What is the research status of lithium-ion power battery thermal management system?

What is the research status of lithium-ion power battery thermal management system?

1. Research background of lithium-ion power battery thermal management system
The performance, life and safety of lithium-ion power batteries are closely related to the temperature of the battery. If the temperature is too high, the side reactions will be accelerated, the decay will be accelerated (every time the temperature increases by 15°C, the life span will be reduced by half), and even safety accidents will occur. If the temperature is too low, the power and capacity of the battery will be significantly reduced. If the power is not limited, it may lead to the precipitation of lithium ions, causing irreversible attenuation and burying potential safety hazards. Generally, the suitable working temperature of lithium-ion power battery is between 10°C and 30°C. The operating ambient temperature of the lithium-ion battery for electronic products is not much different from this suitable temperature range, and no or only simple heat dissipation components are required. Vehicle power batteries are used in a wide range of ambient temperatures (-20°C to 50°C), and the thermal environment around the battery in the vehicle is often very uneven, which poses a serious challenge to the thermal management of the battery pack. The large-scale and grouped use of power batteries has led to the fact that the heat dissipation capacity of the battery (group) is much lower than the heat generation capacity. Especially for HEVs and PHEVs characterized by high-rate discharge, a complex heat dissipation system needs to be designed. When the single cells are used in parallel (the internal pole pieces of the single cells are also connected in parallel), the uneven temperature of the individual cells will cause thermoelectric coupling, that is, the battery (or part) with a high temperature has a smaller internal resistance and will share more current, resulting in The state of charge is not uniform, thereby accelerating the deterioration of the battery pack. Therefore, the thermal management technology of the power battery system is one of the key technologies to ensure its performance, life and safety.

What is the research status of lithium-ion power battery thermal management system?
battery thermal management system

The thermal management system of the power battery mainly realizes the following functions: first, heat dissipation when the temperature of the battery pack is high to prevent safety accidents caused by overheating of the battery; second, heating the battery pack when the temperature of the battery pack is low to ensure that the battery is in a low temperature environment The safety and use efficiency of lower charging and discharging; third, make the temperature difference between different positions of the battery and different parts of the battery as small as possible, suppress the formation of local hot spots or hot spots, and make the thermally induced decay rates of the batteries at different positions close to Consistent. Generally, the internal temperature difference of the battery pack is less than 5℃. GM’s Volt adopts a water-cooling design of thermoelectric integration, which can control the maximum temperature difference within 2℃, which strongly supports the 8-year life guarantee period (GM’s guarantee period for the internal combustion engine power system is 5 years). Table 4-1 shows typical automobile thermal management methods in the United States and Japan.

2. Research content of thermal management system of lithium-ion power battery
1) The main components of the thermal management system for lithium-ion power batteries
(1) Heat transfer medium: a medium in contact with the heat exchange surface of the battery pack, through which the heat generated in the battery pack is dissipated to the external environment through the flow of the medium.

What is the research status of lithium-ion power battery thermal management system?
lithium-ion power battery thermal management system

(2) Flow field environment: the path through which the heat transfer medium flows and the distribution of velocity and pressure along the way.

(3) Temperature measuring element and control circuit: The temperature measuring element is used to measure the real-time temperature of different positions of the battery pack; the control circuit makes the action decision of the cooling actuator according to the real-time temperature.

(4) Heat dissipation actuator: The device that drives the heat transfer medium to circulate, with fans and pumps being the most common. Thermal management systems with natural ventilation do not contain thermal actuators.

2) The main heat transfer medium of the thermal management system of the lithium-ion power battery pack
(1) Air is used as the heat transfer medium. In a thermal management system that uses air as the heat transfer medium, the air from the outside environment or the passenger compartment enters the flow channel of the thermal management system, directly contacts the heat exchange surface of the battery pack, and takes away heat through the air flow. According to the spontaneous degree of air flow, it is divided into two categories: natural ventilation and forced ventilation. Natural ventilation includes natural convection and air movement that occurs with the vehicle. Forced ventilation is primarily driven by fans whose instantaneous power is determined by the control circuit of the thermal management system.

(2) Use liquid as heat transfer medium. Thermal management systems using liquid as heat transfer medium are mainly divided into contact and non-contact thermal management systems. The contact type uses highly insulating liquids such as silicon-based oil, mineral oil, etc., and the battery pack can be directly immersed in the heat transfer liquid. The non-contact type uses conductive liquids such as water, ethylene glycol or coolant, and the battery pack cannot be in direct contact with the heat transfer liquid. At this time, distributed closed pipes must be arranged inside the battery pack, and the heat transfer liquid flows through the pipes to take away the heat. The material of the pipe and its tightness ensure the electrical insulation between the conductive liquid and the battery body. The liquid flow in the contact or non-contact liquid cooling system is mainly driven by oil pumps/water pumps.
Since the specific heat capacity and thermal conductivity of liquid are much higher than that of air, the heat dissipation effect of liquid-cooled thermal management system is theoretically better than that of air-cooled system. However, the following two characteristics of the liquid cooling system reduce its heat dissipation efficiency in practical use:
①The heat transfer medium insulating oil of the contact liquid cooling system has a high viscosity, which requires a high oil pump power to maintain the required flow rate.
②The non-contact liquid cooling system needs to design distributed closed flow channels inside the battery pack, which increases the overall mass of the battery pack and reduces the heat transfer efficiency between the battery surface and the heat transfer medium.

