CN112269137A - Battery health state estimation method based on dynamic parameter identification - Google Patents
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Abstract
The invention discloses a battery state of health estimation method based on dynamic parameter identification, which comprises the following steps: carrying out cycle life test on the battery to obtain an open-circuit voltage spectrum; the method comprises the steps that a second-order RC equivalent circuit model and a charge state are obtained through combination based on an EKF-RLS parameter identification method; extracting RC parameters according to the second-order RC equivalent circuit model and the parameters of the open-circuit voltage spectrum and carrying out correlation analysis to obtain key factors; establishing a battery health state estimation model based on an SVR algorithm by combining an open-circuit voltage spectrum, a charge state, an RC parameter and a key factor; and acquiring the current SoC of the battery to be tested and inputting the current SoC into the battery health state estimation model to obtain the health state of the battery. By using the invention, the health state of the battery can be estimated in real time. The battery health state estimation method based on dynamic parameter identification can be widely applied to the field of battery health state estimation.
Description
Technical Field
The invention relates to the field of battery health state estimation, in particular to a battery health state estimation method based on dynamic parameter identification.
Background
The estimation of the health state of the battery is one of the important technologies of the BMS, the accuracy of the estimation can cooperatively influence other management functions of a battery management system, and the internal state and parameters of the lithium ion battery serving as a comprehensive system with a complex mechanism cannot be directly measured. The state of health of the battery cannot be simply estimated in a table look-up manner due to individual differences in the manufacturing process and the use conditions. At present, a data-driven battery state of health estimation method is commonly used, data are directly obtained from a BMS and are used as model input, the method is simple in feature acquisition, but model input is often large in quantity and large in dimensionality, and time and labor cost is high.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a battery state of health estimation method based on dynamic parameter identification, which can estimate the state of health of a battery in real time.
The first technical scheme adopted by the invention is as follows: a battery state of health estimation method based on dynamic parameter identification includes the following steps:
carrying out cycle life test on the battery to obtain an open-circuit voltage spectrum;
the method comprises the steps that a second-order RC equivalent circuit model and a charge state are obtained through combination based on an EKF-RLS parameter identification method;
extracting RC parameters according to the second-order RC equivalent circuit model and the parameters of the open-circuit voltage spectrum and carrying out correlation analysis to obtain key factors;
establishing a battery health state estimation model based on an SVR algorithm by combining an open-circuit voltage spectrum, a charge state, an RC parameter and a key factor;
and acquiring the current charge state of the battery to be detected and inputting the current charge state into a battery health state estimation model to obtain the health state of the battery.
Further, the step of performing cycle life test on the lithium ion battery to obtain an open-circuit voltage spectrum specifically includes:
and carrying out uninterrupted cyclic charge and discharge test on the battery.
When the charging and discharging test times reach the preset times, carrying out capacity evaluation and open-circuit voltage test on the battery to obtain a battery effective capacity value, an open-circuit voltage curve and an equivalent internal resistance curve;
obtaining a capacity ratio according to the effective capacity value of the battery and the nominal capacity of the battery;
judging that the capacitance ratio is smaller than a preset value, and stopping the battery cyclic charge and discharge test;
and generating an open-circuit voltage spectrum according to the plurality of open-circuit voltage curves.
Further, the preset number of times is set to 50, the preset value is set to 0.7, and the capacity ratio expression specifically is as follows:
in the above formula, CnowIs a value of effective capacity of the battery, CratedIs the nominal capacity of the battery, said ScapIs the capacity ratio.
Further, the EKF-RLS-based parameter identification method combines the step of obtaining a second-order RC equivalent circuit model and a state of charge, and specifically comprises the following steps:
constructing an open-circuit voltage model and a second-order RC equivalent circuit model based on the RLS;
constructing a state of charge estimation model based on an EKF algorithm;
obtaining a state of charge according to a state of charge estimation model;
and correcting the parameters of the open-circuit voltage model and the parameters of the second-order RC equivalent circuit model according to the state of charge to obtain a corrected open-circuit voltage model and a corrected second-order RC equivalent circuit model.
