CN108872861B - Method for evaluating health state of battery on line - Google Patents
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Abstract
The invention provides a method for evaluating the health state of a battery on line, which comprises the steps of determining the variation values of temperature, internal resistance, voltage and current in an on-line acquisition time period and the discharge of the battery corresponding to the on-line acquisition time period as the input of a neural network and training the network by taking the SOC value of the battery as the output to obtain the SOC estimation value in the acquisition time period; calculating the current real-time discharge capacity of the battery according to the current change value, calculating the initial discharge capacity of the battery according to the SOC estimated value, and comparing the initial discharge capacity and the SOC estimated value to obtain a first estimated value; acquiring internal resistance when the service life of the battery is finished, selecting one from the SOC estimated values as a reference value, acquiring the internal resistance input into the neural network when the reference value is acquired and the internal resistance corresponding to the initial value of the new battery and the obtained reference value at the same time, and acquiring a second estimated value according to the three internal resistances; a final estimate is derived based on the first and second estimates. By implementing the method, the limitation of the existing single parameter judgment method for estimating the state of health of the battery is avoided, the estimation precision is improved, and online estimation is supported.
Description
Technical Field
The invention relates to the technical field of battery detection, in particular to a method for evaluating the health state of a battery on line.
Background
With the increasing emphasis on energy crisis and environmental pollution. The rapid development of electric vehicles is an inevitable trend. The performance of the power battery, which is used as a source of energy for electric vehicles, has become a focus in the field of battery research. The battery state parameters are characterized by a state of charge (SOC) of the battery and a state of health (SOH) of the battery; the state of charge of the battery represents the performance of the electric automobile such as endurance mileage, and the health state of the battery represents the service life of the battery, and directly influences the safety and the economy of the electric automobile. Therefore, the health state of the battery is accurately estimated in real time, the performance optimization of the battery is facilitated, the practicability and the safety of the electric automobile are improved, and the method has great significance for the development of the electric automobile industry.
Currently, the methods for evaluating the state of the battery generally used include the following: the capacity method comprises the following mathematical expression from the viewpoint of battery capacity:
wherein, CMThe amount of electricity discharged by the battery at the present time from full discharge, CNThe amount of electricity discharged by the battery from a fully charged state at the initial moment is completely discharged, but the method can only carry out off-line estimation, does not support on-line estimation, has long period and needs the battery to be completely discharged. Particularly, in the actual use process of the electric automobile, the battery is rarely completely discharged, so that the SOH of the battery cannot be estimated on line in real time on the automobile; and (II) considering that the internal resistance of the battery is correspondingly changed from the viewpoint of the internal resistance of the battery, wherein the mathematical expression of the method is as follows:
wherein R isEOLInternal resistance, R, corresponding to the end of battery lifeNEWResistance, R, corresponding to the battery's initialNOWThe internal resistance value corresponding to the current moment is obtained, but the method has higher requirement on the accuracy of internal resistance measurement, and the SOH of the battery is generally estimated under the specific condition that the battery is in a fully charged state or in a fully discharged state: and (III) combining a capacity method and an internal resistance method, respectively obtaining various external characteristic quantities of the internal resistance, the capacity and the discharge curve of the battery by using a specific test method, and jointly judging the current health state of the battery by fusing the characteristic quantities. However, the method has a long estimation period, needs to discharge the battery to the lower limit cut-off voltage, obtains a discharge curve of the SOC of the battery from 100% to 0% after standing, does not support on-line estimation, and simultaneously needs to calculate the SOH by using internal resistance in the state that the SOC is 100% and 0%, and does not support estimation in any SOC state.
In summary, the inventors found that the method for evaluating the state of health of the battery by estimating a single parameter has great limitations, and the estimation accuracy is not high, and online estimation is not supported.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a method for online estimating a battery health status, which can avoid the limitation of the existing single parameter determination method for estimating a battery health status, improve estimation accuracy, and support online estimation.