(3) The phase change material is used as the heat transfer medium. Certain substances undergo a phase change at a specific temperature and absorb or release energy, and these substances are called phase change materials (PCM). The phase change temperature can be adjusted near the upper limit of the suitable working range of the battery by adjusting the types and composition ratios of phase change materials and additives. Using this type of phase change material to wrap the battery pack, when the battery temperature rises to the phase change temperature, the phase change material will absorb a large amount of latent heat, so that the battery temperature is maintained within the suitable working range of the battery, and the battery pack is effectively prevented from overheating.
The thermal management system using phase change material as heat transfer medium has the advantages of simple overall structure, high system reliability and safety. At 40℃~45℃ and high rate discharge, the effect of using composite PCM material to dissipate heat from the battery pack is better than using a fan within the general power range for air cooling. At present, paraffin wax (and its additives) has received more attention as the mainstream battery thermal management phase change material, because the phase change temperature of paraffin wax is close to the upper limit of the optimal operating temperature of the battery, and the cost is low and the latent heat is high. But the main problem is its low thermal conductivity. Therefore, other substances with high thermal conductivity are often added to paraffin to make composite PCM materials. The results show that the mechanical properties are gradually improved with the increase of the paraffin mass fraction at low temperature, while the mechanical properties are gradually deteriorated with the increase of the paraffin mass fraction at high temperature. In addition, adding heat pipes, foamed aluminum and aluminum heat sinks inside the battery pack phase change material can further improve the heat dissipation capacity of the PCM.

Lithium-ion power battery system cycle life fitting

Lithium-ion power battery system cycle life fitting

Using the data obtained from the lithium-ion power battery pack cycle life test, combined with the Matlab genetic algorithm toolbox, the functional relationship between the battery system capacity retention rate η and the number of charge-discharge cycles n was fitted. Referring to the research methods of general engineering problems, combined with the observation of the relationship between the capacity retention rate and the number of cycles in Figure 1, a 3rd degree polynomial can be used to fit the relationship between the two, namely:
η=a0+a1n+a2n²+a3n3——(1)

Lithium-ion power battery system cycle life fitting
Figure 1 Relationship between the capacity retention rate of lithium-ion power battery system and the number of cycles of charge and discharge

According to a certain test result, we can know the test data [ηi, ni ] of the capacity retention rate η and the number of charge-discharge cycles n. In order to prevent the magnitude of the undetermined coefficients a0, a1, a2, and a3 from being too small, the number of cycles of charge and discharge is converted to 1000 times as a unit. The data of ηi,ni are shown in Table 1.

Number of cycles ni (1000 times)Capacity retention rate ηi (%)
0.001103.53034
0.3698.451615
0.7296.411574
1.0894.625677
1.4493.271964
1.892.118205
2.1690.863933
2.5289.384969
Table 1 Experimental data of cycle times and capacity retention

1 Steps for fitting based on Matlab genetic algorithm toolbox
The following describes the process of using the Matlab Genetic Algorithm Toolbox to determine the undetermined coefficients a0, a1, a2, a3 in the polynomial (1):

① Determine the fitness function. The fitness function designed in Matlab Genetic Algorithm can only obtain its minimum value. If the maximum value is to be solved, appropriate changes must be made. Let η’²i be the capacity decay rate corresponding to the number of cycles ni calculated by the fitting function formula (3-26), then the sum of squares of the total errors of ηi and η’i is

Generally, it can be considered that the smaller the error squared sum e, the better the fitting effect, even if the values ​​of the undetermined coefficients a0, a1, a2, and a3 with the smallest e are the optimal results.

② Write the m file of the fitness function and save it to the Matlab working path with the function name “bat_cyclelife3”. The function written in this article is as follows:
function y=bat_cyclelife3(a)
C=[103.53035498.45161596.41157494.62567793.27196492.11820590.86393389.384969]; Ne=[0.001, 0.36, 0.72, 1.08, 1.44, 1.80, 2.16, 2.52];
[r, s] = size(c);
y=0
For i=1: s
y=y+(C(i)-(a(1)+a(2)Ne(i)+a(3)(Ne(i)^2)+a(4)*(Ne(i)^ 3)))^2
% Error sum of squared minimization principle
end

③ Open the Matlab Genetic Algorithm Toolbox to make relevant settings, run and obtain the test results. Enter the handle of the fitness function “@bat_cyclelife3” in the Fitness function, enter “4” for the number of variables, check Best fitness and Best individual in the running display (Plots), and set the Selection function in the selection parameter (Selection) to roulette For Roulette, in Stopping Criteria, Generations is set to 100, Fitness limit is set to 0, Stall generations is set to 100, and Stall time limit is set to Inf. The parameters such as crossover and mutation are default values, which are set in the default toolbox. After completing the settings, click the “Start” button, and the genetic algorithm starts to operate.

2 Algorithm calculation process and results
When the maximum number of iterations (Generations) of the stopping condition is set to 100, the display of the best fitness and the current best individual during the calculation process is shown in Figure 2. It can be seen from this that the optimal fitness (that is, the sum of squares of errors) gradually decreases as the number of iterations progresses, and the optimal fitness of each generation is gradually approaching the average fitness, indicating that the algorithm is constantly being optimized. The best fitness after 100 iterations is 21542.86, and the values ​​of a0, a1, a2, and a3 are shown in Table 2.

number of iterationsa0a1a2a3total error sum of squares
10017.261413.5558412.16966-1.0023921541.8613
30060.7495131.812388.51764-7.231032947.9990
2191102.5959-10.79853.99807-0.70871.5282
Table 2 Coefficient optimization results based on genetic algorithm
Lithium-ion power battery system cycle life fitting
Figure 2 The best/average fitness and the best individual for 100 iterations of the algorithm

It can be seen from Figure 2 that the optimal fitness continues to decrease in the process of 100 iterations, indicating that the optimization can be continued by increasing the number of iterations. Set the “Selection” to Stochastic uniform, the Generations in the Stopping Criteria to “Inf”, the Time limit to “Inf”, the Stall Generations to “Inf”, and then click “Start” restarts the operation. Through the display of the best fitness in Plots, we can see the change between the fitness and the best individual. When the desired effect is achieved, click “Stop” in the toolbox to stop the operation, and record the optimization results of the algorithm as shown in Table 2.

