user. The method solves the problem that the prior art does not quantitatively evaluate the running condition and the environmental condition of the battery to obtain the energy condition of the battery.
Description
Technical Field
The present invention relates to the field of battery management technology, and more particularly, to a battery energy state detection method for a battery management system.
Background
The energy state of a battery is a very important parameter in a battery management system, which helps a driver or an energy management system to better manage and use the battery, improving the efficiency and life of the battery. Meanwhile, by monitoring the energy state of the battery, the battery fault or hidden danger can be found in time, and the safety accident is avoided. Battery Management Systems (BMS) are used to monitor and manage the performance and safety of batteries, one important task of which is to detect the energy state of the batteries.
The existing battery energy detection method obtains the battery energy condition through the charge-discharge cycle test of the battery under the normal temperature condition, and the complete charge-discharge process is usually required when the charge-discharge cycle test of the battery is carried out. This means that the battery needs to be fully charged, then discharged to an empty state, and recharged, and this process is repeated a number of times to evaluate the battery's cycle life and performance.
The existing battery energy detection method is different from the actual battery use condition, namely, the battery test environment and the actual battery use environment are different, the influence of the battery operation environment and the battery operation environment in actual use is not considered, and the battery energy condition is not obtained from the battery operation condition and the battery environment condition quantitative evaluation.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, the present invention provides a battery energy state detection method for a battery management system, which corrects the obtained battery energy state evaluation coefficient based on the battery running environment characteristics and the operation behavior characteristics by enhancing the processing and analysis of data, so as to solve the problems set forth in the above-mentioned background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: a battery energy state detection method for a battery management system, comprising the steps of:
collecting big data of historical operation behaviors of a battery, running environment information and battery state information, and dividing the collected data into a plurality of groups of big data according to battery charging and discharging behaviors;
preprocessing the collected data, including data cleaning, denoising and normalization operations, so as to ensure the accuracy and reliability of the data;
obtaining a battery fixed performance loss index Cp and a battery operation performance loss index Bp based on the battery state information;
obtaining a battery energy state evaluation coefficient Dn based on the combined analysis of the battery fixed performance loss index and the battery operation performance loss index;
correcting the battery energy state evaluation coefficient Dn based on the charging behavior influence factor cx, the discharging behavior influence factor fx and the operating environment influence parameter Hy to obtain a corrected battery energy state evaluation coefficient D' n;
and comparing the corrected battery energy state evaluation coefficient D 'n with a preset value tha, and when the battery energy state evaluation coefficient D' n is lower than the preset value tha, giving a warning of abnormal battery conditions to a user.
Preferably, the battery fixing performance loss index is obtained by the following steps: by the formula Obtaining a battery fixed performance loss index Cp, wherein sl represents a battery capacity loss rate, rz represents a battery internal resistance increase rate, zp represents a self-discharge rate deviation parameter, mu 1 represents a weight coefficient of battery capacity, mu 2 represents a weight coefficient of battery internal resistance, mu 3 represents a weight coefficient of battery self-discharge rate, and the values of mu 1, mu 2 and mu 3 are in the range of [0-1 ]]And μ1+μ2+μ3=1.0.
Preferably, the battery operation performance loss index is obtained by the following steps: based on the battery state information, a charge rate loss rate q1 and a discharge rate loss rate q2 of the battery are obtained, a temperature rise abnormal parameter wy1 during battery charging and a temperature rise abnormal parameter wy2 during battery discharging are obtained, and a battery operation performance loss index Bp is obtained through calculation according to a formula bp=q1 xwy1+q2 xwy 2.
Preferably, the calculation model of the battery energy state evaluation coefficient satisfies a formula Wherein min isBp represents the minimum value of the battery operation performance loss index, maxBp represents the maximum value of the battery operation performance loss index, minCp represents the minimum value of the battery stationary performance loss index, and maxCp represents the maximum value of the battery stationary performance loss index.