Further, the step of correcting the parameters of the open-circuit voltage model and the parameters of the second-order RC equivalent circuit model according to the state of charge to obtain a corrected open-circuit voltage model and a corrected second-order RC equivalent circuit model specifically includes:
setting a state of charge estimation model as a first priority, setting a second-order RC lamp box circuit model as a second priority and setting an open-circuit voltage model as a third priority through a configuration estimator;
and taking the state of charge obtained by the state of charge estimation model as the input quantity of the open-circuit voltage model and the second-order RC equivalent circuit model, and modifying the parameters of the open-circuit voltage model and the parameters of the second-order RC equivalent circuit model to obtain a modified open-circuit voltage model and a modified second-order RC equivalent circuit model.
Further, the method is characterized in that the step of extracting the RC parameter according to the second-order RC equivalent circuit model and the parameter of the open-circuit voltage spectrum and performing correlation analysis to obtain the key factor specifically includes:
extracting RC parameters according to the second-order RC equivalent circuit model and the parameters of the open-circuit voltage spectrum;
performing relevance analysis on the RC parameters through a Spearman correlation coefficient to obtain the importance degree ranking of the RC parameters;
and ranking according to the importance degree of the RC parameters to obtain a key factor.
Further, the step of constructing a battery state of health estimation model based on an SVR algorithm by combining an open circuit voltage spectrum, a state of charge, RC parameters and key factors specifically comprises:
determining a key charge state according to the key factors and acquiring three groups of RC parameters under the key charge state;
and (4) taking the three groups of RC parameters as input quantity and capacity ratio as output quantity, and constructing a battery health state estimation model through an SVR algorithm.
Further, the preprocessing of the uncertainty change matrix is specifically to set 1 to an edge part element in the uncertainty change matrix, which fails to serve as a central point to form a pattern block.
Further, still include:
the hyper-parameters of the battery state of health estimation model are optimized and performance is evaluated using K-fold cross validation.
The method and the system have the beneficial effects that: the method comprises the steps of testing the cycle life and constructing a battery health state estimation model in advance, dynamically identifying corresponding second-order equivalent circuit model parameters by acquiring the open-circuit voltage of the battery on line, taking key health factors, and taking three groups of key RC parameters as model input, thereby estimating the health state of the battery in real time.
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FIG. 1 is a flow chart of the steps of a method for estimating state of health of a battery based on dynamic parameter identification according to the present invention;
FIG. 2 is a flowchart illustrating the steps for optimizing hyper-parameters of a battery state of health estimation model, in accordance with an embodiment of the present invention;
FIG. 3 is a block diagram of an implementation framework for configuring an estimator to set model priorities in accordance with an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the specific embodiments. The step numbers in the following embodiments are provided only for convenience of illustration, the order between the steps is not limited at all, and the execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art.
As shown in fig. 1, the present invention provides a battery state of health estimation method based on dynamic parameter identification, which includes the following steps:
and S1, carrying out cycle life test on the battery to obtain an open-circuit voltage spectrum.
Specifically, the cycle life test selects the environmental temperature and the discharge rate as control variables to perform uninterrupted cycle charge and discharge on the battery. The charge and discharge multiplying power is set to be 0.5C, 1C and 2C, the temperature conditions are set to be 20 ℃, 30 ℃ and 40 ℃, and the discharge depth is 100%. And arranging a plurality of samples for the lithium ion batteries of the same type under each test condition.
S2, combining the EKF-RLS-based parameter identification method to obtain a second-order RC equivalent circuit model and a charge state;
s3, extracting RC parameters according to the second-order RC equivalent circuit model and the parameters of the open-circuit voltage spectrum and carrying out correlation analysis to obtain key factors;
s4, constructing a battery health state estimation model based on an SVR algorithm by combining an open-circuit voltage spectrum, a charge state, RC parameters and key factors;
and S5, acquiring the current state of charge of the battery to be detected and inputting the current state of charge into the battery state of health estimation model to obtain the state of health of the battery.
Specifically, SoC is the state of charge as described below.
Further, as a preferred embodiment of the method, the step of performing a cycle life test on the lithium ion battery to obtain an open circuit voltage spectrum specifically includes:
and carrying out uninterrupted cyclic charge and discharge test on the battery.
