Lithium ion battery sensorless temperature estimation method and device

Abstract

The invention discloses a sensorless temperature estimation method and device for a lithium ion battery, and the method comprises the steps: describing the nonlinear dynamic time-varying characteristics of the lithium ion battery through a state-dependent model under the condition of considering the state of charge, temperature and current; parameters of the state dependent model and the lumped mass thermal model are optimized in an off-line mode according to experimental data; and finally, on the basis of a state-dependent lithium ion battery model, through measurement feedback of terminal voltage, unscented Kalman filtering is combined to realize sensorless temperature estimation of the lithium ion battery. According to the method, a large-scale energy storage system can monitor the temperature of each battery monomer in real time under the condition that a temperature sensor is not adopted, and a battery management system can conveniently carry out thermal management on the battery.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery management, and in particular relates to a sensorless temperature estimation method and device for a lithium-ion battery. Background Art

[0002] At present, energy storage systems have been widely used in the fields of transportation electrification and smart grid. Lithium-ion batteries are the preferred energy storage units for energy storage systems because of their advantages such as long cycle life, high energy density and low self-discharge rate. In the application process of the above energy storage system, temperature is a key state quantity related to the safety and economy of lithium-ion batteries. For example, lithium-ion batteries need to work at a suitable temperature to ensure their best performance; and under some high current conditions, lithium-ion batteries will heat up sharply, resulting in excessive real-time temperature. At this time, cooling measures need to be taken to prevent thermal runaway and heat diffusion. Therefore, it is necessary to monitor the temperature of lithium-ion batteries that make up the energy storage system in real time and accurately to ensure the safe and stable operation of the system. The existing large-scale energy storage system is composed of thousands of battery cells connected in series and parallel. Considering the cost and safety, temperature sensors can only be installed at key locations to obtain temperature information at limited points. This makes it impossible for the battery management system to accurately monitor the temperature of each battery cell; more seriously, once one of the battery cells has thermal runaway, it will cause the adjacent batteries to also have thermal runaway, and then produce a chain reaction, leading to catastrophic consequences. Therefore, estimating the temperature of each battery cell without the aid of sensors is an urgent problem to be solved in the thermal management technology of energy storage systems. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a sensorless temperature estimation method and device for lithium-ion batteries, which can estimate the temperature of lithium-ion battery cells in an energy storage system by using collected battery data without installing a temperature sensor, thereby achieving real-time and effective battery temperature estimation.

[0004] In order to solve the above technical problems, the present invention discloses a sensorless temperature estimation method for a lithium-ion battery, comprising:

[0005] Obtain the data set of the battery to be tested, input it into the ampere-hour integration formula and the parameterized lumped mass thermal model for processing to obtain the predicted state of charge and the predicted temperature value;

[0006] Inputting the predicted state of charge and the predicted temperature value into a trained terminal voltage model to obtain a predicted terminal voltage;

[0007] Combining the actually measured terminal voltage of the battery to be tested and the predicted terminal voltage, an unscented Kalman filter is used for feedback correction to output a temperature estimation value of the battery to be tested;

[0008] The terminal voltage model is constructed based on a state-dependent model.

[0009] Furthermore, the terminal voltage model is:

[0010]

[0011] Where U t is the terminal voltage, I is the current, p and q are the terminal voltage U t and the regression order of the current I, ζ 0 , Ut,i , I,i is the functional coefficient, w(k) is the state quantity, and e is Gaussian white noise.

[0012] Furthermore, the state quantity w(k)=[SOC(k) I(k) T(k)];

[0013] Where SOC is the state of charge, I is the current, and T is the temperature of the lithium-ion battery.

[0014] Furthermore, the functional coefficient ζ 0 , Ut,i , I,i Determined by radial basis function neural network:

[0015]

[0016] Where m is the number of neurons in the radial basis function neural network. and are the linear weights of the radial basis function neural network, is the center of the radial basis function neural network, is the scaling factor of the radial basis function neural network.

[0017] Furthermore, the lumped mass thermal model is:

[0018]

[0019] Where T is the temperature of the lithium-ion battery, h is the equivalent convection heat transfer coefficient, A is the battery surface area, m q is the battery mass, c is the battery specific heat capacity, Δt is the sampling period, is the heat generation rate, T a is the ambient temperature.

[0020] Furthermore, the heat generation rate Heat generation model Calculated; wherein OCV is the open circuit voltage, and the open circuit voltage OCV is a function of the state of charge SOC.