After 2191 iterations, the best approximations of the coefficients a0, a1, a2, and a3 are 102.5959, -10.7985, 3.99807, and -0.7087, respectively, which is the difference between the capacity retention rate η of the lithium-ion power battery pack and the number of charge-discharge cycles n. The functional relationship between them is shown in formula (2). The maximum value of the error between the calculated value of the fitting equation (2) and the actual capacity retention rate obtained by the experiment is 0.9, and the sum of squares of the total error is only 1.5282, indicating that the equation (2) has a good degree of fit. Using the Matlab function to program and draw, the data points obtained from the experiment and the curve of the fitting function are shown in Figure 3. According to the capacity retention rate lower than 80% as the end of life, the total cycle life of the lithium-ion power battery pack is calculated to be 3836 times according to formula (2).
η=102.5959-10.7985n+3.99807n²-0.7087n3——(3)

Lithium-ion power battery system cycle life fitting
Figure 3 GA fitting curve of cycle life of lithium-ion power battery
Matlab genetic algorithm toolbox

Matlab Genetic Algorithm Toolbox

What is the Matlab genetic algorithm toolbox?

Genetic algorithm has the advantages of simple thinking and obvious application effect. Experts and scholars in various fields have conducted in-depth research on it, and used C, C++ and other programming languages ​​to implement algorithms. However, these languages ​​require users to write genetic algorithm programs, which brings certain difficulties to researchers who are not familiar with programming languages. The genetic algorithm toolbox of Matlab can realize the operation of genetic algorithm through a graphical user interface (GUI) . The problem can be quickly defined by writing a small amount of fitness function program and setting the corresponding parameters in the toolbox. Flexible, easy to use, and easy to modify parameters.

This article takes the genetic algorithm toolbox in Matlab7.1 version as an example to introduce the structure and parameter settings of the toolbox. Type “gatool” in the Matlab working window. After the command is run, you can open the Genetic Algorithm Tool box, or find and open the tool box in the Start menu in Matlab. The operation interface is shown in Figure.

Matlab genetic algorithm toolbox
Figure Matlab genetic algorithm toolbox

The genetic algorithm toolbox is divided into 5 parts from left to right, mainly including defining function handles and variable numbers, running displays (Plots), constraints (Constraints), running commands and results (Run Solver), parameter settings (Options), etc.

(1) When solving actual problems, first determine the fitness function of the problem, and write it as an M file and store it on the working path of Matlab. Fill in the handle of the compiled fitness function in Fitness Function, the format is “@funtname”, and enter the number of variables to be solved in Number of vari.

(2) Constraints include constraints such as Linear inequalities, Linear equalities, and Bounds. For example, boundary constraints limit the minimum and maximum values ​​of variables, and the maximum value constraints of multiple variables can be expressed in matrix form.

(3) Operation display (Plots) displays the operation process of the selected item in image form during operation. For example, after checking Best fitness, the algorithm operation process will display the best function value and average value in each generation of the group; checking Best individual will display the individual corresponding to the best fitness value under the current iteration number.

(4) The run command and result (Run solver) includes operation buttons such as run, pause, stop, etc. The number of iterations and operation status will be displayed during the running process, and the final optimization result will be displayed in the final point after the algorithm stops.

(5) Parameter settings (Options) mainly affect the calculation speed and accuracy of the algorithm, mainly including population (Population), fitness scale (Fitness scaling), selection (Selection), mutation (Mutation), stop condition (Stopping Criteria), Crossover (Crossover) and other parameter settings. The relevant parameters can be selected and set through the drop-down menu.

Genetic Algorithm Fitting

Genetic Algorithm Fitting

What is genetic algorithm fitting?

Genetic Algorithm (Genetic Algorithm, GA) is a global optimization algorithm, which is based on the evolutionary theory of natural selection and genetics to find the optimal solution to the function problem. It is not only suitable for general fitting problems, but also can solve the traditional fitting methods. Deal with non-linear and highly complex data fitting problems.

Therefore, this paper uses genetic algorithm to fit the test data obtained from the lithium-ion power battery cycle life test, and obtain the optimal coefficient and equation between the capacity retention rate and the number of cycles.

Genetic algorithm (GA) simulates the evolutionary process of gene recombination and mutation in the process of biological reproduction. When solving actual problems, the potential solution of the problem (randomly generated) is used as the initial population of the algorithm. The population is used as an individual after binary coding by a computer. These individuals perform operations similar to natural selection, crossover, and mutation in biological evolution, and reproduce according to the rule of survival of the fittest, and finally obtain the optimal individual that meets the convergence condition is the optimization result of the problem.

When the genetic algorithm is used for curve fitting, the algorithm uses group evolution to process multiple individuals at the same time. It does not need to know the derivative of the problem to be sought, does not depend on the complexity of the problem, and the initial population, recombination, mutation and other operations in the genetic algorithm It is performed randomly, which can avoid the local optimum in the optimization process and achieve the global optimum effect, and can effectively solve the optimal estimation problems such as polynomial coefficients.

The main calculation steps of genetic algorithm are:

① Randomly generate an initial population based on actual problems.

②Use the designed fitness function to calculate the fitness of the individual.

③ Perform operations such as selection, crossover, and mutation.

Selection refers to the use of certain methods to select individuals with high adaptability to inherit to the next generation according to the fitness of the individual.
Crossover refers to selecting two individuals with higher probability from the group, and exchanging part of the genes in each pair of individuals.
Commonly used methods such as single-point crossover and multi-point crossover. Variation refers to changing the value of part of the gene position of the individual in the population after crossover with a small probability.

④ Determine whether the convergence conditions are met.

When the set convergence conditions are met, the individual with the greatest fitness is the optimal solution to the problem, otherwise, proceed to steps ③ and ④.

The fitness function is used to measure the fitness of an individual, which is equivalent to the objective function in actual problems. It can only find the minimum value in the Matlab genetic algorithm toolbox. The genetic algorithm controls the operation of the algorithm through the fitness function, uses the size of the individual fitness to determine the probability of an individual being inherited to the next generation, and then changes the group structure. It is the basis for the algorithm’s natural selection and the driving force of evolution. When the genetic algorithm is used to fit the function, the fitness function f(x) that can be selected is as shown in the formula (formula diagram), where Cmax is a sufficiently large positive number, and ER is the objective function.

Fitness function
Fitness function

Lithium ion power battery life estimation and optimization,You can learn more.

Life Test and Analysis of Lithium Ion Power Battery System

Life Test and Analysis of Lithium Ion Power Battery System

What is the life test and analysis of lithium-ion power battery system?