In order to solve the above technical problem, an embodiment of the present invention provides a method for online evaluating a state of health of a battery, including the following steps:
determining an online collection time period of battery parameters, obtaining the change values of temperature, internal resistance, voltage and current of the battery during discharge in the collection time period, and further training a preset neural network by taking the change values of the temperature, the internal resistance, the voltage and the current of the battery obtained in the collection time period as the input of the preset neural network and taking the SOC value of the battery as the output of the preset neural network to obtain the SOC estimated value of the battery in the collection time period;
calculating the real-time discharge capacity of the battery at the current moment in the acquisition time period according to the change value of the battery current acquired in the acquisition time period, calculating the estimated discharge capacity of the battery at the initial moment in the acquisition time period according to the SOC estimated value of the battery in the acquisition time period, and further comparing the calculated real-time discharge capacity of the battery in the acquisition time period with the corresponding estimated discharge capacity to obtain a first estimated value of the health state of the battery;
acquiring internal resistance when the service life of the battery is finished, taking one SOC estimated value in the SOC estimated values of the battery in the acquisition time period as a reference value, acquiring the internal resistance input into the preset neural network when the reference value is obtained, acquiring the internal resistance actually acquired when the new battery is initially equal to the reference value, and further acquiring a second estimated value of the health state of the battery according to the internal resistance when the service life of the battery is finished, the internal resistance input into the preset neural network when the reference value is obtained, and the internal resistance corresponding to the new battery when the new battery is initially equal to the reference value;
and obtaining a final estimated value of the battery health state according to the first estimated value and the second estimated value of the battery health state.
Wherein the first estimation value of the battery state of health is
Wherein, SOHcA first estimate of the state of health of the battery; cNew(SOCt1-SOCt2)For the battery present time acquisition period t1, t2]Real-time discharge capacity of the internal battery; cNow(SOCt1-SOCt2)For the initial time acquisition period of the battery t1, t2]An estimated discharge capacity of the internal battery; t1 is acquisition period [ t1, t2]]The initial time of (a); t2 is acquisition period [ t1, t2]]The termination time of (c).
Wherein the second estimation value of the battery state of health is
Wherein, SOHRA second estimate of the state of health of the battery; SOCtThe reference value obtained from the SOC estimation value of the battery in the acquisition time period; rEOLInternal resistance at the end of the battery life; rnowCorrespondingly inputting the internal resistance of the preset neural network for the obtained reference value; rnewThe internal resistance of the new battery corresponding to the obtained reference value is initially equal.
Wherein the final estimation value of the battery state of health is SOH aSOHC+bSOHR(ii) a Wherein SOH is a final estimate of the state of health of the battery; a is>b and a + b is 1.
Wherein, a is 0.8, and b is 0.2.
The embodiment of the invention has the following beneficial effects:
compared with the ratio of the total discharge capacity from full charge to full discharge at the present stage after the battery is used in the traditional capacity method to the total discharge capacity from full charge to full discharge at the initial stage, online detection cannot be realized, the SOC at any two moments without the need of performing a full discharge state can be obtained, and the online estimation of the SOH of the battery is supported; meanwhile, compared with the traditional internal resistance method in which the internal resistance is limited at the time when the initial SOC is 100 and cannot be measured in real time, the method takes the SOC as the limitation, can estimate at any SOC state time, and supports the real-time online estimation of the SOH of the battery; therefore, the method can avoid the limitation of the single parameter judgment method for estimating the SOH of the battery in the prior art, can improve the accuracy of estimating the SOH, can estimate under any SOC state and supports online estimation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for online evaluation of a state of health of a battery according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, a method for online assessing a state of health of a battery according to an embodiment of the present invention includes the following steps:
step S1, determining an online collection time period of battery parameters, obtaining the variation values of temperature, internal resistance, voltage and current of the battery during discharge in the collection time period, and further training a preset neural network by taking the variation values of the temperature, the internal resistance, the voltage and the current of the battery obtained in the collection time period as the input of the preset neural network and the SOC value of the battery as the output of the preset neural network to obtain the SOC estimation value of the battery in the collection time period;
step S2, calculating the real-time discharge capacity of the battery at the current moment in the acquisition time period according to the change value of the battery current acquired in the acquisition time period, calculating the estimated discharge capacity of the battery at the initial moment in the acquisition time period according to the SOC estimated value of the battery in the acquisition time period, and further comparing the calculated real-time discharge capacity of the battery at the current moment in the acquisition time period with the estimated discharge capacity corresponding to the initial moment of the battery to obtain a first estimated value of the health state of the battery;
step S3, obtaining internal resistance when the service life of the battery is finished, taking one SOC estimated value in the SOC estimated values of the battery in the collection time period as a reference value, inputting the internal resistance of the preset neural network when the reference value obtained by calculation is obtained, obtaining the internal resistance actually collected when the new battery is initially equal to the reference value obtained, and further obtaining a second estimated value of the health state of the battery according to the internal resistance when the service life of the battery is finished, the internal resistance correspondingly input into the preset neural network when the reference value is obtained, and the internal resistance corresponding to the new battery when the new battery is initially equal to the reference value obtained;
and step S4, obtaining a final estimated value of the battery health state according to the first estimated value and the second estimated value of the battery health state.
