CN112305426B - Lithium ion battery power state estimation system under multi-constraint condition - Google Patents

Lithium ion battery power state estimation system under multi-constraint condition Download PDF

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CN112305426B
CN112305426B CN202011161636.6A CN202011161636A CN112305426B CN 112305426 B CN112305426 B CN 112305426B CN 202011161636 A CN202011161636 A CN 202011161636A CN 112305426 B CN112305426 B CN 112305426B
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CN112305426A (en
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沈佳妮
贺益君
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Shanghai Jiaotong University
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    • 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]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • 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
    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • 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]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G01R31/388—Determining ampere-hour charge capacity or SoC involving voltage measurements

Abstract

The invention relates to a lithium ion battery power state estimation system under multiple constraint conditions, which is applied to a lithium ion battery energy storage device with a microcontroller and a memory, and comprises the microcontroller, an SOP estimator, a current meter and a voltage meter which are controlled by the microcontroller, and the memory for storing programs executed by the microcontroller, wherein the SOP estimator performs SOP estimation according to the control of the microcontroller and sends estimation results to the microcontroller, and the SOP estimator realizes the estimation of the lithium ion battery power state sequentially through an off-line model construction step and an on-line algorithm implementation step. Compared with the prior art, the method has the advantages of realizing higher-precision SOP estimation, ensuring the accuracy and reliability of the SOC estimation value of the lithium battery and the like.

Description

Lithium ion battery power state estimation system under multi-constraint condition
Technical Field
The invention relates to the technical field of battery management, in particular to a lithium ion battery power state estimation system under a multi-constraint condition.
Background
Lithium ion batteries have been widely used in consumer electronics, electric vehicles, energy storage power stations, and other fields because of their advantages of high energy density, high output power, long charge-discharge life, and the like. The lithium ion battery State includes a State of Charge (SOC), a State of Health (SOH), and a Power State (SOP). The power state is a necessary parameter for implementing power distribution of the lithium ion battery system, and is important for guaranteeing efficient and safe operation of the system. Currently, most commercial battery management systems lack an SOP estimation function. In order to solve the problem, research proposes that current, voltage, power, temperature and other limitations are considered, and a battery equivalent circuit model is combined to perform SOP estimation in real time. However, the method often has taylor expansion errors and lacks a multi-constraint synchronous processing mechanism, so that the accuracy and reliability of the SOP estimation are difficult to ensure.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a lithium ion battery power state estimation system under the multi-constraint condition, which can realize high-precision SOP estimation.
The purpose of the invention can be realized by the following technical scheme:
a lithium ion battery power state estimation system under multiple constraint conditions is applied to a lithium ion battery energy storage device with a microcontroller and a memory, and comprises the microcontroller, an SOP estimator, a current meter and a voltage meter which are controlled by the microcontroller, and the memory for storing programs executed by the microcontroller, wherein the SOP estimator performs SOP estimation according to the control of the microcontroller and sends estimation results to the microcontroller, and the SOP estimator realizes the estimation of the lithium ion battery power state sequentially through an off-line model construction step and an on-line algorithm implementation step.
The specific steps of the off-line model building step of the SOP estimator comprise:
1.1) carrying out an open-circuit voltage experiment on the lithium ion battery, establishing an open-circuit voltage model, determining a functional relation between the open-circuit voltage and the SOC, and determining the open-circuit voltage; specifically, the method comprises the following steps:
111) charging the lithium ion battery to a cut-off voltage in a constant-current and constant-voltage manner, and standing for a certain time;
112) Continuously discharging the battery to a specific SOC by using current with the rate of 1C, and standing for 1 hour; the discharge current is defined as a positive value, the charge current is defined as a negative value, and in the whole process, the terminal voltage and the load current of the lithium ion battery are synchronously acquired at the sampling frequency of 1 Hz.
113) And establishing a functional relation between the open-circuit voltage and the SOC according to the SOC of each standing point and the corresponding open-circuit voltage measured value, and determining the open-circuit voltage.
1.2) carrying out peak power test characteristic test on the lithium ion battery, establishing an equivalent circuit model based on the measured voltage response curve data, and completing off-line model construction by identifying each parameter of the equivalent circuit model.