Electric vehicles are most concerned about the life of the entire battery system, not just the life of the single battery. Due to the limitations of test equipment, test time, test cost and other conditions, most of the current research on life estimation of lithium-ion power batteries only focuses on battery cells rather than battery systems. This chapter aims at the life test and analysis of a certain vehicle-mounted lithium-ion power battery system, and provides more reference value data for the life estimation of the power battery system.

1.1 Test object and test bench

The test object is a lithium-ion power battery system for a hybrid electric vehicle. The battery system is a lithium iron phosphate battery with a combined structure of 90 strings, a nominal capacity of 6.5Ah, and a working voltage range of 207~342V. The cycle life test benches for vehicle-mounted lithium-ion battery systems mainly include lithium-ion power battery systems, Dicaron battery test systems, desktop computers, CANcaseXL, etc.

1.2 Test equipment

The main experimental equipment used in the cycle life test of the lithium-ion power battery system includes: Dicaron battery test system, hybrid test bench and HTH1920-40A high and low temperature humidity and heat test box.

The Dicaron battery test system is suitable for performance testing and data analysis of most power battery systems. The test system can load different test conditions for the test objects according to the test requirements and purposes. During the test, the actual vehicle conditions and the actual vehicle environment can be simulated, and the test can be as close as possible to the actual use environment. The hybrid test bench can meet the power system with a power of no more than 200kW, and is suitable for the powertrain test requirements of most models. HTH1920-40A high and low temperature damp heat test chamber can meet the test requirements of -40℃~150℃, the humidity range is adjustable from 25%RH to 98%RH (20℃~150℃), the maximum load is 250kg, which can meet the battery System testing requirements.

1.3 Test methods and results

The cycle life test of the lithium-ion power battery system is carried out at a temperature of (25±2) ℃, and a 1C constant current is used for the cyclic charge and discharge test. The charge and discharge capacity of the battery system is measured after 360 cycles during the test. The test was carried out 2520 cycles in total.

The method of measuring the charge and discharge capacity of the battery pack: First, use 1C constant current to charge the battery system until it reaches the charging cut-off condition specified by the manufacturer (BMS automatic protection). After standing for 30 minutes, use 1C constant current to discharge the battery system to BMS automatic protection, and record the discharge capacity value. After standing for another 30 minutes, use 1C constant current to charge to the BMS automatic protection, record the charging capacity value, and finally stand for another 30 minutes. The charge and discharge capacity test needs to be carried out 3 times to accurately obtain the charge and discharge remaining capacity value of the battery system. The recorded test results are shown in Figure 1.

 Cycle life test results of lithium ion power battery system
Cycle life test results of lithium ion power battery system

Figure 1 Cycle life test results of lithium ion power battery system

Cyclic charge and discharge data of lithium ion power battery system
Cyclic charge and discharge data of lithium ion power battery system

Figure 2 Data processing of cyclic charge and discharge test of lithium-ion power battery system

Simple processing of the data obtained from the cycle life test of the lithium-ion power battery system, in which the coulomb efficiency is equal to the percentage of the discharge capacity to the charge capacity, and the discharge capacity retention rate is equal to the percentage of the average discharge capacity to the nominal capacity (6.5Ah), the result is obtained as shown in picture 2. According to the data of the average charge and discharge capacity, the relationship curve between the charge and discharge capacity and the number of cycles is drawn, as shown in Figure 3. It can be seen from Figure 3 that with the increase in the number of cycles of charge and discharge, the irreversible reaction and the expansion of inconsistency within the battery cause the charge and discharge capacity of the battery system to continue to decrease. The test data is used to calculate the average discharge capacity attenuation rate of the battery pack. It is 0.365Ah/1000 times. During the test, the coulombic efficiency of the battery pack fluctuates around 97.5%, and the maximum difference is less than 0.6%. The distance between the charging curve and the discharging curve in Figure 3 remains stable, which also reflects that the Coulomb efficiency of the battery pack is basically unchanged.

The relationship between charge and discharge capacity and cycle charge and discharge times
The relationship between charge and discharge capacity and cycle charge and discharge times

Figure 3 The relationship between the charging and discharging capacity of the lithium-ion power battery system and the number of cycles of charging and discharging

The relationship between capacity retention rate and cycle charge and discharge times
The relationship between charge and discharge capacity and cycle charge and discharge times

Figure 4 The relationship between the capacity retention rate of the lithium-ion power battery system and the number of cycles of charge and discharge

The relationship between the capacity retention rate of the lithium-ion power battery system and the number of cycles of charge and discharge is shown in Figure 4. After 2520 cycles, the battery pack capacity retention rate is 89.38%. The nominal capacity of the lithium-ion battery system for the test is 6.5Ah. If the remaining available capacity of the battery system is less than 80% of the nominal capacity as the end of life, the average attenuation rate obtained from the test is 0.365Ah/1000 times for calculation. The cycle life of the battery pack is 4191.9 times.

The above is the life test and analysis of the lithium-ion power battery system.

Second-order RC model of lithium-ion power battery

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Second-order RC model of lithium-ion power battery

Second-order RC model of lithium-ion power battery

What is the second-order RC model of lithium-ion power battery? Next, the second-order RC model of lithium-ion power battery is introduced.

We have learned How does the lithium ion power battery work.The charge and discharge process of lithium-ion power batteries is a very complex electrochemical process, and its performance parameters are affected by many factors such as charge and discharge depth, current intensity, temperature, and vibration. It takes a lot of time and cost to detect the changes of various parameters of the battery through the method of battery charge and discharge test, and there are potential safety hazards such as fire and explosion. By establishing an accurate battery model to simulate the change state of various performance parameters of the battery during the charging and discharging process, not only can safety be improved, but also a lot of test time and cost can be saved. Therefore, this section studies the establishment of a lithium-ion power battery model..