When the charging and discharging test times reach the preset times, carrying out capacity evaluation and open-circuit voltage test on the battery to obtain a battery effective capacity value, an open-circuit voltage curve and an equivalent internal resistance curve;
obtaining a capacity ratio according to the effective capacity value of the battery and the nominal capacity of the battery;
judging that the capacitance ratio is smaller than a preset value, and stopping the battery cyclic charge and discharge test;
and generating an open-circuit voltage spectrum according to the plurality of open-circuit voltage curves.
Specifically, the single cycle life test is composed of 50 complete charge and discharge cycles, the battery is placed for 2 hours after the test is finished until the battery is recovered to a stable state, and then the battery is comprehensively evaluated, so that the change rule of the battery health state and each parameter of the equivalent circuit model along with the cycle working condition is obtained. The comprehensive evaluation sets the unified environmental temperature to be 20 ℃, the charging and discharging multiplying power to be 0.5C and 1C respectively, and the capacity evaluation and the open-circuit voltage test are mainly divided. And the direct current equivalent internal resistance evaluation and the open-circuit voltage test are carried out synchronously. The maximum available electric quantity C of the battery can be obtained through comprehensive evaluation testnowThe open-circuit voltage curve and the direct-current equivalent internal resistance, and RC parameters of the battery model under different degradation states can be extracted based on the open-circuit voltage spectrum.
Further, as a preferred embodiment of the method, it is characterized in that the preset number of times is set to 50, the preset value is set to 0.7, and the capacity ratio expression specifically is:
in the above formula, CnowIs a value of effective capacity of the battery, CratedIs the nominal capacity of the battery, said ScapIs a capacity ratio;
in particular, the capacity ratio when the lithium ion battery loses storage capacity
Then, the cycle life test is ended, wherein CratedIs rated capacity of battery, CnowThe current maximum capacity of the battery.
Further, as a preferred embodiment of the method, the EKF-RLS-based parameter identification method combines the step of obtaining a second-order RC equivalent circuit model and a state of charge, and specifically includes:
constructing an open-circuit voltage model and a second-order RC equivalent circuit model based on the RLS;
constructing a state of charge estimation model based on an EKF algorithm;
obtaining a state of charge according to a state of charge estimation model;
and correcting the parameters of the open-circuit voltage model and the parameters of the second-order RC equivalent circuit model according to the state of charge to obtain a corrected open-circuit voltage model and a corrected second-order RC equivalent circuit model.
Specifically, an OCV estimation model is established based on an empirical functional relationship between OCV and SoC:
Uoc＝p0+p1·SoC+p2·SoC2+p3·SoC3+p4·SoC4+p5·SoC5 (1)
wherein the state variables are:
the parameters are as follows: thetak＝[p0,p1，p2，p3,p4，p5]TThe observable value of the parameter identification:
establishing a voltage-current relation according to a second-order RC equivalent circuit model, and selecting a first-order backward difference transformation method to obtain:
Uoc，k-UL，k＝k1(Uoc，k-1-UL，k-1)+k2(Uoc，k-2-UL，k-2)+k3IL，k+k4IL，k-1+k5IL，k-2 (2)
let Ek＝Uoc，k-UL，kThe state variable is
Parameter is thetak＝[k1，k2，k3，k4，k5]TIs obtained by
Obtaining direct parameter k using RLS iteration1，k2，k3，k4，k5Solving in the reverse direction to obtain Ro、Rs，Rm，Cs，Cm。
Model-based determination of corresponding state variables
Parameter thetakAnd observable value Uoc。
The more accurate the estimated parameter θ, the predicted value of the model
The closer to ykThe objective function is therefore to minimize the error of the estimate：
The objective function is derived from θ to yield:
according to the Recursive Least Squares (RLS), a correction is performed based on the model parameters at the previous time and the data at the current time to update the model parameters at the next time, that is:
wherein γ represents a correction amount, f (y)k，xk) Denotes ykAnd
the functional relationship of (a). Further, it is necessary to establish a relationship between two adjacent time points and define a variable
To obtain finally
Sequentially calculating K according to the RLS iteration updatek-1，Pk-1，θkWherein, K isk-1Is the algorithm gain.
