CN113567864A - Method and device for determining state of charge of battery, computer equipment and storage medium - Google Patents
Method and device for determining state of charge of battery, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method and a device for determining the state of charge of a battery, computer equipment and a storage medium, wherein an optimization target is determined according to the current value of battery model parameters and the estimated value of the battery model parameters, the battery model parameters are used for indicating the working state of the battery, and the estimated value of the battery model parameters is obtained by numerical calculation according to a noise group; and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter. On the basis of calculating the SOC by the traditional Kalman filtering algorithm, an optimization target based on a particle algorithm is added, noise can be optimized by the optimization target in an off-line process, the accuracy and the calculation speed of the SOC calculation method are improved, and the influence of the noise on the calculation process is reduced.
Description
Technical Field
The invention belongs to the technical field of power batteries, and particularly relates to a method and a device for determining the state of charge of a battery, computer equipment and a storage medium.
Background
Batteries, motors and electric control are three major core technologies for electric vehicles. The battery management system BMS (battery management system) is the brain of the power battery and is responsible for monitoring, coordinating, diagnosing and maintaining each module of the battery, and the quality of the BMS directly relates to the performance, service life and safety of the power battery. The SOC of the battery is calculated as a core algorithm in the BMS, and the calculation precision directly influences the quality of the BMS performance.
The common calculation methods of the state of charge (soc) of the battery mainly include the following methods: ampere-hour integration method, open-circuit voltage method, neural network estimation method, and Kalman filtering method. The ampere-hour integration method calculates the SOC through the integration of current to time, although the algorithm is simple, once the current measurement has an error, the SOC calculation error value is larger and larger due to the existence of an integration link; the open-circuit voltage method obtains the SOC by measuring the terminal voltage of the battery and inquiring the open-circuit voltage-state of charge relation table, and the method is used on the premise that the battery has a sufficient standing process, so the method is not suitable for BMS online estimation of the SOC; the neural network estimation method is used for training a model through a large number of data samples so as to obtain an SOC estimation value, but the method is not suitable for BMS online estimation of SOC due to the fact that the required training sample amount is extremely large and the requirement on BMS computing capacity is extremely high; the Kalman filtering method has a closed-loop observer, on the premise that model parameters are accurate, the calculation precision of the method is very high, but the requirement on the precision of the parameters is high, the traditional Kalman filtering method adopts fixed parameters and is influenced by uncertain factors such as sensor noise and the like in the actual application process, and the calculation precision of the method is low on the contrary, so that the traditional Kalman filtering method is not suitable for BMS online SOC estimation.
In view of the above, it is necessary to develop an SOC calculation method with high accuracy, fast calculation speed, and less influence of noise.
Disclosure of Invention
In view of the above, it is necessary to provide an SOC calculation method, an SOC calculation apparatus, a computer device, and a storage medium with high accuracy, high calculation speed, and little influence of noise.
A method of determining a state of charge of a battery, the method comprising:
determining an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter, wherein the battery model parameter is used for indicating the working state of the battery, the estimated value of the battery model parameter is obtained by calculating according to the value of the noise group, and the battery model parameter comprises the state of charge of the battery;
and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter.
In one embodiment, the battery model parameters include battery terminal voltage; accordingly, the process of obtaining the current value of the battery terminal voltage includes:
if the power-off duration of the battery management system exceeds a first preset threshold, the battery management system is awakened, and the current open-circuit voltage of the battery is recorded as the current value of the battery terminal voltage.
In one embodiment, the process of obtaining the current value of the battery state of charge comprises:
if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage;
and determining the current state of charge of the battery corresponding to the current value of the battery terminal voltage according to a relation curve between the battery terminal voltage and the state of charge of the battery, wherein the relation curve between the battery terminal voltage and the state of charge of the battery is data carried by the battery when the battery leaves a factory.
In one embodiment, the battery model parameters include battery terminal voltage; accordingly, the optimization objective is to minimize the sum of the following two absolute values, which are: the absolute value of the difference between the current value of the battery state of charge and the estimated value of the battery state of charge and the absolute value of the difference between the current value of the battery terminal voltage and the estimated value of the battery terminal voltage.
In one embodiment, the battery model parameters include battery terminal voltage; accordingly, the process of obtaining the estimated values of the parameters of the battery model comprises the following steps:
and solving a state space equation to obtain a current estimation value of the battery model parameter, wherein the state space equation is determined by deforming a battery second-order RC model based on a Kalman filtering algorithm.
