CN113807039A - Power state prediction method of series battery system - Google Patents

Power state prediction method of series battery system Download PDF

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CN113807039A
CN113807039A CN202111097016.5A CN202111097016A CN113807039A CN 113807039 A CN113807039 A CN 113807039A CN 202111097016 A CN202111097016 A CN 202111097016A CN 113807039 A CN113807039 A CN 113807039A
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彭思敏
徐璐
吴冬春
王前进
朱家骥
沈翠凤
阚加荣
王建冈
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Yancheng Institute of Technology
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Abstract

The invention discloses a power state prediction method of a series battery system, which comprises the following steps: establishing a series battery system model according to battery monomer model parameters and series circuit characteristics to further obtain a battery system space state equation, obtaining a battery system SOC through a battery system SOC estimation module by combining detected battery system voltage U and current I, generating a battery system power state base value SOP by using a power state base value prediction module by combining I, U, SOC as input and a battery system modelb. Then, the voltage U of each battery monomer in the battery system1～UnThe voltage U of the battery system is used as input, and the voltage deviation of each battery monomer is obtained through a voltage controllerValue of Δ U1～ΔUnThen, an SOP corrector based on a BP neural network is utilized to obtain a power state compensation value delta SOP of the battery systemb(ii) a Finally, the SOP is carried outbAnd Δ SOPbSuperposing to obtain the predicted value SOP of the power state of the series battery systemr。