[0021] Furthermore, the open circuit voltage OCV is obtained by calculating an open circuit voltage-state of charge curve; the open circuit voltage-state of charge curve is fitted by the following steps:

[0022] 001. Perform a small current charge and discharge test on the lithium-ion battery to collect small current charge and discharge data of the lithium-ion battery, wherein the small current charge and discharge data includes terminal voltage and state of charge;

[0023] 002. Take 1% of the state of charge as an interval as a state of charge point, extract the terminal voltage corresponding to the state of charge point as an open circuit voltage point, and fit the state of charge point and the open circuit voltage point into an open circuit voltage-state of charge curve through a polynomial function.

[0024] Furthermore, the equivalent convection heat transfer coefficient h and specific heat capacity c are optimized using a particle swarm algorithm.

[0025] Furthermore, the ampere-hour integral formula is:

[0026]

[0027] In the formula, C b is the capacity of a lithium-ion battery.

[0028] Based on the same inventive concept, another aspect of the present invention further provides a sensorless temperature estimation device for a lithium ion battery, the device comprising:

[0029] The sensorless temperature prediction module of the lithium-ion battery obtains the data set of the battery to be tested, inputs the ampere-hour integration formula and the parameterized lumped mass thermal model for processing to obtain the predicted state of charge and the predicted temperature value; the predicted state of charge and the predicted temperature value are input into the trained terminal voltage model to obtain the predicted terminal voltage;

[0030] The sensorless temperature correction output module for lithium-ion batteries combines the measured terminal voltage of the battery to be tested and the predicted terminal voltage, uses unscented Kalman filtering for feedback correction, and outputs the estimated temperature value of the battery to be tested.

[0031] The present invention has the following beneficial effects:

[0032] The present invention proposes a sensorless temperature estimation method for lithium-ion batteries. The method realizes terminal voltage modeling of lithium-ion batteries through a state-dependent model while considering the state of charge, temperature, and current. The established state-dependent terminal voltage model has the ability to reflect the nonlinearity and time-varying nature of lithium-ion batteries under different temperature ranges and working conditions; and a dynamic experiment is designed to optimize the parameters of the state-dependent model and the lumped mass thermal model offline according to the experimental data; finally, based on the state-dependent model, the terminal voltage measurement feedback is combined with an unscented Kalman filter to realize accurate lithium-ion battery temperature estimation. The method can monitor the temperature of battery cells in real time without using a temperature sensor, which is convenient for the battery management system to perform thermal management on the battery. It is particularly suitable for energy storage systems containing a large number of batteries, reduces the cost of installing temperature sensors in the energy storage system, and provides thermal safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is one of the flow charts of the sensorless temperature estimation method for lithium-ion batteries provided in some embodiments of the present invention,

[0034] Figure 2 This is a second flow chart of a sensorless temperature estimation method for a lithium-ion battery provided in some embodiments of the present invention.

[0035] Figure 3 is a schematic diagram of data collected by some embodiments of the present invention, including terminal voltage, current diagram, state of charge SOC, surface temperature and ambient temperature,

[0036] Figure 4 is a schematic diagram comparing the actual temperature and the estimated temperature of a battery provided by an embodiment of the present invention,

[0037] Figure 5 It is a schematic diagram of estimation result error provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to more clearly and completely describe the technical solution of the present invention, the present invention is further described in detail through specific embodiments below. It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention. Various changes can be made within the scope of the rights of the present invention.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0040] like Figure 1As shown, the present invention provides a sensorless temperature estimation method for a lithium-ion battery, comprising the following steps:

[0041] S1. Obtain a data set of the battery to be tested, input it into the ampere-hour integration formula and the parameterized lumped mass thermal model for processing to obtain predicted state of charge data and predicted temperature value;

[0042] S2, inputting the predicted state of charge data and the predicted temperature value into a trained terminal voltage model to obtain a predicted terminal voltage;

[0043] S3, combining the measured terminal voltage of the battery to be tested and the predicted terminal voltage, using unscented Kalman filtering to perform feedback correction, and outputting a temperature estimation value of the battery to be tested;

[0044] Among them, the terminal voltage model is constructed based on the state-dependent model.

[0045] In some implementations provided by the present invention, the terminal voltage model is:

[0046]

[0047] Where U t is the model output, i.e., terminal voltage, I is the model input, i.e., current, and p and q are the output-terminal voltage U t and the regression order of the input-current I, ζ 0 , Ut,i , I,i is the functional coefficient, w(k) is the state quantity, and e is Gaussian white noise.

[0048] In some embodiments provided by the present invention, based on the active material, size, winding core form and appearance of the lithium-ion battery to be tested, a battery with the same model and specification as the lithium-ion battery to be tested is selected as the modeling and collection object, and a dynamic operating condition test is performed on it to collect the terminal voltage, current, temperature, ambient temperature, and SOC data of the lithium-ion battery. Then, the temperature data in the dynamic operating condition test data is Gaussian average filtered to reduce noise.

