CN116381538A - Method and device for correcting SOH based on big data platform, electronic equipment and medium - Google Patents
Method and device for correcting SOH based on big data platform, electronic equipment and medium Download PDFInfo
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Abstract
The invention discloses a method, a device, electronic equipment and a medium for correcting SOH based on a big data platform. The method comprises the following steps: obtaining maximum allowable charging power, maximum allowable discharging power and current value of the current residual electric quantity of a power battery of a target vehicle when the power battery works; calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; calculating the cycle times of the battery according to the current value to obtain target cycle times of the battery; and correcting the current SOH of the target vehicle by using the target charge/discharge times, the target deep discharge times, the target battery cycle times and the predetermined vehicle data to obtain the target SOH. According to the technical scheme, the adaptability to different driving habits is strong, the SOH precision of the power battery is greatly improved, and the service life of the power battery is prolonged.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to a method, a device, electronic equipment and a medium for correcting SOH based on a big data platform.
Background
The battery capacity of the new energy vehicle power battery decays after a plurality of charge and discharge cycles. Excessive current accelerates the decay of battery life if SOH (State of Health, which is a State of Health of the battery in the industry) is not known. Therefore, accurate estimation of SOH is of great importance.
The current SOH calculation method of the new energy vehicle battery comprises the following steps: one method is a discharge experiment method, in which a discharge device is used to discharge the battery from full charge to the cut-off voltage of the battery, and the SOH of the battery can be calculated by comparing the discharged electric quantity with rated capacity. One method is an internal resistance calculation method, wherein the larger the internal resistance is, the smaller the SOH is, and the internal resistance of the battery is calculated through battery data such as current and voltage, so that the SOH of the battery is estimated. The other method is a battery cycle number folding algorithm, and the SOH of the battery is corresponding to the battery cycle number, wherein the more the cycle number is, the smaller the SOH is, namely the worse the battery state of health is.
The discharge experiment method cannot calculate SOH on line, and can only be used for testing in a laboratory. The internal resistance calculation method has larger SOH estimation error as the battery internal resistance changes smaller relative to SOH. The battery cycle number folding algorithm roughly corresponds to SOH according to the cycle number of the battery, for example, if the battery is charged and discharged twice rated capacity every time, the cycle number of the battery is increased by one, and finally, the SOH at the moment is obtained by looking up a table according to the relation between the cycle number of the battery and the SOH. The method has single calculation dimension and poor adaptability.
Disclosure of Invention
The method, the device, the electronic equipment and the medium for correcting the SOH based on the big data platform are strong in adaptability to different driving habits, greatly improve the SOH precision of the power battery, have low requirements on hardware operation, ensure that the power battery operates in good working conditions as much as possible, and prolong the service life of the power battery.
According to an aspect of the present invention, there is provided a method of correcting SOH based on a big data platform, the method comprising:
acquiring maximum allowable charging power, maximum allowable discharging power and current value of a power battery of a target vehicle when the power battery works; obtaining the current residual electric quantity of a power battery of the target vehicle;
calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; calculating the cycle times of the battery according to the current value to obtain target cycle times of the battery;
correcting the current SOH of the target vehicle by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and the predetermined vehicle data to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
According to another aspect of the present invention, there is provided an apparatus for correcting SOH based on a big data platform, the apparatus comprising:
the power battery parameter acquisition module is used for acquiring the maximum allowable charging power, the maximum allowable discharging power and the current value of the power battery of the target vehicle when in operation; obtaining the current residual electric quantity of a power battery of the target vehicle;
the power battery parameter calculation module is used for calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; calculating the cycle times of the battery according to the current value to obtain target cycle times of the battery;
the current SOH correction module is used for correcting the current SOH of the target vehicle by utilizing the target charge and discharge times, the target deep discharge times, the target battery cycle times and the predetermined vehicle data to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the SOH-based method of any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the SOH correction method based on a big data platform according to any of the embodiments of the present invention when executed.
