CN113076688A

CN113076688A - Lithium ion power battery efficiency state evaluation method

Info

Publication number: CN113076688A
Application number: CN202110314194.2A
Authority: CN
Inventors: 欧阳剑; 李菁; 莫志东; 袁维维
Original assignee: Guangzhou Yiwe Electric Vehicle Co ltd
Current assignee: Foshan Tuopu Technology Co ltd
Priority date: 2021-03-24
Filing date: 2021-03-24
Publication date: 2021-07-06

Abstract

The invention relates to the technical field of batteries, in particular to a lithium ion power battery performance state evaluation method which can realize real-time dynamic evaluation of the performance state of a lithium ion power battery; the method comprises the following steps: s1, designing a fuzzy logic control algorithm for battery efficiency state evaluation according to the principle of the fuzzy logic control algorithm and the actual situation of the SOF index of the power battery in the application of the new energy automobile, wherein the fuzzy logic control algorithm comprises the following steps: determining the electric quantity state, the temperature and the charge-discharge multiplying power of the power battery as input variables, dividing the discourse domain of the input variables, dividing fuzzy subsets and designing fuzzy logic rules; s2, evaluating the performance status of the lithium-ion power battery by using the designed fuzzy logic control algorithm to output a lithium-ion power battery performance status SOF, including: fuzzification processing is carried out on the input variable by using the membership function, fuzzy reasoning is carried out through a preset calculation rule in a knowledge base, and defuzzification processing is carried out on the fuzzy reasoning result to obtain the SOF (state of performance) of the lithium ion power battery.

Description

Lithium ion power battery efficiency state evaluation method

Technical Field

The invention relates to the technical field of lithium ion power batteries, in particular to a lithium ion power battery efficiency state evaluation method.

Background

The global exploration on the storage amount of petroleum is gradually reduced, the energy shortage is the biggest problem in the automobile industry in the world nowadays, and the popularization of new energy automobiles (especially electric automobiles) is expected to solve the problem in order to meet the requirements of energy conservation and emission reduction. The intelligent, networking, electromotion and sharing modes change the traditional traffic travel modes of people, meanwhile, the life style of people is influenced, the development and extension of new energy traffic technologies represented by electric vehicles form the foundation of future intelligent traffic and smart cities, and the foundation is a necessary development trend.

The power battery provides electric energy for a driving motor of the new energy automobile, is the primary key of the development of the new energy automobile, and depends on a safe, reliable, durable and low-price power battery pack for application in a larger range and even popularization of the new energy automobile. The lithium ion power battery has the advantages of high monomer voltage, large specific energy, high specific power, small self-discharge, no memory effect, good cycle characteristic, rapid charge and discharge, high energy efficiency and the like, so that the lithium ion power battery becomes a promising new energy automobile energy storage scheme.

The battery management system is an intelligent core and an important component of a new energy vehicle energy system. In a battery system, the capacity of a single battery is small, the voltage of the single battery is low, and the requirements of high voltage and large capacity are difficult to meet. Therefore, it is usually necessary to make up the battery pack by connecting the single batteries in series and in parallel to increase the total voltage and the total capacity to meet the requirements of the vehicle applications. The difference of the single batteries can cause self-charging and self-discharging between batteries in a system, and extremely easily cause over-charging and over-discharging of the batteries, so that the service life of the batteries is shortened, the use efficiency of energy sources is reduced, and even the lithium ion power batteries are overheated and spontaneously combust. As an electrochemical system, the internal reaction of the lithium ion power battery has uncertainty, is easily influenced by environmental factors, and has the characteristics of uncertain capacity, unstable charge and discharge power, time-varying internal resistance along with the reaction process and the like in practical application. These factors, if not reasonably effective, necessarily lead to reduced battery life, battery state changes, and reduced battery performance. Therefore, the battery management depends on a high-precision sensor to sample parameters such as voltage, current and temperature in real time, and then depends on the basic data to estimate indirectly measured variables such as residual capacity and efficiency state of the battery in real time; meanwhile, the method is used as the basis for balance control and heat balance management; moreover, through monitoring the basic sampling data, the safety accidents of undervoltage, overcurrent, overtemperature and the like of the battery can be avoided. The problems caused by the use of a large number of single batteries after being grouped need to be solved by a battery management system, and the stronger the function of the battery management system is, the lower the fault rate of the battery system is, and the higher the use efficiency is.