Description

Power state prediction method of series battery system
Technical Field
The invention belongs to the technical field of control and management of a high-capacity battery energy storage system in a smart power grid, and relates to a power state prediction method of a series battery system.
Background
Since the 21 st century, with the long-term development of traditional fuel automobiles, the environmental pollution is becoming more serious, and electric automobiles are coming, becoming one of the future development trends of the automobile industry. The Power battery is used as a main Power source of the electric automobile, and the current Power State (State of Power, SOP) of the electric automobile, namely the peak Power which can be provided by the electric automobile within a period of time, is accurately obtained, so that the Power battery is of great importance for prolonging the service life of the Power battery, reducing the cost, safely operating and the like. However, there are many factors that affect the accurate acquisition of the SOP of a battery, and the SOP of a battery cannot be measured directly with difficulty. Meanwhile, due to the influence of factors such as use environment and different driving modes, the battery system in the electric automobile has inconsistency of battery cells, so that the SOP of the series battery system is more difficult to accurately obtain.
At present, research on SOP prediction methods at home and abroad is mostly concentrated on single batteries, documents related to SOP prediction methods of a series battery system are not abundant, and a patent (CN201911425010.9) discloses a power state method of the battery system, wherein a battery health state index determined according to internal resistance and current power of the battery system is used as a correction index to correct an initial power parameter to reflect the capability of the battery system for providing power, but the method is complex and has high requirements on estimation value precision of the battery health state. The patent (CN201610799603.1) discloses a method for obtaining the maximum output power of a battery pack at different states of charge at a preset temperature, according to which the battery pack is at different temperaturesThe method has the advantages that the internal resistance is equivalent, a first preset coefficient is obtained, and the output power of the battery pack is estimated according to the first preset coefficient and the maximum output power. In order to further improve the SOP measurement and calculation precision of the series battery system, the invention discloses a power state prediction method of the series battery system, which improves the SOP prediction precision through two approaches: firstly, aiming at the problem of more factors influencing the SOP prediction of the battery system, the SOP prediction method based on the multi-constraint methods such as current constraint, voltage constraint, SOC constraint and the like is adopted to obtain the SOP prediction base value SOP of the battery systemb(ii) a Second, obtaining SOPbOn the basis, aiming at the problem that the inconsistency of the battery monomers is not considered in the SOP of the battery system based on the multi-constraint method, the difference of the terminal voltage of each battery monomer in the series circuit is utilized, and the SOP corrector based on the BP neural network is adopted to obtain the SOP compensation value delta SOP capable of reflecting the influence of the inconsistency of each battery monomerbFurther, the SOP prediction method of the battery system based on the multi-constraint method is perfected, and the SOP prediction precision of the battery system is improved.
Disclosure of Invention
Based on this, the present invention provides a method for predicting the power state of a series battery system, which can consider the voltage constraint, the battery constraint and the inconsistency of battery cells, wherein the series battery system is formed by connecting n battery cells in series, wherein n is a natural number greater than 1, the method comprises the following steps, the structure diagram of which is shown in fig. 1:
step 1: establishing a second-order battery system equivalent circuit model (2) containing 2 RC parallel circuits according to the battery monomer model parameters (1) and the circuit characteristics of the sum of the impedance voltages such as the current equal at all positions and the total voltage in the series circuit, wherein as shown in figure 2, the battery system model parameters (1) comprise the open-circuit terminal voltage U of the battery systemb0Internal resistance R of the battery system b2 resistors R in RC parallel circuitbs、RblAnd a capacitor Cbs、Cbl；
Step 2: battery system voltage detection value UbBattery system detection value IbAs input, combined with battery systemsThe battery system space state equation established by the equivalent circuit model is used for obtaining the SOC of the battery system through the SOC estimation module (3) of the battery systemb；
And step 3: by the voltage U of the battery systembBattery system current IbAnd SOC of the battery systembAs input, the power state base value SOP of the battery system is generated by a power state base value prediction module (4) in combination with a battery system equivalent modelb；
And 4, step 4: by the voltage U of each battery in the battery system1～UnVoltage U of battery systembAs input, the voltage deviation value delta U of each battery cell is obtained through a voltage controller (5)1～ΔUnWherein the voltage controller is designed as
Figure BDA0003269425920000021
i is a natural number which is more than or equal to 1 and less than or equal to n;