According to the technical scheme, the maximum allowable charging power, the maximum allowable discharging power and the current value of the power battery of the target vehicle during operation are obtained; the current residual electric quantity of the power battery of the target vehicle is obtained, and then the high-power charge and discharge times of the power battery are calculated according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; and calculating the battery cycle number according to the current value to obtain a target battery cycle number, and correcting the current SOH of the target vehicle by using the target charge/discharge number, the target deep discharge number, the target battery cycle number and predetermined vehicle data to obtain the target SOH. According to the technical scheme, SOH is calculated through the high-power charge and discharge times, the deep discharge times and the power battery circulation times, adaptability to different driving habits is high, SOH precision of the power battery is greatly improved, the requirement on hardware operation is low, the power battery is ensured to run under good working conditions as much as possible, and the service life of the power battery is prolonged.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for SOH correction based on big data platform according to a first embodiment of the present invention;
FIG. 2 is a flow chart of another method for SOH correction based on big data platform provided in accordance with an embodiment of the present application;
FIG. 3 is a schematic diagram of a SOH correction process based on a big data platform according to a second embodiment of the present invention;
FIG. 4 is a schematic diagram of another SOH correction process based on a big data platform according to the third embodiment of the present invention;
FIG. 5 is a schematic diagram of a SOH correction process based on a big data platform according to a fourth embodiment of the present invention;
Fig. 6 is a schematic structural diagram of an apparatus for SOH correction based on a big data platform according to a fifth embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device implementing a method for correcting SOH based on a big data platform according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a method for correcting SOH based on a big data platform according to an embodiment of the present invention, where the method may be performed by a device for correcting SOH based on a big data platform, and the device for correcting SOH based on a big data platform may be implemented in hardware and/or software, and the device for correcting SOH based on a big data platform may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, acquiring maximum allowable charge power, maximum allowable discharge power and current value of a power battery of a target vehicle when the power battery works; and obtaining the current residual quantity of the power battery of the target vehicle.
In this scheme, because the driver drives the difference of habit and charging habit, lead to there is the deviation in SOH of the power battery of target vehicle, and then cause the deviation in power battery's life-span, in order to be stronger to the adaptability of different driving habits and charging habit, better to the protectiveness of power battery life-span, can calculate SOH through high-power charge-discharge number of times, degree of depth discharge number of times, battery cycle number of times, guarantee that power battery's operation is at good operating mode as far as to extend power battery's life-span.
The target vehicle may be a new energy vehicle.
In this embodiment, the power battery may refer to a power source that provides a source of power for the target vehicle.
In the present solution, the maximum allowable charge power, the maximum allowable discharge power, and the current value when the power battery of the target vehicle is operated may be acquired based on the sensor installed on the target vehicle; and obtaining a current state of charge (SOC) of a power battery of the target vehicle.
Optionally, obtaining the maximum allowable charge power and the maximum allowable discharge power of the power battery of the target vehicle during operation includes steps A1-A2:
a1, acquiring the current residual capacity, battery temperature and current SOH of a power battery of a target vehicle;
in the present embodiment, the current remaining power of the power battery of the target vehicle, the battery temperature may be acquired in real time based on the sensor mounted on the target vehicle. For example, a temperature sensor may be utilized to monitor battery temperature; the current remaining power may be monitored and calculated using a voltage sensor, a current sensor.
In this scheme, the current SOH of the power battery of the target vehicle may be obtained experimentally. For example, a discharge test may be used to measure the current SOH of the power battery of the target vehicle.
And A2, calculating the maximum allowable charge power and the maximum allowable discharge power of the power battery in the target vehicle when the power battery works according to a preset power calculation formula and the current residual electric quantity, the battery temperature and the current SOH.
Further, fig. 2 is a flowchart of another method for correcting SOH based on a big data platform according to the first embodiment of the present application, as shown in fig. 2, various power calculation formulas may be used to calculate the maximum allowable charge power and the maximum allowable discharge power when the power battery in the target vehicle works according to the current remaining power, the battery temperature and the current SOH.
The maximum allowable charge power and the maximum allowable discharge power when the power battery of the target vehicle is operated are calculated, so that the target charge/discharge number can be calculated based on the maximum allowable charge power and the maximum allowable discharge power when the power battery of the target vehicle is operated.
S120, calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; and calculating the battery cycle number according to the current value to obtain the target battery cycle number.
In this embodiment, as shown in fig. 2, by calculating the target charge/discharge number, the target deep discharge number, and the target battery cycle number, SOH can be calculated based on the target charge/discharge number, the target deep discharge number, and the target battery cycle number pair, so as to adapt to SOH deviation caused by different driving habits and charging habits.
Specifically, as shown in fig. 2, the number of times of high-power charging and discharging of the power battery can be calculated according to the maximum allowable charging power and the maximum allowable discharging power in a predetermined calculation mode; calculating the deep discharge times of the power battery according to the current residual electric quantity; and calculating the battery cycle number according to the current value.