In one embodiment, after taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter, the method further includes:
and setting the numerical value of the noise group corresponding to the optimization target result meeting the preset condition as the initial value of the noise group for the next optimization calculation process.
In one embodiment, the noise group includes process noise and voltage noise; accordingly, the process of adjusting the magnitude of the noise group includes:
adjusting process noise within a first preset range based on a first preset interval, and adjusting the voltage noise within a second preset range based on a second preset interval; alternatively, the first and second electrodes may be,
and adjusting the process noise within a first preset range based on a third preset interval, and adjusting the voltage noise within a second preset range based on a fourth preset interval after the adjustment of the process noise is finished.
A battery state of charge determination apparatus, the apparatus comprising:
the first determination module is used for determining an optimization target according to the current value of the battery model parameter and the estimation value of the battery model parameter;
the second determination module is used for continuously adjusting the numerical value of the noise group until the optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter;
the battery model parameters are used for indicating the working state of the battery, the estimated values of the battery model parameters are obtained through calculation according to the numerical values of the noise group, and the battery model parameters comprise the state of charge of the battery.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
determining an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter, wherein the battery model parameter is used for indicating the working state of the battery, and the estimated value of the battery model parameter is obtained by calculating according to the value of the noise group;
and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
determining an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter, wherein the battery model parameter is used for indicating the working state of the battery, and the estimated value of the battery model parameter is obtained by calculating according to the value of the noise group;
and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
determining an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter, wherein the battery model parameter is used for indicating the working state of the battery, and the estimated value of the battery model parameter is obtained by calculating according to the value of the noise group;
and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter.
According to the method and the device for determining the state of charge of the battery, the computer equipment and the storage medium, the optimization target is determined according to the current value of the battery model parameter and the estimated value of the battery model parameter, the battery model parameter is used for indicating the working state of the battery, and the estimated value of the battery model parameter is obtained by numerical calculation according to the noise group; and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter. On the basis of calculating the SOC by the traditional Kalman filtering algorithm, an optimization target based on a particle algorithm is added, noise can be optimized by the optimization target in an off-line process, the accuracy and the calculation speed of the SOC calculation method are improved, and the influence of the noise on the calculation process is reduced.
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FIG. 1 is a schematic flow chart diagram of a method for determining battery state of charge in one embodiment;
FIG. 2 is a schematic flow chart diagram of a method for determining battery state of charge in another embodiment;
FIG. 3 is a schematic flow chart diagram of a method for determining battery state of charge in yet another embodiment;
FIG. 4 is a diagram of a battery circuit model illustrating a method for determining battery state of charge according to one embodiment;
fig. 5 is a block diagram showing a configuration of a battery state of charge determination apparatus according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various terms, but these terms are not limited by these terms unless otherwise specified. These terms are only used to distinguish one term from another. For example, the third preset threshold and the fourth preset threshold may be the same or different without departing from the scope of the present application.
Batteries, motors and electric control are three major core technologies for electric vehicles. The battery management system BMS (battery management system) is the brain of the power battery and is responsible for monitoring, coordinating, diagnosing and maintaining each module of the battery, and the quality of the BMS directly relates to the performance, service life and safety of the power battery. The SOC of the battery is calculated as a core algorithm in the BMS, and the calculation precision directly influences the quality of the BMS performance.
The common calculation methods of the state of charge (soc) of the battery mainly include the following methods: ampere-hour integration method, open-circuit voltage method, neural network estimation method, and Kalman filtering method. The ampere-hour integration method calculates the SOC through the integration of current to time, although the algorithm is simple, once the current measurement has an error, the SOC calculation error value is larger and larger due to the existence of an integration link; the open-circuit voltage method obtains the SOC by measuring the terminal voltage of the battery and inquiring the open-circuit voltage-state of charge relation table, and the method is used on the premise that the battery has a sufficient standing process, so the method is not suitable for BMS online estimation of the SOC; the neural network estimation method is used for training a model through a large number of data samples so as to obtain an SOC estimation value, but the method is not suitable for BMS online estimation of SOC due to the fact that the required training sample amount is extremely large and the requirement on BMS computing capacity is extremely high; the Kalman filtering method has a closed-loop observer, on the premise that model parameters are accurate, the calculation precision of the method is very high, but the requirement on the precision of the parameters is high, the traditional Kalman filtering method adopts fixed parameters and is influenced by uncertain factors such as sensor noise and the like in the actual application process, and the calculation precision of the method is low on the contrary, so that the traditional Kalman filtering method is not suitable for BMS online SOC estimation.