and 5: based on the voltage deviation value delta U of each battery unit1～ΔUnAs an input vector, a battery system power state compensation value delta SOP is obtained by using an SOP corrector (6) based on a BP neural networkbI.e. the output vector of the BP neural network, wherein the SOP corrector (6) based on the BP neural network is designed as follows: (1) determining input layer Δ U separately1～ΔUnIntermediate layer and output layer Δ SOPbThe number of neurons; (2) setting training parameters according to the determined neuron number of the BP neural network model, and performing network training on the SOP corrector based on the BP neural network, wherein the training parameters comprise a training target, training times and a learning gradient; (3) establishing sample acquisition data and testing, wherein the sample data comprises training data, verification data and test data, and performing network test and delta SOP judgment on the test databIf the error is smaller than the set threshold, the established SOP corrector based on the BP neural network meets the set requirement, otherwise, the loop (2) is switched to;
step 6: finally, the power state base value SOP of the battery systembAnd compensation value Δ SOPbAre superposed to produce a cascadeSOP (state of Power) prediction value of battery systemr。
The power state base value prediction module adopts a multi-constraint condition method to carry out SOP (state of charge) on the power state base value of the battery systembThe specific design of the prediction is as follows: (1) calculating open circuit voltage Ub0Sustained peak current under the constraint of
Figure BDA0003269425920000031
In the formula (I), the compound is shown in the specification,
Figure BDA0003269425920000032
respectively the maximum discharge current and the maximum charge current of the battery system in the sampling time L, delta t is unit sampling time, L is sampling length, U isbs,k、Ubs,kThe voltages, U, across 2 RC circuits at k sampling times, respectivelyb,min、Ub,maxDischarge cutoff voltage, charge cutoff voltage for battery system, Cb0For the rated capacity, eta, of the battery system0For charge-discharge conversion efficiency, τ1、τ2Is a constant number of times, and is,
Figure BDA0003269425920000033
calculating a deviation derivative;
(2) calculating SOCbSustained peak current under the constraint of
Figure BDA0003269425920000034
In the formula (I), the compound is shown in the specification,
Figure BDA0003269425920000035
maximum current, SOC, of the battery system during discharging and charging, respectivelymin、SOCmaxRespectively the SOC of the battery system during dischargingbMinimum value, battery system SOC during chargingbMaximum value, SOCb,kFor the k-time battery system SOCb；
(3) Obtaining a power state base value SOP of a battery systembI.e., the peak power of the battery system at time k,
Figure BDA0003269425920000036
in the formula of Ub,kFor the battery system terminal voltage at time k,
Figure BDA0003269425920000037
respectively the continuous peak current when the battery system is charged and discharged,
Figure BDA0003269425920000038
the maximum discharge current and the maximum charge current are respectively designed for the battery system.
The parameters of the battery monomer model comprise battery open-circuit voltage U0(SOC), dynamic resistance R of batterys(t)、Rl(t) and dynamic capacitance of Battery Cs(t)、Cl(t) internal cell resistance R (t).
The battery system model parameters are calculated as follows: u shapeb0(SOCb)＝nU0(SOCb)、Rb(t)＝nR(t)、Rbs(t)＝nRs(t)、
Figure BDA0003269425920000039
Rbl(t)＝nRl(t)、
Figure BDA00032694259200000310
The battery system space state equation is as follows:
Figure BDA0003269425920000041
Figure BDA0003269425920000042
[Ub,k]＝Ub0,k-Rb,kIb,k-Ubs,k-Ubl,k+vk，vk、wkrespectively system observation noise and process noise, and k is a natural number greater than 1.
The SOC estimation method of the battery system is not only suitable for a Kalman filtering method, but also suitable for an extended Kalman filtering method, an unscented Kalman filtering method and an improved algorithm thereof.
The power state prediction method is not only suitable for lithium ion batteries, but also suitable for lead-acid batteries, nickel-hydrogen batteries and nickel-cadmium batteries.
Compared with the published document (CN201911425010.9), the invention has the following beneficial technical effects: the method has the advantages that firstly, the influence of factors such as current, voltage and SOC on the SOP predicted value is considered through a multi-constraint method in the prediction of the SOP basic value of the battery system, and the prediction precision of the SOP basic value of the battery system is improved; secondly, a voltage corrector and a Back Propagation (BP) neural network algorithm-based SOP corrector are used for obtaining a SOP compensation value of the battery system, the influence of battery inconsistency is considered, and the SOP prediction precision of the battery system is improved;
drawings
FIG. 1 is a block diagram of a method for predicting a power state of a series battery system;
fig. 2 is a schematic diagram of an equivalent circuit model of a series-connected battery system.
Detailed Description
The present invention will now be described in further detail with reference to specific examples, which are intended to be illustrative, but not limiting, of the invention.
(1) Establishing equivalent circuit model of battery system
