Energy storage battery state evaluation method and device, electronic equipment and storage system
1. The method for evaluating the state of the energy storage battery is characterized by comprising the following steps of:
acquiring historical data of an energy storage battery;
obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
Calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
2. The method for evaluating the state of the energy storage battery according to claim 1, wherein the historical data of the energy storage battery comprises: current, voltage, temperature and time.
3. The method for evaluating the state of the energy storage battery according to claim 2, wherein the step of obtaining the accumulated work loss Δ Q of the energy storage battery according to the historical data of the energy storage battery specifically comprises:
obtaining the work accumulated loss delta Q of the energy storage battery through the established coupling relation according to the current, the voltage, the temperature and the time of the energy storage battery:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
4. The method for evaluating the state of the energy storage battery according to claim 2, wherein the accumulated resting loss Q of the energy storage battery is obtained according to historical data of the energy storage batteryloss(t) the step specifically comprises:
establishing a coupling mechanism between the shelf time and the battery calendar life, and calculating the shelf accumulated loss Qloss (t)：
Alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of 0.1-1.
5. The method for evaluating the state of the energy storage battery according to claim 1, wherein the temperature weighting coefficient β is calculated by the following formula:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this time.
6. The method according to claim 1, wherein the temperature weighting coefficient β is related to the work accumulated loss Δ Q and the rest accumulated lossQloss(t) coupling, and obtaining the state of health (SOH) of the energy storage battery, wherein the calculation formula of the state of health (SOH) is as follows:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
7. The method for evaluating the state of the energy storage battery is characterized by comprising the following steps of:
calibrating the state of health of the energy storage battery to obtain a calibrated state of health (SOH 1);
acquiring historical data of continuous operation of the energy storage battery after calibration;
obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
Calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
8. The method for evaluating the state of the energy storage battery according to claim 7, wherein the historical data of the energy storage battery comprises: current, voltage, temperature and time.
9. The method for evaluating the state of the energy storage battery according to claim 8, wherein the step of obtaining the accumulated work loss Δ Q of the energy storage battery according to the historical data of the energy storage battery specifically comprises:
obtaining the work accumulated loss delta Q of the energy storage battery through the established coupling relation according to the current, the voltage, the temperature and the time of the energy storage battery:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
10. The method for evaluating the state of the energy storage battery according to claim 8, wherein the accumulated resting loss Q of the energy storage battery is obtained according to historical data of the energy storage batteryloss(t) the step specifically comprises:
establishing a coupling mechanism between the shelf time and the battery calendar life, and calculating the shelf accumulated loss Qloss (t)：
Alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of 0.1-1.
11. The method for evaluating the state of the energy storage battery according to claim 7, wherein the temperature weighting coefficient β is calculated by the formula:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this time.
12. The method according to claim 7, wherein the temperature weighting coefficient β is related to the work accumulated loss Δ Q and the rest accumulated loss Qloss(t) coupling, and obtaining the state of health (SOH) of the energy storage battery, wherein the calculation formula of the state of health (SOH) is as follows:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
13. An energy storage battery state evaluation device, characterized by comprising:
the acquisition module is used for acquiring historical data of the energy storage battery;
the loss determining module is used for obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
The health state determining module is used for calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
14. The device for evaluating the state of the energy storage battery according to claim 13, wherein the historical data of the energy storage battery comprises: current, voltage, temperature and time.
15. The energy storage battery state evaluation device according to claim 14, wherein the calculation formula of the operation accumulated loss Δ Q is as follows:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
16. The apparatus according to claim 14, wherein the cumulative loss Q is set asidelossThe formula for calculation of (t) is:
alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of 0.1-1.
17. The energy storage battery state evaluation device according to claim 13, wherein the temperature weighting coefficient β is calculated by the formula:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this time.
18. The energy storage battery state evaluation device according to claim 13, wherein the state of health SOH is calculated by the formula:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
19. An energy storage battery state evaluation device, characterized by comprising:
the calibration module is used for calibrating the health state of the energy storage battery to obtain a calibrated health state SOH 1;
the acquisition module is used for acquiring historical data of the energy storage battery;
the loss determining module is used for obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
The health state determining module is used for calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
20. The device for evaluating the state of the energy storage battery according to claim 19, wherein the historical data of the energy storage battery comprises: current, voltage, temperature and time.
