CN109986997B - Power battery SOC prediction device, automobile and method - Google Patents
Power battery SOC prediction device, automobile and method Download PDFInfo
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Abstract
The invention relates to the field of the health state of a power battery, and discloses a power battery SOC prediction device based on fuzzy multi-model Kalman filtering. The invention improves the accuracy of describing the charging and discharging characteristics of the power battery.
Description
Technical Field
The invention relates to the field of the health state of a power battery, in particular to a power battery SOC prediction device, an automobile and a method.
Background
The accurate measurement of the state of charge of the power battery has important significance on energy management and battery protection of the power battery. However, as the service life of the battery increases, the internal resistance of the battery increases, and the capacity of the battery decreases. And a complex nonlinear relation exists between the state of charge of the battery and the ambient temperature and between the state of charge of the battery and the charging and discharging current, so that the charging and discharging characteristics of the power battery are difficult to describe by establishing an accurate mathematical model. And due to the precision problem of the mathematical model, the application of the traditional Kalman filtering in engineering is limited.
Disclosure of Invention
The invention aims to provide a power battery SOC prediction device, an automobile and a method, and the power battery SOC prediction device solves the problem that an accurate mathematical model cannot be established in the prior art to describe the charge and discharge characteristics of a power battery, and improves the precision of describing the charge and discharge characteristics of the power battery.
In order to achieve the above object, the present invention provides a power battery SOC prediction device based on fuzzy multi-model kalman filtering, which includes a microcontroller connected to the power battery and recording the charging and discharging time of the power battery, a temperature sensor measuring the temperature of the power battery, a current sensor measuring the charging current of the power battery, and a voltage sensor measuring the discharging voltage of the charging battery, wherein the microcontroller is connected to the temperature sensor, the current sensor, and the voltage sensor, respectively, and the microcontroller obtains the state of charge value of the power battery through a weighted summation algorithm after establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge.
Preferably, the power battery SOC prediction device includes: the controller can establish a plurality of mathematical models according to the measured charging current and discharging voltage, and predict the state of charge of the power battery aiming at the mathematical models through an extended Kalman filtering method; the microcontroller can obtain the weight of the state of charge of each mathematical model prediction power battery after fuzzy reasoning according to the charging and discharging time and the temperature of the power battery; and predicting the state of charge of the power battery through a plurality of mathematical models and obtaining the state of charge value of the power battery through a weighted summation algorithm of the state of charge and the weight of the power battery.
Preferably, the microcontroller is further connected with a charged erasable programmable read only memory, and the total discharge time of the power battery is recorded in the charged erasable programmable read only memory.
Preferably, the power battery SOC prediction device further comprises a power supply connected to the microcontroller, the temperature sensor, the current sensor and the voltage sensor to provide an operating voltage.
Preferably, the power battery SOC prediction device further comprises a CAN controller, the input end of the CAN controller is connected to the microcontroller, and the output end of the CAN controller is connected to the vehicle-mounted ECU.
The invention also provides an automobile which comprises the power battery SOC prediction device based on the fuzzy multi-model Kalman filtering.
The invention also provides a power battery SOC prediction method based on the fuzzy multi-model Kalman filtering, which comprises the steps of obtaining the SOC value of the power battery through a weighted summation algorithm after establishing a plurality of mathematical models for predicting the SOC of the power battery and obtaining the weight of each SOC.
Preferably, after establishing a plurality of mathematical models for predicting the states of charge of the power battery and obtaining the weight of each state of charge, the method comprises the following steps:
establishing a plurality of mathematical models according to the measured charging current and discharging voltage of the power battery, and predicting the state of charge of the power battery aiming at the mathematical models through an extended Kalman filtering method;
and obtaining the weight of the state of charge of each mathematical model prediction power battery according to the charging and discharging time and the temperature of the power battery after fuzzy reasoning.
Preferably, the state of charge value of the power battery is obtained through a weighted summation algorithm, which comprises the following steps:
and predicting the state of charge of the power battery through a plurality of mathematical models and obtaining the state of charge value of the power battery through a weighted summation algorithm of the state of charge and the weight of the power battery.