In view of the problems in the related art, an embodiment of the present invention provides a method for determining a state of charge of a battery, and with reference to fig. 1, the method is applied to a server, and an execution subject is described as an example of the server, where the method includes the following steps:
s11, determining an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter, wherein the battery model parameter is used for indicating the working state of the battery, the estimated value of the battery model parameter is obtained by calculating according to the value of the noise group, and the battery model parameter comprises the state of charge of the battery;
and S12, continuously adjusting the numerical value of the noise group until the optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter.
The state of charge (soc) of a battery, which reflects the remaining capacity of the battery, is numerically defined as the ratio of the remaining capacity to the total capacity of the battery, and is usually expressed as a percentage. The value range of the SOC of the battery in the embodiment of the invention is 0-1, the SOC is 0 to indicate that the battery is completely discharged, and the SOC is 1 to indicate that the battery is completely charged. The SOC of the battery cannot be directly measured, and the SOC can be estimated only by using battery model parameters such as battery terminal voltage, charge-discharge voltage, internal resistance, and the like.
In the embodiment of the invention, the SOC of the battery is calculated by combining off-line calculation and on-line calculation, wherein based on the size of a set noise group, the calculation of the estimated value of the battery model parameter by using a Kalman filtering algorithm is completed in the BMS on-line state, the determination of the optimization target and the result of the optimization target are completed in the BMS off-line state, and the whole off-line process does not occupy the BMS on-line calculation memory resource.
In the method provided by the embodiment of the invention, an optimization target is determined according to the current value of the battery model parameter and the estimation value of the battery model parameter, the battery model parameter is used for indicating the working state of the battery, and the estimation value of the battery model parameter is obtained by calculating according to the value of the noise group; and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter. On the basis of calculating the SOC by the traditional Kalman filtering algorithm, an optimization target based on a particle algorithm is added, noise can be optimized by the optimization target in an off-line process, the accuracy and the calculation speed of the SOC calculation method are improved, and the influence of the noise on the calculation process is reduced.
In combination with the above embodiments, in one embodiment, the battery model parameters include a battery terminal voltage; accordingly, the process of obtaining the current value of the battery terminal voltage comprises the following steps:
if the power-off duration of the battery management system exceeds a first preset threshold, the battery management system is awakened, and the current open-circuit voltage of the battery is recorded as the current value of the battery terminal voltage.
The first preset threshold may be set according to the requirements of a specific application scenario, and is mainly used to trigger determining and calculating an optimization target in the BMS offline state, for example, if the first preset threshold is set to 1 hour, when the BMS power-off duration exceeds 1 hour, the BMS system of the vehicle is actively awakened, and an initial value of the battery terminal voltage is obtained, where the initial value of the battery terminal voltage is the current open-circuit voltage of the battery circuit detected by the sensor.
According to the above, it can be proposed that the optimization target according to the embodiment of the present invention is an absolute value of a difference between a current value of the battery terminal voltage and an estimated value of the battery terminal voltage, and when the absolute value is the smallest, the result is the optimization target.
In the method provided by the embodiment of the invention, the battery model parameter comprises a battery terminal voltage; accordingly, the process of obtaining the current value of the battery terminal voltage includes: if the power-off duration of the battery management system exceeds a first preset threshold, the battery management system is awakened, and the current open-circuit voltage of the battery is recorded as the current value of the battery terminal voltage. By utilizing the time of the BMS offline state, the optimization target and the result thereof are determined, the time and the memory for online SOC calculation can be saved, the accuracy and the calculation speed of the SOC calculation method are improved, and the influence of noise on the calculation process is reduced.
In combination with the above description of the embodiment, referring to fig. 2, in an embodiment, the process of obtaining the current value of the battery state of charge includes:
s21, if the power-off duration of the battery management system exceeds a first preset threshold, waking up the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage;
and S22, determining the current state of charge of the battery corresponding to the current value of the battery terminal voltage according to the relation curve between the battery terminal voltage and the state of charge of the battery, wherein the relation curve between the battery terminal voltage and the state of charge of the battery is data carried by the battery when the battery leaves a factory.
When the battery model parameters include the battery charge state, the battery SOC cannot be directly measured, and the battery SOC can only be estimated through the battery model parameters such as the battery terminal voltage, the charge-discharge voltage, the internal resistance and the like, so that under the condition of obtaining the initial value of the battery terminal voltage, a relation curve between the battery terminal voltage and the battery charge state is inquired, and the current battery charge state corresponding to the current value of the battery terminal voltage is determined. The relation curve between the battery terminal voltage and the battery charge state is data obtained by tests when each type of battery leaves a factory, and may be a relation corresponding to a table meaning or a functional relation expression between the battery charge state and the battery terminal voltage.
