CN115421042A - Vehicle SOC estimation method, device, equipment and storage medium - Google Patents
Vehicle SOC estimation method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN115421042A CN115421042A CN202211019593.7A CN202211019593A CN115421042A CN 115421042 A CN115421042 A CN 115421042A CN 202211019593 A CN202211019593 A CN 202211019593A CN 115421042 A CN115421042 A CN 115421042A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- temperature
- parameters
- soc
- soc estimation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Power Engineering (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Secondary Cells (AREA)
Abstract
The invention belongs to the technical field of automobile driving, and discloses a method, a device, equipment and a storage medium for estimating vehicle SOC; the method comprises the following steps: acquiring the current vehicle temperature; matching in a circuit parameter database according to the current vehicle temperature to obtain preset circuit parameters, wherein the circuit parameter database is established by a temperature compensation battery model according to the circuit parameters obtained at different temperatures; estimating according to preset parameters to obtain a current vehicle SOC estimated value; according to the invention, the preset circuit parameters corresponding to the temperature in the circuit parameter database are obtained through the current vehicle temperature, the vehicle SOC value is estimated according to the preset circuit parameters, the problem that the estimation of the vehicle SOC is not accurate enough due to the influence of the ambient temperature on the battery is fully considered, the circuit parameters with different temperatures are established according to the temperature compensation battery model, and the SOC estimation is directly performed according to the circuit parameters, so that the problem that the estimation of the vehicle SOC is not accurate due to the influence of the ambient temperature in the driving process is effectively solved, and the estimation of the vehicle SOC is rapidly and accurately realized.
Description
Technical Field
The invention relates to the technical field of automobile driving, in particular to a method, a device, equipment and a storage medium for estimating vehicle SOC.
Background
Accurate estimation of the State of Charge (SOC) of a Battery can improve the utilization rate of the Battery and reduce the anxiety of the mileage of a driver, which is a precondition for energy management and fault diagnosis of a Battery Management System (BMS). The SOC of the battery cannot be directly calculated due to the fact that the battery is susceptible to factors such as time-varying ambient temperature in the vehicle-mounted use process, and therefore how to achieve SOC estimation of the power battery under complex environmental conditions is always a difficult point of the BMS.
The SOC prediction method commonly used at the present stage comprises the following steps: data-driven SOC estimation, direct-measure SOC estimation, and model-based SOC estimation. The direct measurement method is simple and easy to implement, but the calculation accuracy is influenced by the accumulated error. The SOC estimation method based on data driving utilizes a large number of battery test data samples to learn and predict by establishing a battery black box model, and has higher SOC estimation precision. However, the method depends on a large amount of training data, and has the defects of high complexity, difficulty in online application and the like.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a vehicle SOC estimation method, and aims to solve the technical problem that in the prior art, the estimation of the vehicle SOC is not accurate due to the influence of the environmental temperature in the driving process.
To achieve the above object, the present invention provides a vehicle SOC estimation method, comprising the steps of:
acquiring the current vehicle temperature;
matching in a circuit parameter database according to the current vehicle temperature to obtain preset circuit parameters, wherein the circuit parameter database is established by a temperature compensation battery model according to circuit parameters obtained at different temperatures;
and estimating according to the preset parameters to obtain the SOC estimated value of the current vehicle.
Optionally, the matching in a circuit parameter database according to the current vehicle temperature to obtain preset circuit parameters, before the circuit parameter database is established by a temperature compensation battery model according to circuit parameters obtained at different temperatures, the method further includes:
acquiring experiment temperature, experiment battery terminal voltage and experiment working current;
establishing a temperature compensation battery model based on the experiment temperature, the experiment battery terminal voltage and the experiment working current;
calculating to obtain preset circuit parameters according to an artificial bee colony algorithm based on a temperature compensation battery model;
and establishing a circuit parameter database according to the experiment temperature, the experiment battery terminal voltage, the working current and the experiment parameters.
Optionally, the obtaining of the preset circuit parameter by calculating according to an artificial bee colony algorithm includes:
setting initialization parameters of an artificial bee colony algorithm;
searching and generating a leading bee group according to the initialization parameters;
generating a follower bee population in the lead bee population based on the follower bee generation probability;
searching a honey source by the leading bee group and the following bee group;
judging whether the honey source meets a termination condition, if so, obtaining experiment parameters;
and calculating to obtain preset circuit parameters according to the experiment parameters.
Optionally, before the leading bee colony generates the following bee colony, the method further includes:
acquiring an experimental real terminal voltage and a model terminal voltage;
according to the mean square error of the experimental real terminal voltage and the model terminal voltage;
calculating a fitness function according to the mean square error;
and calculating the generation probability of the follower bee according to the fitness function.
Optionally, before obtaining the current vehicle SOC estimation value according to the preset parameter estimation, the method includes:
acquiring the measurement noise and the process noise;
predicting the SOC of the vehicle according to preset parameters, the measurement noise and the process noise to obtain a predicted state value;
acquiring a reference value of the SOC of the vehicle;
and correcting the vehicle SOC according to the predicted state value and the reference value to obtain a vehicle SOC estimated value.