21. The device for evaluating the state of the energy storage battery according to claim 20, wherein the calculation formula of the operation accumulated loss Δ Q of the energy storage battery is as follows:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
22. The apparatus according to claim 20, wherein the accumulated loss Q of the energy storage battery is determined by the resting of the energy storage batterylossThe formula for calculation of (t) is:
alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of 0.1-1.
23. The energy storage battery state evaluation device according to claim 19, wherein the temperature weighting coefficient β is calculated by the formula:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this time.
24. The apparatus according to claim 19, wherein the temperature weighting factor β is related to the operating cumulative loss Δ Q and the resting cumulative loss Qloss(t) coupling, and obtaining the state of health (SOH) of the energy storage battery, wherein the calculation formula of the state of health (SOH) is as follows:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
25. An electronic device, characterized in that the electronic device comprises a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement an energy storage battery state evaluation method according to any one of claims 1 to 6.
26. A computer-readable storage medium storing at least one instruction which, when executed by a processor, implements an energy storage battery state evaluation method according to any one of claims 1 to 6.
Background
By the end of 2020, the accumulated loading capacity of lithium ion batteries is the largest among various electrochemical energy storage technologies, which reaches 2.9GW, and a high-speed growth situation is still maintained in the coming years, and the electrochemical energy storage system has the disadvantages of large number of batteries, large scale, complex use working condition and high requirements on the safety and service life of the energy storage battery.
The aging of the lithium battery is a long-term gradual change process, and the health state of the battery is influenced by various factors such as temperature, current multiplying power, cut-off voltage and the like. The state of health (SOH) is an important index of safety and stability of a lithium ion battery, and accurate prediction thereof is one of the preconditions and key technologies for operation of a battery management system, is very important for safety of a power grid and prolonging of service life of the battery, and is a hotspot and difficult problem of research all the time.
Chinese patent publication No. CN107505575A discloses a method for rapidly evaluating retired power batteries, which utilizes the data of batteries during the steady current charging process and adopts a method of fusing the data of capacity, internal resistance, power, self-discharge rate, etc. to estimate the health status of batteries, the process is complicated, long standing time is required, and the method is not suitable for the practical engineering application.
In the prior art, a battery state of health (SOH) calculation is also performed by using a definition method. The SOH is generally defined as the current maximum available capacity of the battery divided by the nominal capacity of the battery. Under the condition that the nominal capacity of the battery is known, the SOH of the battery can be calculated only by obtaining the maximum available capacity of the battery at the current moment, so that the battery can be discharged from a full-charge state to a cut-off voltage, and the capacity released in the process is the maximum available capacity of the battery at the current moment.
However, the definition method has poor practicability, the maximum available electric quantity of the battery can be obtained only after the battery is completely charged and discharged, the number of the batteries in the energy storage system is large, the time cost is high, and the test cost is also high; the battery health state is calculated by using a definition method, and an energy storage system needs to be shut down, so that the operation of the whole system is influenced.
Disclosure of Invention
The invention aims to provide an energy storage battery state evaluation method, an energy storage battery state evaluation device, electronic equipment and a storage system, and aims to solve the technical problems of low accuracy, long time consumption and high cost of the existing evaluation method.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for evaluating the state of an energy storage battery, including the following steps:
acquiring historical data of an energy storage battery;
obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
Calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
The invention further improves the following steps: the energy storage battery historical data comprises: current, voltage, temperature and time.
The invention further improves the following steps: the step of obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery specifically comprises the following steps:
obtaining the work accumulated loss delta Q of the energy storage battery through the established coupling relation according to the current, the voltage, the temperature and the time of the energy storage battery:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
The invention further improves the following steps: obtaining the accumulated shelving loss Q of the energy storage battery according to the historical data of the energy storage batteryloss(t) the step specifically comprises:
establishing a coupling mechanism between the shelf time and the battery calendar life, and calculating the shelf accumulated loss Qloss (t)：
Alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of 0.1-1.
The invention further improves the following steps: the temperature weighting coefficient β is calculated by the formula:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this time.
The invention further improves the following steps: the temperature weighting coefficient beta, the work accumulated loss Delta Q and the rest accumulated loss Q are calculatedloss(t) coupling, and obtaining the state of health (SOH) of the energy storage battery, wherein the calculation formula of the state of health (SOH) is as follows:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
In a second aspect, the invention provides a method for evaluating the state of an energy storage battery, which comprises the following steps:
calibrating the state of health of the energy storage battery to obtain a calibrated state of health (SOH 1);
acquiring historical data of continuous operation of the energy storage battery after calibration;
obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
Calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
The invention further improves the following steps: the energy storage battery historical data comprises: current, voltage, temperature and time.