The peak power test characteristic test is used for charging the lithium ion battery to a full charge state, then discharging to a specific SOC at a 1C multiplying power, then charging and discharging under a simplified DST working condition, finally sequentially performing pulse charging and discharging at a multiplying power of first positive and then negative for multiple times, continuously repeating each pulse current for 10s according to the operation until a discharge cut-off voltage is reached.
The equivalent circuit model comprises open-circuit voltage, ohmic internal resistance and a first-order or multi-order RC network, wherein the open-circuit voltage is obtained according to the step 1.1), and the first-order or multi-order RC network comprises a polarization resistor and an equivalent capacitor.
The online algorithm implementation steps of the SOP estimator comprise the following specific steps:
2.1) based on the definition of the SOP, adding multi-parameter constraint limits of current, voltage, SOC and peak power to the battery peak power at any k moment in the charging and discharging process, and respectively converting a discharging SOP estimation problem and a charging SOP estimation problem into a multi-constraint nonlinear optimization problem for searching sustainable peak power in a time window L in the future by combining the multi-parameter constraint limits and an equivalent circuit model;
the specific content of the multi-parameter constraint limits of current, voltage, SOC and peak power is as follows:
Figure BDA0002744526440000021
in the formula, V b,min And V b,max Upper and lower limits, I, designed for the terminal voltage of the lithium ion battery, respectively min And I max Respectively designing the upper limit and the lower limit of current, SOC for the lithium ion battery min And SOC max Respectively designing upper and lower limits, P, for SOC min And P max And designing an upper limit and a lower limit for the sustainable power peak value of the lithium ion battery respectively.
The specific contents of the multi-constraint nonlinear optimization for finding the sustainable discharge peak power in a period of time L in the future by converting the discharge estimation of the SOP are as follows:
for the discharge process, the current is constrained not to exceed the maximum allowable discharge current, the terminal voltage is constrained to be between the discharge cut-off voltage and the charge cut-off voltage, the SOC is constrained to be that the discharge cut-off SOC is not more than 1, and the peak power is constrained not to exceed the maximum allowable discharge power.
The specific contents of the multi-constraint nonlinear optimization for converting the charge estimation of the SOP into the search of the sustainable charge peak power within a future time window L are as follows:
for the charging process, the current is constrained to be not less than the maximum allowable discharging current, the terminal voltage is constrained to be between the discharging cut-off voltage and the charging cut-off voltage, the SOC is constrained to be that the charging cut-off SOC is greater than 0, and the peak power is constrained to be not less than the maximum allowable discharging power.
2.2) solving the multi-constraint nonlinear optimization problem in a time interval [ k, k + L ] according to the detected voltage and current to obtain the discharging SOP and the charging SOP at any k moment.
Compared with the prior art, the method is based on the equivalent circuit model, adopts a multi-constraint optimization algorithm, synchronously considers a plurality of constraints such as maximum allowable charge/discharge current, maximum allowable charge/discharge voltage, charge/discharge cut-off SOC (state of charge/discharge) and maximum allowable charge/discharge power, can simultaneously process the plurality of constraints, avoids Taylor expansion errors in the equivalent circuit model simplification, and can realize higher-precision SOP estimation, thereby ensuring the accuracy and reliability of the SOC estimation value of the lithium battery and finally improving the overall performance of the battery management system.
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FIG. 1 is a schematic structural diagram of a lithium ion battery power state estimation system under multiple constraint conditions in an embodiment;
The reference numbers in the figures indicate:
100. microcontroller, 102, memory, 104, current and voltage meter, 106, SOP estimator;
FIG. 2 is a schematic diagram of a lithium ion battery power state estimation system under multiple constraints in an embodiment;
FIG. 3 is a waveform diagram of current excitation and voltage response in an embodiment;
FIG. 4 is a schematic diagram of an equivalent circuit of a lithium ion battery in an embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
The invention relates to a lithium ion battery power state estimation system under multiple constraint conditions, which is applied to a lithium battery energy storage device with a microcontroller and a memory, and the structure of the estimation system is shown in fig. 1, and the estimation system comprises a microcontroller 100, a memory 102, a current and voltage meter 104 and an SOP estimator 106.