Optionally, the measurement noise and the process noise comprise:
acquiring a noise correction matrix;
and correcting the process noise and the measurement noise according to the noise correction matrix to obtain new measurement noise and new process noise.
Optionally, the obtaining a noise correction matrix includes:
acquiring an experimental real voltage and an analog circuit voltage;
calculating the difference value of the experimental real voltage and the analog circuit voltage;
and establishing a noise correction matrix according to the difference.
Further, to achieve the above object, the present invention also proposes a vehicle SOC estimation device including:
the temperature acquisition module is used for acquiring the current vehicle temperature;
the parameter acquisition module is used for matching in a circuit parameter database according to the current vehicle temperature to obtain preset circuit parameters, and the circuit parameter database is established by a temperature compensation battery model according to the circuit parameters obtained at different temperatures;
and the vehicle SOC estimation module is used for estimating and obtaining the current vehicle SOC estimation value according to the preset parameters.
Further, to achieve the above object, the present invention also proposes a vehicle SOC estimation device including: a memory, a processor and a vehicle SOC estimation program stored on the memory and operable on the processor, the vehicle SOC estimation program configured to implement the steps of the vehicle SOC estimation method as described above.
Furthermore, to achieve the above object, the present invention also proposes a storage medium having stored thereon a vehicle SOC estimation program that, when executed by a processor, implements the steps of the vehicle SOC estimation method as described above.
According to the invention, the preset circuit parameters corresponding to the temperature in the circuit parameter database are obtained through the current vehicle temperature, the vehicle SOC value is estimated according to the preset circuit parameters, the problem that the estimation of the vehicle SOC is not accurate enough due to the influence of the ambient temperature on the battery is fully considered, the circuit parameters with different temperatures are established according to the temperature compensation battery model, and the SOC estimation is directly carried out according to the circuit parameters, so that the problem that the estimation of the vehicle SOC is inaccurate due to the influence of the ambient temperature in the driving process is effectively solved, and the estimation of the vehicle SOC is fast and accurately realized.
Drawings
FIG. 1 is a schematic structural diagram of a vehicle SOC estimation apparatus according to a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a vehicle SOC estimation method according to the present invention;
FIG. 3 is a circuit diagram of a temperature compensated battery model according to an embodiment of the vehicle SOC estimation method of the present invention;
FIG. 4 is a schematic diagram of experimental results of a vehicle SOC estimation method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an experimental result of an embodiment of a vehicle SOC estimation method according to the present invention;
FIG. 6 is a schematic diagram of an experimental result of an embodiment of a vehicle SOC estimation method according to the present invention;
FIG. 7 is a schematic diagram of experimental results of a vehicle SOC estimation method according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of experimental results of an embodiment of a vehicle SOC estimation method according to the present invention;
FIG. 9 is a schematic diagram of experimental results of an embodiment of a vehicle SOC estimation method according to the present invention;
FIG. 10 is a flowchart illustrating a second embodiment of a vehicle SOC estimation method according to the present invention;
FIG. 11 is a schematic diagram of experimental parameters of a vehicle SOC estimation method according to an embodiment of the present invention;
FIG. 12 is a flowchart illustrating a vehicle SOC estimation method according to a third embodiment of the present invention;
FIG. 13 is a schematic diagram illustrating experimental results of a method for estimating a vehicle SOC according to an embodiment of the present invention;
FIG. 14 is a schematic diagram illustrating experimental results of an embodiment of a vehicle SOC estimation method according to the present invention;
fig. 15 is a block diagram showing the configuration of the first embodiment of the vehicle SOC estimating apparatus of the invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a vehicle SOC estimation device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the vehicle SOC estimation device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the vehicle SOC estimation device, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a vehicle SOC estimation program.
In the vehicle SOC estimation device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the vehicle SOC estimation device of the present invention may be provided in the vehicle SOC estimation device that calls the vehicle SOC estimation program stored in the memory 1005 through the processor 1001 and executes the vehicle SOC estimation method provided by the embodiment of the present invention.
An embodiment of the present invention provides a vehicle SOC estimation method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of the vehicle SOC estimation method according to the present invention.
In the present embodiment, the vehicle SOC estimation method includes the steps of:
step S10: the current vehicle temperature is obtained.
It is understood that the current vehicle temperature refers to the ambient temperature of the vehicle when the vehicle SOC budget is needed.
It should be understood that the current vehicle temperature is measured by a thermometer or the like, and may be directly obtained when it is desired to obtain the current vehicle temperature.
It should be noted that the present invention refers to a range of the current vehicle temperature from-20 ℃ to 60 ℃, in which the vehicle SOC is estimated. The current vehicle temperature may also be-20 deg.C, 0 deg.C, 20 deg.C, 40 deg.C and 60 deg.C.
It should be emphasized that the current vehicle temperature may be an internal vehicle ambient temperature, an external ambient temperature, or a battery ambient temperature, and the present invention is described with the battery temperature as the current vehicle temperature.