[0049] In some embodiments provided by the present invention, taking into account the significant effects of current, SOC and temperature of the lithium-ion battery on the terminal voltage of the lithium-ion battery, the state quantity w(k) in the terminal voltage model constructed by the state-dependent model is determined, w(k) = [SOC(k) I(k) T(k)], where SOC is the state of charge, I is the current, and T is the temperature of the lithium-ion battery.

[0050] In some embodiments provided by the present invention, the functional coefficient ζ in the terminal voltage model constructed according to the state-dependent model is 0 , Ut,i ,I,i Determined by radial basis function neural network:

[0051]

[0052] Where m is the number of neurons in the radial basis function neural network. and are the linear weights of the radial basis function neural network, is the center of the radial basis function neural network, is the scale factor of the radial basis function neural network. All parameters of the state-dependent model include linear weights, centers, and scale factors.

[0053] According to the above-mentioned processed dynamic operating data, all parameters in the state-dependent model are optimized by the gradient descent method, and the terminal voltage model (1-1) established based on the state-dependent model is stored as an observation equation.

[0054]

[0055] In some implementations provided by the present invention, the lumped mass thermal model is:

[0056]

[0057] Where T is the temperature of the lithium-ion battery, h is the equivalent convection heat transfer coefficient, A is the battery surface area, m q is the battery mass, c is the battery specific heat capacity, Δt is the sampling period, is the heat generation rate, T a is the ambient temperature.

[0058] In some embodiments provided by the present invention, the heat generation rate Heat generation model Calculated; where OCV is the open circuit voltage, and the open circuit voltage OCV is a function of the state of charge SOC.

[0059] In some embodiments provided by the present invention, the open circuit voltage OCV is obtained by calculating an open circuit voltage-state of charge curve; the open circuit voltage-state of charge curve is fitted by the following steps:

[0060] 001. Perform a small current charge and discharge test on the lithium-ion battery. For example, the lithium-ion battery is charged and discharged at a small current of 1 / 20C, and the small current charge and discharge data of the lithium-ion battery is collected. The small current charge and discharge data includes the terminal voltage and the state of charge;

[0061] 002. Take 1% of the state of charge as an interval as a state of charge point, extract the terminal voltage corresponding to the state of charge point as an open circuit voltage point, and fit the state of charge point and the open circuit voltage point into an open circuit voltage-state of charge curve through a polynomial function.

[0062] In some implementations provided by the present invention, based on the above-processed dynamic operating condition data, the equivalent convection heat transfer coefficient h and the specific heat capacity c are optimized using a particle swarm algorithm, and the optimized lumped mass thermal model is stored:

[0063]

[0064] In some embodiments provided by the present invention, the state of charge SOC of the lithium-ion battery is obtained by the ampere-hour integration formula:

[0065]

[0066] In the formula, C b is the capacity of the lithium-ion battery, obtained from the battery manual.

[0067] (1-2) and (1-3) are used as state equations; the state equations combined with the observation equations can predict the terminal voltage, temperature and state of charge of the lithium-ion battery to be tested and output the estimated value of the lithium-ion temperature in combination with the feedback correction of the unscented Kalman filter, thereby obtaining the temperature of the lithium-ion battery without the need for a temperature sensor. Figure 2 As shown in the figure, the specific process is as follows: First, the measured ambient temperature, current and calculated heat generation rate are input into the state equation to obtain the state of charge and temperature predicted by the model. Then the state of charge and temperature are input into the observation equation to obtain the terminal voltage predicted by the model. There is a difference between the actual measured terminal voltage of the lithium-ion battery and the terminal voltage predicted by the model. Through the error value of the two, combined with the feedback correction capability of the unscented Kalman filter, the predicted temperature is corrected, and the corrected temperature can be output, that is, the estimated temperature of the lithium-ion battery.

[0068] The following is a description of the sensorless temperature estimation method for a lithium-ion battery provided by the present invention in conjunction with a specific embodiment:

[0069] Example

[0070] This embodiment provides a sensorless temperature estimation method for a lithium-ion battery, which includes two stages: offline establishment and online application.

[0071] Offline establishment phase:

[0072] According to the active material, size, winding core form and appearance of the lithium-ion battery to be tested, batteries with the same model and specifications as the lithium-ion battery to be tested are selected as the collection objects.

[0073] (1.1) Perform a low current (1 / 20C) charge and discharge test on the lithium-ion battery as the collection object, and collect the terminal voltage and state of charge (SOC) data of the lithium-ion battery.