The series battery system is formed by connecting 3 battery monomers in series, wherein the rated voltage of each battery monomer is 3.7V, the rated capacity is 860mAh, and the discharge cut-off voltage is 3V. The equivalent circuit model (2) of the series battery system is a second-order equivalent circuit model, and a main circuit of the equivalent circuit model is composed of 2 RC parallel circuits and a controlled voltage source Ub0(SOC) and internal resistance R of batterybAnd the equivalent circuit diagram of the battery system is shown in FIG. 2, and the model parameters (1) of the battery system are calculated as follows: u shapeb0(SOC)＝3U0(SOC)、Rb(t)＝3R(t)、Rbs(t)＝3Rs(t)、
Figure BDA0003269425920000043
Rbl(t)＝3Rl(t)、
Figure BDA0003269425920000044
In the formula of U0(SOC) is the open circuit voltage of the cell, R (t) is the internal resistance of the cell, Rs(t)、Rl(t) and Cs(t)、Cl(t) resistance and capacitance, respectively, describing the cell dynamics, the cell model parameters (1) versus SOC can be obtained by the method described in patent (ZL 2015104171032).
(2) Obtaining battery system SOCb
According to the established equivalent circuit model of the battery system, the SOC of the battery system is usedb2 RC terminal voltage Ubs、UblAs a state vector with the battery system current IbVoltage UbEstablishing a space state equation for the input quantity and the output quantity of the system as follows:
Figure BDA0003269425920000051
[Ub,k]＝Ub0,k-Rb,kIb,k-Ubs,k-Ubl,k+vk，vk、wkrespectively system observation noise and process noise, and k is a natural number greater than 1.
In the SOC estimation module (3) of the battery system, the SOC of the battery system is obtained by adopting an unscented Kalman filtering methodbThe specific steps can be carried out according to the description of the literature (CN 105182245A): 1) initializing a state variable mean value and a mean square error; 2) acquiring sampling points and corresponding weights; 3) state estimation and time update of mean square error; 4) calculating a gain matrix; 5) state estimation and mean square error measurement update.
(3) Obtaining a battery system power state base value
By the voltage U of the battery systembBattery system current IbAnd SOC of the battery systembAs input, the power state base value SOP of the battery system is generated by a power state base value prediction module (4) in combination with a battery system equivalent modelbThe specific design is as follows:
1) calculating open circuit voltage Ub0Sustained peak current under the constraint of
Figure BDA0003269425920000052
In the formula (I), the compound is shown in the specification,
Figure BDA0003269425920000053
respectively the maximum discharge current and the maximum charge current of the battery system in the sampling time L, delta t is unit sampling time, L is sampling length, U isbs,k、Ubs,kThe voltages, U, across 2 RC circuits at k sampling times, respectivelyb,min、Ub,maxDischarge cutoff voltage, charge cutoff voltage for battery system, Cb0For the rated capacity, eta, of the battery system0For charge-discharge conversion efficiency, τ1、τ2Is a constant number of times, and is,
Figure BDA0003269425920000061
calculating a deviation derivative;
2) calculating battery system SOCbSustained peak current under constraint
Figure BDA0003269425920000062
In the formula (I), the compound is shown in the specification,
Figure BDA0003269425920000063
maximum current, SOC, of the battery system during discharging and charging, respectivelymin、SOCmaxRespectively the SOC of the battery system during dischargingbMinimum value, battery system SOC during chargingbMaximum value, SOCb,kFor the k-time battery system SOCb；
3) Obtaining a power state base value SOP of a battery systembI.e., the peak power of the battery system at time k,
Figure BDA0003269425920000064
in the formula of Ub,kFor the battery system terminal voltage at time k,
Figure BDA0003269425920000065
respectively discharging and charging the battery systemThe continuous peak current at the time of the current,
Figure BDA0003269425920000066
the maximum discharge current and the maximum charge current are respectively designed for the battery system.
(4) Obtaining a battery system power state compensation value
By the voltage U of each battery in the battery system1～U3Voltage U of battery systembAs input, the voltage deviation value delta U of each battery cell is obtained through a voltage controller (5)1～ΔU3Wherein the voltage controller is designed as
Figure BDA0003269425920000067
Based on the voltage deviation value delta U of each battery unit1～ΔU3As an input vector, a battery system power state compensation value delta SOP is obtained by using an SOP corrector (6) based on a BP neural networkbI.e. the output vector of the BP neural network, wherein the SOP corrector (6) based on the BP neural network is designed as follows: 1) determining input layer Δ U separately1～ΔU3The number of neurons is 3, the middle layer is 15 neurons, and the output layer Δ SOPbThe number of neurons in the population is 1; 2) setting training parameters according to the determined number of neurons of the BP neural network model, and performing network training on the SOP corrector based on the BP neural network, wherein the training parameters comprise 250000 training targets, the training frequency is 1000, and the learning gradient is 11.8; 3) establishing sample acquisition data and testing, wherein the sample data comprises 175000 training data, 375000 verification data and 37500 test data, and performing network test and judgment on the test data to determine delta SOPbAnd (3) whether the error is smaller than a set threshold value of 0.3, if so, indicating that the established SOP corrector based on the BP neural network meets the set requirement, otherwise, turning to the 2) loop.
(5) Generating a series battery system power state prediction value
Finally, the power state base value SOP of the battery systembAnd compensation value Δ SOPbSuperposed to generate series-connected cell system workRate state prediction SOPr。