The microcontroller 100 is used to control the SOP estimator 106, the current and voltage meters 104 as a whole. The memory 102 is used for storing programs executed by the microcontroller 100. The current and voltage meter 104 measures the current and voltage of the lithium ion battery according to the control of the microcontroller 100. The SOP estimator 106 estimates the SOP according to the control of the microcontroller 100 and provides the estimated result to the microcontroller 100. The construction of the SOP estimator 106 includes off-line model construction and on-line implementation.
The off-line model construction of the SOP estimator comprises the following two steps:
step S11: and performing an open-circuit voltage experiment on the lithium ion battery to establish an open-circuit voltage model.
In this embodiment, a ternary system lithium ion battery is taken as an example, and the capacity of the ternary system battery is 16 Ah. Firstly, charging the battery to a cut-off voltage in a constant-current and constant-voltage mode, and standing for a certain time, wherein the standing time is confirmed according to experimental requirements and can be generally 1 hour; after the battery is continuously discharged to a specific SOC by the current with the rate of 1C, the battery is kept standing for 1 hour, the specific SOC can be self-defined according to the requirement, and usually 10% SOC per interval can be adopted. The discharge current is defined as a positive value, and the charge current is defined as a negative value. In the whole process, the battery terminal voltage and the load current are synchronously acquired at the sampling frequency of 1Hz, so that the test requirement can be met. And establishing a functional relation between the open-circuit voltage and the SOC according to the SOC of each standing point and the corresponding open-circuit voltage measured value, wherein the SOC of each standing point can be defined by self, and usually 10% SOC at each interval can be adopted. In the present embodiment, a 12 th order polynomial form is adopted to represent the functional relationship between the open-circuit voltage VOC and the SOC:
Figure BDA0002744526440000041
in the formula, the required identificationHas a parameter of polynomial coefficient beta 1j The required SOC is calculated according to a current integration method:
Figure BDA0002744526440000042
in the formula, SOC (0) is an initial SOC value of the battery, C is a battery capacity, and I is a load current. Combining the relational expressions (1) and (2), and adopting a least square method to carry out beta 1j Performing parameter identification to determine the functional relation between the open-circuit voltage and the SOC; using least square method to measure beta 1j The parameter identification is performed in the prior art and is not described herein in detail.
Step S12: and carrying out peak power test characteristic test on the lithium ion battery, and establishing an equivalent circuit model based on the tested data. The peak power test characteristic test is shown in fig. 3. The battery is charged to a full charge state. And then, discharging to a specific SOC at a 1C multiplying power, then performing charging and discharging under a simplified DST working condition, and finally performing pulse charging and discharging respectively at 3C, -1C, 5C, -2C, 6C, -3C, 7C and-4C multiplying powers in sequence, wherein each pulse current lasts for 10 s.
And continuously repeating the steps until the discharge cut-off voltage is reached. In high and medium SOC regions, the maximum pulse discharge multiplying power is 7C; in the low SOC region, the maximum pulse discharge rate is reduced to 5C subject to the discharge cutoff voltage. The SOC interval of the obtained power test curve is [ 100%, 3% ]. The charge cut-off voltage is set to be 3.8V, and the discharge cut-off voltage is set to be 2.7V.
The equivalent circuit model comprises three parts: open circuit voltage VOC, ohmic internal resistance R 0 And a first or multi-stage RC network, wherein the RC network is composed of a polarization resistor R 1 And an equivalent capacitance C 1 The open-circuit voltage VOC is determined by step S11. In this embodiment, a first order equivalent circuit model is used, as shown in FIG. 4, where V b Is the battery voltage and I is the load current. The equivalent circuit model conforms to the following voltage-current relationship:
Figure BDA0002744526440000051
V b ＝VOC＝V 1 -IR 0 (4)
for one sampling period Δ t, the discretized form of relations (3) and (4) can be expressed as:
Figure BDA0002744526440000052
V b,k ＝V OC (SOC k )-V 1,k -I k R 0,k (6)
wherein, for the time k, the battery voltage is V b,k Ohmic internal resistance of R 0,k Polarization resistance of R 1,k Equivalent capacitance of C 1,k A polarization voltage of V 1,k Time constant τ k ＝R k C k . In the present embodiment, Δ t is 1 s.