Preferably, the operation behavior influencing parameters of the battery include a charge behavior influencing factor cx and a discharge behavior influencing factor fx, and the acquiring of the operation behavior influencing parameters of the battery includes the steps of:
dividing the operation behaviors into a plurality of intervals according to the charging and discharging behaviors, numbering, and arranging m1 times of charging behaviors and m2 times of discharging behaviors, wherein the charging behaviors and the discharging behaviors are alternately arranged;
acquiring the proportion of the charge amount of each charging behavior to the battery capacity and the charging current variance, marking as sr1i and cfi, acquiring the charging frequency as pc, and marking the number of the interval represented by i;
acquiring the discharge quantity of each discharge behavior to account for the battery capacity proportion and the discharge voltage variance, marking as sr2i and dfi, acquiring the discharge duration time as ct, and indicating the number of the interval by i;
by the formulaObtaining a charge behavior influencing factor cx of the battery, wherein +.>Mean value of charge amount of m1 charge actions in proportion to battery capacity, +.>Representing the charging current variance mean value in m1 charging behaviors;
by the formulaObtaining a discharge behavior influence factor fx of the battery, obtaining an operation behavior influence parameter of the battery, wherein ∈ ->The average value of the discharge amount representing the m2 discharge behaviors in proportion to the battery capacity is shown.
Preferably, the acquisition of the battery operation environment influence parameter Hy includes the steps of:
dividing the operation behavior into a plurality of sections according to the charging and discharging behaviors, numbering the sections, and setting n sections;
acquiring an ambient temperature mean value, an ambient temperature difference and an ambient temperature variance of each interval, and marking the ambient temperature mean value, the ambient temperature difference and the ambient temperature variance as wci, wdavg_i and wfi, wherein i represents the number of the interval;
by the formulaAnd calculating to obtain an operating environment influence parameter Hy.
Preferably, the battery energy state evaluation coefficient Dn is corrected in such a manner that Where Bp represents the corrected battery running performance loss index, satisfying Bp "=q1×wy1+q2×wy2×fx.
Preferably, the modified battery energy state evaluation coefficient D "n is compared with a preset value tha, when the modified battery energy state evaluation coefficient D" n is lower than the preset value tha, the abnormal battery quality is indicated, a warning of abnormal battery condition is sent to a user, and when the modified battery energy state evaluation coefficient D "n is not lower than the preset value tha, the abnormal battery quality is indicated, and no measures are taken.
Preferably, the method for obtaining the preset value tha is as follows: acquiring an initial battery health condition evaluation coefficient of a battery from a database; obtaining the operation behavior characteristics and the operation environment characteristics of the battery based on the characteristic extraction algorithm; based on the initial battery health evaluation coefficient, the battery operation behavior characteristic and the battery operation environment characteristic of the battery, the method comprises the following steps ofObtaining a preset value tha, wherein Deltac represents the initial state of the batteryThe initial battery state of health evaluation coefficient yti represents the ith operating behavior feature of the battery, qti is the weight coefficient corresponding to the operating behavior feature, yhi represents the ith operating environment feature of the battery, qhi is the weight coefficient corresponding to the operating environment feature, w1 represents the behavior influencing factor, w2 represents the environmental influencing factor, and 0 < w1 < 1,0 < w2 < 1, w1+w2=1.0.
Preferably, the obtaining mode of the weight coefficient corresponding to the feature is as follows: and performing a cyclic test on the battery, acquiring a battery fixed performance loss index and a battery operation performance loss index under the cyclic test based on data obtained by the cyclic test on the battery, obtaining a battery energy state evaluation coefficient based on joint analysis of the battery fixed performance loss index and the battery operation performance loss index, taking the battery health state evaluation coefficient as a target variable, recording the battery health state evaluation coefficient obtained under the cyclic test as X_Dn, respectively taking the battery operation behavior characteristic and the battery operation environment characteristic as independent variables, obtaining the battery operation behavior characteristic weight coefficient and the battery operation environment characteristic weight coefficient through training of a deep learning model, and storing the battery operation behavior characteristic weight coefficient and the battery operation environment characteristic weight coefficient of each battery in a database.