Through the technical scheme, the states of the batteries are described by respectively establishing mathematical models aiming at the batteries at different stages and under different environments. And estimating the state of charge of the battery by a Kalman filtering method aiming at each mathematical model. And then determining the weight of each model prediction result through fuzzy reasoning, and performing weighted summation on each prediction result to improve the final prediction precision of the state of charge of the power battery.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of the structure of a power battery SOC prediction device based on fuzzy multi-model Kalman filtering according to a preferred embodiment of the invention;
fig. 2 is a flow chart of a power battery SOC prediction method based on fuzzy multi-model kalman filtering according to a preferred embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The invention provides a power battery SOC prediction device based on fuzzy multi-model Kalman filtering, as shown in figure 1, the power battery SOC prediction device comprises a microcontroller, a temperature sensor, a current sensor and a voltage sensor, wherein the microcontroller is connected with the following components of a power battery and used for recording the charging and discharging time of the power battery, the temperature sensor is used for measuring the temperature of the power battery, the current sensor is used for measuring the charging current of the power battery, the voltage sensor is used for measuring the discharging voltage of the charging battery, the microcontroller is respectively connected with the temperature sensor, the current sensor and the voltage sensor, and the microcontroller obtains the state of charge value of the power battery through a weighted summation algorithm after establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge.
Through the technical scheme, the states of the batteries are described by respectively establishing mathematical models aiming at the batteries at different stages and under different environments. And estimating the state of charge of the battery by a Kalman filtering method aiming at each mathematical model. And then determining the weight of each model prediction result through fuzzy reasoning, and performing weighted summation on each prediction result to improve the final prediction precision of the state of charge of the power battery. The microprocessor may use stm32c8t 6.
The temperature sensor, the current sensor and the voltage sensor are respectively connected to the power battery so as to respectively feed back the measured power battery value to the microcontroller, so that the microcontroller can conveniently execute the next operation, and finally, a more accurate state of charge value can be obtained.
In one embodiment of the present invention, the power battery SOC prediction device may include: the controller can establish a plurality of mathematical models according to the measured charging current and discharging voltage, and predict the state of charge of the power battery aiming at the mathematical models through an extended Kalman filtering method; the microcontroller can obtain the weight of the state of charge of each mathematical model prediction power battery after fuzzy reasoning according to the charging and discharging time and the temperature of the power battery; and predicting the state of charge of the power battery through a plurality of mathematical models and obtaining the state of charge value of the power battery through a weighted summation algorithm of the state of charge and the weight of the power battery.
Through the implementation mode, a large number of operation modules need to be integrated in the microcontroller, the integrated operation modules can realize functions of model building, weighting operation and the like, and finally, the SOC prediction of the power battery can be realized.
In one embodiment of the present invention, in order to store the total discharge time, the microcontroller is further connected to a charged erasable programmable read only memory, and the total discharge time of the power battery is recorded in the charged erasable programmable read only memory.
Among them, the most preferable memory is the electrically erasable programmable read-only memory, which aims to store the total discharge time continuously in case of power failure.
In an embodiment of the present invention, the power battery SOC prediction apparatus may further include a power supply connected to the microcontroller, the temperature sensor, the current sensor, and the voltage sensor to provide an operating voltage.
Through the embodiment, the power supply of the microcontroller, the temperature sensor, the current sensor and the voltage sensor can be realized, the supply of electric quantity can be realized, and the supply of working voltage can be realized.
In an embodiment of the invention, the power battery SOC prediction device may further include a CAN controller having an input end connected to the microcontroller, and an output end of the CAN controller is connected to the vehicle-mounted ECU.
Through the embodiment, the invention can be used on an automobile, so that the vehicle-mounted ECU can read the electric quantity of the battery, and the visual effect of detection is ensured.
In a most preferred embodiment of the invention, for batteries with different service lives, a current sensor in the device collects the current of a power battery as input quantity, a voltage sensor collects the terminal voltage of the battery as output quantity, and n mathematical models are respectively established in a microprocessor to describe the charging and discharging characteristics of the battery.
Xi(k)＝fi(X(k-1))+Wi(k)；
Yi(k)＝Hi(k)X(k)+Vi(k)；
Wherein (i ═ 1,2, … n).
Then, the state of charge of the battery is predicted by aiming at n battery models through an extended Kalman filtering method respectively to obtain n calculation results Xi(k|k)。
The temperature T is collected by a temperature sensor in the device. The device records the total discharge time t of the battery through the microprocessor and records the total discharge time t in the EEPROM, so that the power failure information of the device is not lost. The power supply in the device supplies power to the microprocessor and the sensor.
Then carrying out fuzzy reasoning according to the total discharge time T and the ambient temperature T of the current battery to obtain the weight alpha of the prediction result of each modeli. The end result is
After the measurement is finished, the device sends the final measurement result to a network through the CAN controller for the vehicle-mounted ECU to use.
The invention also provides an automobile which comprises the power battery SOC prediction device based on the fuzzy multi-model Kalman filtering.
The automobile can be a new energy automobile or a hybrid automobile as long as the electric energy supply of the power battery can be realized.
