CN113848486A - SOC (System on chip) joint estimation method - Google Patents
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Abstract
The invention discloses a SOC (state of charge) combined estimation method which comprises a first-order RC (resistance-capacitance) battery equivalent circuit model, an offline parameter identification and extraction module, a two-dimensional lookup table module, a UKF (unscented Kalman Filter) algorithm module, an RLS (recursive least squares) algorithm module and an SOC initial value module, wherein the offline parameter identification and extraction module comprises a pulse discharge experiment module and a parameter identification and extraction module, the two-dimensional lookup table module comprises an R0 two-dimensional lookup table, an R1 two-dimensional lookup table, a C1 two-dimensional lookup table and an OCV two-dimensional lookup table, the UKF algorithm module comprises a first-order RC battery equivalent circuit model module and a UKF formula module, the RLS algorithm comprises a first-order RC battery equivalent circuit model module and an RLS formula module, and the SOC initial value module comprises an OCV two-dimensional lookup table and an SOC two-dimensional lookup table. According to the SOC combined estimation algorithm, a first-order RC battery equivalent circuit model is established, the calculated amount is reduced, the realization is simple, the precision is ensured, the influence of the environmental temperature is considered in the SOC estimation process, and the estimation result is closer to the true value.
Description
Technical Field
The invention relates to the technical field of battery residual capacity calculation, in particular to a SOC joint estimation method.
Background
SOC (State of charge) is an important index for representing the residual capacity of the battery, and the real-time accurate estimation of the SOC value is the basis and the key for improving the performance of a power management system (BMS) and has important significance for improving the utilization efficiency of battery energy. At present, an SOC estimation method based on a battery equivalent circuit model is common, an RC parallel network is mostly used for describing transient response of a battery in the battery equivalent circuit model, the more the RC parallel networks are connected in series, the higher the model accuracy is, but the higher the corresponding model order is, the larger the calculated amount is, and the more difficult the realization is. The battery equivalent circuit model can realize accurate SOC estimation by combining Extended Kalman Filtering (EKF) and Recursive Least Squares (RLS), but the EKF utilizes Taylor to extract a first-order approximate term from a nonlinear equation, which can introduce linearization error, in addition, the Jacobian matrixes of a state equation and an observation equation of a computing system are not easy to realize under general conditions, the computing complexity of the algorithm is increased, the Recursive Least Squares (RLS) needs to give a model parameter initial value first, and if the model parameter initial value is not appropriate, the convergence time of the algorithm is too long or even diverges, so that the robustness of the algorithm is reduced. The ambient temperature has a large influence on the SOC estimation, and the estimation result without taking the ambient temperature into account is not a true value.
Disclosure of Invention
The invention aims to solve the problems of complex algorithm, more calculation resource consumption and poorer accuracy in the prior art, and provides an SOC joint estimation method.
In order to achieve the purpose, the invention adopts the following technical scheme: a SOC joint estimation method comprises the following steps:
the system comprises a first-order RC battery equivalent circuit model, an off-line parameter identification and extraction module, a two-dimensional lookup table module, a UKF algorithm module, an RLS algorithm module and an SOC initial value module;
the off-line parameter identification and extraction module comprises a pulse discharge experiment module and a parameter identification and extraction module;
the two-dimensional lookup table module comprises an R0 two-dimensional lookup table, an R1 two-dimensional lookup table, a C1 two-dimensional lookup table and an OCV two-dimensional lookup table;
the UKF algorithm module comprises a first-order RC battery equivalent circuit model module and a UKF formula module;
the RLS algorithm comprises a first-order RC battery equivalent circuit model module and an RLS formula module;
the SOC initial value module comprises an OCV two-dimensional lookup table and an SOC two-dimensional lookup table.
As a further description of the above technical solution:
the first-order RC battery equivalent circuit model comprises a voltage source OCV, a resistor R0, a resistor R1 and a capacitor C1, wherein the positive electrode of the voltage source OCV is connected with one end of the resistor R1 and one end of the capacitor C1, the negative electrode of the voltage source OCV is connected with a power ground, the other end of the resistor R1 is connected with the other end of the capacitor C1 and one end of the resistor R0, and the other end of the resistor R0 and the power ground output are E (t).