The invention further improves the following steps: the step of obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery specifically comprises the following steps:
according to the current, voltage, temperature and operation time of the energy storage battery, the accumulated loss delta Q of the battery work is obtained through the established coupling relation:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
The invention further improves the following steps: obtaining the accumulated shelving loss Q of the energy storage battery according to the historical data of the energy storage batteryloss(t) the step specifically comprises:
establishing a coupling mechanism between the shelf time and the battery calendar life, and calculating the shelf accumulated loss Qloss (t)：
Alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of 0.1-1.
The invention further improves the following steps: the temperature weighting coefficient β is calculated by the formula:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this time.
The invention further improves the following steps: the temperature weighting coefficient beta, the work accumulated loss Delta Q and the rest accumulated loss Q are calculatedloss(t) coupling, and obtaining the state of health (SOH) of the energy storage battery, wherein the calculation formula of the state of health (SOH) is as follows:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
In a third aspect, the present invention provides an energy storage battery state evaluation apparatus, including:
the acquisition module is used for acquiring historical data of the energy storage battery;
the consumption determining module is used for obtaining the work accumulated consumption delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
The health state determining module is used for calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
The invention further improves the following steps: the energy storage battery historical data comprises: current, voltage, temperature and time.
The invention further improves the following steps: the calculation formula of the work accumulated loss delta Q is as follows:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
The invention further improves the following steps: shelf cumulative loss QlossThe formula for calculation of (t) is:
alpha is the first empirical coefficientValue of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of 0.1-1.
The invention further improves the following steps: the temperature weighting coefficient β is calculated by the formula:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this time.
The invention further improves the following steps: the state of health SOH is calculated as:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
In a fourth aspect, the present invention provides another energy storage battery state evaluation apparatus, including:
the calibration module is used for calibrating the health state of the energy storage battery to obtain a calibrated health state SOH 1;
the acquisition module is used for acquiring historical data of the energy storage battery;
the loss determining module is used for obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
The health state determining module is used for calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta and the work accumulated loss delta are calculatedQ and shelf cumulative loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
The invention further improves the following steps: the energy storage battery historical data comprises: current, voltage, temperature and time.
The invention further improves the following steps: the calculation formula of the work accumulated loss delta Q of the energy storage battery is as follows:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
The invention further improves the following steps: accumulated energy storage battery shelving loss QlossThe formula for calculation of (t) is:
alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of 0.1-1.
The invention further improves the following steps: the temperature weighting coefficient β is calculated by the formula:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this time.
The invention further improves the following steps: the temperature is increasedWeight coefficient beta, work cumulative loss Delta Q and shelf cumulative loss Qloss(t) coupling, and obtaining the state of health (SOH) of the energy storage battery, wherein the calculation formula of the state of health (SOH) is as follows:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
In a fifth aspect, the present invention provides an electronic device, which includes a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the method for evaluating the state of the energy storage battery.
In a sixth aspect, the present invention provides a computer-readable storage medium, where at least one instruction is stored, and the at least one instruction, when executed by a processor, implements the method for evaluating the state of an energy storage battery.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides an energy storage battery state evaluation method, an energy storage battery state evaluation device, electronic equipment and a storage system, aiming at the data type and granularity conditions of a real energy storage power station, the battery data are analyzed, processed and calculated, different evaluation methods under two application situations of work and shelving are combined, temperature parameters are weighted and coupled, a lithium ion battery state evaluation method in an energy storage system is comprehensively established, the single level of a battery cell can be evaluated, and the problems of low accuracy, long time consumption and high cost of a conventional battery state evaluation method are solved; in addition, the parameters used by the method are generally and easily obtained, and the practicability is strong; during evaluation, the energy storage system does not need to be stopped, and an evaluation result and an operation and maintenance strategy can be obtained in real time. And a solution is provided for the degradation or failure of the balancing strategy after the battery state changes in the aging process.