The State of performance (SOF) is one of the important parameters of a battery management system, and is used for evaluating the performance of a lithium ion power battery system. Defined as the performance output that the battery can achieve under certain constraints. Currently, the research on the evaluation problem of battery SOF is still in the beginning stage, and no systematic and operable evaluation method has been proposed, even though there is no consensus in the industry on the specific definition of SOF. Some scholars take SOF as a general term of various states of the battery (such as charge retention capacity and battery starting capacity); the other part of researchers defines the SOF as a state flag of the battery function, namely two states of 0 and 1, wherein "0" represents a battery function failure state, and "1" represents a battery function valid state; researchers also treat SOF equally to the state of charge of the battery. The method embodies the definition of the SOF, provides a quantitative index of percentage between 0 and 100 to describe the performance of the battery under the current condition, and has strong operability in practical system application.

Therefore, a real-time, concise and system status evaluation means and method is needed. The lithium ion power battery performance state evaluation method and system provided by the invention are the scheme meeting the requirements.

Disclosure of Invention

In order to solve the problems in the prior art, the invention provides a method for evaluating the performance state of a lithium-ion power battery, which can realize real-time dynamic evaluation of the performance state of the lithium-ion power battery.

The technical scheme adopted by the invention is as follows: a lithium ion power battery performance state evaluation method comprises the following steps:

s1, designing a fuzzy logic control algorithm for battery efficiency state evaluation according to the principle of the fuzzy logic control algorithm and by combining the actual situation of the SOF index of the power battery in the application of the new energy automobile;

s2, evaluating the lithium ion power battery performance state by using the designed fuzzy logic control algorithm to output the lithium ion power battery performance state SOF;

step S1 includes: determining the state of charge (SOC), the temperature (T) and the charge-discharge multiplying power (C-rate) of the power battery as input variables, and dividing discourse domains of the input variables, dividing fuzzy subsets and designing fuzzy logic rules;

step S2 includes: fuzzification processing is carried out on the input variable by using the membership function, fuzzy reasoning is carried out through a preset calculation rule in a knowledge base, defuzzification processing is carried out on the result of the fuzzy reasoning, and the final lithium ion power battery efficiency state SOF is obtained and serves as the output variable.

Compared with the prior art, the invention has the following advantages and beneficial effects:

1. modeling is carried out on the performance state evaluation of the lithium ion power battery based on a fuzzy logic control algorithm, a brand-new SOF evaluation system is designed, and a practical and effective evaluation method is provided for practical application;

2. through computer software simulation and real vehicle test display, the design of the performance state evaluation algorithm has certain operability;

3. some suggestions in the application of the actual system are provided by combining practical experience, and the method has theoretical and practical significance.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

FIG. 1 is a flow chart of a fuzzy logic control algorithm in an embodiment of the present invention;

FIG. 2 is a schematic diagram of a method for evaluating the performance status of a lithium-ion power battery according to an embodiment of the present invention;

FIG. 3 is a fuzzy logic relationship diagram in an embodiment of the present invention; the relation graph of the battery working environment temperature in the moderate range when the low-rate discharge is adopted, (b) the relation graph of the battery working environment temperature in the high range when the low-rate discharge is adopted, (c) the relation graph of the battery working environment temperature in the moderate range when the high-rate discharge is adopted, and (d) the relation graph of the battery working environment temperature in the low range;

FIG. 4 is a graph of total discharge current and discharge rate of a battery in an experimental practical vehicle; wherein (a) is a total discharge current curve of the battery module, and (b) is a curve graph obtained after the total discharge current is converted into discharge rate;

fig. 5 is a graph of the performance state of the lithium ion power battery according to the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, embodiments of the present invention or technical solutions in the prior art will be clearly and completely described below with reference to the accompanying drawings and embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Examples

As shown in fig. 1, in the fuzzy logic control algorithm flowchart, after fuzzification, fuzzy inference and defuzzification are performed on input variables by a fuzzy logic controller with the help of a knowledge base, control variables are output to a controlled object, and output variables generated by the controlled object are fed back to the fuzzy logic controller. That is, within the fuzzy logic controller is mainly done: judging a knowledge base, fuzzifying, carrying out fuzzy reasoning operation and defuzzifying.

The method is characterized in that a fuzzy logic control algorithm for battery efficiency state evaluation is designed according to the principle of the fuzzy logic control algorithm and the actual situation of SOF indexes of the power battery in the application of the new energy automobile; the designed fuzzy logic control algorithm comprises the following aspects:

(one), determination of input variables

As is well known, the SOC is an important reference for safety protection and state control of the power battery in the working process. However, since the SOC cannot be directly measured and obtained, different methods are often used for estimation in practical applications, and the corresponding relationship between the battery SOC and the battery open-circuit voltage is a common method for estimating the SOC of the power battery at present. In the process of charging and discharging the power battery, the SOC directly determines the working capacity of the battery, so that the SOC is selected as one of input variables for evaluating the SOF of the power battery.