Under a certain time scale, the undetermined parameters { p } and { k } can be directly obtained by using RLS iterative update, and R can be obtained by { k } and inverse solutiono、Rs，Rm，Cs，Cm。
Estimating the state of charge by utilizing an EKF algorithm, wherein a state space equation of the state of charge estimation is as follows:
as a preferred embodiment of the method, the step of correcting the parameters of the open-circuit voltage model and the parameters of the second-order RC equivalent circuit model according to the state of charge to obtain a corrected open-circuit voltage model and a corrected second-order RC equivalent circuit model specifically includes:
setting a state of charge estimation model as a first priority, setting a second-order RC lamp box circuit model as a second priority and setting an open-circuit voltage model as a third priority through a configuration estimator;
and taking the state of charge obtained by the state of charge estimation model as the input quantity of the open-circuit voltage model and the second-order RC equivalent circuit model, and modifying the parameters of the open-circuit voltage model and the parameters of the second-order RC equivalent circuit model to obtain a modified open-circuit voltage model and a modified second-order RC equivalent circuit model.
Specifically, the estimated priorities and parameter change speeds of the three models included in the improved algorithm are not consistent, the state of charge is the basis and basis of the input of the other two models, and therefore the estimated Est is configured to have the highest updating priority relative to other parameters1The time scale is set to Δ t1. Secondly, the equivalent circuit model parameter reflects the health state of the battery, the parameter value changes along with the state of charge, and an estimator Est is configured for the equivalent circuit model parameter2The time scale is set to Δ t2. Finally, since the open-circuit voltage model is only used for the parameter correction of the open-circuit voltage, the updating priority is lower relative to the other two estimators, so that the estimator Est is configured for the model3The time scale is set to Δ t3. Wherein, Δ t3>Δt2>Δt1The detailed schematic diagram refers to fig. 3.
Further, as a preferred embodiment of the method, the step of extracting the RC parameter according to the second-order RC equivalent circuit model and the parameter of the open-circuit voltage spectrum and performing correlation analysis to obtain the key factor specifically includes:
extracting RC parameters according to the second-order RC equivalent circuit model and the parameters of the open-circuit voltage spectrum;
performing relevance analysis on the RC parameters through a Spearman correlation coefficient to obtain the importance degree ranking of the RC parameters;
and ranking according to the importance degree of the RC parameters to obtain a key factor.
Specifically, the change rule of the battery model parameters under different charge states and battery health states is obtained. The selection range of the SoC for setting the model parameters is 20-90%, the RC parameters of the battery samples in different degradation states are extracted by taking 10% SoC as the step length, and 8 groups (40 in total) of RC parameters are obtained to be used as health factors.
And performing correlation analysis on the relationship between the 40 health factors and the battery health state by using a Spearman correlation coefficient, and ranking according to the importance degree of each health factor to obtain a key factor.
Further, as a preferred embodiment of the method, the step of constructing the battery state of health estimation model based on the SVR algorithm in combination with the open circuit voltage spectrum, the state of charge, the RC parameter and the key factor specifically includes:
determining a key charge state according to the key factors and acquiring three groups of RC parameters under the key charge state;
and (4) taking the three groups of RC parameters as input quantity and capacity ratio as output quantity, and constructing a battery health state estimation model through an SVR algorithm.
Specifically, three groups of state-of-charge socs corresponding to the key factors are determined, three groups of RC parameters under the state-of-charge socs are used as input quantities of the SVR algorithm, the capacity ratio is used as model output quantity, a nonlinear regression model for capacity estimation is constructed by adopting a support vector regression algorithm, and a battery state-of-health estimation model is obtained.
Further as a preferred embodiment of the method, it is characterized in that:
the hyper-parameters of the battery state of health estimation model are optimized and performance is evaluated using K-fold cross validation.
Specifically, the K-fold cross validation is to divide the training data into K parts, take K-1 groups of data as a training set in turn, take the other group of data as a validation set, test the trained model, finally obtain K trained models, measure the effect of the model through the average value of evaluation indexes of the K models, and finally obtain the optimal result of the hyperparameter, wherein the specific steps refer to FIG. 2.
While the preferred embodiments of the present invention have been illustrated and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A battery state of health estimation method based on dynamic parameter identification is characterized by comprising the following steps:
carrying out cycle life test on the battery to obtain an open-circuit voltage spectrum;
the method comprises the steps that a second-order RC equivalent circuit model and a charge state are obtained through combination based on an EKF-RLS parameter identification method;
extracting RC parameters according to the second-order RC equivalent circuit model and the parameters of the open-circuit voltage spectrum and carrying out correlation analysis to obtain key factors;
establishing a battery health state estimation model based on an SVR algorithm by combining an open-circuit voltage spectrum, a charge state, an RC parameter and a key factor;
and acquiring the current charge state of the battery to be detected and inputting the current charge state into a battery health state estimation model to obtain the health state of the battery.