As a further description of the above technical solution:
the off-line parameter identification and extraction module is used for carrying out a pulse discharge experiment on the target battery in the thermostat to obtain a terminal voltage value and a discharge current value of the target battery, and the off-line parameter identification and extraction result of the model parameter is obtained by the parameter identification and extraction module.
As a further description of the above technical solution:
the two-dimensional lookup table module utilizes the parameters extracted by the offline parameter identification and extraction module to establish an R0 two-dimensional lookup table, an R1 two-dimensional lookup table, a C1 two-dimensional lookup table and an OCV two-dimensional lookup table of which the model parameters are related to the SOC and the ambient temperature T.
As a further description of the above technical solution:
the UKF algorithm module establishes a state equation and a measurement equation of the UKF according to a first-order RC battery equivalent circuit model, an SOC initial value module is used as an input to form an initial value of a state vector of the UKF, real-time terminal voltage values and current values are also input into the UKF algorithm module, an SOC value at a new moment is obtained according to the UKF algorithm, and the OCV value at the moment is determined by utilizing the SOC value, the environment temperature T and the OCV two-dimensional lookup table and is given to the RLS algorithm module.
As a further description of the above technical solution:
the battery parameter online identification method comprises the steps that the RLS algorithm module determines a conversion relation between battery model parameters and RLS parameter vectors according to a first-order RC battery equivalent circuit model, the OCV value at a new moment obtained by the UKF algorithm module is added with a real-time terminal voltage value and a real-time current value to update the state vector of the RLS algorithm module, so that an online identification result of the model parameters is obtained, the battery model parameter values are updated, and then the online identification result is fed back to the UKF algorithm module, and a state equation and a measurement equation in the UKF algorithm module are updated.
As a further description of the above technical solution:
the initial value module of SOC obtains the initial value of SOC through the two-dimensional lookup table of SOC, the input of the two-dimensional lookup table of SOC is initial OCV value and ambient temperature T, and the two-dimensional lookup table of SOC is got from OCV two-dimensional lookup table conversion, and initial OCV value is got from the initial terminal voltage value when the battery just worked.
Advantageous effects
The invention provides a SOC joint estimation method. The method has the following beneficial effects:
(1) and a first-order RC battery equivalent circuit model is established, so that the calculated amount is reduced, the realization is simple, and the precision is ensured.
(2) The UKF algorithm does not ignore high-order terms, does not introduce linearization errors, does not need to conduct derivation calculation on a Jacobian matrix, and reduces the operation amount while improving the accuracy.
(3) The initial value of the battery model parameter of the RLS algorithm is determined by establishing a battery model parameter two-dimensional lookup table through an optimized off-line parameter identification method, so that the time for convergence of the model parameter is reduced, and a positive definite matrix in which covariance is not symmetrical in the UKF algorithm due to the fact that the initial value is randomly designated can be avoided;
(4) the influence of the ambient temperature is considered in the SOC estimation process, so that the estimation result is closer to the true value.
Drawings
FIG. 1 is a schematic diagram of a combined estimation method of SOC according to the present invention;
FIG. 2 is a flowchart of obtaining an initial SOC value according to the present invention;
FIG. 3 is a circuit diagram of a first-order RC battery equivalent circuit model according to the present invention;
FIG. 4 is a diagram illustrating simulation results of a SOC estimation method;
FIG. 5 is a diagram illustrating the result of SOC estimation error.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
As shown in fig. 1-3, a method for jointly estimating SOC includes a first-order RC battery equivalent circuit model, an offline parameter identification and extraction module, a two-dimensional lookup table module, a UKF algorithm module, an RLS algorithm module, and an SOC initial value module;
the off-line parameter identification and extraction module comprises a pulse discharge experiment module and a parameter identification and extraction module;
the two-dimensional lookup table module comprises an R0 two-dimensional lookup table, an R1 two-dimensional lookup table, a C1 two-dimensional lookup table and an OCV two-dimensional lookup table;
the UKF algorithm module comprises a first-order RC battery equivalent circuit model module and a UKF formula module;
the RLS algorithm comprises a first-order RC battery equivalent circuit model module and an RLS formula module;
the SOC initial value module comprises an OCV two-dimensional lookup table and an SOC two-dimensional lookup table.