According to the above, it can be proposed that the optimization target according to the embodiment of the present invention is an absolute value of a difference between a current value of the battery state of charge and an estimated value of the battery state of charge, and when the absolute value is minimum, the result is the optimization target.
In the method provided in the embodiment of the present invention, the battery model parameter includes a battery state of charge; accordingly, the process of obtaining the current value of the battery state of charge comprises: if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage; and determining the current state of charge of the battery corresponding to the current value of the battery terminal voltage according to a relation curve between the battery terminal voltage and the state of charge of the battery, wherein the relation curve between the battery terminal voltage and the state of charge of the battery is data carried by the battery when the battery leaves a factory. The parameter of the battery state of charge needing to be measured and the estimated value of the battery state of charge are directly set as optimization targets, and the result of calculation of the battery state of charge by noise can be directly reduced. The optimization target and the result thereof are determined by using the time of the BMS offline state, so that the time and the memory for calculating the SOC online can be saved, and the accuracy and the calculation speed of the SOC calculation method are improved.
In combination with the above embodiments, in one embodiment, the battery model parameters include a battery terminal voltage; accordingly, the optimization objective is to minimize the sum of the following two absolute values, which are: the absolute value of the difference between the current value of the battery state of charge and the estimated value of the battery state of charge and the absolute value of the difference between the current value of the battery terminal voltage and the estimated value of the battery terminal voltage.
The optimization objective of the particle swarm optimization algorithm mentioned in the above embodiments can be expressed by the following mathematical expression:
wherein, therein
Represents the current value of the battery terminal voltage at the k-th moment, UkAn estimated value representing the terminal voltage of the battery at the k-th time, SOCkAn estimate representing the state of charge of the battery at time k,
represents the current value of the battery state of charge at the k-th moment, UkAnd SOCkIs estimated on line by Kalman filtering algorithm.
In the method provided by the embodiment of the invention, the battery model parameter comprises the battery terminal voltage; accordingly, the optimization objective is to minimize the sum of the following two absolute values, which are: the absolute value of the difference between the current value of the battery state of charge and the estimated value of the battery state of charge and the absolute value of the difference between the current value of the battery terminal voltage and the estimated value of the battery terminal voltage. And meanwhile, the error analysis is carried out on the battery terminal voltage and the current value and the estimated value of the battery charge state, so that a noise group can be adjusted, and the accuracy of the battery charge state calculation result is further improved.
In combination with the above embodiments, in one embodiment, the battery model parameters include a battery terminal voltage; accordingly, the process of obtaining the estimated value of the battery model parameter includes:
and solving a state space equation to obtain a current estimation value of the battery model parameter, wherein the state space equation is determined by deforming a battery second-order RC model based on a Kalman filtering algorithm.
The circuit diagram of the second order RC model of the battery is shown in fig. 4, where: r1For polarizing internal resistance 1, R2To polarize internal resistance 2, C1To polarize the capacitance 1, C2Is a polarized capacitor 2, OCV is the open circuit voltage, R0The ohmic resistance of the battery pack. Accordingly, the state space equation of the second-order RC model of the battery is as follows:
in the above state space equation, SOCk+1Is the state of charge, SOC, of the battery at time k +1kIs the state of charge of the battery at time k,
polarizes the voltage on the internal resistance 1 at the time k +1,
polarizes the voltage of the internal resistance 2 at the time k +1,
to polarize the voltage at the internal resistance 1 at the time k,
is the voltage of the polarized internal resistance 2 at the k-th moment, Δ t is the time interval, τ1And τ2Is a time constant, τ1＝R1C1，τ2＝R2C2Eta is coulombic efficiency, Cap is battery capacity, ikIs the current at the k-th time, UkTerminal voltage of battery at the k-th time, wkIs the process noise at time k, vkIs the voltage noise at the k-th instant.
SOC calculated from the above state space equationkIs an estimate of the state of charge of the battery at time k, UkIs an estimate of the battery terminal voltage at time k.
In the method provided by the embodiment of the invention, the battery model parameter comprises a battery terminal voltage; accordingly, the process of obtaining the estimated value of the battery model parameter includes: and solving a state space equation to obtain a current estimation value of the battery model parameter, wherein the state space equation is determined by deforming a battery second-order RC model based on a Kalman filtering algorithm. The estimation value of the battery model parameter is calculated by utilizing the traditional Kalman filtering algorithm, the calculation error of the SOC can be gradually eliminated in an iteration mode, the accuracy and the calculation speed of the SOC calculation method are improved, and the influence of noise on the calculation process is reduced.