S130, correcting the current SOH of the target vehicle by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and predetermined vehicle data to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
The vehicle data may refer to related data such as a battery specification, a vehicle model, and the like.
In this scheme, as shown in fig. 2, after calculating the target charge/discharge frequency, the target deep discharge frequency, and the target battery cycle frequency, the target charge/discharge frequency, the target deep discharge frequency, and the target battery cycle frequency may be uploaded to the big data platform, and the current SOH of the target vehicle may be adjusted by using the multidimensional table of the SOH in the big data platform, to obtain the target SOH. Wherein adjusting the current SOH of the target vehicle includes turning the current SOH up and turning the current SOH down. The multi-dimensional table of SOH may be set based on experimental data and historical vehicle data.
Optionally, the current SOH of the target vehicle is corrected by using the target charge/discharge frequency, the target deep discharge frequency, the target battery cycle frequency and predetermined vehicle data to obtain a target SOH, which includes steps B1-B2:
step B1, calculating an SOH correction coefficient by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and predetermined vehicle data to obtain a target correction coefficient;
in this embodiment, the target charge/discharge frequency, the target deep discharge frequency, the target battery cycle frequency, and predetermined vehicle data may be used as query and calculation conditions, and table lookup interpolation calculation may be performed on the multi-dimensional table of SOH in the big data, to obtain the target correction coefficient. Optionally, the target charge-discharge frequency, the target deep-discharge frequency, the target battery cycle frequency, and the historical charge-discharge frequency, the historical deep-discharge frequency, the historical battery cycle frequency, and the historical vehicle data in the multidimensional table of the vehicle data and the SOH may be compared, a range in which the target SOH is located is determined, and different target correction coefficients are set according to the range in which the target SOH is located. Preferably, the range of the target SOH may be compared with a threshold value, and different target correction coefficients may be set.
And B2, adjusting the current SOH of the target vehicle according to the target correction coefficient to obtain a target SOH.
Specifically, after the target correction coefficient is obtained, the current SOH of the target vehicle may be increased or decreased according to the target correction coefficient to obtain the target SOH. And issues the target SOH back to the current vehicle BMS (Battery Management System). The BMS stores the target SOH issued by the platform, performs smoothing processing on the target SOH value, and then updates the target SOH value to the bus.
By calculating SOH, the method has strong adaptability to different driving habits, greatly improves SOH precision of the power battery, ensures that the power battery operates under good working conditions as much as possible, and prolongs the service life of the power battery.
According to the technical scheme, the maximum allowable charging power, the maximum allowable discharging power and the current value of the power battery of the target vehicle during operation are obtained; and obtaining the current residual capacity of the power battery of the target vehicle, calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times, calculating the deep discharge times of the power battery according to the current residual capacity to obtain target deep discharge times, calculating the battery cycle times according to the current value to obtain target battery cycle times, and correcting the current SOH of the target vehicle by utilizing the target charge and discharge times, the target deep discharge times, the target battery cycle times and predetermined vehicle data to obtain target SOH. By executing the technical scheme, the SOH is calculated through the high-power charge and discharge accumulated times, the deep discharge accumulated times and the power battery circulation times, the adaptability to different driving habits is strong, the SOH precision of the power battery is greatly improved, the power battery is ensured to run under good working conditions as much as possible, and the service life of the power battery is prolonged. And the final SOH complex operation is carried out through the big data platform, so that the calculation requirement of BMS hardware is reduced.
Example two
Fig. 3 is a schematic diagram of a process for correcting SOH based on a big data platform according to a second embodiment of the present invention, and the relationship between the present embodiment and the above embodiment is a detailed description of the process for calculating the target charge and discharge times. As shown in fig. 3, the method includes:
s310, acquiring maximum allowable charge power, maximum allowable discharge power and current value of a power battery of a target vehicle when the power battery works; and obtaining the current residual quantity of the power battery of the target vehicle.
S320, acquiring the current discharging power and the current charging power of the power battery in the target vehicle.
In the present embodiment, the current discharge power and the current charge power of the power battery in the target vehicle may be acquired using the sensor mounted on the target vehicle.
S330, comparing the current discharge power with the maximum allowable discharge power, and comparing the current charge power with the maximum allowable charge power to determine target charge and discharge times.