The first-order RC battery equivalent circuit model comprises a voltage source OCV, a resistor R0, a resistor R1 and a capacitor C1, wherein the positive electrode of the voltage source OCV is connected with one end of the resistor R1 and one end of the capacitor C1, the negative electrode of the voltage source OCV is connected with a power ground, the other end of the resistor R1 is connected with the other end of the capacitor C1 and one end of the resistor R0, and the other end of the resistor R0 and the power ground output are E (t).
The off-line parameter identification and extraction module is used for carrying out a pulse discharge experiment on the target battery in the thermostat to obtain a terminal voltage value and a discharge current value of the target battery, and the off-line parameter identification and extraction result of the model parameters is obtained by the parameter identification and extraction module.
The two-dimensional lookup table module utilizes the parameters extracted by the offline parameter identification and extraction module to establish an R0 two-dimensional lookup table, an R1 two-dimensional lookup table, a C1 two-dimensional lookup table and an OCV two-dimensional lookup table of which the model parameters are related to the SOC and the ambient temperature T.
The UKF algorithm module establishes a state equation and a measurement equation of the UKF according to a first-order RC battery equivalent circuit model, an SOC initial value module is used as an input to form an initial value of a state vector of the UKF, real-time terminal voltage values and current values are also input into the UKF algorithm module, an SOC value at a new moment is obtained according to the UKF algorithm, and the two-dimensional lookup table of the SOC value, the environmental temperature T and the OCV at the moment is used for determining the OCV value at the moment and giving the RLS algorithm module.
The battery model parameter updating method comprises the steps that the RLS algorithm module determines a conversion relation between battery model parameters and RLS parameter vectors according to a first-order RC battery equivalent circuit model, the OCV value at a new moment obtained by the UKF algorithm module is added with a real-time terminal voltage value and a real-time current value to update the state vector of the RLS algorithm module, so that an online identification result of the model parameters is obtained, the battery model parameter values are updated, and then the battery model parameter values are fed back to the UKF algorithm module, and a state equation and a measurement equation in the UKF algorithm module are updated.
The initial value module of SOC obtains the initial value of SOC through SOC two-dimentional look-up table, the input of SOC two-dimentional look-up table is initial OCV value and ambient temperature T, and SOC two-dimentional look-up table is got from OCV two-dimentional look-up table conversion, and initial OCV value is got from the initial terminal voltage value when the battery just worked.
Example (b):
s01, performing a pulse discharge experiment on the target battery in a constant temperature box to respectively obtain voltage data of battery terminal voltage at the environmental temperature of 5 ℃, 20 ℃ and 40 ℃;
s02: the method comprises the steps of utilizing an offline parameter identification and extraction method to carry out parameter identification and extraction on experimental data of a pulse discharge experiment, so that two-dimensional lookup tables of R0, R1, C1 and OCV related to SOC and ambient temperature T are established, and the two-dimensional lookup tables of SOC are obtained through conversion of the OCV two-dimensional lookup tables;
s03: measuring the initial terminal voltage value of the battery, namely the initial OCV value, taking the initial OCV value and the environment temperature value at the moment as input, and determining the initial value of the SOC through a two-dimensional SOC table look-up;
s04: the initial value of the state variable of the UKF algorithm module is determined by the initial value of the SOC, and the updated SOC value is calculated by the UKF algorithm module;
SO 5: determining the OCV value at the moment by using the SOC value at the moment and the environmental temperature value at the moment as input through an OCV two-dimensional table lookup;
s06: updating a state vector of the RLS algorithm module according to the OCV value, the terminal voltage value and the current value at the moment, and obtaining the latest online model parameter value through the RLS algorithm module;
s07: updating a state equation and a measurement equation in the UKF algorithm module by using the new model parameter values, and updating the SOC value in real time through the UKF algorithm module;
s08: the steps S05, S06 and S07 are continuously cycled to achieve real-time estimation of SOC.