The temperature has a large influence on the performance of the power battery, which is also a significant reason for the need of thermal balance management in the current battery management system. When the power battery is in a low-temperature environment, the electrochemical reaction is slow, the effective electric quantity which can be discharged is sharply reduced compared with the normal temperature, the effective output of the battery cannot be ensured by the low-temperature work, and the performance is greatly influenced; on the contrary, when the power battery is in a high-temperature environment, the electrochemical reaction is active, and the effective electric quantity capable of being discharged is increased compared with the normal temperature. However, high temperature operation is prone to further elevated temperatures and dangerous, and long term activation of chemical reactions can reduce the cycle life of the battery. Therefore, the ambient temperature is important in the battery charging and discharging process, so the temperature T is selected as another input variable for the SOF evaluation of the power battery.

The charge and discharge multiplying power is also a key factor influencing the performance of the lithium ion power battery, and when the charging multiplying power is discharged at a small multiplying power, the electrochemical reaction in the battery is smooth and can fully react, so that the discharging curve is smooth and the voltage platform is high; during high-rate discharge, the electrochemical reaction in the battery is insufficient, so that the voltage of the battery is sharply reduced and the voltage plateau is low. Although the voltage and the capacity are recovered to some extent after the high-rate discharge is stopped, the overall performance of the power battery is also affected to some extent. For the reasons, the invention simultaneously selects the charge-discharge rate C-rate as a further input variable for the SOF evaluation of the power battery.

In addition to the above-described class 3 state variables, there are also some state variables that are also important factors affecting the performance of the battery, such as: the more perfect the established evaluation system is, the more scientific the use of the battery is. The fuzzy control algorithm of the invention reserves input interfaces for the parameters, and provides convenience for the next expansion work.

There are many factors affecting the working condition of the power battery, but from the perspective of practical application and computational complexity, the input variables are not too many, so the invention adopts three input variables to evaluate the battery efficiency:

1) the State of Charge (SOC) of the power battery;

2) temperature (T) of the power battery;

3) and the charge-discharge rate (C-rate) of the power battery.

(II) dividing discourse domain of input variable and dividing fuzzy subset

The domain of discourse for the three input variables determined is defined as follows:

1) and the argument of SOC is as follows: SOC ═ 0%, 100%;

2) and the discourse domain of T is as follows: t [ -40 ℃,70 ℃) ];

3) the domain of discourse for C-rate is: c-rate ═ 0.3C, 5C.

The three input variables are divided into three fuzzy subsets of Low (Low), Medium (Medium) and High (High) on the domains of the three input variables, and the fuzzy subsets are as follows:

1) the fuzzy set of SOC is: f (soc) { L, M, H }. Wherein, Low (Low), Medium (Medium), and High (High) are defined as: less than 25%, 25% to-75%, greater than 75%;

2) and the fuzzy set of T is as follows: f (t) { L, M, H }. Wherein, Low (Low), Medium (Medium), and High (High) are defined as: less than 5 ℃,5 ℃ to-45 ℃, more than 45 ℃;

3) the fuzzy set of C-rates is: f (C-rate) { L, M, H }. Wherein, Low (Low), Medium (Medium), and High (High) are defined as: less than 1.3C, 1.3C-3.3C and more than 3.3C.

The membership functions of the three fuzzy subsets of the variable SOC at its domain are:

the membership functions of the variable T over its domain for the three fuzzy subsets are:

the membership functions of the variable C-rate over its domain for the three fuzzy subsets are:

(III) designing fuzzy logic rules

The rule base is built by using IF [ … ] THEN [ … ] rule statement description, and since there are three input variables, each of which is divided into three fuzzy sets, a total of 3 × 3 × 3 ═ 27 rule statements are built, as shown in table 1:

table 1 list of rule statements

As shown in fig. 2, in the present embodiment, the designed fuzzy logic control algorithm is used to evaluate the performance state of the lithium-ion power battery to output the performance state SOF of the lithium-ion power battery, and the process mainly includes:

fuzzification processing

Fuzzification processing is carried out on input variables by using membership functions, namely, the classification of domains is carried out, and the basic formula is as follows:

for the 3 input variables SOC, T and C-rate selected by the invention, the basic formula is substituted and the specific formula after value range is respectively selected.