In the scheme, the current discharge power and the maximum allowable discharge power can be compared to obtain a first comparison result, and the high-power charge and discharge times of the power battery are calculated according to the first comparison result to obtain the target charge and discharge times.
In this embodiment, the current charging power and the maximum allowable charging power may be compared to obtain a second comparison result, and the number of times of high-power charging and discharging of the power battery may be calculated according to the second comparison result to obtain the target number of times of charging and discharging.
Optionally, comparing the current discharge power with the maximum allowable discharge power, and comparing the current charge power with the maximum allowable charge power, to determine a target charge-discharge frequency, including steps C1-C4:
step C1, subtracting the maximum allowable discharge power from a preset first threshold value to obtain a first discharge power;
step C2, if the duration time of the current discharge power larger than the first discharge power meets the preset first time condition under the condition that the current discharge power is larger than the first discharge power, increasing the high-power charge-discharge frequency of the power battery by 1 to obtain target charge-discharge frequency; the method comprises the steps of,
step C3, subtracting the maximum allowable charging power from a preset second threshold value to obtain a second charging power;
and C4, under the condition that the current charging power is larger than the second charging power, if the duration time of the current charging power larger than the second charging power meets the preset second time condition, increasing the high-power charging and discharging times of the power battery by 1 to obtain target charging and discharging times.
The first threshold value and the second threshold value can be obtained through calibration of experimental data. The first threshold may be denoted by C1 and the second threshold may be denoted by C2.
In this embodiment, the first time condition may refer to a duration in which the current discharge power is greater than the first discharge power being greater than the first time. The first time may be set according to the SOH correction requirement, and the first time may be denoted by t 1.
In this aspect, the second time condition may refer to a duration in which the current charging power is greater than the second charging power being greater than the second time. The second time may also be set according to the SOH correction requirement, where the second time is denoted by t 2.
Specifically, the maximum allowable discharge power is denoted by P1, and the maximum allowable charge power is denoted by P2. And under the condition that the current discharge power is larger than P1-C1 for t1 seconds or the current charge power is larger than P2-C2 for t2 seconds, increasing the high-power charge and discharge times of the power battery by 1 to obtain target charge and discharge times, storing the target charge and discharge times into a memory, and uploading the target charge and discharge times to a large data platform.
By calculating the target charge and discharge times, SOH of the target vehicle can be calculated based on the target charge and discharge times, adaptability to different driving habits and charging habits is higher, and the life protection of the power battery is better.
S340, calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; and calculating the battery cycle number according to the current value to obtain the target battery cycle number.
In this embodiment, steps S320 and S340 may be performed simultaneously, or S320 and S330 may be performed first, then S340 may be performed, or S340 may be performed first, and S320 and S330 may be performed. The execution order is not particularly limited in this embodiment.
S350, correcting the current SOH of the target vehicle by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and predetermined vehicle data to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
According to the technical scheme, the maximum allowable charging power, the maximum allowable discharging power and the current value of the power battery of the target vehicle during operation are obtained; the current residual electric quantity of the power battery of the target vehicle is obtained, then the current discharging power and the current charging power of the power battery in the target vehicle are obtained, the current discharging power is compared with the maximum allowable discharging power, the current charging power is compared with the maximum allowable charging power, and the target charging and discharging times are determined; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; and calculating the battery cycle number according to the current value to obtain a target battery cycle number, and correcting the current SOH of the target vehicle by using the target charge/discharge number, the target deep discharge number, the target battery cycle number and predetermined vehicle data to obtain the target SOH. Through executing this technical scheme, calculate SOH through high-power charge and discharge number of times, degree of depth discharge number of times, power battery circulation number of times, strong adaptability to different driving habits, great improvement power battery SOH precision, guarantee that power battery's operation is at good operating mode as far as possible, extension power battery's life-span. And the final SOH complex operation is carried out through the big data platform, so that the calculation requirement of BMS hardware is reduced.
Example III
Fig. 4 is a schematic diagram of another process for correcting SOH based on a big data platform according to the third embodiment of the present invention, and the relationship between the present embodiment and the above embodiment is a detailed description of the process for calculating the target deep discharge frequency. As shown in fig. 4, the method includes:
s410, acquiring maximum allowable charge power, maximum allowable discharge power and current value of a power battery of a target vehicle when the power battery works; and obtaining the current residual quantity of the power battery of the target vehicle.