According to the invention, a simulation model is established in Matlab, the simulation result of the SOC estimation method is shown in FIG. 4, and FIG. 5 shows that the SOC estimation error is lower than 0.87%.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. A SOC joint estimation method is characterized by comprising a first-order RC battery equivalent circuit model, an offline parameter identification and extraction module, a two-dimensional lookup table module, a UKF algorithm module, an RLS algorithm module and an SOC initial value module;
the off-line parameter identification and extraction module comprises a pulse discharge experiment module and a parameter identification and extraction module;
the two-dimensional lookup table module comprises an R0 two-dimensional lookup table, an R1 two-dimensional lookup table, a C1 two-dimensional lookup table and an OCV two-dimensional lookup table;
the UKF algorithm module comprises a first-order RC battery equivalent circuit model module and a UKF formula module;
the RLS algorithm comprises a first-order RC battery equivalent circuit model module and an RLS formula module;
the SOC initial value module comprises an OCV two-dimensional lookup table and an SOC two-dimensional lookup table.
2. The method of claim 1, wherein the first-order RC battery equivalent circuit model comprises a voltage source OCV, a resistor R0, a resistor R1, and a capacitor C1, wherein a positive terminal of the voltage source OCV is connected to one terminal of the resistor R1 and one terminal of the capacitor C1, a negative terminal of the voltage source OCV is connected to the power ground, another terminal of the resistor R1 is connected to another terminal of the capacitor C1 and one terminal of the resistor R0, and another terminal of the resistor R0 and the power ground output are e (t).
3. The method of claim 1, wherein the offline parameter identification and extraction module performs a pulse discharge experiment on the target battery in the oven to obtain the terminal voltage and discharge current values of the target battery, and the parameter identification and extraction module is used to obtain the offline parameter identification and extraction results of the model parameters.
4. The method of claim 1, wherein the two-dimensional lookup table module uses the parameters extracted by the offline parameter identification and extraction module to build an R0 two-dimensional lookup table, an R1 two-dimensional lookup table, a C1 two-dimensional lookup table, and an OCV two-dimensional lookup table, wherein the model parameters are related to SOC and the ambient temperature T.
5. The SOC combined estimation method according to claim 1, wherein the UKF algorithm module establishes a state equation and a measurement equation of the UKF according to a first-order RC battery equivalent circuit model, and takes the SOC initial value module as input to form an initial value of a state vector of the UKF, real-time terminal voltage value and current value are also input into the UKF algorithm module, a new-time SOC value is obtained according to the UKF algorithm, and the current moment OCV value is determined by using the current moment SOC value, the environment temperature T and the OCV two-dimensional lookup table and is given to the RLS algorithm module.
6. The SOC joint estimation method of claim 1, wherein the RLS algorithm module determines a conversion relationship between battery model parameters and RLS parameter vectors according to a first-order RC battery equivalent circuit model, and the OCV value at a new moment obtained by the UKF algorithm module plus a real-time terminal voltage value and a real-time current value are used for updating the state vectors of the RLS algorithm module, so as to obtain an online identification result of the model parameters, update battery model parameter values, feed back the online identification result to the UKF algorithm module, and update a state equation and a measurement equation in the UKF algorithm module.
7. The method of claim 1, wherein the initial SOC module obtains the initial value of SOC by a SOC two-dimensional lookup table, the inputs of which are the initial OCV value and the ambient temperature T, and the SOC two-dimensional lookup table is transformed from an OCV two-dimensional lookup table, and the initial OCV value is obtained from the initial terminal voltage value of the battery immediately after the battery is operated.
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| CN107064811A (en) * | 2017-03-01 | 2017-08-18 | 华南理工大学 | A kind of lithium battery SOC On-line Estimation methods |
| CN110208707A (en) * | 2019-06-14 | 2019-09-06 | 湖北锂诺新能源科技有限公司 | A kind of lithium ion battery parameter evaluation method based on equivalent-circuit model |
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| CN107064811A (en) * | 2017-03-01 | 2017-08-18 | 华南理工大学 | A kind of lithium battery SOC On-line Estimation methods |
| CN110208707A (en) * | 2019-06-14 | 2019-09-06 | 湖北锂诺新能源科技有限公司 | A kind of lithium ion battery parameter evaluation method based on equivalent-circuit model |
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