The invention provides technical support for the adjustment of the battery pack balancing strategy, and meanwhile, the invention can also improve the management level of mass batteries of the energy storage power station, accurately grasp the online response characteristic of the batteries, provide technical guarantee for the long-term safe operation of the energy storage system, delay the retirement time of the batteries, reduce the times of replacing the batteries and reduce the operation cost of companies. Compared with the prior art, the method has the advantages of higher accuracy, stronger real-time property and higher universality, and can be popularized to batteries of various systems.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a method for evaluating the state of an energy storage battery according to embodiment 1 of the present invention;
fig. 2 is a schematic flow chart of a method for evaluating the state of an energy storage battery according to embodiment 2 of the present invention;
fig. 3 is a block diagram of an energy storage battery state evaluation apparatus according to embodiment 3 of the present invention;
fig. 4 is a block diagram of an energy storage battery state evaluation apparatus according to embodiment 4 of the present invention;
fig. 5 is a block diagram of an electronic device according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The working state and the resting state of the battery alternate in actual operation, more and longer resting processes exist under the energy storage working condition, and the loss of the battery during resting is generally ignored by the conventional evaluation method. Considering that different degradation mechanisms exist when the battery works and stands, the battery state evaluation method under the two degradation mechanisms is respectively established, the battery state evaluation is carried out by judging, extracting and calculating the time, temperature, voltage and current parameter data of the power station data under the two states in combination with the two methods, and a weighted coupling mechanism of temperature parameters at different moments to the battery state is established, so that the actual characteristics of the battery are better met, and the evaluation accuracy is improved.
For the conditions such as the data type and granularity of the real energy storage power station, the battery state is evaluated based on the operation data, and the method can be divided into the following two conditions.
Example 1
Referring to fig. 1, in a situation that historical data of a battery is clear and complete, the method for evaluating the state of an energy storage battery according to the present invention includes the following steps:
s1, acquiring historical data of the energy storage battery, and extracting parameters of the battery in the historical data during working: current, voltage, time (run time and shelf time) and temperature;
s2, obtaining the accumulated energy loss delta Q of the battery work through the established coupling relation;
delta t is the time interval for selecting the operation data; the historical data of the energy storage power station is usually uploaded once in 1min or a few seconds, and the Δ t is the uploading data interval. The granularity of historical data is different, and the delta t is also different. In the uploaded data, if the current is 0, the battery can be judged to be parked in the time delta t, and if the current is not 0, the battery can be judged to be operated in the time delta t;
v is the average voltage in the time delta t;
i is the average current in the time delta t;
establishing a coupling mechanism between the shelving time and the service life of the battery calendar, cleaning and extracting parameters of the battery when shelving, and calculating the shelving accumulated loss Qloss (t)；
Alpha is a first experience coefficient and takes the value of (10)-4-10-2) A certain value therebetween;
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes a value of a certain value between (0.1 and 1);
s3, finally determining the influence degree of the temperature parameters on the battery degradation under different situations, and calculating the temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment, wherein the calculation method comprises the following steps:
a is a first fitting coefficient, and the value of a first fitting coefficient is a certain value between (0.9-1.1);
b is a second fitting coefficient with the value of (10)-6-10-2) A certain value therebetween;
k is a third fitting coefficient with a value of (10)-3-0.5) of a value;
e is a natural constant, a constant in mathematics, and has a value of about 2.718281828459045.
T is the temperature at this time.
Coupling the temperature weighting coefficient beta into the work accumulated loss Delta Q and the shelving accumulated loss Qloss(t) in the whole period calculation process, comprehensively obtaining the health state of the battery at the moment:
and then, the operation data can be called in real time, the SOH of the battery is updated, and a specific operation and maintenance strategy is given according to the SOH. The specific operation and maintenance strategy given according to the SOH is a conventional technical means in the art, and is not described herein in detail.
The method of the invention is adopted to process the real historical data of a newly-built energy storage power station, and the feasibility of the method is verified. The conventional battery management system of the energy storage power station only has the SOH value of the whole battery cluster, the shelf time loss is not included in the evaluation process, the numerical value change is not credible (the data of the power station does not have SOH estimation data of single batteries), the high-risk battery cannot be accurately found, and the operation and maintenance cost and the safety risk are increased. The method provided by the invention realizes the accurate evaluation of the SOH of the 224 battery monomers in the whole cluster of the energy storage system, can evaluate and monitor the battery monomers in real time, and provides a corresponding operation and maintenance strategy. The accurate and intelligent operation and maintenance and early warning of the energy storage system are realized.
Example 2
Secondly, under the conditions of battery historical data loss, low granularity and the like, firstly calibrating the current state of the battery to obtain a calibrated state of health (SOH 1); and giving out a specific operation and maintenance strategy according to the finally calculated specific SOH.
The conventional method for calibrating SOH is to obtain the existing capacity by completely charging and discharging, and then to obtain the SOH by dividing the existing capacity of the battery. The calibration method of the embodiment can adopt a conventional method or a rapid impedance test method; firstly, the SOH is calibrated, and then the change condition of the subsequent SOH is obtained in real time by the method.