The battery models commonly used at this stage mainly include equivalent circuit models, mechanism models, empirical models based on experimental data, electrochemical models, neural network models, and random models. Among them, the equivalent circuit model can express the relationship between the output characteristics of the battery and the internal parameters, and can be used to predict the state of charge SOC, the state of health SOH and other parameters of the battery. It can well reflect the working state of the battery. It has a simple structure, easy modeling, Convenient parameter identification and other advantages. Since the order of the second-order RC equivalent circuit model is appropriate, the engineering is easier to realize, and the steady-state and transient characteristics of the battery can be taken into account, so this article chooses the second-order RC model as the lithium-ion power battery model, as shown in Figure Shown.

Second-order RC equivalent circuit model of lithium-ion power battery
Figure Second-order RC equivalent circuit model of lithium-ion power battery

Among them, VOC represents the ideal voltage source, representing the open circuit voltage of the lithium-ion power battery; R0 represents the ohmic internal resistance; two RC structures are used to represent the polarization reaction of the battery, where RS, and RL are the polarization internal resistance, CS, and CL is the polarization capacitance;
I(t) represents the current, and Vbat represents the measurable battery terminal voltage. Let τ1=Rs. Cs,τ2=RL. CL, τ1 and τ2 respectively represent the short time constant and long time constant in the dynamic response process of the lithium-ion power battery.

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What is the method of estimating the SOC of lithium-ion power battery

What is the method of estimating the SOC of lithium-ion power battery

When the lithium-ion power battery is working, whether the vehicle is in a driving state or a parking state, the battery system will cause a series of electrochemical reactions to cause capacity attenuation. When the remaining discharge capacity of the battery system is less than 80% of the rated capacity, it is considered end of life. The life state of the lithium-ion power battery system directly affects the performance of the vehicle, and the length of the life is also an important content that designers and customers care about. This chapter first introduces the commonly used SOC estimation methods for power batteries, establishes a lithium-ion power battery model, fits the life curve of the battery system according to the cycle life test data, and estimates the life of the power battery, which is the rational use of the on-board battery system and prolongs the use of the battery system. Longevity provides an important basis.

1.1 Commonly used state-of-charge estimation methods

What is the method of estimating the SOC of lithium-ion power battery?

The measurement of the state of charge of lithium-ion power batteries can be divided into two categories: based on the measurement of battery internal parameters (active material parameters or electrolyte) or external parameters (voltage, current, temperature), and its characteristics are shown in Figure 3-1. . Since the real-time detection of battery internal parameters is not operational after the battery leaves the factory, this section mainly focuses on the measurement of external parameters such as battery voltage, current, and temperature. It is also because of the complex electrochemical characteristics of the battery itself and many factors that affect the estimation of the battery. The estimation of the battery state of charge is always the difficulty and focus of battery management and electric vehicle applications.

At present, SOC estimation methods are mostly used in open circuit voltage method and Ah integral method. In recent years, researchers have successively developed some new power battery SOC estimation methods, such as neural network method, Kalman filter method, impedance spectroscopy and other smarter algorithms. These methods are usually used in the literature of Power County) The basic method and its advantages and disadvantages of the more commonly used food in pediatrics are briefly introduced.

1.1.1 Open circuit voltage method

The electromotive force of the power battery is not much different from its open circuit voltage in value. The open circuit voltage method can be used to

Short-term measurement, it is closely related to the electrolyte concentration, and can be directly used for the measurement of the state of charge SOC:

State-of-charge SOC measurement

Formula Figure 1-1 State-of-charge SOC measurement

In the formula, Ua represents the open circuit voltage value when the battery is fully charged; Ub represents the open circuit voltage value when the battery is fully discharged. This method is effective in the initial and final stages of battery charging. The disadvantage is that the over-potential may cause the battery to be overcharged for 5h or longer to ensure that its terminal voltage reaches a steady state, and then the true open circuit voltage value can be obtained. This shortcoming causes certain difficulties in the actual measurement, and it is not easy to accurately determine the resting time of the battery. Therefore, this method is suitable for the parking state of electric vehicles. Figure 3-2 is a graph of the relationship between open circuit voltage OCV and battery SOC.

 OCV and SOC relationship curve

Figure 1-2 OCV and SOC relationship curve

It can be found from Figure 1-2 that the relationship curve between open circuit voltage OCV and battery SOC is basically linear. In the initial stage of battery discharge, the battery SOC changes steeply with the voltage drop, and almost shows a complete linear relationship in the middle of the discharge, while the battery SOC value tends to be relatively flat at the end of the discharge.

1.1.2 Neural Network Method

Since the power battery of electric vehicles is a highly nonlinear system, it is difficult to establish an accurate mathematical model for its use parameters. The neural network has the basic properties of non-linearity, and can give corresponding output to the input layer data through training, so it fits the characteristics of the battery and can dynamically simulate its charging and discharging, and predict the SOC value. The estimation of battery SOC usually uses a three-layer typical neural network. The number of neurons in the middle layer depends on the complexity and analysis accuracy of the research problem. The input and output layer neurons are usually linear functions, and the number is determined In the actual situation of the network. To estimate the state of charge of the battery, the commonly used input variables are the current battery temperature, internal resistance, voltage, accumulated discharge capacity, current, and historical charge and discharge data or changes in related parameters. Whether the choice of input parameter types and the choice of training samples for each parameter is reasonable is closely related to the accuracy of the network model construction and the estimated amount of calculation.

Neural network can be used in various types of batteries, and its application in power battery estimation shows certain advantages, and it is also a current research hotspot. The main disadvantage is that the estimated output is greatly affected by the training method and training data.

1.1.3 Kalman filter method

The essence of the Kalman filter method is a method to make the best estimation of the system state. When applied to the estimation of the power battery SOC algorithm, the power battery is regarded as a system, and the battery S0C is the internal state of the system. By establishing the corresponding power battery SOC estimation model, the recursive algorithm is used to achieve the minimum variance. Excellent estimate.

The Kalman filter method is suitable for all kinds of batteries. The estimation accuracy is high. It can correct the initial error of the battery estimation. It also has a certain inhibitory effect on the interference noise. It is very suitable for the estimation of the SOC battery of electric vehicles with severe current fluctuations. The disadvantage is that the algorithm requires a lot of data to be processed, the algorithm is complex, and the calculation speed of software and hardware is high.