In step S1, setting an acquisition time period from time t1 to time t2, obtaining variation values corresponding to parameters such as temperature, internal resistance, voltage and current of the battery in the acquisition time period [ t1, t2] through the temperature sensing module, the internal resistance measuring module, the voltage and current sensing module, and the like, taking the variation values corresponding to the parameters such as temperature, internal resistance, voltage and current of the battery as preset neural network input, and taking the SOC value of the battery as neural network output to train the neural network, so that the trained neural network is used for estimating the SOC of the battery in real time, and obtaining the SOC estimation value of the battery in the acquisition time period [ t1, t2 ]; wherein t1 is the initial time; t2 is the termination time.
In step S2, according to the acquisition period [ t1, t2]Calculating the discharge acquisition time period [ t1, t2] by using a method of on-time integration according to the acquired change value of the battery current]Battery initial moment real-time discharging electric quantity CNew(SOCt1-SOCt2)And calculates the acquisition time period [ t1, t2] according to the SOC estimation value obtained in the step S1]Estimated discharge C of the internal battery at the present timeNow(SOCt1-SOCt2)；
Obtaining a first estimated value SOH of the state of health of the battery by using the formula (1)cThe method comprises the following steps:
since the conventional capacity method is the ratio of the total discharge capacity from full charge to full discharge at the present stage after the battery is used to the total discharge capacity from full charge to full discharge at the initial stage, it cannot perform online detection. The improved capacity method uses the discharging amount corresponding to the SOC at the current moment as the value of the SOH compared with the discharging amount corresponding to the same SOC at the initial moment. The SOC of any two moments can be obtained, complete discharge is not needed, and online estimation is supported.
In step S3, the internal resistance R at the end of the battery life is acquiredEOLAnd will acquire the time period t1, t2]An SOC estimation value (e.g., SOC at time t) from among SOC estimation values of internal batteryt) As a reference value, obtaining the reference value obtained by calculation and inputting the internal resistance R of the preset neural networknowAnd acquiring the internal resistance R actually acquired when the initial value of the new battery is equal to the obtained reference valuenew；
Obtaining a second estimated value SOH of the battery state of health by using the formula (2)RThe method comprises the following steps:
the traditional internal resistance method only limits the internal resistance at the time when the initial SOC is 100 and cannot measure in real time, while the improved internal resistance method takes the SOC as the limit and can estimate at any SOC state time to support real-time online estimation.
It should be noted that the SOC at time t is extracted in the neural networktThen, the internal resistance R input to the neural network at the time t can be checkednowWhile, according to the equivalent SOCtFinding the internal resistance R acquired at the initial moment of the corresponding new battery by the reference valuenew。
In step S3, using equation (3), the final estimated value SOH of the battery state of health is obtained as follows:
SOH＝aSOHC+bSOHR(3)。
wherein a > b and a + b ═ 1, and a and b represent different weight coefficients.
In one embodiment, a lithium iron phosphate battery is taken as an example, a large amount of experimental data is collected for analysis, and finally the value of a is set to be 0.8, and the value of b is set to be 0.2.