Step S20: and matching in a circuit parameter database according to the current vehicle temperature to obtain preset circuit parameters, wherein the circuit parameter database is established by a temperature compensation battery model according to the circuit parameters obtained at different temperatures.
It can be understood that the temperature compensation battery model is a battery model established by adding temperature parameters in a second-order RC equivalent circuit and can better realize the temperature compensation battery modelThe temperature compensation battery model structure diagram can refer to fig. 3. Wherein U is t Is terminal voltage, U oc Is Open Circuit Voltage (OCV), I is operating current, R is 0 [SOC,T]Is ohmic internal resistance, R 1 [SOC,T]C 1 [SOC,T]And R 2 [SOC,T]C 2 [SOC,T]The polarization effect of the battery can be expressed, wherein R 1 [SOC,T]Is a first resistance, C 1 [SOC,T]A capacitor which is a first resistor; r 2 [SOC,T]Is a second resistance, C 2 [SOC,T]Is the capacitance of the second resistor. The differential equation of the temperature compensation model can be expressed as:
it should be understood that, as known from the differential equation of the temperature compensated battery model, the parameters to be calculated include R 0 ，R 1 ，R 2 ，C 1 ，C 2 And U oc The parameter is a preset circuit parameter.
It should be noted that, the vehicle battery is simulated according to the temperature compensation battery model, the test is performed at different environmental temperatures, the parameters of the vehicle battery at the same terminal voltage and working current and at different environmental temperatures are recorded, and the parameter set is called as a circuit parameter database. Under the real condition, the circuit parameters corresponding to the vehicle battery at the temperature can be obtained only by obtaining the current vehicle environment temperature and matching the current vehicle environment temperature with the existing temperature in the circuit parameter database, and the parameters can be identified in the temperature compensation battery model through an artificial bee colony algorithm.
It is emphasized that the estimation of the vehicle SOC and the parameter R 0 ，R 1 ，R 2 ，C 1 ，C 2 And U oc The size of the temperature compensation battery model is closely related, and U is observed in the experiment of the temperature compensation battery model at different temperatures oc With followingA slow rise in temperature; r is 0 ，R 1 ，R 2 Decreases with increasing temperature, and R 0 ，R 1 ，R 2 The numerical value of (A) is greatly different from other temperatures when the environment is at-20 ℃; c 1 ，C 2 The temperature rises slowly with the rise of the temperature, and the values of C1 and C2 reach a peak at a temperature of 60 ℃. The parameter identification result of the pulse discharge experiment data at the temperature of minus 20 ℃ and 260 ℃ shows that the parameters of the battery model at different temperatures are time-varying, so that a temperature compensation battery model considering the influence of temperature factors is necessary to be established, the experiment result can refer to figures 4, 5, 6, 7, 8 and 9, coordinate systems in figures 4, 5, 6, 7, 8 and 9 are established by taking the parameters, the temperature and the SOC as coordinate axes, wherein the figure 4 is U at the temperature of minus 20 ℃ and 260 DEG C oc A parameter identification result; FIG. 5 shows R at a temperature of-20 ℃ and 260 DEG C 0 Identifying a result of the parameter; FIG. 6 shows R at a temperature of-20 ℃ and 260 DEG C 1 Identifying a result of the parameter; FIG. 7 shows R at a temperature of-20 ℃ and 260 DEG C 2 Identifying a result of the parameter; FIG. 8 shows C at a temperature of-20 ℃ and 260 DEG C 1 A parameter identification result; FIG. 9 shows C at a temperature of-20 ℃ and 260 DEG C 2 And identifying the result of the parameter.
Step S30: and estimating according to the preset parameters to obtain the SOC estimated value of the current vehicle.
It is understood that SOC in the vehicle SOC estimation refers to battery state of charge and the current vehicle SOC estimation refers to an estimation of the current vehicle battery state of charge.
It should be appreciated that accurate estimation of vehicle SOC may improve battery utilization, reduce driver range anxiety, and is a prerequisite for energy management and fault diagnosis by a battery management system.
It should be noted that, in the present application, the battery SOC in the temperature compensation battery model is estimated by an Adaptive Extended Kalman Filtering (AEKF) algorithm.