With reference to the content of the foregoing embodiment, in an embodiment, after taking the estimated value of the battery model parameter corresponding to the optimization target result that meets the preset condition as the final value of the current battery model parameter, the method further includes:
and setting the numerical value of the noise group corresponding to the optimization target result meeting the preset condition as the initial value of the noise group for the next optimization calculation process.
It should be noted that, the noise group is updated by using the values of the process noise and the voltage noise corresponding to the optimization target result meeting the preset condition, and the values are input to the online SOC calculation module for calculation.
In the method provided in the embodiment of the present invention, after taking the estimated value of the battery model parameter corresponding to the optimization target result that meets the preset condition as the final value of the current battery model parameter, the method further includes: and setting the numerical value of the noise group corresponding to the optimization target result meeting the preset condition as the initial value of the noise group for the next optimization calculation process. When the battery starts to work next time, the noise group obtained by the last optimization is used as the initial value of the time, so that the error is smaller, the calculation is more convenient, and the situation that an improper noise group is selected to be brought into the calculation of the SOC of the battery is avoided.
In combination with the above embodiments, in one embodiment, the noise group includes process noise and voltage noise; accordingly, the process of adjusting the magnitude of the noise group comprises:
adjusting the process noise within a first preset range based on a first preset interval, and adjusting the voltage noise within a second preset range based on a second preset interval; alternatively, the first and second electrodes may be,
and adjusting the process noise within a first preset range based on a third preset interval, and adjusting the voltage noise within a second preset range based on a fourth preset interval after the adjustment of the process noise is finished.
No matter the first preset interval, the second preset interval, the third preset interval or the fourth preset interval is used as the preset interval, the value of the preset interval may be fixed, for example, the value of the preset interval is 0.002, and may remain unchanged in the whole adjustment process, or may be variable, for example, the preset interval may be valued according to the number series {0.002, 0.001, 0.002, 0.001, … … } or may be valued according to the number series {0.001, 0.002, 0.003, … … }, and in the embodiment of the present invention, the change mode of the value of the preset interval is not specifically limited. In addition, values of the first preset interval, the second preset interval, the third preset interval and the fourth preset interval may all be the same, may all be different, and may also be locally the same, which is not specifically limited in this embodiment of the present invention.
For convenience of calculation, the first preset range and the second preset range may be the same, the specific range interval may be limited according to the actual application condition, and an example may be given again, that is, the first preset range and the second preset range are both (10)-5,10-3)。
In the method provided by the embodiment of the invention, the noise group comprises process noise and voltage noise; accordingly, the process of adjusting the magnitude of the noise group comprises: adjusting the process noise within a first preset range based on a first preset interval, and adjusting the voltage noise within a second preset range based on a second preset interval; or adjusting the process noise within a first preset range based on a third preset interval, and adjusting the voltage noise within a second preset range based on a fourth preset interval after the adjustment of the process noise is finished. By the method, the values of the noise groups in the preset range can be traversed as much as possible, the more optimized values of the noise groups are found for SOC online calculation, and the accuracy of the calculation result of the SOC online calculation is improved.
For convenience of understanding, an embodiment will be specifically described, in this case, in the present invention, the battery model parameters include a battery terminal voltage and a battery state of charge, and accordingly, there are two optimization targets, the optimization target being that an absolute value of a difference between an estimated value of the battery terminal voltage and a current value of the battery terminal voltage is minimum, and the optimization target being that an absolute value of a difference between a current value of the battery SOC and an estimated value of the battery SOC is minimum. The specific algorithm flow is shown in fig. 3.
It should be understood that although the steps of fig. 1, 2 and 3 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1, 2, and 3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least some of the other steps.
It should be noted that the technical solutions described above may be implemented as independent embodiments in actual implementation processes, or may be combined with each other and implemented as combined embodiments. In addition, when the contents of the embodiments of the present invention are described above, the different embodiments are described according to the corresponding sequence only based on the idea of convenient description, for example, the sequence of the data flow is not limited to the execution sequence between the different embodiments, nor is the execution sequence of the steps in the embodiments limited. Accordingly, in the actual implementation process, if it is necessary to implement multiple embodiments provided by the present invention, the execution sequence provided in the embodiments of the present invention is not necessarily required, but the execution sequence between different embodiments may be arranged according to requirements.