The invention has the technical effects and advantages that:
(1) The invention provides a battery energy state detection method for a battery management system, which is used for obtaining a battery fixed performance loss index and a battery operation performance loss index under a cyclic test, and obtaining a battery energy state evaluation coefficient based on joint analysis of the battery fixed performance loss index and the battery operation performance loss index; taking the battery health condition evaluation coefficient obtained under the cyclic test as a target variable, taking the battery operation behavior characteristic battery operation environment characteristic as an independent variable, obtaining a battery operation behavior characteristic weight coefficient and a battery operation environment characteristic weight coefficient through training of a deep learning model, and storing the battery operation behavior characteristic weight coefficient and the battery operation environment characteristic weight coefficient of each battery in a database; acquiring an initial battery health condition evaluation coefficient of a battery from a database; obtaining the operation behavior characteristics and the operation environment characteristics of the battery based on the characteristic extraction algorithm; the preset value tha is obtained based on the initial battery health condition evaluation coefficient, the battery operation behavior characteristic and the battery operation environment characteristic of the battery, so that the method is beneficial to judging whether the quality of the battery is abnormal;
(2) According to the environmental risk early warning content provided by the invention, the operation risk of the battery is obtained through monitoring the operation of the battery, so that the thermal runaway of the battery is prevented, and the operation safety of the battery is improved.
Drawings
Fig. 1 is a flowchart of a battery energy state detection method according to the present invention.
Fig. 2 is a flowchart of the operation behavior influence parameter acquisition of the battery of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure is embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the application, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
The conventional battery energy detection method is obtained by performing a cycle test on the battery under a preset environment, but the cycle test is generally set before the battery leaves the factory, consumes large resources, and cannot be applied to daily energy state detection of the battery.
In the conventional battery state monitoring method, the charge and discharge cycle is usually performed under a specific environmental condition, and the charge and discharge cycle is usually performed at normal temperature. The actual use condition cannot be simulated, and the ambient temperature of the battery during charge and discharge is a key factor influencing the quality of the battery. However, in actual use, the temperature fluctuation range during battery charging is large, and it is not possible to directly determine whether or not the battery has a quality problem based on the analysis of the operation data and the environmental data of the battery.
Example 1
Referring to fig. 1, the present invention provides a battery energy state detection method for a battery management system as shown in fig. 1, comprising the steps of:
collecting big data of historical operation behaviors of a battery, running environment information and battery state information, and dividing the collected data into a plurality of groups of big data according to battery charging and discharging behaviors;
the method comprises the steps of acquiring battery historical operation behavior big data, running environment information and battery state information, wherein the battery historical operation behavior big data comprise charging frequency, charging capacity proportion, charging current variance, discharging capacity proportion, discharging voltage variance and discharging duration; the operation environment information comprises an environment temperature mean value, an environment temperature difference and an environment temperature variance; the battery state information comprises a battery capacity loss rate, a battery internal resistance increase rate, a self-discharge rate deviation parameter, a charging speed loss rate, a discharging speed loss rate, a battery temperature rise abnormal parameter during charging and a battery temperature rise abnormal parameter during discharging.
Preprocessing the collected data, including data cleaning, denoising and normalization operations, so as to ensure the accuracy and reliability of the data;
obtaining a battery fixed performance loss index Cp and a battery operation performance loss index Bp based on the battery state information;
obtaining a battery energy state evaluation coefficient Dn based on the combined analysis of the battery fixed performance loss index and the battery operation performance loss index;
correcting the battery energy state evaluation coefficient Dn based on the charging behavior influence factor cx, the discharging behavior influence factor fx and the operating environment influence parameter Hy to obtain a corrected battery energy state evaluation coefficient D' n;
and comparing the corrected battery energy state evaluation coefficient D 'n with a preset value tha, and when the battery energy state evaluation coefficient D' n is lower than the preset value tha, giving a warning of abnormal battery conditions to a user.
In the embodiment of the present invention, it should be further explained that the method for obtaining the battery fixed performance loss index is as follows: by the formulaObtaining a battery fixed performance loss index Cp, wherein sl represents a battery capacity loss rate, rz represents a battery internal resistance increase rate, zp represents a self-discharge rate deviation parameter, mu 1 represents a weight coefficient of battery capacity, mu 2 represents a weight coefficient of battery internal resistance, mu 3 represents a weight coefficient of battery self-discharge rate, and the values of mu 1, mu 2 and mu 3 are in the range of [0-1 ]]And μ1+μ2+μ3=1.0.