It is emphasized that, in order to verify the accuracy and robustness of the temperature compensation model, the invention carries out experimental verification on different working conditions at the environment temperature of 20 ℃, wherein the working conditions comprise a UDDS working condition (a circulation working condition) and a hybrid working condition; the experimental data can be referred to the following table:
the voltage error table is based on the UDDS working condition in table 1, the voltage error table is based on the hybrid dynamic working condition in table 2, wherein ME represents the maximum error of the conventional battery model and the temperature compensation battery model, the maximum voltage error under the UDDS working condition of the conventional battery model is on the left side of the ME column in table 1, and the maximum voltage error under the UDDS working condition of the temperature compensation battery model is on the right side of the ME column in table 1; the left side of the ME column in the table 2 is the maximum voltage error of the conventional battery model under the hybrid dynamic working condition, and the right side of the ME column in the table 2 is the maximum voltage error of the temperature compensation battery model under the hybrid dynamic working condition;
the RMSE represents the root mean square errors of the conventional battery model and the temperature compensation battery model, the voltage error root mean square under the working condition of the UDDS of the conventional battery model is arranged on the left side of the RMSE column in the table 1, and the voltage error root mean square under the working condition of the UDDS of the temperature compensation battery model is arranged on the right side of the RMSE column in the table 1; in table 2, the left side of the RMSE column is the root mean square of the voltage error under the hybrid dynamic condition of the conventional battery model, and the right side of the RMSE column in table 2 is the root mean square of the voltage error under the hybrid dynamic condition of the temperature compensation battery model;
based on the test results, the temperature compensation battery model and the artificial bee colony algorithm can accurately reflect the SOC conditions of the real vehicle battery under the conditions of high temperature, low temperature and normal temperature, accurately reflect the battery parameters of the vehicle battery, and have high accuracy.
According to the method, the preset circuit parameters corresponding to the temperature in the circuit parameter database are obtained through the current vehicle temperature, the vehicle SOC value is estimated according to the preset circuit parameters, the problem that the estimation of the vehicle SOC is not accurate enough due to the fact that the battery is influenced by the ambient temperature is fully considered, circuit parameters of different temperatures are established according to the temperature compensation battery model, the SOC estimation is directly carried out according to the circuit parameters, the problem that the estimation of the vehicle SOC is inaccurate due to the influence of the ambient temperature in the driving process is effectively solved, and the estimation of the vehicle SOC is fast and accurately achieved.
Referring to fig. 10, fig. 10 is a flowchart illustrating a second embodiment of a vehicle SOC estimation method according to the present invention.
Based on the first embodiment described above, the vehicle SOC estimation method of the present embodiment, before the step S20, includes:
step S201: and acquiring the experiment temperature, the experiment battery terminal voltage and the experiment working current.
It is understood that the experimental temperature is the temperature that the battery may reach during the simulated driving of the vehicle during the experimental process; the experimental battery terminal voltage refers to the terminal voltage of a circuit in the process of simulating the vehicle battery through the temperature compensation battery model, namely the vehicle-mounted voltage of the vehicle; the experimental working current refers to a working current in a circuit in the process of simulating a vehicle battery through a temperature compensation battery model, namely, a vehicle-mounted current corresponding to vehicle-mounted voltage of a vehicle.
It should be understood that the vehicle-mounted voltage and the vehicle-mounted current of different vehicles may differ, and in the present application, a common terminal voltage and a common working current are taken as an example for explanation, and parameters are recorded through experiments to establish a circuit parameter database, or an experimental battery terminal voltage and an experimental working current may be adjusted according to actual conditions of different vehicles to establish different databases.
It should be noted that the experiment temperature, the experiment battery terminal voltage and the experiment working current referred to in the present application are all performed by taking a 18650 model lithium battery as an example. The battery parameters of a 18650 type lithium battery are as follows:
step S202: and establishing a temperature compensation battery model based on the experiment temperature, the experiment battery terminal voltage and the experiment working current.
It can be understood that the temperature compensation battery model is correspondingly established according to the acquired experimental temperature, the experimental battery terminal voltage and the experimental working current, and the condition of the vehicle battery in the vehicle driving process can be simulated through the temperature compensation battery model.
In specific implementation, pulse current discharge experiments are respectively carried out at the ambient temperature of-20 ℃ to 60 ℃. The experiment comprises the following specific steps: (1) The temperature of the thermostat is set to-20 ℃, and the battery is fully charged and stands for 2 hours; (2) Performing a pulse discharge experiment at a rate of 1/3C (discharging the battery to SOC = 0.9), and standing for 1h; (3) The step (2) is circulated until the discharge voltage approaches the cut-off voltage; (4) The above pulse discharge experiment was performed sequentially with the thermostat temperatures set at 0 ℃,20 ℃,40 ℃ and 60 ℃. The experimental process is divided into 10 stages, the SOC of the battery at each stage is reduced by 0.1, and fig. 11 is a schematic diagram of the impulse discharge response of the battery when SOC = 0.6. The ordinate of the graph in the upper part of the figure is the experimental battery terminal voltage, and the abscissa is time; the ordinate of the lower graph is experimental working current, and the abscissa is time; a. b, c, d and e are experimental battery terminal voltage and experimental working current corresponding to different time points.
As further shown in FIG. 11, the voltage change from point d to point c is due to the ohmic internal resistance R 0 Resulting in an ohmic internal resistance R at SOC =0.6 0 The calculation formula of (c) is:
R 0 ＝|U d -U c |/I
working voltage U from point d to point e due to hysteresis voltage characteristics de Can be expressed as:
for working voltage U from point d to point e de The following formula is obtained by replacing the coefficients of the calculation formula:
further conversion may result in:
by applying an operating voltage U from point d to point e de Fitting the formula obtained by coefficient replacement of the calculation formula to further obtain the numerical values of the parameters c1, c2, c3, c4 and c5, and performing recursive calculation according to the numerical values of the parameters c1, c2, c3, c4 and c5 to obtain the SOC stage R 1 ，R 2 ，C 1 ，C 2 And U oc The numerical value of (c).