The embodiment of the invention has the following beneficial effects:
compared with the ratio of the total discharge capacity from full charge to full discharge at the present stage after the battery is used in the traditional capacity method to the total discharge capacity from full charge to full discharge at the initial stage, online detection cannot be realized, the SOC at any two moments without the need of performing a full discharge state can be obtained, and the online estimation of the SOH of the battery is supported; meanwhile, compared with the traditional internal resistance method in which the internal resistance is limited at the time when the initial SOC is 100 and cannot be measured in real time, the method takes the SOC as the limitation, can estimate at any SOC state time, and supports the real-time online estimation of the SOH of the battery; therefore, the method can avoid the limitation of the single parameter judgment method for estimating the SOH of the battery in the prior art, can improve the accuracy of estimating the SOH, can estimate under any SOC state and supports online estimation.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (2)
1. A method for online assessment of battery state of health, comprising the steps of:
determining an online collection time period of battery parameters, obtaining the change values of temperature, internal resistance, voltage and current of the battery during discharge in the collection time period, and further training a preset neural network by taking the change values of the temperature, the internal resistance, the voltage and the current of the battery obtained in the collection time period as the input of the preset neural network and taking the SOC value of the battery as the output of the preset neural network to obtain the SOC estimated value of the battery in the collection time period;
calculating the real-time discharge capacity of the battery at the current moment in the acquisition time period according to the change value of the battery current acquired in the acquisition time period, calculating the estimated discharge capacity of the battery at the initial moment in the acquisition time period according to the SOC estimated value of the battery in the acquisition time period, and further comparing the calculated estimated discharge capacity of the battery at the initial moment in the acquisition time period with the estimated discharge capacity of the battery at the initial moment corresponding to the SOC estimated value to obtain a first estimated value of the health state of the battery;
acquiring internal resistance when the service life of the battery is finished, taking one SOC estimated value in the SOC estimated values of the battery in the acquisition time period as a reference value, acquiring the internal resistance input into the preset neural network when the reference value is obtained, acquiring the internal resistance actually acquired when the new battery is initially equal to the reference value, and further acquiring a second estimated value of the health state of the battery according to the internal resistance when the service life of the battery is finished, the internal resistance input into the preset neural network when the reference value is obtained, and the internal resistance corresponding to the new battery when the new battery is initially equal to the reference value;
obtaining a final estimated value of the battery health state according to the first estimated value and the second estimated value of the battery health state;
the first estimate of the state of health of the battery is
Wherein, SOHcA first estimate of the state of health of the battery;
is an acquisition period [ t1, t2]]Real-time discharge capacity of the internal battery at the initial moment;
is an acquisition period [ t1, t2]]The estimated discharge capacity of the internal battery at the current moment; t1 is acquisition period [ t1, t2]]The initial time of (a); t2 is acquisition period [ t1, t2]]The termination time of (2);
the second estimate of the state of health of the battery is
Wherein, SOHRIs the state of health of the batteryA second estimated value; SOCtThe reference value obtained from the SOC estimation value of the battery in the acquisition time period; rEOLInternal resistance at the end of the battery life; rnowCorrespondingly inputting the internal resistance of the preset neural network for the obtained reference value; rnewThe internal resistance of the new battery which corresponds to the obtained reference value is initially equal;
the final estimation value of the state of health of the battery is SOH-aSOHC+bSOHR(ii) a Wherein SOH is a final estimate of the state of health of the battery; a is>b and a + b is 1.
2. The method for online state of health assessment of a battery as claimed in claim 1, wherein a-0.8 and b-0.2.
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| CN109932663A (en) * | 2019-03-07 | 2019-06-25 | 清华四川能源互联网研究院 | Cell health state appraisal procedure, device, storage medium and electronic device |
| CN110187283A (en) * | 2019-06-14 | 2019-08-30 | 湖北锂诺新能源科技有限公司 | A kind of estimation method of lithium ion battery SOC |
| CN113514771A (en) * | 2020-04-10 | 2021-10-19 | 广州汽车集团股份有限公司 | Battery impedance service life estimation method and system |
| CN111521945B (en) * | 2020-05-06 | 2022-04-08 | 美智纵横科技有限责任公司 | Battery health state detection method and device, electronic equipment and storage medium |
| CN112255554A (en) * | 2020-10-29 | 2021-01-22 | 国网江苏省电力有限公司盐城供电分公司 | Direct current system battery health status and internal fault monitoring device |
| CN116400227A (en) * | 2023-06-08 | 2023-07-07 | 长安大学 | SOH prediction method, system, equipment and medium for power battery of electric automobile |
| CN116774091B (en) * | 2023-08-24 | 2023-10-17 | 南京市计量监督检测院 | High-precision power battery pack SOH online measurement system and method thereof |
| CN117406125B (en) * | 2023-12-15 | 2024-02-23 | 山东派蒙机电技术有限公司 | Battery health state confirmation method, device, equipment and storage medium |
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| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20181123 Assignee: Hefei Jinhe Electronic Technology Co.,Ltd. Assignor: Wenzhou University Contract record no.: X2021330000834 Denomination of invention: A method for on-line evaluation of battery health Granted publication date: 20200721 License type: Common License Record date: 20211222 |