In combination with the above embodiments, in one embodiment, referring to fig. 5, there is provided a battery state of charge determination apparatus, comprising:
a first determining module 501, configured to determine an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter;
a second determining module 502, configured to continuously adjust the magnitude of the noise group until the optimization target result meeting a preset condition is determined, and use the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as a final value of the current battery model parameter;
the battery model parameters are used for indicating the working state of the battery, the estimated values of the battery model parameters are obtained through calculation according to the numerical values of the noise group, and the battery model parameters comprise the state of charge of the battery.
In one embodiment, the battery model parameters include a battery terminal voltage, and the first determining module 501 includes: if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage.
In one embodiment, the first determining module 501 includes: if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage; and determining the current state of charge of the battery corresponding to the current value of the battery terminal voltage according to the relation curve between the battery terminal voltage and the state of charge of the battery, wherein the relation curve between the battery terminal voltage and the state of charge of the battery is data carried when the battery leaves a factory.
In one embodiment, the battery model parameters include a battery terminal voltage, and the first determining module 501 includes: determining the optimization target as the minimum sum of the following two absolute values: the absolute value of the difference between the current value of the battery state of charge and the estimated value of the battery state of charge and the absolute value of the difference between the current value of the battery terminal voltage and the estimated value of the battery terminal voltage.
In one embodiment, the battery model parameters include a battery terminal voltage, and the second determining module 502 includes: and solving a state space equation to obtain the current estimation value of the battery model parameter, wherein the state space equation is determined by deforming a battery second-order RC model based on a Kalman filtering algorithm.
In one embodiment, the second determining module 502 includes: after the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition is used as the final value of the current battery model parameter, the method further comprises the following steps:
and setting the numerical value of the noise group corresponding to the optimization target result meeting the preset condition as the initial value of the noise group for the next optimization calculation process.
In one embodiment, the first determining module 501 includes: adjusting the process noise within a first preset range based on a first preset interval, and adjusting the voltage noise within a second preset range based on a second preset interval; alternatively, the first and second electrodes may be,
and adjusting the process noise within the first preset range based on a third preset interval, and adjusting the voltage noise within the second preset range based on a fourth preset interval after the adjustment of the process noise is finished.
In the device provided by the embodiment of the invention, an optimization target is determined according to the current value of the battery model parameter and the estimation value of the battery model parameter, the battery model parameter is used for indicating the working state of the battery, and the estimation value of the battery model parameter is obtained by calculating according to the value of the noise group; and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter. On the basis of calculating the SOC by the traditional Kalman filtering algorithm, an optimization target based on a particle algorithm is added, noise can be optimized by the optimization target in an off-line process, the accuracy and the calculation speed of the SOC calculation method are improved, and the influence of the noise on the calculation process is reduced.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the preset threshold value. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of recognizing unwanted speech.
Those skilled in the art will appreciate that the configuration shown in fig. 6 is a block diagram of only a portion of the configuration associated with the present application and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
determining an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter, wherein the battery model parameter is used for indicating the working state of the battery, the estimated value of the battery model parameter is obtained by calculating according to the value of the noise group, and the battery model parameter comprises the state of charge of the battery;
and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the battery model parameters include battery terminal voltage; accordingly, the process of obtaining the current value of the battery terminal voltage comprises the following steps:
if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the process of obtaining the current value of the battery state of charge comprises:
if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage;
and determining the current state of charge of the battery corresponding to the current value of the battery terminal voltage according to a relation curve between the battery terminal voltage and the state of charge of the battery, wherein the relation curve between the battery terminal voltage and the state of charge of the battery is data carried by the battery when the battery leaves a factory.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the battery model parameters include battery terminal voltage; accordingly, the optimization objective is to minimize the sum of the following two absolute values, which are: the absolute value of the difference between the current value of the battery state of charge and the estimated value of the battery state of charge and the absolute value of the difference between the current value of the battery terminal voltage and the estimated value of the battery terminal voltage.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the battery model parameters include battery terminal voltage; accordingly, the process of obtaining the estimated value of the battery model parameter includes:
and solving a state space equation to obtain a current estimation value of the battery model parameter, wherein the state space equation is determined by deforming a battery second-order RC model based on a Kalman filtering algorithm.