Two, fuzzy reasoning

The method is mainly carried out through preset calculation rules in a knowledge base, namely IF … THEN … statements, and the specific rules are shown in a table 1, IF [ how SOC, T, C-rate ] THEN [ how SOF is output ].

In fuzzy logic, fuzzy rules are important grounds for fuzzy inference. The fuzzy logic rule is described in the form of:

IF < x is A > THEN < y is B > (11)

In the formula, "X is A" is called a premise, "Y is B" is a conclusion, and A and B are fuzzy sets defined on domains of interest X and Y, respectively, and represent values of linguistic variables X and Y. The form of the fuzzy rule is the same as the generative rule in the expert system. In order to model and analyze the system effectively, the fuzzy conditional sentence must be clearly expressed by a mathematical expression. The rule equation may be expressed in the form of A → B, and the logical relationship between the hypothesis and the result may be expressed as a fuzzy relationship:

in the formula (I), the compound is shown in the specification,

representing a certain operator. Or can be described by the following equation, with the total output equal to the sum of each condition

Third, defuzzification

The fuzzy logic control algorithm can be fuzzy in internal calculation, but the output result is required to be clear, so that the fuzzy inference result needs to be defuzzified before being output. Output variable u_ωThe precise value of (c) can be calculated from the discrete system expression:

in the above formula, u_ωRepresenting an output variable, μ_B'(u_j) Is a weight coefficient, reflecting the element u_jThe weight is occupied in the output variable. The weighting coefficients of the three input variables can be equally distributed in the domain range, and can also be respectively set in different domain intervals.

As shown in fig. 3, for the fuzzy logic relationship diagram of this embodiment, there are two methods for calculating the SOF value in the practical application process, one is real-time calculation, that is, the result of real-time calculation of the output variable according to the values of 3 input variables by using the above-mentioned design; another way is to preset possible situations in the memory by analogy with table 2, where the example in table 2 is a case where when T is 25 ℃, SOC varies between 0% and 100%, and C-rate varies between 0.5 and 5, and this method of course occupies a larger memory space, but once put in, the requirement for actual calculation will be reduced. The resolution of SOC is 10% and the resolution of C-rate is 0.5 in this example, and may be further increased, except that the amount of data preset may be increased.

TABLE 2 fuzzy control tables of SOC, C-rate and SOF at 25 ℃

As shown in fig. 3, the change situation of the value range of the SOF output variable when three input variables change in the full definition domain is covered, and because there are many corresponding states, the present invention selects four relatively representative states for description:

as can be seen from the graph (a) in fig. 3, when the low-rate discharge is adopted, the battery operating environment temperature is in a moderate range, and the output of the SOF is in a higher range even if the battery charge is in an intermediate state, so that it can be seen that the low-rate discharge plays a relatively critical role in effectively exerting the battery performance.

As can be seen from the graph (b) in fig. 3, when the battery operating environment temperature is high and the SOC is low when the low-rate discharge is adopted, the SOF state of the battery is at a low level. It can be seen that the state of SOF at this time is basically dependent on the state of SOC, and the battery management system should adopt a protection mechanism at any time when the performance is weak when the battery is in a low state of charge, especially when the battery is close to a discharge cut-off. In application, the situation that the temperature of the working environment of the battery is high is avoided as much as possible, the electrochemical reaction is active, if the battery is in an active state for a long time, certain damage is caused to the battery, even irreversible damage is caused to the battery in serious conditions, and accidents such as fire and explosion are easily caused by high temperature.

As can be seen from the graph (c) in fig. 3, when high-rate discharge is adopted, the SOF value of the battery is at a low level even if the battery is in a high state in which the battery operating environment temperature is moderate. In practical application, a user is not advised to use the power battery in a high-rate discharge mode, the high-rate discharge is strictly limited in indexes of the energy type power battery, and the high-rate discharge time is also limited by the power type power battery so as to protect the battery from being damaged and prolong the service life of the battery.

As can be seen from the graph (d) in fig. 3, when the battery operating environment temperature is low, the SOF value level of the battery is also low even if the battery is in a low-rate discharge and high-charge state. The working environment temperature of the battery is an important factor influencing the performance of the battery, and experiments prove that the function and the performance of the battery are seriously influenced when the working environment temperature of the battery is lower than 5 ℃, and the battery can hardly work when the working environment temperature of the battery is lower than-20 ℃.

