CN106610478A - Energy storage battery characteristic estimation method and system based on mass data - Google Patents

Abstract

The present invention provides a method and system for evaluating characteristics of an energy storage battery based on massive data, the method comprising the following steps: (1) acquiring monitoring data of each battery cell, including the voltage, current, SOC and temperature of the battery; 2) Calculate the health characteristic index of the battery cell according to the monitoring data of each battery cell; (3) integrate the health characteristic index of each evaluation point of the battery cell, calculate the overall health characteristic of the current cell, and store the analysis results . The system includes a mass battery monitoring data storage subsystem, a battery characteristic analysis subsystem and a battery characteristic analysis result storage subsystem connected in sequence. The invention can adapt to the rapid analysis of the characteristics of all single batteries in a large-scale energy storage power station, and can more accurately reflect the characteristics of the batteries.

Description

A method and system for evaluating characteristics of energy storage batteries based on massive data

technical field

The invention relates to an evaluation method and system for an energy storage battery, in particular to a method and system for evaluating characteristics of an energy storage battery based on massive data.

Background technique

The development of large-scale energy storage technology, especially battery energy storage technology, has become a hot topic today, and many practical applications have appeared in recent years. However, the completion and trial operation of the energy storage power station is only the beginning. A large-capacity battery energy storage system includes a large number of battery cells and battery packs. With the application of battery energy storage systems, the characteristics of battery cells, battery packs and battery energy storage systems will change. How to accurately understand the current state of the battery, master the characteristics of the energy storage system, and apply it to the operation, maintenance, management and control of the battery energy storage system has become an important research content of the energy storage system. And with the continuous deepening and advancement of the construction of energy storage power stations, the amount of data in the monitoring system of energy storage power stations has increased exponentially, forming a massive amount of data. Analysis of energy storage battery characteristics by using massive data management and processing technology has become a research direction that has attracted wide attention.

However, there are the following difficulties in monitoring the characteristics of energy storage battery cells: firstly, there are many types of energy storage batteries, and the evaluation methods of different types of batteries are quite different, lacking a unified evaluation system; secondly, in the energy storage power station system, The number of battery cells is huge, often reaching hundreds of thousands. It is very difficult to accurately monitor and locate so many cells; in addition, because the power station system is in a dynamic operation process, the characteristics of the battery will gradually change. A more feasible method is also needed for the dynamic evaluation of battery characteristics on a long time scale.

Contents of the invention

In order to overcome the deficiencies of the prior art above, the present invention provides a method for evaluating the characteristics of an energy storage battery based on massive data. The invention can adapt to the rapid analysis of the characteristics of all single batteries of large-scale energy storage power stations, and can more accurately reflect the characteristics of the batteries.

In order to realize the above-mentioned purpose of the invention, the present invention takes the following technical solutions:

A method for evaluating characteristics of an energy storage battery based on massive data, the method comprising the steps of:

(1) Obtain the monitoring data of each battery cell, including the voltage, current, SOC and temperature of the battery;

(2) Calculate the health characteristic index of the battery cell according to the monitoring data of each battery cell;

(3) Synthesize the health characteristic index of each evaluation point of the monomer, calculate the overall health characteristic of the current monomer, and store the analysis results.

Preferably, said step (2) comprises the steps of:

Step 2-1. For each monomer, divide the monitoring data based on the SOC variation trend of the monomer to form a set of evaluation points;

Step 2-2. Calculate the average variation of the cell voltage at each evaluation point, and correct the voltage variation according to the voltage, current, SOC and temperature data;

Step 2-3: Calculate the characteristics of the battery cell at each evaluation point according to the predetermined health characteristic classification index and the correction value of the voltage change of the cell at each evaluation point.

Preferably, in the step 2-1, the evaluation point is a group of time intervals, and the length of the time interval exceeds 1 hour; the SOC changes monotonously within the time interval; and the SOC variation range exceeds 30% within the time interval.

Preferably, in the step 2-2, the correction formula of the voltage variation is as follows:

In the formula, δv is the calculated voltage change before correction, is the corrected voltage variation, w _soc is the current battery charging capacity influencing factor, w _t is the current battery temperature influencing factor, w _v is the current battery voltage specification and current voltage influencing factor, and w _i is the current charging and discharging current influencing factor.