Step S203: and calculating to obtain preset circuit parameters according to an artificial bee colony algorithm based on the temperature compensation battery model.
Understandably, the corresponding R can be quickly obtained under the temperature compensation battery model through the artificial bee colony algorithm at the experimental temperature 1 ，R 2 ，C 1 ，C 2 And U oc And (4) parameters.
It should be understood that the artificial bee colony has a plurality of stages, including an initialization stage, a leading bee stage, a following bee stage and a bee detecting stage, in the initialization stage, an initial value of the artificial bee colony is set, a working range and a working result (optimization result) of the bee colony are defined, the leading bee stage and leading bees are generated in the bee colony, the leading bees search targets, namely bee sources, within the range of the initial setting, and a feasible solution randomly generated by the bee colony is represented as:
xi'd＝xid+rid(xid-xkd)
X’ id to generate a new feasible solution, where x id ＝[c 1 c 2 c 3 c 4 c 5 ] T ，i＝k+1(k＝1,...,99)，d＝1,...,5，r id ∈[0，1]。
The bee following stage means that each leading bee has a part of following bees, and when the leading bee finds a bee source, a new leading bee is formed, and the searching expression of the new leading bee group is as follows:
v id ＝x id +η id (x id -x kd )
wherein v is id Is a new honey source, eta ∈ [ -1,1]. And when the leading bees search the honey source, judging whether the honey source meets the initial value setting, and if so, obtaining the optimal parameters for the artificial bee colony algorithm by the honey source.
The calculation process of the artificial bee colony algorithm is to set initialization parameters of the artificial bee colony algorithm; searching and generating a leading bee group according to the initialization parameters; generating a follower bee population in the lead bee population based on the follower bee generation probability; searching honey sources by the leading bee group and the following bee group; judging whether the honey source meets a termination condition, if so, obtaining experiment parameters; and calculating to obtain preset circuit parameters according to the experiment parameters.
It should be noted that the optimal parameters are based on formulas
Calculating parameters c1, c2, c3, c4 and c5, and further converting according to values of c1, c2, c3, c4 and c5 to obtain an SOC stage R 1 ，R 2 ，C 1 ，C 2 And U oc The numerical value of (c).
The method comprises the following steps that before a leading bee group generates a following bee group, the real terminal voltage of an experiment and the terminal voltage of a model are obtained; (the experimental real terminal voltage is the real voltage obtained by measuring through a tool, and the model terminal voltage is the terminal voltage given by calculation) according to the mean square error of the experimental real terminal voltage and the model terminal voltage; calculating a fitness function according to the mean square error; and calculating the generation probability of the follower bee according to the fitness function.
The fitness function formula is as follows:
wherein U is exp To test the true terminal voltage, U sim Is the model terminal voltage.
Further calculating the following bee based screening probability P according to the fitness function i Forming new following bee colonyIn vivo screening probability P i The calculation formula of (c) is:
step S204: and establishing a circuit parameter database according to the experiment temperature, the experiment battery terminal voltage, the working current and the experiment parameters.
It can be understood that the circuit parameter database is a circuit parameter corresponding to the temperature obtained by performing parameter calculation according to the temperature compensation battery model at different temperatures, and all the temperatures and the circuit parameters corresponding to the temperatures are collected.
In the embodiment, parameters corresponding to the battery at different temperatures are experimentally established through an artificial bee colony algorithm based on the temperature compensation battery, the different temperatures and the corresponding parameters are integrated to obtain a circuit parameter database, when the vehicle SOC is estimated, the ambient temperature of the vehicle is matched with the temperature in the circuit parameter database, the vehicle SOC is estimated according to the circuit parameters corresponding to the matched temperatures, and the estimation of the vehicle SO can be accurately and rapidly performed in the driving process of the vehicle through the temperature compensation battery model and the circuit parameter database established by the experimental data.
Referring to fig. 12, fig. 12 is a flowchart illustrating a vehicle SOC estimation method according to a third embodiment of the present invention.
Based on the above-described first embodiment, the vehicle SOC estimation method of the present embodiment, before the step S30, includes:
step S301: and acquiring the measurement noise and the process noise.
Understandably, the measurement noise and the process noise are parameters of a vehicle state equation in the vehicle SOC estimation process; the measured noise and the process noise are detected by the device as known parameters to solve for the vehicle SOC in the vehicle state equation.
It should be appreciated that by modifying the measurement noise and the process noise in the vehicle state equation, the modified measurement noise and the process noise can better calculate the vehicle SOC estimation value via the vehicle state equation.
It should be noted that the measurement noise and the process noise are corrected by a noise correction matrix, where the noise correction matrix is:
where, HK is the noise correction matrix, M is the known parameter, and ek is the difference between the experiment voltage at K moment and the temperature compensation battery model voltage.