Referring to fig. 2, an embodiment 2 of a method for evaluating a state of an energy storage battery includes the following steps:
s0, calibrating the health state of the energy storage battery to obtain a calibrated health state SOH 1;
s1, acquiring historical data of continuous operation of the energy storage battery after calibration;
s2, obtaining the accumulated work loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
S3, calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
The difference from embodiment 1 is that a step of calibrating the state of health of the energy storage battery to obtain a calibrated state of health SOH1 is added, and the formula for obtaining the state of health SOH of the energy storage battery is calculated according to the following formula:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
Example 3
Referring to fig. 3, the present invention further provides an apparatus for evaluating the state of an energy storage battery, including:
the acquisition module is used for acquiring historical data of the energy storage battery; the energy storage battery historical data comprises: current, voltage, temperature, run time, and shelf time;
the loss determining module is used for obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; the calculation formula of the accumulated loss delta Q of the battery work is as follows:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
i is the average current of the energy storage battery within the time delta t;
and obtaining the accumulated rest loss Q of the energy storage batteryloss(t); shelf cumulative loss QlossThe formula for calculation of (t) is:
alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes the value of 0.1-1;
the health state determining module is used for calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss Qloss(t) coupling to obtain the state of health (SOH) of the energy storage battery;
wherein, the calculation formula of the temperature weighting coefficient beta is as follows:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this moment;
the state of health SOH is calculated as:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
Example 4
Referring to fig. 4, the present invention further provides an apparatus for evaluating the state of an energy storage battery, including:
the calibration module is used for calibrating the health state of the energy storage battery to obtain a calibrated health state SOH 1;
the acquisition module is used for acquiring historical data of the energy storage battery; the energy storage battery historical data comprises: current, voltage, temperature, run time, and shelf time;
the loss determining module is used for obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; the calculation formula of the accumulated loss delta Q of the battery work is as follows:
delta t is the time interval for selecting the operation data;
v is the average voltage of the energy storage battery within the time delta t;
i is the average current of the energy storage battery within the time delta t;
and obtaining the accumulated rest loss Q of the energy storage batteryloss(t); shelf cumulative loss QlossThe formula for calculation of (t) is:
alpha is a first experience coefficient and takes the value of 10-4-10-2；
t is the shelf time of the energy storage battery;
z is a second empirical coefficient and takes the value of 0.1-1;
the health state determining module is used for calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss Qloss(t) coupling to obtain the state of health (SOH) of the energy storage battery;
wherein, the calculation formula of the temperature weighting coefficient beta is as follows:
a is a first fitting coefficient, and the value of a is 0.9-1.1;
b is a second fitting coefficient with a value of 10-6-10-2；
k is a third fitting coefficient with a value of 10-3-0.5；
T is the temperature at this moment;
the state of health SOH is calculated as:
wherein, delta t is the time interval of selecting operation data;
v is the average voltage of the energy storage battery within the time delta t;
and I is the average current of the energy storage battery in the time delta t.
Example 5
Referring to fig. 5, the present invention further provides an electronic device 100 for evaluating the state of an energy storage battery; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
The memory 101 may be configured to store the computer program 103, and the processor 102 implements the method steps of the energy storage battery state evaluation method according to any one of embodiments 1 to 2 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the electronic apparatus 100, and the like. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one Processor 102 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, and the processor 102 is a control center of the electronic device 100 and connects various parts of the whole electronic device 100 by various interfaces and lines.
The memory 101 of the electronic device 100 stores a plurality of instructions to implement a method for evaluating the state of an energy storage battery, and the processor 102 can execute the plurality of instructions to implement:
acquiring historical data of an energy storage battery;
obtaining the work accumulated loss delta Q of the energy storage battery according to the historical data of the energy storage battery; and obtaining the accumulated rest loss Q of the energy storage batteryloss (t)；
Calculating a temperature weighting coefficient beta of the influence of the temperature on the battery state at each moment; the temperature weighting coefficient beta is compared with the work accumulated loss Delta Q and the rest accumulated loss QlossAnd (t) coupling to obtain the state of health (SOH) of the energy storage battery.
Specifically, the processor 102 may refer to the description of the relevant steps in embodiment 1 or 2 for a specific implementation method of the instruction, which is not described herein again.
Example 6
The modules/units integrated by the electronic device 100 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, and Read-Only Memory (ROM).
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