In the present embodiment, the circuit parameter R in the expressions (5) and (6) 0 、R 1 And C 1 Expressed in 6 th order polynomial form as a function of SOC and current I:
Figure BDA0002744526440000053
Figure BDA0002744526440000054
Figure BDA0002744526440000055
wherein the required identification parameter is polynomial coefficient beta 2i 、β 3i 、β 4i 、β 5i 、β 6i And beta 7i . In the identification process, based on the formulas (5) and (6), the voltage response curve in the figure 3 is fitted by adopting a least square method to obtain beta 2i 、β 3i 、β 4i 、β 5i 、β 6i And beta 7i . At this point, the off-line model is constructedAnd (4) obtaining.
The online implementation of the SOC estimator comprises the following two steps:
step S21: according to the definition of SOP, the peak power at any time t in the charging and discharging process t,peak Can be defined as formula (10). Wherein, since the charging current is defined as a negative value, the charging peak power is expressed as a minimum charging power:
Figure BDA0002744526440000056
from the perspective of the optimization strategy, for the current time k, the SOP estimation problem can be converted into an optimization problem for finding the sustainable peak power within a future time window L, and the optimization objective can be expressed as formula (11):
Figure BDA0002744526440000061
when the time window is short, the current can be considered constant, denoted as I k+L . At this time, the voltage at the battery terminal is continuously reduced in a window interval in the discharging process under the action of ohmic drop and polarization, and the minimum power appears at the terminal moment and is represented as P k+L (ii) a During the charging process, the voltage of the battery terminal continuously rises in a window interval, and the maximum power in the window interval appears at the initial moment and is represented as P k . Accordingly, the battery peak power optimization objective may be converted to equation (12):
Figure BDA0002744526440000062
in order to ensure the operation safety of the battery and slow down the aging speed of the battery, the peak power needs to be limited by current, voltage, SOC and the like. Therefore, when implementing peak power estimation, the following constraints are considered simultaneously:
Figure BDA0002744526440000063
wherein, V b,min And V b,max Designing upper and lower limits for terminal voltage, I min And I max To design the upper and lower current limits, SOC min And SOC max Design upper and lower limits for SOC, P min And P max And designing an upper limit and a lower limit for the sustainable peak value of the battery. For the charging and discharging processes, the upper and lower limits of each parameter are preset by manufacturers respectively.
At this time, in combination with the equivalent circuit model and the multi-parameter constraint, the SOP estimation problem can be transformed into a multi-constraint nonlinear optimization problem within the time window L. The discharging SOP estimation problem may be represented as a multi-constrained nonlinear optimization problem P (1), and the charging SOP estimation problem may be represented as a multi-constrained nonlinear optimization problem P (2):
and (3) discharging:
Figure BDA0002744526440000064
s.t.P k+L ＝I k+L V b,k+L
formulas (1), (2), (5) - (9), (13)
Figure BDA0002744526440000065
Figure BDA0002744526440000066
Figure BDA0002744526440000067
Figure BDA0002744526440000068
And (3) charging process:
Figure BDA0002744526440000069
s.t.P k+L ＝I k+L V b,k+L
formulas (1), (2), (5) - (9), (13)
Figure BDA0002744526440000071
Figure BDA0002744526440000072
Figure BDA0002744526440000073
Figure BDA0002744526440000074
Wherein, for the discharge process, the current is constrained to the maximum allowable discharge current
Figure BDA0002744526440000075
The terminal voltage is constrained to be the discharge cut-off voltage
Figure BDA0002744526440000076
And charge cut-off voltage
Figure BDA0002744526440000077
The SOC constraint is the discharge cutoff SOC, denoted as
Figure BDA0002744526440000078
And not more than 1, peak power being constrained to maximum allowable discharge power
Figure BDA0002744526440000079
For the charging process, the current is constrained to a maximum allowable discharge current
Figure BDA00027445264400000710
The terminal voltage is constrained to be the discharge cut-off voltage
Figure BDA00027445264400000711
And charge cut-off voltage
Figure BDA00027445264400000712
The SOC constraint is the charge cut-off SOC, denoted as
Figure BDA00027445264400000713
And is greater than 0, the peak power is restricted to the maximum allowable discharge power
Figure BDA00027445264400000714
Step S22: according to the detected voltage and current, a proper nonlinear optimization algorithm commonly used in the field is adopted to solve the multi-constraint nonlinear optimization problems P (1) and P (2) in a time interval [ k, k + L ], and then the discharging SOP and the charging SOP at any k moment can be obtained.