2. The method according to claim 1, wherein the step of performing a cycle life test on the battery to obtain an open circuit voltage spectrum comprises:
and carrying out uninterrupted cyclic charge and discharge test on the battery.
When the charging and discharging test times reach the preset times, carrying out capacity evaluation and open-circuit voltage test on the battery to obtain a battery effective capacity value, an open-circuit voltage curve and an equivalent internal resistance curve;
obtaining a capacity ratio according to the effective capacity value of the battery and the nominal capacity of the battery;
judging that the capacitance ratio is smaller than a preset value, and stopping the battery cyclic charge and discharge test;
and generating an open-circuit voltage spectrum according to the plurality of open-circuit voltage curves.
3. The method according to claim 2, wherein the preset number is set to 50, the preset value is set to 0.7, and the capacity ratio expression is specifically:
in the above formula, CnowIs a value of effective capacity of the battery, CratedIs the nominal capacity of the battery, said ScapIs the capacity ratio.
4. The method according to claim 3, wherein the EKF-RLS-based parameter identification method combines the steps of obtaining a second-order RC equivalent circuit model and a state of charge, and specifically comprises:
constructing an open-circuit voltage model and a second-order RC equivalent circuit model based on the RLS;
constructing a state of charge estimation model based on an EKF algorithm;
obtaining a state of charge according to a state of charge estimation model;
and correcting the parameters of the open-circuit voltage model and the parameters of the second-order RC equivalent circuit model according to the state of charge to obtain a corrected open-circuit voltage model and a corrected second-order RC equivalent circuit model.
5. The method according to claim 4, wherein the step of modifying the open-circuit voltage model parameters and the second-order RC equivalent circuit model parameters according to the state of charge to obtain a modified open-circuit voltage model and a modified second-order RC equivalent circuit model specifically comprises:
setting a state of charge estimation model as a first priority, setting a second-order RC equivalent circuit model as a second priority and setting an open-circuit voltage model as a third priority through a configuration estimator;
and taking the state of charge obtained by the state of charge estimation model as the input quantity of the open-circuit voltage model and the second-order RC equivalent circuit model, and correcting the parameters of the open-circuit voltage model and the parameters of the second-order RC equivalent circuit model to obtain a corrected open-circuit voltage model and a corrected second-order RC equivalent circuit model.
6. The method for estimating the state of health of the battery according to claim 5, wherein the step of extracting RC parameters according to the second-order RC equivalent circuit model and the parameters of the open circuit voltage spectrum and performing correlation analysis to obtain the key factors specifically comprises:
extracting RC parameters according to the second-order RC equivalent circuit model and the parameters of the open-circuit voltage spectrum;
performing relevance analysis on the RC parameters through a Spearman correlation coefficient to obtain the importance degree ranking of the RC parameters;
and ranking according to the importance degree of the RC parameters to obtain a key factor.
7. The battery state of health estimation method based on dynamic parameter identification as claimed in claim 6, wherein the step of constructing the battery state of health estimation model based on SVR algorithm in combination with the open circuit voltage spectrum, the state of charge, the RC parameters and the key factors specifically comprises:
determining a key charge state according to the key factors and acquiring three groups of RC parameters under the key charge state;
and (4) taking the three groups of RC parameters as input quantity and capacity ratio as output quantity, and constructing a battery health state estimation model through an SVR algorithm.