Preferably, in the step 2-3, the predetermined health characteristic classification indicators are excellent, medium, and poor, and the corresponding voltage change intervals are set to [a, b], [b, c] and [c, d], where the values of a, b, c, and d are adjusted according to the battery type, operating conditions, and actual demand factors, and the health characteristics of the evaluation point are obtained according to the voltage change interval where the voltage change correction value of the evaluation point falls classification index.

Preferably, in the step (3), the health characteristics of all evaluation points are counted, and the health characteristics with the largest number are taken as the overall health characteristics of the current monomer.

Preferably, an energy storage battery characteristic evaluation system based on massive data, the system includes: a massive battery monitoring data storage subsystem connected in sequence, a battery characteristic analysis subsystem and a battery characteristic analysis result storage subsystem; the massive battery The monitoring data storage subsystem is used to store dynamic data collected by various types of batteries over time, the dynamic data includes battery cell voltage, current, SOC and temperature; the battery characteristic analysis subsystem for each battery cell, according to Monitoring data, calculating the health characteristic index of the battery cell, the monitoring data includes cell voltage, current, SOC and temperature; the battery characteristic analysis result storage subsystem is used to store the analysis result of each battery cell.

Compared with prior art, the beneficial effect of the present invention is:

The invention uses a distributed storage and computing framework, which can adapt to the rapid analysis of the characteristics of all single batteries in large-scale energy storage power stations; comprehensively utilizes various monitoring data of battery monomers, and can reflect battery characteristics more accurately;

The invention uses the reduced voltage variation as the evaluation index of battery characteristic classification, which not only simplifies the difficulty of evaluation, but also can adapt to different types of batteries, and can be applied to electric energy storage batteries, electric vehicle power batteries and the like.

Description of drawings

Figure 1 is a flowchart of a method for evaluating the characteristics of a single energy storage battery based on massive data;

Figure 2 is a schematic diagram of an energy storage battery cell characteristic evaluation system based on massive data;

Figure 3 is a schematic diagram of the division of battery evaluation points;

FIG. 4 is a schematic diagram of evaluation characteristics of each evaluation point of a battery cell.

detailed description

The present invention will be described in further detail below in conjunction with the accompanying drawings.

As shown in Figure 1, the present invention provides a method for evaluating the characteristics of an energy storage battery based on massive data, the method comprising the following steps:

Step 1. Obtain the monitoring data of each battery cell, including the voltage, current, SOC and temperature of the battery;

Group massive monomer data and input them to different computing units of the distributed computing system for processing. The battery monitoring data adopts a distributed storage method, and the storage content includes the monitoring point, monitoring time and monitoring value of each cell.

Step 2. Calculate the health characteristic index of the battery cell according to the monitoring data of each battery cell;

Step 2-1. Each calculation unit processes a set of individual data. The individual data is multi-year data containing multiple monitoring points at intervals of minutes. These collected data are divided based on the SOC change trend to form a Group evaluation points. Each evaluation point is a time interval, and within this time interval, the SOC changes monotonously, and the range of change of the SOC in the entire interval is within a limited range.

The following table intercepts the battery cell sampling data in about 2 hours:

SOC 70% 60% 50% 40% 30% time 8:49 9:17 9:44 10:03 10:19 electric current 37.4 38.9 51 66.2 67.2 Voltage 3.268 3.247 3.247 3.205 3.184 temperature twenty one twenty two twenty two twenty two twenty three

When δsoc=r=10%, four evaluation points e ₁ , e ₂ , e ₃ and e ₄ can be formed within this time period, and the corresponding time intervals are: [8:49,9:17], [9:17,9:44], [9:44,10:03] and [10:03,10:19], as shown in Figure 3.

Step 2-2. Calculate the voltage variation of the cell at each evaluation point. The voltage changes at several evaluation points during the time period shown in the table above are as follows:

evaluation point e ₁ e ₂ e ₃ e ₄ δv 0.02 0 0.04 0.02

Different monitoring data will have a certain impact on the voltage change, so it is necessary to correct the voltage change according to the voltage, current, SOC, temperature and other data, and set w _soc depends on the current battery charge, w _t depends on the current battery temperature, w _v depends on the current battery voltage specification and current voltage, and w _i depends on the current charge and discharge current.

In the intercepted time period, the set influence factor and the corrected voltage variation are respectively:

Step 2-3: Calculate the battery cell characteristics of the current cell at each evaluation point according to the predetermined classification index and the correction value of the voltage change of the cell at each evaluation point.