Claims (7)

1. A power state prediction method of a series battery system is provided, the series battery system is formed by connecting n battery monomers in series, wherein n is a natural number larger than 1, the method comprises the following steps:
step 1: establishing a second-order battery system equivalent circuit model containing 2 RC parallel circuits according to the battery monomer model parameters and the circuit characteristics of the sum of the impedance voltages such as equal current everywhere and total voltage in the series circuit, wherein the battery system model parameters comprise the open-circuit terminal voltage U of the battery systemb0Internal resistance R of the battery systemb2 resistors R in RC parallel circuitbs、RblAnd a capacitor Cbs、Cbl；
Step 2: battery system voltage detection value UbBattery system detection value IbAs input, the battery system SOC is obtained by combining a battery system space state equation established by a battery system equivalent circuit model and a battery system SOC estimation moduleb；
And step 3: by the voltage U of the battery systembBattery system current IbAnd SOC of the battery systembGenerating a battery system power state base value SOP by combining a battery system equivalent model and a power state base value prediction module as inputb；
And 4, step 4: by the voltage U of each battery in the battery system1～UnVoltage U of battery systembAs input, obtaining the voltage deviation value delta U of each battery cell after passing through a voltage controller1～ΔUnWherein the voltage controller is designed as
Figure FDA0003269425910000011
i is a natural number which is more than or equal to 1 and less than or equal to n;
and 5: based on the voltage deviation value delta U of each battery unit1～ΔUnObtaining a battery system by using an SOP corrector based on a BP neural network as an input vectorPower state compensation value Δ SOPbI.e., the output vector of the BP neural network, wherein the SOP corrector based on the BP neural network is designed as follows: (1) determining input layer Δ U separately1～ΔUnIntermediate layer and output layer Δ SOPbThe number of neurons; (2) setting training parameters according to the determined number of neurons of the BP neural network model, and performing network training on the SOP corrector based on the BP neural network, wherein the training parameters comprise a training target, training times and learning speed; (3) establishing sample acquisition data and testing, wherein the sample data comprises training data, verification data and test data, and performing network test and delta SOP judgment on the test databIf the error is smaller than the set threshold, the established SOP corrector based on the BP neural network meets the set requirement, otherwise, the loop (2) is switched to;
step 6: finally, the power state base value SOP of the battery systembAnd compensation value Δ SOPbSuperposition to generate predicted value SOP of power state of series-connected battery systemr。
2. The method of claim 1, wherein the power state base prediction module employs a multi-constraint condition method to perform the SOP of the battery systembThe specific design of the prediction is as follows: (1) calculating open circuit voltage Ub0Sustained peak current under the constraint of
Figure FDA0003269425910000021
In the formula (I), the compound is shown in the specification,
Figure FDA0003269425910000022
respectively the maximum discharge current and the maximum charge current of the battery system in the sampling time L, delta t is unit sampling time, L is sampling length, U isbs,k、Ubs,kThe voltages, U, across 2 RC circuits at k sampling times, respectivelyb,min、Ub,maxDischarge cutoff voltage, charge cutoff voltage for battery system, Cb0For the rated capacity, eta, of the battery system0For charge-discharge conversion efficiency, τ1、τ2Is a constant number of times, and is,
Figure FDA0003269425910000023
calculating a deviation derivative;
(2) calculating SOCbSustained peak current under the constraint of
Figure FDA0003269425910000024
In the formula (I), the compound is shown in the specification,
Figure FDA0003269425910000025
maximum current, SOC, of the battery system during discharging and charging, respectivelymin、SOCmaxRespectively the SOC of the battery system during dischargingbMinimum value, battery system SOC during chargingbMaximum value, SOCb,kFor the k-time battery system SOCb；
(3) Obtaining a power state base value SOP of a battery systembI.e., the peak power of the battery system at time k,
Figure FDA0003269425910000026
in the formula of Ub,kFor the battery system terminal voltage at time k,
Figure FDA0003269425910000027
respectively the continuous peak current when the battery system is charged and discharged,
Figure FDA0003269425910000028
the maximum discharge current and the maximum charge current are respectively designed for the battery system.
3. The method as claimed in claim 1, wherein the battery cell model parameter includes a battery open-circuit voltage U0(SOCb) Battery dynamic resistance Rs(t)、Rl(t) and dynamic capacitance of Battery Cs(t)、Cl(t) internal cell resistance R (t).
4. The method of claim 1, wherein the battery system model parameters are calculated as follows: u shapeb0(SOCb)＝nU0(SOCb)、Rb(t)＝nR(t)、Rbs(t)＝nRs(t)、
Figure FDA0003269425910000029
Rbl(t)＝nRl(t)、
Figure FDA00032694259100000210
5. The method of claim 1, wherein the battery system space state equation is:
Figure FDA0003269425910000031
Figure FDA0003269425910000032
[Ub,k]＝Ub0,k-Rb,kIb,k-Ubs,k-Ubl,k+vk，vk、wkrespectively system observation noise and process noise, and k is a natural number greater than 1.
6. The method according to claim 1, wherein the battery system SOC estimation module employs a kalman filter, an extended kalman filter, or an unscented kalman filter, and an improved algorithm thereof.
7. The method of claim 1, wherein the battery is a lithium ion battery, a lead acid battery, a nickel metal hydride battery, or a nickel cadmium battery.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114895189A (en) * 2022-05-16 2022-08-12 盐城工学院 Energy state prediction method for series battery system
CN114910806A (en) * 2022-05-16 2022-08-16 盐城工学院 Parallel battery system modeling method
CN115144760A (en) * 2022-09-01 2022-10-04 中创新航科技股份有限公司 Estimation method and device for battery system SOP
CN116470623A (en) * 2023-06-01 2023-07-21 苏州精控能源科技有限公司 Large energy storage system charge and discharge power state prediction method, electronic equipment and medium

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114895189A (en) * 2022-05-16 2022-08-12 盐城工学院 Energy state prediction method for series battery system
CN114910806A (en) * 2022-05-16 2022-08-16 盐城工学院 Parallel battery system modeling method
CN114910806B (en) * 2022-05-16 2024-05-07 盐城工学院 Modeling method for parallel battery system
CN114895189B (en) * 2022-05-16 2024-05-10 盐城工学院 Energy state prediction method for series battery system
CN115144760A (en) * 2022-09-01 2022-10-04 中创新航科技股份有限公司 Estimation method and device for battery system SOP
CN116470623A (en) * 2023-06-01 2023-07-21 苏州精控能源科技有限公司 Large energy storage system charge and discharge power state prediction method, electronic equipment and medium
CN116470623B (en) * 2023-06-01 2023-08-29 苏州精控能源科技有限公司 Large energy storage system charge and discharge power state prediction method, electronic equipment and medium

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Application publication date: 20211217