The battery capacity loss rate is obtained by the following steps: acquiring the preset capacity and the actual capacity of the battery, and dividing the battery capacity difference by the preset capacity to obtain the battery capacity loss rate; the method for obtaining the internal resistance growth rate of the battery comprises the following steps: obtaining the preset internal resistance and the actual internal resistance of the battery, dividing the difference value of the internal resistances of the battery by the preset internal resistance to obtain the internal resistance increase rate of the battery; the self-discharge rate deviation parameter is obtained by obtaining the preset self-discharge rate of the battery and the actual self-discharge rate of the battery, and dividing the difference value of the self-discharge rates of the battery by the preset self-discharge rate.
In the embodiment of the invention, it is further explained that the battery operation performance loss index is obtained by the following steps: based on the battery state information, a charge rate loss rate q1 and a discharge rate loss rate q2 of the battery are obtained, a temperature rise abnormal parameter wy1 during battery charging and a temperature rise abnormal parameter wy2 during battery discharging are obtained, and a battery operation performance loss index Bp is obtained through calculation according to a formula bp=q1 xwy1+q2 xwy 2.
The battery charge rate loss q1 is obtained by: obtaining the charging speed of the battery based on the charging behavior of the battery, obtaining the charging speed of each interval, obtaining the charging speed loss rate of the battery by weighting, summing and averaging, and setting the charging speed loss rate atm1 charging intervals, the charging speed of the ith interval is cvi, the charging time of the ith interval is cti, and the charging speed is calculated by the formula Calculating to obtain a charging speed loss rate q1 of the battery, wherein cv0 represents a preset charging speed of the battery; the discharge rate loss rate q2 is obtained by the following steps: obtaining the discharge speed of the battery based on the discharge behavior of the battery, obtaining the discharge speed of each section, obtaining the charge speed loss rate of the battery by weighting and summing to average, setting the discharge speed of the m2 discharge sections, marking the discharge speed of the i-th section as fvi, obtaining the charge time of the i-th section as fti, and obtaining the charge time of the i-th section by the formula-> Calculating to obtain a charging speed loss rate q1 of the battery, wherein fv0 represents a preset charging speed of the battery; the method for acquiring the abnormal temperature rise parameter wy1 during battery charging is as follows: obtaining the charging temperature of the battery based on the charging behavior of the battery, obtaining the charging temperature rise speed of each section, obtaining the abnormal temperature rise parameter of the battery during charging by weighting, summing and averaging, setting the charging temperature rise speed of the battery in m1 charging sections, marking the charging temperature rise speed of the ith section as csi, obtaining the charging time of the ith section as cti, and obtaining the charging time of the ith section by the formula->Wherein cw0 represents a preset charging temperature rising speed of the battery; the method for acquiring the abnormal temperature rise parameter wy2 during battery discharge is as follows: obtaining the charging temperature of the battery based on the charging behavior of the battery, obtaining the charging temperature rise speed of each interval, obtaining the battery discharging temperature anomaly coefficient of the battery by weighting summation and averaging, and obtaining the average by weighting summationAnd obtaining abnormal temperature rise parameters of the battery during discharging.
In the embodiment of the present invention, it should be further explained that the calculation model of the battery energy state evaluation coefficient satisfies the formulaWhere minBp represents the minimum value of the battery operating performance loss index, maxBp represents the maximum value of the battery operating performance loss index, minCp represents the minimum value of the battery fixed performance loss index, and maxCp represents the maximum value of the battery fixed performance loss index.
Referring to the operation behavior influence parameter acquisition flowchart of the battery of fig. 2, the operation behavior influence parameters of the battery include a charge behavior influence factor cx and a discharge behavior influence factor fx, and the acquisition of the operation behavior influence parameters of the battery includes the steps of:
dividing the operation behaviors into a plurality of intervals according to the charging and discharging behaviors, numbering, and arranging m1 times of charging behaviors and m2 times of discharging behaviors, wherein the charging behaviors and the discharging behaviors are alternately arranged;
acquiring the charge quantity of each charging behavior to account for the battery capacity proportion and the charging current variance, marking as sr1i and cfi, acquiring the charging frequency to be recorded as pc, i representing the number of the interval, and acquiring a charging behavior influence factor based on the charge quantity of each charging behavior to account for the battery capacity proportion, the charging current variance and the charging frequency;
and obtaining the discharge quantity of each discharge behavior to occupy the battery capacity proportion and the discharge voltage variance, marking as sr2i and dfi, obtaining the discharge duration time as ct, and obtaining the discharge behavior influence factor based on the discharge quantity of the discharge behavior to occupy the battery capacity proportion, the discharge voltage variance and the discharge time, wherein i represents the number of the interval.