It is emphasized that the noise correction matrix is established by testing the difference between the real voltage and the analog circuit voltage.
Step S302: and predicting the SOC of the vehicle according to preset parameters, the measurement noise and the process noise to obtain a predicted state value.
It can be understood that the preset parameters, the measurement noise and the process noise are brought into a vehicle state equation, and a preset state value of the vehicle SOC can be obtained through solving.
It should be noted that the vehicle state equation can be expressed as:
x[k+1]＝f(x[k],v[k])＝A d x[k]+B d u[k]+w[k]
y[k]＝h(x[k],v[k])+v[k]
wherein x [ k ]]＝[U 1 [k]U 2 [k]SOC[k]] T As a state variable of a system (battery internal system at the time of temperature compensation battery model SOC estimation), y [ k ]]＝U t [k]As an observed quantity of the system, u [ k ]]＝I[k]Is the input quantity of the system; in the vehicle state equation:
where vk is the measurement noise, wk is the process noise, T is the time value, Q is the process noise covariance, x k +1 is the vehicle SOC estimate at the current time, and x k is the vehicle SOC estimate at the previous time.
It should be emphasized that the measurement noise and the process noise can seriously affect the estimation result of the AEKF on the vehicle SOC, the estimation of the vehicle SOC by the AEKF can be obtained by manual debugging at the beginning, and the process noise and the measurement noise are corrected by continuously estimating the vehicle SOC in the following process, so that the influence of the process noise and the measurement noise on the estimation of the vehicle SOC is reduced, and the estimation of the vehicle SOC is more accurate.
Step S303: a reference value of a vehicle SOC is obtained.
It can be understood that the reference value of the vehicle SOC can be understood as the SOC value of the vehicle battery at the current time calculated according to the vehicle state equation, and the vehicle SOC value at the next time can be further predicted according to the vehicle state equation from the vehicle battery SOC at the current time.
Step S304: and correcting the vehicle SOC according to the predicted state value and the reference value to obtain a vehicle SOC estimated value.
It can be understood that the predicted state value is calculated according to the SOC estimation at the previous moment, but the SOC estimation value is known from the vehicle state formula to be influenced by measurement noise and process noise, and the current vehicle is corrected according to the predicted state value and the vehicle SOC reference value, which may be an average of the predicted state value and the reference value, or a ratio of the predicted state value and the reference value to determine the vehicle SOC estimation value.
It should be understood that the predicted state value of the current time is calculated by measuring noise, process noise and the estimated value of the previous time, the measuring noise and the process noise are corrected by the correction matrix, the vehicle SOC reference value is calculated by the corrected measuring noise and the process noise, the vehicle SOC estimated value of the current time is re-corrected according to the predicted state value and the estimated value, and the corrected vehicle SOC estimated value is output as the final SOC estimated value.
It should be noted that the SOC estimation verification experiment is performed based on the time-varying temperature environment of-20 ℃ and 260 ℃, and the SOC estimation verification experiment is performed under the UDDS working condition and the hybrid dynamic working condition, and the experiment results are shown in fig. 13 to 14. FIG. 13 is a comparison graph of the estimation result of the UDDS operating condition SOC under the time-varying temperature environment, wherein the abscissa is time, the time unit is second, the ordinate is the vehicle SOC value, three curves in the graph are dense, and the upper right corner of the graph is at time 1.25X 10 4 -1.40*10 4 The enlarged diagram of the three curves in time is shown, wherein the uppermost curve in the enlarged diagram is the actual vehicle SOC value, the middle curve is the temperature compensation model SOC estimated value, and the lowermost curve is the temperature model SOC estimated value not considered.
Fig. 14 is a comparison graph of SOC estimation results of a hybrid dynamic condition in a time-varying temperature environment, where the abscissa in the graph is time, the time unit is second, the ordinate is a vehicle SOC value, three curves in the graph are relatively dense, the upper right corner of the graph is an enlarged view of the three curves within a time range of 9600S-15000S, the uppermost curve in the enlarged view is an estimated value of SOC without considering a temperature model, the middle curve is an estimated value of SOC based on a temperature compensation model, and the lowermost curve is a true value of SOC of the vehicle. It can be seen that the vehicle SOC can be estimated more accurately based on the temperature compensated battery model.
In the embodiment, based on a battery parameter database established by a temperature compensation battery model, the SOC of the vehicle is estimated by circuit parameters corresponding to different temperatures through a vehicle state equation in an AEKF algorithm, parameters such as measurement noise and process noise in the vehicle state equation are further corrected through a noise correction matrix, then the SOC of the vehicle is corrected according to a real value of the SOC of the vehicle, and the vehicle state equation is continuously optimized through continuous correction of the noise, so that the SOC of the vehicle is estimated more accurately.
Furthermore, an embodiment of the present invention also proposes a storage medium having stored thereon a vehicle SOC estimation program that, when executed by a processor, implements the steps of the vehicle SOC estimation method as described above.