In one embodiment, the processor, when executing the computer program, further performs the steps of: after the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition is used as the final value of the current battery model parameter, the method further comprises the following steps:
and setting the numerical value of the noise group corresponding to the optimization target result meeting the preset condition as the initial value of the noise group for the next optimization calculation process.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the noise group includes process noise and voltage noise; accordingly, the process of adjusting the magnitude of the noise group comprises:
adjusting the process noise within a first preset range based on a first preset interval, and adjusting the voltage noise within a second preset range based on a second preset interval; alternatively, the first and second electrodes may be,
and adjusting the process noise within a first preset range based on a third preset interval, and adjusting the voltage noise within a second preset range based on a fourth preset interval after the adjustment of the process noise is finished.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
determining an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter, wherein the battery model parameter is used for indicating the working state of the battery, the estimated value of the battery model parameter is obtained by calculating according to the value of the noise group, and the battery model parameter comprises the state of charge of the battery;
and continuously adjusting the numerical value of the noise group until an optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter.
In one embodiment, the computer program when executed by the processor further performs the steps of: the battery model parameters include battery terminal voltage; accordingly, the process of obtaining the current value of the battery terminal voltage comprises the following steps:
if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage.
In one embodiment, the computer program when executed by the processor further performs the steps of: the process of obtaining the current value of the battery state of charge comprises:
if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage;
and determining the current state of charge of the battery corresponding to the current value of the battery terminal voltage according to a relation curve between the battery terminal voltage and the state of charge of the battery, wherein the relation curve between the battery terminal voltage and the state of charge of the battery is data carried by the battery when the battery leaves a factory.
In one embodiment, the computer program when executed by the processor further performs the steps of: the battery model parameters include battery terminal voltage; accordingly, the optimization objective is to minimize the sum of the following two absolute values, which are: the absolute value of the difference between the current value of the battery state of charge and the estimated value of the battery state of charge and the absolute value of the difference between the current value of the battery terminal voltage and the estimated value of the battery terminal voltage.
In one embodiment, the computer program when executed by the processor further performs the steps of: the battery model parameters include battery terminal voltage; accordingly, the process of obtaining the estimated value of the battery model parameter includes:
and solving a state space equation to obtain a current estimation value of the battery model parameter, wherein the state space equation is determined by deforming a battery second-order RC model based on a Kalman filtering algorithm.
In one embodiment, the computer program when executed by the processor further performs the steps of: after the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition is used as the final value of the current battery model parameter, the method further comprises the following steps:
and setting the numerical value of the noise group corresponding to the optimization target result meeting the preset condition as the initial value of the noise group for the next optimization calculation process.
In one embodiment, the computer program when executed by the processor further performs the steps of: the noise group includes process noise and voltage noise; accordingly, the process of adjusting the magnitude of the noise group comprises:
adjusting the process noise within a first preset range based on a first preset interval, and adjusting the voltage noise within a second preset range based on a second preset interval; alternatively, the first and second electrodes may be,
and adjusting the process noise within a first preset range based on a third preset interval, and adjusting the voltage noise within a second preset range based on a fourth preset interval after the adjustment of the process noise is finished.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of determining a state of charge of a battery, the method comprising:
determining an optimization target according to the current value of the battery model parameter and the estimated value of the battery model parameter, wherein the battery model parameter is used for indicating the working state of the battery, the estimated value of the battery model parameter is obtained by numerical calculation according to the noise group, and the battery model parameter comprises the battery charge state;
and continuously adjusting the numerical value of the noise group until an optimization target result meeting a preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter.
2. The method of claim 1, wherein the battery model parameters include battery terminal voltage; accordingly, the process of obtaining the current value of the battery terminal voltage comprises the following steps:
if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage.
3. The method of claim 1, wherein the obtaining of the current value of the battery state of charge comprises:
if the power-off duration of the battery management system exceeds a first preset threshold, awakening the battery management system, and recording the current open-circuit voltage of the battery as the current value of the battery terminal voltage;
and determining the current state of charge of the battery corresponding to the current value of the battery terminal voltage according to the relation curve between the battery terminal voltage and the state of charge of the battery, wherein the relation curve between the battery terminal voltage and the state of charge of the battery is data carried when the battery leaves a factory.
4. The method of claim 1, wherein the battery model parameters include battery terminal voltage; accordingly, the optimization objective is to minimize the sum of the following two absolute values, which are: the absolute value of the difference between the current value of the battery state of charge and the estimated value of the battery state of charge and the absolute value of the difference between the current value of the battery terminal voltage and the estimated value of the battery terminal voltage.
5. The method of claim 1, wherein the battery model parameters include battery terminal voltage; accordingly, the process of obtaining the estimated value of the battery model parameter includes:
and solving a state space equation to obtain the current estimation value of the battery model parameter, wherein the state space equation is determined by deforming a battery second-order RC model based on a Kalman filtering algorithm.