The method is based on an equivalent circuit model, adopts an optimization strategy, synchronously considers a plurality of constraints such as maximum allowable charge/discharge current, maximum allowable charge/discharge voltage, charge/discharge cut-off SOC (state of charge/discharge) and maximum allowable charge/discharge power, and can realize higher-precision SOP (state of charge) estimation compared with an estimation method lacking a multi-constraint synchronous processing mechanism.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (9)

1. A lithium ion battery power state estimation system under multiple constraint conditions is applied to a lithium ion battery energy storage device with a microcontroller and a memory, and is characterized by comprising the microcontroller, an SOP estimator, a current meter and a voltage meter which are controlled by the microcontroller, and the memory for storing a program executed by the microcontroller, wherein the SOP estimator performs SOP estimation according to the control of the microcontroller and sends an estimation result to the microcontroller, and the SOP estimator realizes the estimation of the lithium ion battery power state sequentially through an offline model construction step and an online algorithm implementation step;
The online algorithm implementation steps of the SOP estimator comprise the following specific steps:
21) based on the definition of the SOP, in the charging and discharging process, multi-parameter constraint limits of current, voltage, SOC and peak power are added to the battery peak power at any k moment, and the discharging SOP estimation problem and the charging SOP estimation problem are respectively converted into a multi-constraint nonlinear optimization problem for searching sustainable peak power in a time window L in the future by combining the multi-parameter constraint limits and an equivalent circuit model;
and (3) discharging:
Figure FDA0003537084160000011
s.t.P k+L ＝I k+L V b,k+L
Figure FDA0003537084160000012
Figure FDA0003537084160000013
Figure FDA0003537084160000014
V n,k ＝V OC (SOC k )-V 1,k -I k R 0,k
Figure FDA0003537084160000015
Figure FDA0003537084160000016
Figure FDA0003537084160000017
Figure FDA0003537084160000021
Figure FDA0003537084160000022
Figure FDA0003537084160000023
Figure FDA0003537084160000024
Figure FDA0003537084160000025
and (3) charging process:
Figure FDA0003537084160000026
s.t.P k+L ＝I k+L V b,k+L
Figure FDA0003537084160000027
Figure FDA0003537084160000028
Figure FDA0003537084160000029
V b,k ＝V OC (SOC k )-V 1,k -I k R 0,k
Figure FDA00035370841600000210
Figure FDA00035370841600000211
Figure FDA00035370841600000212
Figure FDA00035370841600000213
Figure FDA00035370841600000214
Figure FDA00035370841600000215
Figure FDA00035370841600000216
Figure FDA00035370841600000217
wherein, for the discharge process, the current is constrained to the maximum allowable discharge current
Figure FDA00035370841600000218
The terminal voltage is constrained to be the discharge cut-off voltage
Figure FDA00035370841600000219
And charge cut-off voltage
Figure FDA00035370841600000220
The SOC constraint is the discharge cutoff SOC, denoted as
Figure FDA00035370841600000221
And not more than 1, peak power being constrained to maximum allowable discharge power
Figure FDA0003537084160000031
For the charging process, the current is constrained to a maximum allowable discharge current
Figure FDA0003537084160000032
The terminal voltage is constrained to be the discharge cut-off voltage
Figure FDA0003537084160000033
And charge cut-off voltage
Figure FDA0003537084160000034
The SOC constraint is the charge cut-off SOC, denoted as
Figure FDA0003537084160000035
And is greater than 0, the peak power is restricted to the maximum allowable discharge power
Figure FDA0003537084160000036
VOC is open circuit voltage; beta is a 1j The parameter to be identified is a polynomial coefficient; SOC (0) is the initial SOC value of the battery, C is the battery capacity, and I is the load current; for time k, the battery voltage is V b,k Ohmic internal resistance of R 0,k Polarization resistance of R 1,k Equivalent capacitance of C 1,k Polarization voltage of V 1,k Time constant τ k ＝R k C k (ii) a Δ t is 1 s; circuit parameter R 0 、R 1 And C 1 As a function of SOC and current I; polynomial coefficient beta 2i 、β 3i 、β 4i 、β 5i 、β 6i And beta 7i Identifying the required parameters; v b,min And V b,max Designing upper and lower limits for terminal voltage, I min And I max To design the upper and lower current limits, SOC min And SOC max Design upper and lower limits for SOC, P min And P max Designing upper and lower limits for the sustainable peak value of the battery;
22) and solving the multi-constraint nonlinear optimization problem in the time interval [ k, k + L ] according to the detected voltage and current to obtain the discharging SOP and the charging SOP at any k moment.