In addition to the above, the invention also provides a power battery SOC prediction method based on the fuzzy multi-model kalman filtering, and the power battery SOC prediction method may include obtaining a state of charge value of the power battery through a weighted summation algorithm after establishing a plurality of mathematical models for predicting states of charge of the power battery and obtaining a weight of each state of charge.
Through the embodiment, the state of charge can be predicted, the state of charge of the power battery can be finally obtained, and the method can improve the final prediction precision of the state of charge of the power battery.
In this embodiment, as described above, after establishing a plurality of mathematical models for predicting the states of charge of the power battery and obtaining the weight of each state of charge, the method includes:
establishing a plurality of mathematical models according to the measured charging current and discharging voltage of the power battery, and predicting the state of charge of the power battery aiming at the mathematical models through an extended Kalman filtering method;
and obtaining the weight of the state of charge of each mathematical model prediction power battery according to the charging and discharging time and the temperature of the power battery after fuzzy reasoning.
In this kind of embodiment, the state of charge value of power battery is obtained through the weighted sum algorithm, including:
and predicting the state of charge of the power battery through a plurality of mathematical models and obtaining the state of charge value of the power battery through a weighted summation algorithm of the state of charge and the weight of the power battery.
By the method, a weighted summation mode can be realized, the state of charge value is obtained, and the subsequent use can be ensured finally.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (7)
1. The SOC prediction device of the power battery based on the fuzzy multi-model Kalman filtering is characterized by comprising a microcontroller, a temperature sensor, a current sensor and a voltage sensor, wherein the microcontroller is connected with the following components of the power battery and used for recording the charging and discharging time of the power battery, the temperature sensor is used for measuring the temperature of the power battery, the current sensor is used for measuring the charging current of the power battery, and the voltage sensor is used for measuring the discharging voltage of the charging battery;
the power battery SOC prediction device comprises: the controller can establish a plurality of mathematical models according to the measured charging current and discharging voltage, and predict the state of charge of the power battery aiming at the mathematical models through an extended Kalman filtering method; the microcontroller can obtain the weight of the state of charge of each mathematical model prediction power battery after fuzzy reasoning according to the charging and discharging time and the temperature of the power battery; and predicting the state of charge of the power battery through a plurality of mathematical models and obtaining the state of charge value of the power battery through a weighted summation algorithm of the state of charge and the weight of the power battery.
2. The fuzzy multi-model kalman filter-based power battery SOC prediction apparatus according to claim 1, wherein the microcontroller is further connected with a charged erasable programmable read only memory, and the total discharge time of the power battery is recorded in the charged erasable programmable read only memory.
3. The fuzzy multi-model kalman filter-based power battery SOC prediction apparatus according to claim 1, further comprising a power supply connected to the microcontroller, the temperature sensor, the current sensor and the voltage sensor to provide an operating voltage.
4. The fuzzy multi-model Kalman filtering based power battery SOC prediction device of claim 1, characterized in that, the power battery SOC prediction device further comprises a CAN controller with an input end connected to the microcontroller, and an output end of the CAN controller is connected to a vehicle ECU.
5. An automobile, characterized in that the automobile comprises a power battery SOC prediction device based on the fuzzy multi-model Kalman filtering according to any one of claims 1 to 4.
6. A power battery SOC prediction method based on fuzzy multi-model Kalman filtering is characterized in that the power battery SOC prediction method comprises the steps of obtaining a state of charge value of a power battery through a weighted summation algorithm after establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge;
after establishing a plurality of mathematical models for predicting the state of charge of the power battery and obtaining the weight of each state of charge, the method comprises the following steps:
establishing a plurality of mathematical models according to the measured charging current and discharging voltage of the power battery, and predicting the state of charge of the power battery aiming at the mathematical models through an extended Kalman filtering method;
and obtaining the weight of the state of charge of each mathematical model prediction power battery according to the charging and discharging time and the temperature of the power battery after fuzzy reasoning.
7. The method for predicting the SOC of the power battery based on the fuzzy multi-model Kalman filtering is characterized in that the obtaining of the SOC value of the power battery through a weighted summation algorithm comprises the following steps:
and predicting the state of charge of the power battery through a plurality of mathematical models and obtaining the state of charge value of the power battery through a weighted summation algorithm of the state of charge and the weight of the power battery.
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| CN113071507B (en) * | 2021-03-22 | 2022-03-01 | 江铃汽车股份有限公司 | Electric automobile energy management control method based on fuzzy control |
| CN113071373B (en) * | 2021-04-25 | 2022-07-29 | 北京新能源汽车股份有限公司 | Temperature prediction and device based on cloud intelligent interconnected big data |
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