6. The method according to claim 1, wherein the step of taking the estimated value of the battery model parameter corresponding to the optimization goal result satisfying the preset condition as the final value of the current battery model parameter further comprises:
and setting the numerical value of the noise group corresponding to the optimization target result meeting the preset condition as the initial value of the noise group for the next optimization calculation process.
7. The method of claim 6, wherein the set of noises includes process noise and voltage noise; accordingly, the process of adjusting the magnitude of the noise group comprises:
adjusting the process noise within a first preset range based on a first preset interval, and adjusting the voltage noise within a second preset range based on a second preset interval; alternatively, the first and second electrodes may be,
and adjusting the process noise within the first preset range based on a third preset interval, and adjusting the voltage noise within the second preset range based on a fourth preset interval after the adjustment of the process noise is finished.
8. An apparatus for determining a state of charge of a battery, the apparatus comprising:
the first determination module is used for determining an optimization target according to the current value of the battery model parameter and the estimation value of the battery model parameter;
the second determination module is used for continuously adjusting the numerical value of the noise group until the optimization target result meeting the preset condition is determined, and taking the estimated value of the battery model parameter corresponding to the optimization target result meeting the preset condition as the final value of the current battery model parameter;
the battery model parameters are used for indicating the working state of the battery, and the estimated values of the battery model parameters are obtained through calculation according to the numerical values of the noise group.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115587654A (en) * | 2022-10-17 | 2023-01-10 | 国网河北省电力有限公司邯郸供电分公司 | Method and system for optimizing discharge of working vehicle, electronic device and readable storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120050053A1 (en) * | 2008-01-31 | 2012-03-01 | Basler Electric Company | System and Method for Detecting Generator Incipient Failures |
| CN102998629A (en) * | 2012-12-16 | 2013-03-27 | 天津大学 | Method for estimating lithium battery charge state |
| CN104617623A (en) * | 2015-01-30 | 2015-05-13 | 武汉理工大学 | Balance control method for power battery pack of electric vehicle |
| CN109444758A (en) * | 2018-12-03 | 2019-03-08 | 湖南金杯新能源发展有限公司 | Battery charge state estimation method, device, storage medium and computer equipment |
| CN111007400A (en) * | 2019-11-22 | 2020-04-14 | 西安工程大学 | Lithium battery SOC estimation method based on self-adaptive double-extended Kalman filtering method |
| CN111426968A (en) * | 2020-05-28 | 2020-07-17 | 山东交通学院 | Method and system for estimating SOP of power battery of electric vehicle |
| CN111693877A (en) * | 2020-05-15 | 2020-09-22 | 江苏大学 | SOC (System on chip) online estimation method and system of lithium ion battery |
-
2021
- 2021-06-25 CN CN202110708736.4A patent/CN113567864A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120050053A1 (en) * | 2008-01-31 | 2012-03-01 | Basler Electric Company | System and Method for Detecting Generator Incipient Failures |
| CN102998629A (en) * | 2012-12-16 | 2013-03-27 | 天津大学 | Method for estimating lithium battery charge state |
| CN104617623A (en) * | 2015-01-30 | 2015-05-13 | 武汉理工大学 | Balance control method for power battery pack of electric vehicle |
| CN109444758A (en) * | 2018-12-03 | 2019-03-08 | 湖南金杯新能源发展有限公司 | Battery charge state estimation method, device, storage medium and computer equipment |
| CN111007400A (en) * | 2019-11-22 | 2020-04-14 | 西安工程大学 | Lithium battery SOC estimation method based on self-adaptive double-extended Kalman filtering method |
| CN111693877A (en) * | 2020-05-15 | 2020-09-22 | 江苏大学 | SOC (System on chip) online estimation method and system of lithium ion battery |
| CN111426968A (en) * | 2020-05-28 | 2020-07-17 | 山东交通学院 | Method and system for estimating SOP of power battery of electric vehicle |
Non-Patent Citations (1)
| Title |
|---|
| 黄兵锋;杨正才;傅佳宏;: "基于六参数RC等效电路模型的锂离子电池SOC估计", 重庆交通大学学报(自然科学版), no. 05 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115587654A (en) * | 2022-10-17 | 2023-01-10 | 国网河北省电力有限公司邯郸供电分公司 | Method and system for optimizing discharge of working vehicle, electronic device and readable storage medium |
| CN115587654B (en) * | 2022-10-17 | 2024-03-01 | 国网河北省电力有限公司邯郸供电分公司 | Operation vehicle discharge optimization method and system, electronic equipment and readable storage medium |
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