1.1.4 Ah integration method

This is a method of measuring the discharge capacity or ampere-hours, and it is a relatively simple and commonly used method to determine the state of charge of the battery. If the initial state of the power battery during operation is SOC0, then the state of charge estimation formula of the battery during charging and discharging is shown in formula (1-3)

The state of charge of the battery during charging and discharging

Formula Figure 1-3 The state of charge of the battery during charging and discharging

Among them, CN represents the rated capacity of the battery; Ⅰ represents the working current of the battery.

However, the above ampere-hour method does not consider the battery’s charge and discharge efficiency, self-discharge, etc., and the current integration is usually used to correct this deviation. The Ah measurement method mainly has the following problems:

(1) Measurement offset: including difficulty in obtaining the initial state SOCO accurately and inaccurate measurement of current.

Before using the ampere-hour integration method, the initial state of charge value of the battery must be determined. The OCV-SOC experimental relationship table is usually used to calculate S0C0. However, for the working power battery, the battery does not have enough standing time. The initial voltage value detected at this moment and the electromotive force of the battery have a certain error, and the resulting SOC0 is also inaccurate, leading to subsequent battery S0C estimation It’s not accurate enough. When the power battery of an electric vehicle is working, the temperature of the battery will constantly change, and the working current will also fluctuate sharply. Therefore, the current measurement will be inaccurate, which will lead to battery estimation errors. With the increase of time, the integral value of the current will be larger and larger, and the error will be larger.

(2) The estimation of the charge and discharge efficiency is wrong. The self-discharge speed and aging characteristics of the battery during use have not been accurately considered. In this regard, a large number of experiments must be done in advance to establish the empirical formula of the charge and discharge efficiency of this type of battery. At the same time, after the battery has been running for a period of time, the ampere-hour model needs to be modified according to the battery’s usage.

In general, the Ah measurement method has a wide range of applications and is also a relatively simple and reliable method for estimating battery SOC. It usually needs to be combined with the open circuit voltage method.

1.1.5 Internal resistance method

The internal resistance of the battery is divided into DC internal resistance and AC internal resistance. In a period of time, the DC internal resistance of the power battery is expressed as the ratio of the change of the terminal voltage and the current. In practical applications, when the power battery is charged and discharged, the open circuit voltage value and the load voltage value are measured, and the two values ​​are made the difference, and the difference is compared with the current to get the battery’s DC internal resistance. According to the inherent characteristics of lead-acid batteries, in the later stage of battery discharge, the DC internal resistance changes significantly, which is suitable for battery estimation.

The AC internal resistance of the battery is a complex variable and needs to be measured with an instrument. The AC impedance of the power battery is easily affected by external factors, and at the same time, researchers have not yet reached a conclusion about the application of its measurement, so it is rarely used in real vehicles.

Comparing the two internal resistance methods, the AC internal resistance is not practical, and the estimation of the DC internal resistance is easily affected by the time period: if only a short-term estimation is performed, the polarization internal resistance of the battery will not be detected; otherwise, the estimated time If it is too long, the internal resistance will be more complicated. Therefore, the internal resistance method of battery estimation is only applicable to the later stage of battery discharge.

What is the capacity degradation of lithium-ion power battery

What is the capacity degradation of lithium-ion power battery

Causes of capacity degradation of lithium-ion power batteries

The internal factors leading to the failure of lithium-ion power batteries mainly include the performance degradation of the positive/negative electrode materials and the aging of the electrolyte decomposition membrane. The external factors include the battery temperature, the intensity of charge and discharge current, and the depth of discharge. Lithium-ion dynamics. When the battery fails, if it can reach the design life specified by the manufacturer, it is called normal failure, otherwise it is called premature failure. The main reasons leading to the premature failure of the hammer ion power battery are excessive use (excessive strength, excessive depth, overload, etc.), external short circuit, internal damage, etc. Excessive use aggravates the irreversible side reactions inside the battery, accelerates the attenuation of battery life, and may even cause fire, explosion, etc.

The ideal working state of the lithium ion power battery is that only Li+ intercalation and deintercalation between the positive and negative electrodes occur, and there are no other side reactions to consume Li+. In the actual use process, as the charge and discharge progress, the lithium-ion power battery will have metal lithium deposition, active material dissolution, electrolyte decomposition and other phenomena, resulting in irreversible loss of the capacity of the lithium-ion power battery. The main mechanisms that cause the capacity degradation of lithium-ion power batteries are:

(1) Cathode material dissolution. The cathode material will dissolve during the use of lithium-ion power batteries. This is mainly caused by factors such as structural defects of the positive electrode material and overcharging during use. With the increase in the number of battery charging and discharging, the dissolution rate of the cathode material is also increasing. The dissolution of the positive electrode material causes the formation of simple metal near the negative electrode, which increases the battery impedance and causes the capacity of the lithium-ion battery to decline.

(2) Phase change of positive/negative electrode materials. There are two types of phase changes in lithium ion battery electrode materials:

①The phase change caused by the deintercalation and intercalation of Li+ during the normal operation of the lithium-ion power battery. This phase change causes physical damage to the positive and negative materials and reduces the electrical contact between the internal materials of the battery.

②The over-use of overcharge and over-discharge during use will cause the phase change of the positive electrode material. This phase change changes the volume structure of the cathode material.

Both of these two phase transitions affect the propagation process of Li+ in the battery, which leads to the degradation of battery capacity.

(3) The electrolyte causes capacity attenuation. The decomposition of the electrolyte causes a series of irreversible reactions in the battery, which produces lithium oxides and LiOH and other deposits, which consumes the electrolyte, which leads to an increase in battery polarization, a decrease in Li+ concentration, and an increase in resistance to expansion.

(4) Overcharge causes capacity loss. When overcharged, Li+ is reduced and deposited on the negative electrode, which thickens the negative SEI film. Inert materials and oxygen are also formed near the positive electrode, which hinders the deintercalation and insertion of lithium ions and causes irreversible loss of battery capacity.

(5) Self-discharge. The self-discharge phenomenon of lithium-ion power batteries is inevitable. Only a small part of the battery capacity loss caused by self-discharge is irreversible loss, and most can be recovered by recharging. The irreversible loss caused by self-discharge is caused by the loss of Li+ and the blockage of the electrode pores by the oxide of the electrolyte.