Referring to fig. 15, fig. 15 is a block diagram showing the structure of a first embodiment of the vehicle SOC estimating apparatus of the present invention.
As shown in fig. 15, a vehicle SOC estimation device according to an embodiment of the present invention includes:
the temperature acquisition module 10 is used for acquiring the current vehicle temperature;
the parameter acquisition module 20 is configured to perform matching in a circuit parameter database according to the current vehicle temperature to obtain preset circuit parameters, where the circuit parameter database is established by a temperature compensation battery model according to circuit parameters obtained at different temperatures;
and the vehicle SOC estimation module 30 is used for estimating and obtaining the current vehicle SOC estimation value according to the preset parameters.
According to the method, the preset circuit parameters corresponding to the temperature in the circuit parameter database are obtained through the current vehicle temperature, the vehicle SOC value is estimated according to the preset circuit parameters, the problem that the estimation of the vehicle SOC is not accurate enough due to the fact that the battery is influenced by the ambient temperature is fully considered, the circuit parameters of different temperatures are established according to the temperature compensation battery model, the SOC estimation is directly carried out according to the circuit parameters, the problem that the estimation of the vehicle SOC is inaccurate due to the influence of the ambient temperature in the driving process is effectively solved, and the estimation of the vehicle SOC is fast and accurately achieved.
In an embodiment, the parameter obtaining module 20 is further configured to obtain an experimental temperature, an experimental battery terminal voltage, and an experimental working current; establishing a temperature compensation battery model based on the experiment temperature, the experiment battery terminal voltage and the experiment working current; calculating to obtain preset circuit parameters according to an artificial bee colony algorithm based on a temperature compensation battery model; and establishing a circuit parameter database according to the experiment temperature, the experiment battery terminal voltage, the working current and the experiment parameters.
In an embodiment, the parameter obtaining module 20 is further configured to set an initialization parameter of an artificial bee colony algorithm; searching and generating a leading bee group according to the initialization parameters; generating a follower bee population in the lead bee population based on the follower bee generation probability; searching honey sources by the leading bee group and the following bee group; judging whether the honey source meets a termination condition, if so, obtaining experiment parameters; and calculating to obtain preset circuit parameters according to the experiment parameters.
In an embodiment, the parameter obtaining module 20 is further configured to obtain an experimental real terminal voltage and a model terminal voltage; according to the mean square error of the experimental real terminal voltage and the model terminal voltage; calculating a fitness function according to the mean square error; and calculating the generation probability of the follower bee according to the fitness function.
In one embodiment, the vehicle SOC estimation module 30 is further configured to obtain the measured noise and the process noise; predicting the SOC of the vehicle according to preset parameters, the measurement noise and the process noise to obtain a predicted state value; acquiring a reference value of the SOC of the vehicle; and correcting the vehicle SOC according to the predicted state value and the reference value to obtain a vehicle SOC estimated value.
In one embodiment, the vehicle SOC estimation module 30 is further configured to obtain a noise correction matrix; and correcting the process noise and the measurement noise according to the noise correction matrix to obtain new measurement noise and new process noise.
In one embodiment, the vehicle SOC estimation module 30 is further configured to obtain an experimental real voltage and an analog circuit voltage; calculating the difference value of the experimental real voltage and the analog circuit voltage; and establishing a noise correction matrix according to the difference.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or system comprising the element.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A vehicle SOC estimation method, characterized by comprising:
acquiring the current vehicle temperature;
matching in a circuit parameter database according to the current vehicle temperature to obtain preset circuit parameters, wherein the circuit parameter database is established by a temperature compensation battery model according to the circuit parameters obtained at different temperatures;
and estimating according to the preset parameters to obtain the SOC estimated value of the current vehicle.
2. The vehicle SOC estimation method according to claim 1, wherein said obtaining preset circuit parameters by matching in a circuit parameter database according to the current vehicle temperature, said circuit parameter database being established by a temperature-compensated battery model according to circuit parameters obtained at different temperatures, further comprises:
acquiring experiment temperature, experiment battery terminal voltage and experiment working current;
establishing a temperature compensation battery model based on the experiment temperature, the experiment battery terminal voltage and the experiment working current;
calculating to obtain preset circuit parameters according to an artificial bee colony algorithm based on a temperature compensation battery model;
and establishing a circuit parameter database according to the experiment temperature, the experiment battery terminal voltage, the working current and the experiment parameters.
3. The vehicle SOC estimation method according to claim 2, wherein the calculating preset circuit parameters according to an artificial bee colony algorithm includes:
setting initialization parameters of an artificial bee colony algorithm;
searching and generating a leading bee group according to the initialization parameters;
generating a follower bee population in the leading bee population based on the follower bee generation probability;
searching a honey source by the leading bee group and the following bee group;
judging whether the honey source meets a termination condition, if so, obtaining experiment parameters;
and calculating to obtain preset circuit parameters according to the experiment parameters.