Further, by the formulaObtaining a charge behavior influencing factor cx of the battery, wherein +.>Mean value of charge amount of m1 charge actions in proportion to battery capacity, +.>Representing the charging current variance mean value in m1 charging behaviors; by the formula-> Obtaining a discharge behavior influence factor fx of the battery, obtaining an operation behavior influence parameter of the battery, wherein ∈ ->The average value of the discharge amount representing the m2 discharge behaviors in proportion to the battery capacity is shown.
It should be further explained in the embodiment of the present invention that the acquisition of the operating environment influence parameter Hy of the battery includes the following steps:
dividing the operation behavior into a plurality of sections according to the charging and discharging behaviors, numbering the sections, and setting n sections;
acquiring an ambient temperature mean value, an ambient temperature difference and an ambient temperature variance of each interval, and marking the ambient temperature mean value, the ambient temperature difference and the ambient temperature variance as wci, wdavg_i and wfi, wherein i represents the number of the interval;
by the formulaAnd calculating to obtain an operating environment influence parameter Hy.
It should be further explained in the embodiment of the present invention that the manner of correction of the battery energy state estimation coefficient Dn satisfiesWhere Bp represents the corrected battery running performance loss index, satisfying Bp "=q1×wy1+q2×wy2×fx.
In the embodiment of the invention, it is further explained that the modified battery energy state evaluation coefficient D "n is compared with the preset value tha, when the modified battery energy state evaluation coefficient D" n is lower than the preset value tha, the abnormal battery quality is indicated, the abnormal battery condition is warned to the user, and when the modified battery energy state evaluation coefficient D "n is not lower than the preset value tha, the abnormal battery quality is indicated, and no measures are taken.
In the embodiment of the present invention, it should be further explained that the method for obtaining the preset value tha is: acquiring an initial battery health condition evaluation coefficient of a battery from a database; obtaining the operation behavior characteristics and the operation environment characteristics of the battery based on the characteristic extraction algorithm; based on the initial battery health evaluation coefficient, the battery operation behavior characteristic and the battery operation environment characteristic of the battery, the method comprises the following steps of Obtaining a preset value tha, wherein deltac represents an initial battery health condition evaluation coefficient of the battery, yti represents an ith operating behavior feature of the battery, qti represents a weight coefficient corresponding to the ith operating environment feature of the battery, yhi represents a weight coefficient corresponding to the ith operating environment feature of the battery, qhi represents a behavior influence factor, w1 represents an environment influence factor, w2 represents an environment influence factor, and 0 < w1 < 1,0 < w2 < 1, w1+w2=1.0.
In the embodiment of the invention, it is further explained that the obtaining mode of the weight coefficient corresponding to the feature is as follows: and performing a cyclic test on the battery, acquiring a battery fixed performance loss index and a battery operation performance loss index under the cyclic test based on data obtained by the cyclic test on the battery, obtaining a battery energy state evaluation coefficient based on joint analysis of the battery fixed performance loss index and the battery operation performance loss index, taking the battery health state evaluation coefficient as a target variable, recording the battery health state evaluation coefficient obtained under the cyclic test as X_Dn, respectively taking the battery operation behavior characteristic and the battery operation environment characteristic as independent variables, obtaining the battery operation behavior characteristic weight coefficient and the battery operation environment characteristic weight coefficient through training of a deep learning model, and storing the battery operation behavior characteristic weight coefficient and the battery operation environment characteristic weight coefficient of each battery in a database.
Example 2
In order to ensure the operation safety of the battery, embodiment 2 further includes environmental risk early warning content compared with embodiment 1, and the risk coefficient of the battery environment is acquired based on the sensor, and whether the battery has thermal runaway risk is judged based on the risk coefficient.
The risk coefficient of the battery environment is obtained by the following steps:
the method comprises the steps of installing sensors at the periphery of a battery, wherein the sensors comprise a hydrogen sensor, a temperature and humidity sensor and a smart meter, obtaining a topological structure diagram of the sensors relative to the battery, and obtaining real-time voltage dy, battery center temperature zd, environment temperature and hydrogen concentration q of the battery Hydrogen gas ；
Acquiring the space environment parameters of the sensor through a formula Calculating to obtain an operating environment influence parameter Hy;
dividing the battery operation behavior into a plurality of sections according to the charging and discharging behaviors, numbering the sections, and setting n sections; acquiring an ambient temperature mean value, an ambient temperature difference and an ambient temperature variance of each interval, marking the ambient temperature mean value, the ambient temperature difference and the ambient temperature variance as wci, wdavg_i and wfi, wherein i represents the number of the interval, and passing through a formula Calculating to obtain a risk coefficient HF of the battery environment;
and comparing the acquired risk coefficient HF of the battery environment with a preset value thb, and when the risk coefficient HF of the battery environment is larger than the preset value thb, indicating that the thermal runaway risk exists, giving an early warning to a manager, and controlling the running condition and the environment of the battery through a relay.
Further, the method for obtaining the thb includes that through analyzing the environmental risk of the battery history, a corresponding curve of the environmental risk and the occurrence probability of the thermal runaway is constructed, when the environmental risk coefficient is larger than a critical value, the occurrence probability of the thermal runaway exceeds a controllable range, and the critical point is marked as the thb.
Further, the hydrogen concentration is acquired based on the hydrogen sensor, the hydrogen sensor comprises a sensing material, the sensing material is a conductor or a semiconductor, one surface of the sensing material is coated with a catalyst, the other surface of the sensing material is not coated, the catalyst is coated on one surface of the sensing material, the other surface of the sensing material is not coated, the difference of temperature between two points of the sensing material can cause a potential difference, an electrical sensing signal is obtained, and the hydrogen sensor generates the electrical sensing signal based on the heating of catalytic oxidation of hydrogen, so that the hydrogen concentration is obtained.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A battery energy state detection method for a battery management system, characterized by: comprises the following steps:
collecting big data of historical operation behaviors of a battery, running environment information and battery state information, and dividing the collected data into a plurality of groups of big data according to battery charging and discharging behaviors;
obtaining a battery fixed performance loss index Cp and a battery operation performance loss index Bp based on the battery state information;
obtaining a battery energy state evaluation coefficient Dn based on the combined analysis of the battery fixed performance loss index and the battery operation performance loss index;
correcting the battery energy state evaluation coefficient Dn based on the charging behavior influence factor cx, the discharging behavior influence factor fx and the operating environment influence parameter Hy to obtain a corrected battery energy state evaluation coefficient D' n;
and comparing the corrected battery energy state evaluation coefficient D 'n with a preset value tha, and when the battery energy state evaluation coefficient D' n is lower than the preset value tha, giving a warning of abnormal battery conditions to a user.
2. The battery energy state detection method for a battery management system according to claim 1, wherein: the battery fixing performance loss index is obtained by the following steps: by the formula Obtaining a battery fixed performance loss index Cp, wherein sl represents a battery capacity loss rate, rz represents a battery internal resistance increase rate, zp represents a self-discharge rate deviation parameter, mu 1 represents a weight coefficient of battery capacity, mu 2 represents a weight coefficient of battery internal resistance, mu 3 represents a weight coefficient of battery self-discharge rate, and the values of mu 1, mu 2 and mu 3 are in the range of [0-1 ]]And μ1+μ2+μ3=1.0.
3. The battery energy state detection method for a battery management system according to claim 2, wherein: the battery operation performance loss index is obtained by the following steps: based on the battery state information, a charge rate loss rate q1 and a discharge rate loss rate q2 of the battery are obtained, a temperature rise abnormal parameter wy1 during battery charging and a temperature rise abnormal parameter wy2 during battery discharging are obtained, and a battery operation performance loss index Bp is obtained through calculation according to a formula bp=q1 xwy1+q2 xwy 2.
4. A battery energy state detection method for a battery management system according to claim 3, characterized in that: the calculation model of the battery energy state evaluation coefficient meets the formula Wherein minBp represents the battery operation performanceThe minimum value of the loss index, maxBp represents the maximum value of the battery running performance loss index, minCp represents the minimum value of the battery fixed performance loss index, and maxCp represents the maximum value of the battery fixed performance loss index.
5. The battery energy state detection method for a battery management system according to claim 1, wherein: the operation behavior influence parameters of the battery include a charge behavior influence factor cx and a discharge behavior influence factor fx, and the acquisition of the operation behavior influence parameters of the battery includes the steps of:
dividing the operation behaviors into a plurality of intervals according to the charging and discharging behaviors, numbering, and arranging m1 times of charging behaviors and m2 times of discharging behaviors, wherein the charging behaviors and the discharging behaviors are alternately arranged;
acquiring the proportion of the charge amount of each charging behavior to the battery capacity and the charging current variance, marking as sr1i and cfi, acquiring the charging frequency as pc, and marking the number of the interval represented by i;
acquiring the discharge quantity of each discharge behavior to account for the battery capacity proportion and the discharge voltage variance, marking as sr2i and dfi, acquiring the discharge duration time as ct, and indicating the number of the interval by i;
by the formulaObtaining a charge behavior influencing factor cx of the battery, wherein +.>Mean value of charge amount of m1 charge actions in proportion to battery capacity, +.>Representing the charging current variance mean value in m1 charging behaviors;
by the formulaObtaining a discharge behavior influence factor of the batteryfx, obtaining the operational behavior influencing parameters of the battery, wherein +.>The average value of the discharge amount representing the m2 discharge behaviors in proportion to the battery capacity is shown.
6. The battery energy state detection method for a battery management system according to claim 1, wherein: the acquisition of the battery operation environment influence parameter Hy includes the steps of:
dividing the operation behavior into a plurality of sections according to the charging and discharging behaviors, numbering the sections, and setting n sections;
acquiring an ambient temperature mean value, an ambient temperature difference and an ambient temperature variance of each interval, and marking the ambient temperature mean value, the ambient temperature difference and the ambient temperature variance as wci, wdavg_i and wfi, wherein i represents the number of the interval;
by the formulaAnd calculating to obtain an operating environment influence parameter Hy.
7. The battery energy state detection method for a battery management system according to claim 1, wherein: the mode of correcting the battery energy state evaluation coefficient Dn is satisfied Where Bp represents the corrected battery running performance loss index, satisfying Bp "=q1×wy1+q2×wy2×fx.
8. The battery energy state detection method for a battery management system according to claim 7, wherein: and comparing the corrected battery energy state evaluation coefficient D ' n with a preset value tha, when the battery energy state evaluation coefficient D ' n is lower than the preset value tha, indicating that the battery quality is abnormal, giving a warning of abnormal battery condition to a user, and when the battery energy state evaluation coefficient D ' n is not lower than the preset value tha, indicating that the battery quality is not abnormal, and taking no measures.
9. The battery energy state detection method for a battery management system according to claim 8, wherein: the method for acquiring the preset value tha comprises the following steps: acquiring an initial battery health condition evaluation coefficient of a battery from a database; obtaining the operation behavior characteristics and the operation environment characteristics of the battery based on the characteristic extraction algorithm; based on the initial battery health evaluation coefficient, the battery operation behavior characteristic and the battery operation environment characteristic of the battery, the method comprises the following steps of Obtaining a preset value tha, wherein deltac represents an initial battery health condition evaluation coefficient of the battery, yti represents an ith operating behavior feature of the battery, qti represents a weight coefficient corresponding to the ith operating environment feature of the battery, yhi represents a weight coefficient corresponding to the ith operating environment feature of the battery, qhi represents a behavior influence factor, w1 represents an environment influence factor, w2 represents an environment influence factor, and 0 < w1 < 1,0 < w2 < 1, w1+w2=1.0.
10. The battery energy state detection method for a battery management system according to claim 9, wherein: the weight coefficient corresponding to the characteristic is obtained by the following steps: and performing a cyclic test on the battery, acquiring a battery fixed performance loss index and a battery operation performance loss index under the cyclic test based on data obtained by the cyclic test on the battery, obtaining a battery energy state evaluation coefficient based on joint analysis of the battery fixed performance loss index and the battery operation performance loss index, taking the battery health state evaluation coefficient as a target variable, recording the battery health state evaluation coefficient obtained under the cyclic test as X_Dn, respectively taking the battery operation behavior characteristic and the battery operation environment characteristic as independent variables, obtaining the battery operation behavior characteristic weight coefficient and the battery operation environment characteristic weight coefficient through training of a deep learning model, and storing the battery operation behavior characteristic weight coefficient and the battery operation environment characteristic weight coefficient of each battery in a database.
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