And S420, calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times.
And S430, increasing the deep discharge frequency of the power battery by 2 under the condition that the current residual electric quantity is smaller than a preset third threshold value, so as to obtain the target deep discharge frequency.
The third threshold value can be obtained through calibration of experimental data and can be represented by C3.
In this embodiment, if the current remaining power SOC is less than C3, the power battery deep discharge frequency is increased by 2 to obtain the target deep discharge frequency.
S440, increasing the deep discharge frequency of the power battery by 1 to obtain the target deep discharge frequency when the current residual electric quantity is larger than or equal to a preset third threshold value and smaller than a preset fourth threshold value.
The fourth threshold value can be obtained through calibration of experimental data and can be represented by C4.
Specifically, when the SOC of C3 is less than or equal to C4, the deep discharge frequency of the power battery is increased by 1, and the target deep discharge frequency is obtained.
And S450, increasing the deep discharge frequency of the power battery by 0 under the condition that the current residual electric quantity is larger than or equal to a preset fourth threshold value, so as to obtain the target deep discharge frequency.
Further, when the SOC is more than or equal to C4, the number of deep discharge times of the power battery is increased by 0, namely the accumulated number of deep discharge times is not accumulated.
And S460, calculating the battery cycle times according to the current value to obtain the target battery cycle times.
S470, correcting the current SOH of the target vehicle by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and the predetermined vehicle data to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
According to the technical scheme, the maximum allowable charging power, the maximum allowable discharging power and the current value of the power battery of the target vehicle during operation are obtained, the current residual capacity of the power battery of the target vehicle is obtained, then the high-power charging and discharging times of the power battery are calculated according to the maximum allowable charging power and the maximum allowable discharging power to obtain the target charging and discharging times, the current residual capacity is compared with a preset threshold value, the target deep charging and discharging times are determined, the battery cycle times are calculated according to the current value to obtain the target battery cycle times, and the current SOH of the target vehicle is corrected by utilizing the target charging and discharging times, the target deep discharging times, the target battery cycle times and the predetermined vehicle data to obtain the target SOH. Through executing this technical scheme, calculate SOH through high-power charge and discharge number of times, degree of depth discharge number of times, power battery circulation number of times, strong adaptability to different driving habits, great improvement power battery SOH precision, guarantee that power battery's operation is at good operating mode as far as possible, extension power battery's life-span. And the final SOH complex operation is carried out through the big data platform, so that the calculation requirement of BMS hardware is reduced.
Example IV
Fig. 5 is a schematic diagram of a process of correcting SOH based on a big data platform according to a fourth embodiment of the present invention, and the relationship between the present embodiment and the above embodiment is a detailed description of the process of calculating the cycle number of the target battery. As shown in fig. 5, the method includes:
s510, acquiring maximum allowable charge power, maximum allowable discharge power and current value of a power battery of a target vehicle when the power battery works; and obtaining the current residual quantity of the power battery of the target vehicle.
S520, calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times; and calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times.
S530, integrating the current value to obtain current throughput.
In this embodiment, when the power battery of the target vehicle is in operation, the absolute value of the collected current value I may be integrated, so as to obtain the current throughput.
And S540, calculating the cycle times of the battery according to the current throughput and the predetermined rated capacity of the battery to obtain the target cycle times of the battery.
Wherein the battery rated capacity may be determined based on vehicle data of the target vehicle.
In this scheme, the current throughput may be divided by twice the battery rated amount Q to obtain the target battery cycle number S.
Specifically, the target battery cycle number S may be calculated using the following formula:
S＝(∫|I|dt)/Q/2。
s550, correcting the current SOH of the target vehicle by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and predetermined vehicle data to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
According to the technical scheme, the maximum allowable charging power, the maximum allowable discharging power and the current value of the power battery of the target vehicle during operation are obtained, the current residual capacity of the power battery of the target vehicle is obtained, then the high-power charging and discharging times of the power battery are calculated according to the maximum allowable charging power and the maximum allowable discharging power, the target charging and discharging times are obtained, the deep charging and discharging times of the power battery are calculated according to the current residual capacity, the target deep charging and discharging times are obtained, the current throughput is calculated, and the target battery cycle times are calculated based on the current throughput. And then correcting the current SOH of the target vehicle by utilizing the target charge and discharge times, the target deep discharge times, the target battery cycle times and the predetermined vehicle data to obtain the target SOH. Through executing this technical scheme, calculate SOH through high-power charge and discharge number of times, degree of depth discharge number of times, power battery circulation number of times, strong adaptability to different driving habits, great improvement power battery SOH precision, guarantee that power battery's operation is at good operating mode as far as possible, extension power battery's life-span. And the final SOH complex operation is carried out through the big data platform, so that the calculation requirement of BMS hardware is reduced.