2. The lithium ion battery power state estimation system under multiple constraints according to claim 1, wherein the specific steps of the off-line model construction step of the SOP estimator include:
11) performing an open-circuit voltage experiment on the lithium ion battery, establishing an open-circuit voltage model, determining a functional relation between the open-circuit voltage and the SOC, and determining the open-circuit voltage;
12) and carrying out peak power test characteristic test on the lithium ion battery, establishing an equivalent circuit model based on the measured voltage response curve data, and identifying each parameter of the equivalent circuit model to complete off-line model construction.
3. The lithium ion battery power state estimation system under multiple constraint conditions according to claim 2, wherein the specific content in step 11) is:
111) charging the lithium ion battery to a cut-off voltage in a constant-current and constant-voltage manner, and standing for a certain time;
112) continuously discharging the battery to a specific SOC by using current with the rate of 1C, and standing for 1 hour;
113) and establishing a functional relation between the open-circuit voltage and the SOC according to the SOC of each standing point and the corresponding open-circuit voltage measured value, and determining the open-circuit voltage.
4. The system according to claim 3, wherein in step 112), the discharging current is defined as a positive value, the charging current is defined as a negative value, and in the whole process, the terminal voltage and the load current of the lithium ion battery are synchronously collected at a sampling frequency of 1 Hz.
5. The lithium ion battery power state estimation system under multiple constraint conditions according to claim 2, wherein the specific content of step 12) is:
the method comprises the steps of carrying out peak power test characteristic test on a lithium ion battery, establishing an equivalent circuit model based on measured data, carrying out peak power test characteristic test on the lithium ion battery, firstly charging the lithium ion battery to a full charge state, then discharging to a specific SOC at a 1C multiplying power, then carrying out charging and discharging under a simplified DST working condition, finally carrying out pulse charging and discharging sequentially at a multiplying power of firstly positive and then negative for multiple times, keeping each pulse current for 10s, and repeating the operation continuously until a discharge cut-off voltage is reached.
6. The multi-constraint lithium ion battery power state estimation system of claim 5, wherein the equivalent circuit model comprises an open circuit voltage, an ohmic internal resistance, and a first-order or multi-order RC network, the open circuit voltage being obtained according to step 11), the first-order or multi-order RC network comprising a polarization resistance and an equivalent capacitance.
7. The system for estimating the power state of the lithium ion battery under the multi-constraint condition according to claim 1, wherein the multi-parameter constraint limits of the current, the voltage, the SOC and the peak power are as follows:
Figure FDA0003537084160000041
in the formula, V b,min And V b,max Upper and lower limits, I, designed for the terminal voltage of the lithium ion battery, respectively min And I max Respectively designing the upper limit and the lower limit of current, SOC for the lithium ion battery min And SOC max Respectively designing upper and lower limits, P, for SOC min And P max And designing an upper limit and a lower limit for the sustainable power peak value of the lithium ion battery respectively.
8. The lithium ion battery power state estimation system under multiple constraint conditions according to claim 1, wherein the specific content of the transformation of the discharge estimation of the SOP into the multiple constraint nonlinear optimization for finding the sustainable discharge peak power within a future time window L is as follows:
for the discharge process, the current is constrained not to exceed the maximum allowable discharge current, the terminal voltage is constrained to be between the discharge cut-off voltage and the charge cut-off voltage, the SOC is constrained to be that the discharge cut-off SOC is not more than 1, and the peak power is constrained not to exceed the maximum allowable discharge power.
9. The lithium ion battery power state estimation system under multiple constraint conditions according to claim 1, wherein the specific content of the multi-constraint nonlinear optimization for converting the charge estimation of the SOP into the search for the sustainable charge peak power within a future time window L is as follows:
for the charging process, the current is constrained to be not less than the maximum allowable discharging current, the terminal voltage is constrained to be between the discharging cut-off voltage and the charging cut-off voltage, the SOC is constrained to be that the charging cut-off SOC is greater than 0, and the peak power is constrained to be not less than the maximum allowable discharging power.
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