As shown in fig. 4, a curve of total discharge current and discharge rate of the experimental battery of the invention is specifically described as follows:

fig. 4 (a) is a graph showing a total discharge current curve of the battery module, and fig. 4 (b) is a graph showing a conversion of the total discharge current into a discharge rate. As can be seen from (a) of fig. 4, the instantaneous current is large when the electric vehicle accelerates and travels uphill, and the maximum can be reached to be near 220A; when the electric vehicle brakes, the instantaneous current is a negative value; the current is about 50A or so when the electric vehicle is running at a constant speed on a gentle road surface. Since the battery module assembly scheme of this test employed 3 sets of battery modules with a nominal capacity of 50Ah for parallel use, and the actual total capacity after parallel grouping was 138.7Ah, the battery module discharge rate curve in the graph (b) in fig. 4 was calculated from the ratio of the actual current in the graph (a) in fig. 4 to the actual capacity of the battery module, the trend of the curve was completely the same, except that the ordinate was converted to C-rate. As can be seen from the figure, when the electric vehicle accelerates and runs uphill, the instantaneous discharge rate can reach 1.6C at most; when the electric vehicle runs on a flat road surface at a constant speed, the instantaneous discharge rate is basically maintained at about 0.3C.

As shown in fig. 5, a performance state curve of the lithium ion power battery of the present invention is specifically described as follows:

as shown in fig. 5, when the SOC of the battery is high, the battery is discharged at a high rate (1.6C), and the SOF of the battery is instantaneously reduced by about 25%, but can be maintained at a relatively high position around 60%; when the SOC of the battery is high, the low-rate discharge is performed, and the SOF of the battery is substantially maintained at around 80%. When the SOC of the battery is low, large-rate (>1C) discharge is carried out, and the SOF of the battery is instantaneously reduced to about 50%; when the SOC of the battery is low, the low-rate discharge is performed, and the SOF of the battery is substantially maintained at around 75%.

In summary, the discharge rate of the battery is an important variable affecting the performance of the battery, and no matter the SOC state of the battery is "high" or "low", the state of performance of the battery can show a better level (more than 75%) if the discharge rate can be controlled within a range less than 0.5C; however, when an electric vehicle is operated, instantaneous acceleration or climbing is inevitably encountered, and at this time, the discharge rate of the battery is generally large (greater than 1C), and the state of performance of the battery is drastically reduced regardless of the SOC state of the battery. When the charge of the battery is high, large-rate discharge can be allowed, but the discharge duration should be strictly limited; when the electric quantity of the battery is low, the high-rate discharge causes great risk to the system, the high-rate discharge is strictly controlled, and the high-rate discharge is forbidden if necessary, so that the safe operation of the system and the damage to the battery module are ensured.

Practical use recommendations in electric vehicle applications:

1. when the SOF is more than 70%, the electric automobile can advance at full speed on a gentle slope and a flat ground, can overtake at will and can accelerate on a steep slope;

2. when the SOF is less than 70 percent, the electric automobile can advance at full speed on a flat ground, the accelerating and overtaking distance is not suitable to be too long, and the electric automobile can accelerate on a gentle slope;

3. when the SOF is less than 30% and 10%, the electric automobile needs to travel at a limited speed, is not allowed to overtake, cannot travel on a gentle slope, prompts to charge or prompts to search for nearby charging facilities;

4. when the SOF is less than 10%, the electric automobile needs to start a limp mode, automatically start a warning lamp and search for a nearby safe area to stop.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A lithium ion power battery performance state evaluation method is characterized by comprising the following steps:

2. The method according to claim 1, wherein the domains for the three input variables in step S1 are defined as follows:

the domain of charge state SOC is: SOC ═ 0%, 100%;

the domain of temperature T is: t [ -40 ℃,70 ℃) ];

the argument of the charge-discharge multiplying power C-rate is as follows: c-rate ═ 0.3C, 5C;

the three input variables are divided into three fuzzy subsets of low, middle and high on the domains of speaking.

3. The method for evaluating the state of performance of a lithium-ion power battery according to claim 1, wherein in the defuzzification process of step S2, the precise value of the output variable is calculated by an expression of a discrete system.

4. The method according to claim 3, wherein the expression of the discrete system is:

wherein u is_ωRepresenting an output variable, μ_B'(u_j) Is a weight coefficient, reflecting the element u_jThe weight is occupied in the output variable.

5. The method according to claim 4, wherein the weighting coefficients of the three input variables are equally distributed in the domain of discourse or are set in different domain of discourse intervals.