8. The battery state of health estimation method based on dynamic parameter identification as claimed in claim 7, further comprising:
the hyper-parameters of the battery state of health estimation model are optimized and performance is evaluated using K-fold cross validation.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113359035A (en) * | 2021-05-28 | 2021-09-07 | 上海玖行能源科技有限公司 | Open-circuit voltage obtaining method based on actual working condition of battery |
| CN114325454A (en) * | 2021-12-30 | 2022-04-12 | 东软睿驰汽车技术（沈阳）有限公司 | Method, device, equipment and medium for determining influence of multiple characteristics on battery health degree |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140244225A1 (en) * | 2013-02-24 | 2014-08-28 | The University Of Connecticut | Battery state of charge tracking, equivalent circuit selection and benchmarking |
| CN105319512A (en) * | 2014-07-30 | 2016-02-10 | 三星电子株式会社 | Method and apparatus for estimating state of battery |
| CN107741569A (en) * | 2017-11-16 | 2018-02-27 | 温州大学 | A kind of evaluation method of the lithium battery charge state based on segment extension Kalman filtering |
| CN108508371A (en) * | 2018-04-09 | 2018-09-07 | 重庆大学 | A kind of power battery SOC/SOH/SOP combined estimation methods based on equivalent-circuit model |
| CN108646189A (en) * | 2018-05-08 | 2018-10-12 | 国网陕西省电力公司电力科学研究院 | A kind of LIBSVM modeling methods towards the prediction of lead-acid accumulator residual capacity |
| CN110045288A (en) * | 2019-05-23 | 2019-07-23 | 中山大学 | A kind of capacity of lithium ion battery On-line Estimation method based on support vector regression |
| WO2019207399A1 (en) * | 2018-04-27 | 2019-10-31 | 株式会社半導体エネルギー研究所 | Accumulator state-of-charge estimation method and accumulator state-of-charge estimation system |
| CN110703112A (en) * | 2019-10-14 | 2020-01-17 | 重庆大学 | Online estimation method of battery pack state based on local charging data |
| CN111443293A (en) * | 2020-03-30 | 2020-07-24 | 青岛大学 | Lithium battery state of health (SOH) estimation method based on data driving |
-
2020
- 2020-10-19 CN CN202011116879.8A patent/CN112269137B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140244225A1 (en) * | 2013-02-24 | 2014-08-28 | The University Of Connecticut | Battery state of charge tracking, equivalent circuit selection and benchmarking |
| CN105319512A (en) * | 2014-07-30 | 2016-02-10 | 三星电子株式会社 | Method and apparatus for estimating state of battery |
| CN107741569A (en) * | 2017-11-16 | 2018-02-27 | 温州大学 | A kind of evaluation method of the lithium battery charge state based on segment extension Kalman filtering |
| CN108508371A (en) * | 2018-04-09 | 2018-09-07 | 重庆大学 | A kind of power battery SOC/SOH/SOP combined estimation methods based on equivalent-circuit model |
| WO2019207399A1 (en) * | 2018-04-27 | 2019-10-31 | 株式会社半導体エネルギー研究所 | Accumulator state-of-charge estimation method and accumulator state-of-charge estimation system |
| CN108646189A (en) * | 2018-05-08 | 2018-10-12 | 国网陕西省电力公司电力科学研究院 | A kind of LIBSVM modeling methods towards the prediction of lead-acid accumulator residual capacity |
| CN110045288A (en) * | 2019-05-23 | 2019-07-23 | 中山大学 | A kind of capacity of lithium ion battery On-line Estimation method based on support vector regression |
| CN110703112A (en) * | 2019-10-14 | 2020-01-17 | 重庆大学 | Online estimation method of battery pack state based on local charging data |
| CN111443293A (en) * | 2020-03-30 | 2020-07-24 | 青岛大学 | Lithium battery state of health (SOH) estimation method based on data driving |
Non-Patent Citations (2)
| Title |
|---|
| DUO YANG ET AL.: "State-of-health estimation for the lithium-ion battery based on support vector regression", 《APPLIED ENERGY》 * |
| XIAOJUN TAN ET AL.: "Real-Time State-of-Health Estimation of Lithium-Ion Batteries Based on the Equivalent Internal Resistance", 《IEEE ACCESS》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113359035A (en) * | 2021-05-28 | 2021-09-07 | 上海玖行能源科技有限公司 | Open-circuit voltage obtaining method based on actual working condition of battery |
| CN114325454A (en) * | 2021-12-30 | 2022-04-12 | 东软睿驰汽车技术（沈阳）有限公司 | Method, device, equipment and medium for determining influence of multiple characteristics on battery health degree |
| CN114325454B (en) * | 2021-12-30 | 2023-07-04 | 东软睿驰汽车技术（沈阳）有限公司 | Method, device, equipment and medium for determining influence of multiple characteristics on battery health |
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