Example five
Fig. 6 is a schematic structural diagram of a device for correcting SOH based on a big data platform according to a fifth embodiment of the present invention. As shown in fig. 6, the apparatus includes:
the power battery parameter obtaining module 610 is configured to obtain a maximum allowable charging power, a maximum allowable discharging power and a current value when a power battery of the target vehicle is in operation; obtaining the current residual electric quantity of a power battery of the target vehicle;
the power battery parameter calculation module 620 is configured to calculate the number of times of high-power charging and discharging of the power battery according to the maximum allowable charging power and the maximum allowable discharging power, so as to obtain a target number of times of charging and discharging; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; calculating the cycle times of the battery according to the current value to obtain target cycle times of the battery;
the current SOH correction module 630 is configured to correct the current SOH of the target vehicle by using the target charge/discharge times, the target deep discharge times, the target battery cycle times, and predetermined vehicle data, to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
Optionally, the power battery parameter calculation module 620 includes:
a power acquisition unit for acquiring a current discharge power and a current charge power of a power battery in a target vehicle;
and the target charge and discharge frequency determining unit is used for comparing the current discharge power with the maximum allowable discharge power and comparing the current charge power with the maximum allowable charge power to determine the target charge and discharge frequency.
Optionally, the target charge and discharge number determining unit is specifically configured to:
subtracting the maximum allowable discharge power from a preset first threshold value to obtain a first discharge power;
if the duration time of the current discharge power larger than the first discharge power meets the preset first time condition under the condition that the current discharge power is larger than the first discharge power, the high-power charge-discharge frequency of the power battery is increased by 1, and the target charge-discharge frequency is obtained; the method comprises the steps of,
subtracting the maximum allowable charging power from a preset second threshold value to obtain a second charging power;
and under the condition that the current charging power is larger than the second charging power, if the duration time of the current charging power larger than the second charging power meets the preset second time condition, increasing the high-power charging and discharging times of the power battery by 1 to obtain target charging and discharging times.
Optionally, the power battery parameter calculation module 620 is further configured to:
increasing the deep discharge frequency of the power battery by 2 under the condition that the current residual electric quantity is smaller than a preset third threshold value to obtain target deep discharge frequency;
when the current residual electric quantity is larger than or equal to a preset third threshold value and smaller than a preset fourth threshold value, increasing the deep discharge frequency of the power battery by 1 to obtain target deep discharge frequency;
and increasing the deep discharging frequency of the power battery by 0 under the condition that the current residual electric quantity is larger than or equal to a preset fourth threshold value, so as to obtain the target deep discharging frequency.
Optionally, the power battery parameter calculation module 620 is further configured to:
integrating the current value to obtain current throughput;
and calculating the cycle times of the battery according to the current throughput and the predetermined rated capacity of the battery to obtain the target cycle times of the battery.
Optionally, the power battery parameter obtaining module 610 is specifically configured to:
acquiring the current residual capacity, battery temperature and current SOH of a power battery of a target vehicle;
and calculating the maximum allowable charging power and the maximum allowable discharging power of the power battery in the target vehicle when the power battery works according to a preset power calculation formula and the current residual electric quantity, the battery temperature and the current SOH.
Optionally, the current SOH correction module 630 is specifically configured to:
calculating an SOH correction coefficient by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and predetermined vehicle data to obtain a target correction coefficient;
and adjusting the current SOH of the target vehicle according to the target correction coefficient to obtain a target SOH.
The device for correcting SOH based on the big data platform provided by the embodiment of the invention can execute the method for correcting SOH based on the big data platform provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example six
Fig. 7 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 7, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a method of correcting SOH based on a big data platform.