(6) Formation of SEI interface film. At the beginning of the charge-discharge cycle, an irreversible reaction occurs between the negative electrode material of the lithium-ion power battery and the electrolyte, forming a solid electrolyte membrane (SEI film) on the surface of the negative electrode. Its formation and growth will consume the Li+ and electrolyte inside the battery, leading to a decline in the capacity of the lithium-ion power battery. The growth rate of the SEI film is closely related to the battery life, working temperature, and the specific area of ​​the negative electrode material.

(7) Current collector corrosion. In the process of charging and discharging lithium-ion power batteries, the current collector will corrode and produce a corrosive film. In the case of deep discharge, copper ions will form elemental copper deposits on the surface of the negative electrode during the charging process.These films and deposits hinder the intercalation and deintercalation of lithium ions, resulting in a decline in battery capacity.

What is the cycle life of lithium ion power battery

What is the cycle life of lithium-ion power battery

Cycle life of lithium-ion power battery

During the use of electric vehicles and other new energy vehicles, the lithium-ion power battery as the power source is in different charging and discharging states, and at the same time is affected by external conditions such as vibration and temperature. As the number of charging and discharging increases, the capacity of lithium-ion power batteries will inevitably have varying degrees of attenuation. The capacity loss of lithium-ion batteries is divided into reversible loss and irreversible loss: reversible loss can be restored by recharging, generally refers to self-discharge; irreversible loss can not be restored by recharging. The cycle life of lithium-ion power batteries is an important parameter for battery production and use, and the attenuation of battery capacity is a long-term and complicated process of change. Accurately detecting or predicting the life state of power batteries is a common concern for automobile manufacturers and users. This section mainly introduces the relevant regulations of lithium-ion power battery life.

The life of a lithium-ion power battery usually includes storage life, service life and cycle life. The storage life refers to the time that the battery has been stored in a static state until it expires. Service life refers to the total discharge time accumulated before the battery fails. Cycle life refers to the total number of charging and discharging of the battery before failure. The cycle life of lithium-ion power batteries is directly related to the cost-effectiveness of the vehicle and the performance of the vehicle. Quick and accurate evaluation of the cycle life of the battery is also one of the key issues that power batteries must solve urgently. At present, most studies often use cycle life to study the life state of lithium-ion power batteries. This article focuses on the cycle life of lithium-ion power batteries.

The cycle life defined in the “USAB Battery Test Manual” is the number of cycles that the battery can perform before it reaches the end-of-life conditions when the battery is subjected to a cyclic charge and discharge test according to the standard charge and discharge system. “GB/Z18333.1–2001 Lithium-ion Battery for Electric Road Vehicles” stipulates that lithium-ion power batteries follow the prescribed charging and discharging system (charged until the voltage reaches 4.2V, and then discharged with 1I3 current to 80% of the rated capacity) Carry out the charge and discharge cycle until the battery capacity decays to 80% of the rated capacity, then the total number of charge and discharge cycles in the whole process is its cycle life. According to IEE1188-1996, when the remaining capacity of the battery is less than 80% of the rated capacity of the new battery, the performance of the battery cannot meet the needs of the vehicle and should be replaced. Therefore, the decay of the battery capacity to 80% of the rated capacity is often used as the end of battery life. SOH is generally used to represent the health of the battery. It is defined as the percentage of the remaining dischargeable capacity of the battery to the rated capacity under standard operating conditions, as shown in the formula.

SOH=Qres/Qrat×100%

In the formula, Qres represents the remaining dischargeable capacity of the battery, and Qrat represents the rated capacity of the battery.

The life of lithium-ion power battery is affected by the design, production, use conditions and other aspects. For lithium-ion power battery packs for vehicles, its life is also affected by the design and consistency of the battery pack. From the perspective of use, vehicle manufacturers and users are most concerned about the life status of the power battery system, not just the life of the battery cells or individual modules. The main factors affecting the life of lithium-ion power batteries include: battery design plan, production level, battery pack structure design, group structure, monomer consistency, vehicle usage conditions, charge and discharge current intensity, depth of discharge, SOC working window, and charging system Wait.

What are the characteristics of lithium ion power battery

What are the characteristics of lithium ion power battery

The main performance of lithium-ion power battery

1.1 Charging and discharging characteristics of lithium-ion power battery

The charging of lithium-ion power batteries needs to take into account safety, reliability and charging efficiency. Usually, the charging method of constant current charging first, and then converted to constant voltage charging to a certain small current is adopted. The main differences between different types of lithium-ion power battery charging methods are:

(1) Different types of lithium-ion power batteries have different currents in the constant current charging stage. According to the different cathode materials and manufacturing processes used in lithium-ion power batteries, there are some differences in the optimal charging current, and the general charging current is between 0.2C and 0.3C.

(The proportion of the two-stage charging capacity to the total capacity is different. The extension of the constant current charging time helps to shorten the total charging time and facilitate the practical application of electric vehicles.

The voltage of lithium-ion power battery is stable in the middle of the discharge, and the voltage drops rapidly in the latter part of the discharge. Effective control should be carried out at the later stage of discharge to prevent over-discharge. If overdischarge occurs, not only the lattice structure of the positive electrode material will change, but the negative copper current collector will also be oxidized, which will cause irreversible damage to the battery. Related standards stipulate that when a series of power battery packs are discharged, in order to prevent overdischarge of a certain cell, when the voltage of a certain cell reaches the discharge cut-off voltage, a discharge protection circuit should be used to terminate the discharge of the battery pack.

1.2 Discharge capacity of lithium-ion power battery

“GB/Z183331-2001 Lithium-ion battery for electric road vehicles” stipulates that after the lithium-ion battery is fully charged according to the charging method given by the manufacturer, it is allowed to stand for 1~5h at a temperature of (20±5)℃, and discharge at a current of 113A. The capacity is the rated capacity of the battery. The electrode material, charging voltage, and working temperature of lithium-ion power batteries have important effects on the discharge capacity.