Set the battery health characteristics to [Excellent, Medium, Poor]. For example, the corresponding voltage change intervals can be set to [0,0.02], [0.02,0.05] and [0.05,∞] respectively. The battery health characteristics of the evaluation points are respectively excellent, excellent, medium and excellent, as shown in Figure 4 for monomer A.

Step 3. Calculate the monthly health characteristics of the monomer based on the health characteristics of each evaluation point within a month. The monthly health characteristics can not only compare the differences of different monomers horizontally, but also analyze the change trend of monomer characteristics vertically. As shown in Figure 4, the health characteristics of Monomer A are better than Monomer B.

As shown in Fig. 2, a storage battery characteristic evaluation system based on massive data provided by the present invention, the system includes: a massive battery monitoring data storage system connected in sequence, a battery characteristic analysis system and a battery characteristic analysis result storage system; The massive battery monitoring data storage system is used to store dynamic data collected over time by various types of batteries, the dynamic data including battery cell voltage, current, SOC and temperature; the battery characteristic analysis system for each battery cell , calculating the health characteristic index of the battery cell according to the monitoring data, the monitoring data including the cell voltage, current, SOC and temperature; the battery characteristic analysis result storage system is used to store the analysis result of each battery cell.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the present invention can still be Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the scope of the claims of the present invention.

Claims (7)

1. A method for evaluating the characteristics of an energy storage battery based on massive data, characterized in that the method comprises the steps of: (1) Obtain the monitoring data of each battery cell, including the voltage, current, SOC and temperature of the battery; (2) Calculate the health characteristic index of the battery cell according to the monitoring data of each battery cell; (3) Synthesize the health characteristic index of each evaluation point of the monomer, calculate the overall health characteristic of the current monomer, and store the analysis results. 2. evaluation method according to claim 1, is characterized in that, described step (2) comprises the steps: Step 2-1. For each monomer, divide the monitoring data based on the SOC variation trend of the monomer to form a set of evaluation points; Step 2-2. Calculate the average variation of the cell voltage at each evaluation point, and correct the voltage variation according to the voltage, current, SOC and temperature data; Step 2-3: Calculate the characteristics of the battery cell at each evaluation point according to the predetermined health characteristic classification index and the correction value of the voltage change of the cell at each evaluation point. 3. The evaluation method according to claim 2, characterized in that, in the step 2-1, the evaluation points are a group of time intervals, and the length of the time interval exceeds 1 hour; the SOC changes monotonically in the time interval; The range of SOC variation in the interval exceeds 30%. 4. The evaluation method according to claim 3, characterized in that, in the step 2-2, the correction formula of the voltage variation is as follows: $\stackrel{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; \&delta;</span>}\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; c</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; w</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&delta;</span>}\mathrm{<span\; class="notranslate">\; v</span>}$ In the formula, δv is the calculated voltage change before correction, is the corrected voltage variation, w _soc is the current battery charging capacity influencing factor, w _t is the current battery temperature influencing factor, w _v is the current battery voltage specification and current voltage influencing factor, and w _i is the current charging and discharging current influencing factor. 5. The evaluation method according to claim 2, characterized in that, in the step 2-3, the predetermined health characteristic classification indicators are excellent, medium, and poor, and the corresponding voltage change intervals are set as [a, b], [b, c] and [c, d], where the values of a, b, c, and d are adjusted according to the battery type, operating conditions, and actual demand factors, and the correction value falls according to the voltage change of the evaluation point The health characteristic classification index of the evaluation point is obtained from the voltage change interval. 6. The evaluation method according to claim 1, characterized in that, in the step (3), the health characteristics of all evaluation points are counted, and the health characteristics with the largest number are taken as the overall health characteristics of the current monomer. 7. An energy storage battery characteristic evaluation system based on massive data, characterized in that the system includes: a massive battery monitoring data storage subsystem, a battery characteristic analysis subsystem, and a battery characteristic analysis result storage subsystem connected in sequence; The massive battery monitoring data storage subsystem is used to store dynamic data collected by various types of batteries over time, the dynamic data includes battery cell voltage, current, SOC and temperature; the battery characteristic analysis subsystem is specific to each battery cell The body calculates the health characteristic index of the battery cell according to the monitoring data, the monitoring data includes the cell voltage, current, SOC and temperature; the battery characteristic analysis result storage subsystem is used to store the analysis result of each battery cell.