In some embodiments, the method of modifying SOH based on a big data platform may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method of correcting SOH based on a big data platform described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method of correcting SOH based on a big data platform in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The method for correcting SOH based on the big data platform is characterized by comprising the following steps:
acquiring maximum allowable charging power, maximum allowable discharging power and current value of a power battery of a target vehicle when the power battery works; obtaining the current residual electric quantity of a power battery of the target vehicle;
calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; calculating the cycle times of the battery according to the current value to obtain target cycle times of the battery;
Correcting the current SOH of the target vehicle by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and the predetermined vehicle data to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
2. The method of claim 1, wherein calculating the number of times of high power charging and discharging of the power battery based on the maximum allowable charging power and the maximum allowable discharging power to obtain the target number of times of charging and discharging comprises:
acquiring current discharging power and current charging power of a power battery in a target vehicle;
comparing the current discharge power with the maximum allowable discharge power, and comparing the current charge power with the maximum allowable charge power to determine target charge and discharge times.
3. The method of claim 2, wherein comparing the current discharge power to the maximum allowable discharge power and comparing the current charge power to the maximum allowable charge power, determining a target number of charge and discharge times, comprises:
subtracting the maximum allowable discharge power from a preset first threshold value to obtain a first discharge power;
If the duration time of the current discharge power larger than the first discharge power meets the preset first time condition under the condition that the current discharge power is larger than the first discharge power, the high-power charge-discharge frequency of the power battery is increased by 1, and the target charge-discharge frequency is obtained; the method comprises the steps of,
subtracting the maximum allowable charging power from a preset second threshold value to obtain a second charging power;
and under the condition that the current charging power is larger than the second charging power, if the duration time of the current charging power larger than the second charging power meets the preset second time condition, increasing the high-power charging and discharging times of the power battery by 1 to obtain target charging and discharging times.
4. The method of claim 1, wherein calculating the number of deep discharges of the power battery based on the current remaining power to obtain the number of deep discharges of the power battery includes:
increasing the deep discharge frequency of the power battery by 2 under the condition that the current residual electric quantity is smaller than a preset third threshold value to obtain target deep discharge frequency;
when the current residual electric quantity is larger than or equal to a preset third threshold value and smaller than a preset fourth threshold value, increasing the deep discharge frequency of the power battery by 1 to obtain target deep discharge frequency;
And increasing the deep discharging frequency of the power battery by 0 under the condition that the current residual electric quantity is larger than or equal to a preset fourth threshold value, so as to obtain the target deep discharging frequency.
5. The method of claim 1, wherein calculating the number of battery cycles based on the current value to obtain the target number of battery cycles comprises:
integrating the current value to obtain current throughput;
and calculating the cycle times of the battery according to the current throughput and the predetermined rated capacity of the battery to obtain the target cycle times of the battery.
6. The method of claim 1, wherein obtaining a maximum allowable charge power, a maximum allowable discharge power of the power battery of the target vehicle when operating, comprises:
acquiring the current residual capacity, battery temperature and current SOH of a power battery of a target vehicle;
and calculating the maximum allowable charging power and the maximum allowable discharging power of the power battery in the target vehicle when the power battery works according to a preset power calculation formula and the current residual electric quantity, the battery temperature and the current SOH.
7. The method of claim 1, wherein correcting the current SOH of the target vehicle to obtain the target SOH using the target charge-discharge times, the target deep-discharge times, the target battery cycle times, and predetermined vehicle data comprises:
Calculating an SOH correction coefficient by using the target charge and discharge times, the target deep discharge times, the target battery cycle times and predetermined vehicle data to obtain a target correction coefficient;
and adjusting the current SOH of the target vehicle according to the target correction coefficient to obtain a target SOH.
8. The device for correcting SOH based on big data platform is characterized by comprising:
the power battery parameter acquisition module is used for acquiring the maximum allowable charging power, the maximum allowable discharging power and the current value of the power battery of the target vehicle when in operation; obtaining the current residual electric quantity of a power battery of the target vehicle;
the power battery parameter calculation module is used for calculating the high-power charge and discharge times of the power battery according to the maximum allowable charge power and the maximum allowable discharge power to obtain target charge and discharge times; calculating the deep discharge times of the power battery according to the current residual electric quantity to obtain target deep discharge times; calculating the cycle times of the battery according to the current value to obtain target cycle times of the battery;
the current SOH correction module is used for correcting the current SOH of the target vehicle by utilizing the target charge and discharge times, the target deep discharge times, the target battery cycle times and the predetermined vehicle data to obtain a target SOH; wherein the vehicle data is used to characterize data generated when the target vehicle is traveling.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the big data platform based SOH correction method of any of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the method of correcting SOH based on a big data platform of any of claims 1-7.
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