1.2.1 The influence of electrode material on discharge capacity

It can be seen from Figure 1-1 that at different discharge temperatures, the discharge capacity of C/LiCoO2 batteries is greater than that of C/LiMn2O4 batteries. Under the condition of 21℃, the discharge capacity of C/LiCoO2 battery is 18.6% higher than that of C/LiMn2O4 battery, and the average discharge voltage of the latter reaches 3.9V. Therefore, when discussing the energy density of lithium-ion power batteries, batteries with different electrode materials should be treated differently.

Discharge capacity of lithium-ion power battery under different conditions

Figure 1-1 Discharge capacity of lithium-ion power battery under different conditions

1.2.2 The influence of operating temperature on discharge capacity

The 18650-type C/LiMn2O4 battery and C/LiCoO2 battery were subjected to the discharge test under the condition of -20℃~60℃, and the results obtained are shown in Figure 1-1. It can be seen from Figure 1-1 that the operation of lithium-ion batteries under low temperature conditions is significantly affected, and the average discharge voltage and discharge capacity are significantly smaller; when the operating temperature is higher than 20°C, as the temperature increases, the two types of lithium-ion batteries The average working voltage and discharge capacity of the battery no longer change significantly.

1.2.3 The influence of charging voltage on discharge capacity

By increasing the end-of-charge voltage, the discharge capacity and specific energy of the lithium-ion power battery can be increased. Take a certain type of 7Ah square lithium-ion battery as an example. Charge the battery to 4.1V and 4.2V with 1A current at a temperature of 25°C, and then use different powers for constant power discharge. The discharge capacity is shown in Figure 1-2 and the figure. Shown in Figure 1-3. You can see from it

The relationship between the end-of-discharge voltage of a certain lithium-ion power battery and the energy released

Figure 1-2 The relationship between the end-of-discharge voltage of a certain lithium-ion power battery and the energy released

After increasing the end-of-charge voltage, the battery discharge energy increases, and the battery discharge energy increases by 25% when the charging voltage is 4.2V at 45W discharge power. At the same time, it can be seen that as the discharge power increases, the energy released by the battery decreases.

When the charge termination voltage of lithium-ion power battery is high, it will cause partial decomposition of the positive electrode material, deterioration of electrolyte performance, and oxidation of the diaphragm, which accelerates the aging process of the battery and shortens the service life of the battery. Therefore, the charge termination voltage must be strictly controlled.

The relationship between the end-of-charge voltage of lithium-ion power batteries and the energy released

Figure 1-3 The relationship between the end-of-charge voltage of lithium-ion power batteries and the energy released

1.2.4 The influence of discharge current on discharge capacity

Take a 35Ah square lithium-ion power battery as a sample, and use 5 different discharge currents to conduct a discharge test at an ambient temperature of 25°C. The results are shown in Figure 1-4. It can be seen from the curve in Figure 2-5 that as the discharge current increases, the discharge capacity of the square lithium-ion power battery decreases. This is because lithium-ion power batteries use organic electrolytes, and the internal resistance of the battery is greater than that of other types of batteries, so the discharge performance of lithium-ion power batteries under high current conditions is poor.

The relationship between discharge current and discharge capacity of a certain type of lithium-ion power battery

  Figure 1-4 The relationship between discharge current and discharge capacity of a certain type of lithium-ion power battery

1.3 Internal resistance of lithium-ion power battery

Internal resistance is an important parameter of lithium-ion power batteries, an important indicator of battery health, and one of the key data in the research of lithium-ion power batteries. Its value has an important influence on the charging and discharging efficiency of the lithium-ion power battery and the thermal characteristics of the battery.

The internal resistance of lithium-ion power batteries is greatly affected by factors such as state of charge (SOC) and temperature. Under the same conditions, the internal resistance of lithium-ion power batteries is larger than that of power batteries with other structures. For example, the single cell internal resistance of a 10Ah valve-regulated lead-acid battery is 2~3mΩ, while the internal resistance of a lithium-ion power battery cell of 8~10Ah is 10mΩ. The large internal resistance causes the specific energy of the battery to drop rapidly at high power output. There are two main reasons for this:

(1) The cathode materials of lithium-ion power batteries mostly use oxides or salts, and their electronic conductivity is worse than that of metals.

(2) Lithium-ion power batteries use organic materials as electrolyte solvents, and the diffusion rate of lithium ions is also affected by the material lattice.

1.3.1 The influence of temperature on internal resistance

Studies have shown that the internal resistance of lithium-ion power batteries remains basically unchanged in the temperature range of 20°C to 50°C, but the internal resistance increases rapidly in a low temperature environment. At 0°C, the internal resistance at room temperature doubles to -10°C. When the internal resistance increases by more than 2 times. Therefore, the heating and heat preservation of the lithium-ion power battery pack should be strengthened when used in a low-temperature environment.

1.3.2 The influence of SOC on internal resistance

Figure 1-5 and Figure 1-6 show the charge and discharge internal resistance of a certain type of lithium manganese battery and lithium iron phosphate battery in different SOC states measured at a room temperature of 25°C. Through analysis, it can be seen that the internal resistance of the battery increases significantly when the SOC is low, and the internal resistance increases rapidly as the SOC decreases when the SOC is less than 40%. The internal resistance of the battery is the smallest and relatively stable when the SOC is less than 40%, and it has a certain degree of stability. The characteristics of the platform are conducive to working as a vehicle power battery. At the same time, it can be seen that the internal resistance of lithium-ion power battery charging and discharging is not much different. The maximum difference between charge and discharge internal resistance of lithium manganate battery is 4.5%, and the maximum difference between charge and discharge internal resistance of lithium iron phosphate battery is about 5%.

 The relationship between charge and discharge internal resistance and SOC of a certain type of lithium manganate battery

Figure 1-5 The relationship between charge and discharge internal resistance and SOC of a certain type of lithium manganate battery

 The relationship between charge and discharge internal resistance and SOC of a certain type of lithium iron phosphate battery

Figure 1-6 The relationship between charge and discharge internal resistance and SOC of a certain type of lithium iron phosphate battery