4. The vehicle SOC estimation method according to claim 3, wherein before the leading bee population generates the following bee population, further comprising:
acquiring an experimental real terminal voltage and a model terminal voltage;
according to the mean square error of the experimental real terminal voltage and the model terminal voltage;
calculating a fitness function according to the mean square error;
and calculating the generation probability of the follower bee according to the fitness function.
5. The vehicle SOC estimation method according to claim 1, wherein said before obtaining the current vehicle SOC estimation value based on the preset parameter estimation, comprises:
acquiring the measurement noise and the process noise;
predicting the SOC of the vehicle according to preset parameters, the measurement noise and the process noise to obtain a predicted state value;
acquiring a reference value of the SOC of the vehicle;
and correcting the vehicle SOC according to the predicted state value and the reference value to obtain a vehicle SOC estimated value.
6. The vehicle SOC estimation method of any of claims 1-5, wherein the measured noise and the process noise include:
acquiring a noise correction matrix;
and correcting the process noise and the measurement noise according to the noise correction matrix to obtain new measurement noise and new process noise.
7. The vehicle SOC estimation method of any of claims 1-5, wherein the obtaining a noise correction matrix includes:
acquiring an experimental real voltage and an analog circuit voltage;
calculating the difference value of the experimental real voltage and the analog circuit voltage;
and establishing a noise correction matrix according to the difference.
8. A vehicle SOC estimation device, characterized by comprising:
the temperature acquisition module is used for acquiring the current vehicle temperature;
the parameter acquisition module is used for matching in a circuit parameter database according to the current vehicle temperature to obtain preset circuit parameters, and the circuit parameter database is established by a temperature compensation battery model according to the circuit parameters obtained at different temperatures;
and the vehicle SOC estimation module is used for estimating and obtaining the current vehicle SOC estimation value according to the preset parameters.
9. A vehicle SOC estimation apparatus, characterized by comprising: a memory, a processor, and a vehicle SOC estimation program stored on the memory and executable on the processor, the vehicle SOC estimation program configured to implement the vehicle SOC estimation method according to any one of claims 1 to 7.
10. A storage medium characterized in that a vehicle SOC estimation program is stored thereon, which when executed by a processor implements the vehicle SOC estimation method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211019593.7A CN115421042A (en) | 2022-08-24 | 2022-08-24 | Vehicle SOC estimation method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211019593.7A CN115421042A (en) | 2022-08-24 | 2022-08-24 | Vehicle SOC estimation method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115421042A true CN115421042A (en) | 2022-12-02 |
Family
ID=84198408
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211019593.7A Pending CN115421042A (en) | 2022-08-24 | 2022-08-24 | Vehicle SOC estimation method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115421042A (en) |
-
2022
- 2022-08-24 CN CN202211019593.7A patent/CN115421042A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110488194B (en) | Lithium battery SOC estimation method and system based on electrochemical impedance model | |
| Lai et al. | A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries | |
| JP6404832B2 (en) | Method of determining control technical observer for SoC | |
| CN111581904B (en) | Lithium battery SOC and SOH collaborative estimation method considering cycle number influence | |
| CN107003359B (en) | Method for automatically estimating capacity of battery cell of battery pack | |
| CN108089135B (en) | Extreme learning model-based battery state prediction system and implementation method thereof | |
| CN110146822A (en) | A kind of Vehicular dynamic battery capacity On-line Estimation method based on constant-current charge process | |
| CN112858916A (en) | Battery pack state of charge estimation method based on model and data drive fusion | |
| CN111142025A (en) | Battery SOC estimation method and device, storage medium and electric vehicle | |
| CN113466725B (en) | Method and device for determining state of charge of battery, storage medium and electronic equipment | |
| Jin et al. | SOC estimation of lithium-ion battery considering the influence of discharge rate | |
| CN111060822B (en) | State of charge estimation method based on model switching and fusion | |
| CN113608126A (en) | Lithium battery SOC online estimation method at different temperatures | |
| CN114594378A (en) | Lithium battery SOH model training method, prediction method, device and equipment | |
| CN112269133A (en) | SOC estimation method based on pre-charging circuit model parameter identification | |
| CN109782182B (en) | Online estimation method and device for energy state of series battery pack | |
| CN115598540A (en) | Lithium battery parameter identification and state joint estimation method considering wide temperature | |
| CN112269137B (en) | Battery health state estimation method based on dynamic parameter identification | |
| CN110927582B (en) | Lithium battery SOC estimation method based on multiple sigma points | |
| CN115421042A (en) | Vehicle SOC estimation method, device, equipment and storage medium | |
| CN116699414A (en) | Lithium battery SOC estimation method and system based on UKF-LSTM algorithm | |
| CN113933725B (en) | Method for determining state of charge of power battery based on data driving | |
| CN115980583A (en) | Lithium battery equivalent circuit model parameter identification four-step method based on differential algebraic equation | |
| CN113433457B (en) | LSTM network assisted Kalman filtering SOC estimation method | |
| CN113030555B (en) | Energy storage open-circuit voltage estimation method and device, terminal equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |