Patent Application
20210245624
This application is based on and claims priority under 35 USC 119 from Japanese Patent Application No. 2020-018735 filed on Feb. 6, 2020, the disclosure of which is incorporated by reference herein.
The present disclosure relates to a battery degradation evaluation system for evaluating degradation of a battery such as an auxiliary battery of a vehicle or the like, a battery degradation assessment method, and a non-transitory storage medium storing a battery degradation assessment program.
For example, Japanese Patent Application Laid-Open (JP-A) No. 2018-179733 proposes a battery life assessment device including: a storage unit that stores usage durations of a battery that are measured at time intervals and measured values of a degradation index that are measured at the time intervals, the degradation index indicating degradation of the battery; and a processing unit that assesses a life of the battery. Based on the usage durations of the battery and the measured values of the degradation index, the processing unit finds changes over time of the degradation index. Based on the changes over time of the degradation index, the processing unit finds a prediction function of the degradation index for the individual battery. Based on the degradation index prediction function, the processing unit finds predicted values of the degradation index, and based on the predicted value of the degradation index, the processing unit assesses the life of the battery.
A state quantity of a battery such as voltage, resistance or the like varies dependent on temperature and current or the like of the battery. However, for example, the technology disclosed in JP-A No. 2018-179733 gives no consideration to a state quantity such as voltage, resistance or the like of a battery varying dependent on temperature, current or the like of the battery. Therefore, there is scope for improvement in that evaluation accuracy may decline when unmodified state quantities are used to evaluate degradation of a battery.
The present disclosure provides a battery degradation evaluation device, a battery degradation evaluation method and a battery degradation evaluation program that may accurately evaluate degradation of a battery.
To achieve the object described above, a battery degradation evaluation system according to a first aspect includes: an acquisition section that acquires a state quantity of a battery; a correction section that, in a case in which the state quantity acquired by the acquisition section correlates with a physical quantity relating to a temperature of the battery, corrects the state quantity; and an evaluation section that evaluates degradation of the battery based on the state quantity corrected by the correction section.
According to the first aspect, the acquisition section acquires the state quantity of the battery. The acquired state quantity is a physical quantity that changes in association with degradation of the battery. For example, voltage, resistance, temperature and the like of the battery can be mentioned as examples.
In a case in which the state quantity acquired by the acquisition section correlates with the physical quantity relating to temperature of the battery, the correction section corrects the state quantity.
The evaluation section evaluates degradation of the battery based on the state quantity that has been corrected by the correction section. Thus, because the state quantity that correlates with the physical quantity relating to temperature of the battery is corrected and degradation of the battery is evaluated, degradation of the battery may be evaluated more accurately than in a situation in which an unmodified state quantity is used to evaluate battery degradation.
As in a second aspect, the correction section may correct the state quantity with a correction amount that differs in accordance with a usage time of the battery. Thus, the effects of degradation may be taken into account, and cases of chances to identify degradation being missed due to correction may be suppressed.
As in a third aspect, the correction section may correct the state quantity with a pre-specified correction amount in accordance with a magnitude of the state quantity. Thus, the effects of degradation may be taken into account, and cases of chances to identify degradation being missed due to correction may be suppressed.
As in a fourth aspect, in a case in which the state quantity does not correlate with the physical quantity, the evaluation section may evaluate degradation of the battery based on the state quantity acquired by the acquisition section. Thus, degradation of the battery may be evaluated without an unnecessary correction being performed.
As in a fifth aspect, the acquisition section may acquire the physical quantity. As in a sixth aspect, an estimation section that estimates the physical quantity may be further included. Thus, the estimation section may estimate the physical quantity.
As in a seventh aspect, the correction section may correct the state quantity in a case in which a correlation coefficient of the state quantity and the physical quantity is at least a pre-specified value. Alternatively, as in an eighth aspect, the correction section may compare respective values of the state quantity and the physical quantity with average values of the state quantity and the physical quantity in a pre-specified period, judge a correlation of the state quantity with the physical quantity, and correct the state quantity.
As in a ninth aspect, the state quantity that the acquisition section acquires may be detected by a sensor mounted at the vehicle.
As in a tenth aspect, the present disclosure may be a battery degradation evaluation method in which processing executed by a computer includes: acquiring a state quantity of a battery, in a case in which the acquired state quantity correlates with a physical quantity relating to a temperature of the battery, correcting the state quantity; and evaluating degradation of the battery based on the corrected state quantity.
Alternatively, as in an eleventh aspect, a non-transitory storage medium may store a battery degradation evaluation program executable by a processor to perform battery degradation evaluation processing, the battery degradation evaluation processing comprises acquiring a state quantity of a battery; in a case in which the acquired state quantity correlates with a physical quantity relating to a temperature of the battery, correcting the state quantity; and evaluating degradation of the battery based on the corrected state quantity.
According to the present disclosure as described above, a battery degradation evaluation device, battery degradation evaluation method, and non-transitory storage medium storing a battery degradation evaluation program may be provided that may accurately evaluate degradation of a battery.
Exemplary embodiments of the present invention will be described in detail based on the following figures, wherein:
Below, an example of an embodiment of the present disclosure is described in detail with reference to the attached drawings.
In a battery degradation evaluation system 10 according to the present exemplary embodiment, an on-board device 16 mounted at a vehicle 14 is connected with a center 12 via a communications network 18. In the battery degradation evaluation system 10 according to the present exemplary embodiment, state quantities of batteries mounted at multiple on-board devices 16 are sent to the center 12, and the center 12 evaluates degradation of the batteries. The center 12 employs big data of state quantities of batteries gathered from multiple vehicles and machine learning based on artificial intelligence (AI) to evaluate degradation of the batteries, using the state quantities of the batteries as input values.
In the present exemplary embodiment, a case of application to, for example, an auxiliary lead storage battery of the vehicle 14 is described. The state quantity is a state quantity representing a state of a battery. In particular, the state quantity is a physical quantity that changes in association with degradation of the battery. For example, voltage, resistance, temperature and the like of the battery can be mentioned as examples. In the present exemplary embodiment, an example in which the state quantity is, as an example, a voltage of the battery is described.
As shown in
As shown in
The data transmission section 24 sends the battery information acquired by the battery information acquisition section 22 to the center 12 via the communications network 18.
As shown in
As shown in
The data correction section 32 features functions of a temperature substitution processing section 34 and a data correction processing section 36. The data correction section 32 performs processing on the battery information received by the data reception section 30 to correct for a variation amount that is dependent on a temperature of the battery 20.
When there is no sensor at the vehicle 14 to detect the temperature of the battery 20 and temperature information cannot be acquired, the temperature substitution processing section 34 estimates a temperature of the battery 20 to be used in a degradation evaluation or in degradation evaluation learning.
In a situation in a luggage compartment or the like in which the battery 20 is distant from a heat-generating body such as an engine or the like, the temperature of the battery 20 is dependent on ambient air. In this situation, as illustrated in
The state quantity of the battery 20 is affected by the temperature of the battery 20, and the battery temperature is affected by the ambient air. Accordingly, the data correction processing section 36 corrects variations of the state quantity that have correlations with variations of the ambient air to remove the effects of the ambient air. In the present exemplary embodiment, variations in the state quantity that, as illustrated in the upper part of
For a correlation between battery temperature, serving as a physical quantity relating to temperature of the battery 20, and the state quantity, a method of obtaining a correlation coefficient between the two quantities, a method of evaluating a correlation from matches of high-low relationships relative to yearly average values, and so forth are available.
For example, in a case in which a correlation coefficient is to be obtained, as shown in
Alternatively, in a case of evaluation from matches of high-low relationships relative to yearly average values, as shown in
Because the state quantity of a battery varies in accordance with degradation, in a case in which the state quantity is used to estimate degradation, there may be cases of chances to identify degradation being missed due to correction. Accordingly, when the data correction processing section 36 corrects a state quantity that is to be used as an input for degradation evaluation, the data correction processing section 36 determines a correction amount taking into account the effects of degradation. More specifically, correction amounts are determined using a data map of state quantities and correction coefficients. If the state quantity is voltage, correction amounts in a low-voltage range are smaller than in other voltage ranges, and if the state quantity is resistance, correction amounts in a high-resistance range are smaller than in other resistance ranges. For example, as shown in
The data operations section 40 features functions of a probability calculation section 42 and a degradation evaluation section 44. The data operations section 40 evaluates degradation of each battery 20 based on the state quantities acquired from the on-board device 16.
As shown in
Based on the calculation result from the probability calculation section 42, the degradation evaluation section 44 evaluates whether the battery 20 has degraded by making a determination as to whether the state quantity meets a pre-specified degrading condition. For example, the degradation evaluation section 44 evaluates degradation by making a determination as to whether a probability of degrading calculated by the probability calculation section 42 is at least a pre-specified threshold value.
The data output section 46 sends an evaluation result from the degradation evaluation section 44 to the on-board device 16 of the vehicle 14 from which the state quantity was acquired. Hence, a degradation evaluation result of the battery 20 may be reported by the on-board device 16.
Herein, the battery information acquisition section 22 or data reception section 30 corresponds to an acquisition section, the data correction section 32 corresponds to a correction section, the data operations section 40 corresponds to an evaluation section, and the temperature substitution processing section 34 corresponds to an estimation section.
Now, specific processing that is carried out at the center 12 of the battery degradation evaluation system 10 according to the present exemplary embodiment configured as described above is described.
In step 100, the temperature substitution processing section 34 makes a determination as to whether temperature data is present. This determination is a determination as to whether temperature data is received from the on-board device 16 together with the state quantity. If the result of this determination is negative, the data correction section 32 proceeds to step 102, and if the result is affirmative, the data correction section 32 proceeds to step 104.
In step 102, the temperature substitution processing section 34 performs temperature substitution processing, and the data correction section 32 proceeds to step 104. For example, as shown in
In step 104, the data correction processing section 36 makes a determination as to whether the time is within a pre-specified period N. This determination is, for example, a determination as to whether the time is within an initial period of one or two years or the like. If the result of this determination is affirmative, the data correction section 32 proceeds to step 106, and if the result is negative, the data correction section 32 proceeds to step 108.
In step 106, the data correction processing section 36 acquires the third data correction amount, and the data correction section 32 proceeds to step 114. The third data correction amount obtains a correction coefficient corresponding to the initial period shown in
In step 108, the data correction processing section 36 makes a determination as to whether the state quantity is within a correction-enabled range. This determination is a determination as to whether the voltage serving as the state quantity, which corresponds to the later period shown in
In step 110, the data correction processing section 36 acquires the second data correction amount, and the data correction section 32 proceeds to step 114. The second data correction amount is a correction coefficient corresponding to the high-voltage range in the later period shown in
In step 112, the data correction processing section 36 acquires the first data correction amount, and the data correction section 32 proceeds to step 114. The first data correction amount is a correction coefficient corresponding to the low-voltage range in the later period shown in
In step 114, the data correction processing section 36 obtains a correlation value between the state quantity and temperature, and the data correction section 32 proceeds to step 116. That is, in the present exemplary embodiment, a correlation value of temperature with the voltage serving as the state quantity is obtained.
In step 116, the data correction processing section 36 makes a determination as to whether the correlation value is greater than a pre-specified threshold CC. In other words, the data correction processing section 36 makes a determination as to whether there is a correlation. If the result of this determination is affirmative, the data correction section 32 proceeds to step 118, and if the result is negative, the data correction section 32 proceeds to step 120.
In step 118, the data correction processing section 36 uses the correction coefficient acquired in step 106, step 110 or step 112 to correct the voltage serving as the state quantity, and this sequence of processing by the data correction section 32 is ended.
Alternatively, in step 120, the data correction processing section 36 suspends data correction, and this sequence of processing by the data correction section 32 is ended.
Now, processing that is carried out by the data operations section 40, which is carried out following the processing by the data correction section 32, is described.
In step 200, the probability calculation section 42 calculates a degradation probability of the battery 20, and the data operations section 40 proceeds to step 202. That is, the data map created beforehand is used to calculate the degradation probability, with the state quantity of the battery 20 as an input. The degradation probability that is calculated in this step is a probability of degrading in a pre-specified period a, but may be the probability of not degrading in the period a.
In step 202, the degradation evaluation section 44 makes a determination as to whether the calculated degradation probability A is greater than a pre-specified threshold CP. If the result of this determination is affirmative, the data operations section 40 proceeds to step 204, and if the result is negative, the data operations section 40 proceeds to step 206.
In step 204, the degradation evaluation section 44 evaluates that the battery is degrading and sets degradation evaluation to on, and this sequence of processing by the data operations section 40 ends.
Alternatively, in step 206, the degradation evaluation section 44 evaluates that the battery is not degrading and sets the degradation evaluation to off, and this sequence of processing by the data operations section 40 ends.
By carrying out this processing, the present exemplary embodiment may correct state quantities that correlate with temperature changes of the battery 20 and not correct state quantities that do not correlate. Thus, the present exemplary embodiment may suppress the effects of temperature changes on degradation evaluation accuracy of the battery 20.
When a state quantity is corrected, a correction amount is determined in accordance with a usage time of the battery 20 and the state quantity. Thus, because the effect of degradation is taken into consideration, a case of a degradation evaluation period being delayed due to correction may be suppressed.
Even when there is no sensor that detects the temperature of the battery 20 at the vehicle 14 and temperature information cannot be acquired, the temperature substitution processing section 34 may estimate the temperature. Thus, an increase in costs due to the addition of a temperature sensor may be suppressed, and a reduction in evaluation degradation accuracy because temperature information cannot be acquired may be suppressed.
In the exemplary embodiment described above, an example is described in which the second data correction amount and the third data correction amount are the same correction coefficient, but this is not limiting; the second data correction amount and the third data correction amount may be different correction coefficients. The correction coefficients shown in
In the exemplary embodiment described above, an example is described in which the data correction processing section 36 applies correction in accordance with a correlation between the state quantity and temperature, but this is not limiting. For example, it is known that state quantities such as voltage, resistance and the like are dependent on current values. Ordinarily, because resistance of the battery 20 is dependent on temperature, a current load in the vehicle 14 may be controlled in accordance with temperature. In this situation, the current load varies with a certain period. Therefore, similar processing may be performed with current values replacing the temperatures. Similarly, an alternative physical quantity that changes in accordance with temperature, current or the like may be used.
In the exemplary embodiment described above, voltage, resistance, temperature and the like of the battery 20 are mentioned as examples of the state quantity, but the state quantity is not limited thus. For example, a current or power relating to the battery 20, a two-dimensionally obtained state of charge (SOC) or the like, or a quantity in which these quantities are multiplied or added may prospectively be the state quantity. A prospective state quantity that is employed may be a single quantity and may be plural quantities. Which state quantity is most appropriate may vary in accordance with conditions, required accuracy and the like. Accordingly, it is sufficient to select the state quantity as appropriate.
In the exemplary embodiment described above, an example is described in which a communications device is provided at the vehicle 14 for communicating with the center 12, but this is not limiting. For example, as shown in
In the exemplary embodiment described above, an example is described in which degradation of the battery 20 is evaluated using big data and machine learning based on an AI model, but this is not limiting. For example, an evaluation method may be employed that uses single regression analysis, multiple regression analysis or the like. Alternatively, a degradation evaluation method may be employed that uses small-quantity data and a physical model. Alternatively again, a degradation evaluation method may be employed that uses both machine learning and a physical model.
In the exemplary embodiment described above, an example is described in which the data correction processing section 36 uses a data map of state quantities and correction coefficients to determine the correction amount, but this is not limiting. For example, a calculation expression may be used instead of a data map. Further, a correction coefficient may be determined not just from the state quantity and time. Any factors that affect loads on the battery 20 (for example, usage states of the vehicle 14, running states and the like) may be utilized.
In the exemplary embodiment described above, an example is described in which, as an example of a method for estimating temperature, the temperature substitution processing section 34 estimates the temperature of the battery 20 from date data. However, a temperature estimation method is not limited thus. For example, a value other than time that is linked with temperature may be utilized, provided that value interrelates with temperature. For example, when the battery 20 is installed in an engine compartment, the temperature of the battery 20 varies with an engine temperature, such as an engine water temperature or the like. Thus, the temperature of the battery 20 may be estimated from the engine water temperature. In this configuration, an engine temperature detection period, in units of hours or the like, is a shorter period than a date data detection period. A calculation expression may also be utilized as a method for estimating temperature.
The processing executed by the center 12 according to the exemplary embodiment described above is described as software processing that is implemented by a program being executed, but this is not limiting. For example, the processing may be carried out by hardware such as a graphics processing unit (GPU), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or the like. Alternatively, the processing may combine both software and hardware. Further, if the processing is implemented in software, the program may be memorized in any of various non-transitory storage media and distributed.
The present disclosure is not limited by the above recitations. In addition to the above recitations, it will be clear that numerous modifications may be embodied within a technical scope not departing from the gist of the disclosure.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2020-018735 | Feb 2020 | JP | national |