[0074] (1.2) The terminal voltage of the lithium-ion battery is extracted from the low-current charge and discharge data collected in (1.1) at intervals of 1% state of charge SOC. Since the current is very small, the polarization effect can be ignored, and the terminal voltage can be considered as the open circuit voltage (OCV), and the open circuit voltage OCV corresponding to each state of charge SOC point can be obtained. Finally, these points are fitted into the OCV-SOC curve through a polynomial function.

[0075] (1.3) Perform dynamic operating condition tests on lithium-ion batteries and collect terminal voltage, current, temperature, ambient temperature, and state of charge (SOC) data of lithium-ion batteries, such as Figure 3 Then, the temperature data in the dynamic working condition test data is subjected to Gaussian average filtering to reduce noise.

[0076] (1.4) Construct the terminal voltage model of lithium-ion battery through the state-dependent model:

[0077]

[0078] Where U t is the model output, i.e., the terminal voltage; I is the model input, i.e., the current; p and q are the regression orders of the output and input, respectively; e is Gaussian white noise. 0 , Ut,i , I,i is the functional coefficient of the model; w(k) is the state quantity of the model;

[0079] (1.5) Considering the influence of current, SOC and temperature on the terminal voltage of lithium-ion battery, the state quantity in the state-dependent model (1.4) is determined:

[0080] w(k)=[SOC(k)I(k)T(k)]

[0081] Where SOC is the SOC of the lithium-ion battery, I is the current, and T is the temperature of the lithium-ion battery.

[0082] (1.6) The functional coefficients are determined by a radial basis function neural network with local approximation capability:

[0083]

[0084] Where m is the number of neurons in the radial basis function neural network; and are the linear weights of the radial basis function neural network; It is the center of the radial basis function neural network; is the scale factor of the radial basis function neural network. All parameters of the state-dependent model include linear weights, centers, and scale factors.

[0085] (1.7) We further obtain the following complete state-dependent model.

[0086]

[0087] (1.8) Based on the dynamic operating condition data processed in (1.3), all parameters in the state-dependent model are optimized by the gradient descent method, and the model is stored.

[0088] (1.9) Based on the dynamic operating data collected in (1.3), the particle swarm algorithm is used to optimize the parameters of the battery specific heat capacity c and the equivalent convection heat transfer coefficient h of the following lumped mass thermal model, and the lumped mass thermal model is stored.

[0089]

[0090] Where A is the surface area; m q is the mass. These two parameters can be obtained from the battery manual. c is the specific heat capacity of the lithium-ion battery; h is the equivalent convection heat transfer coefficient. These two parameters need to be optimized. Δt is the sampling period. T a is the ambient temperature. is the heat production rate, the heat production rate Calculated by the following heat generation model.

[0091]

[0092] Wherein, OCV is the open circuit voltage (OCV) of the lithium-ion battery, which is a function of the state of charge (SOC) of the lithium-ion battery and is calculated using the OCV-SOC curve in (1.2).

[0093] (1.10) Determine the ampere-hour integral formula for the SOC of a lithium-ion battery:

[0094]

[0095] In the formula, C b is the capacity of the lithium-ion battery, obtained from the battery manual.

[0096] (1.11) The lumped mass thermal model established in (1.9) and the ampere-hour integral formula in (1.10) are used as the state equation:

[0097]

[0098] (1.12) The state-dependent model established in (1.8) is used as the observation equation:

[0099]

[0100] (2) Online application stage:

[0101] (2.1) The process of online estimation of lithium-ion battery temperature is as follows Figure 2 . First, the measured ambient temperature, current and calculated heat generation rate are input into the state equation to obtain the SOC and temperature predicted by the model. Then the predicted SOC and temperature are input into the observation equation to obtain the terminal voltage predicted by the model. Since there is a difference between the actual measured terminal voltage of the lithium-ion battery and the terminal voltage predicted by the model, the error value of the two is combined with the feedback correction capability of the unscented Kalman filter to output the corrected temperature. The schematic diagram of the comparison between the actual temperature and the estimated temperature in this embodiment is shown in Figure 4 As shown, the error diagram of estimated temperature is as follows Figure 5 As shown by Figure 4 , Figure 5 It can be seen that the estimated temperature obtained by the lithium-ion battery temperature estimation method provided by the present invention for estimating the temperature of the battery to be tested is basically consistent with the actual temperature, and the positive and negative errors of the estimated temperature obtained by the lithium-ion battery sensorless temperature estimation method do not exceed 1°C, which means that the estimation method provided by the present invention can accurately estimate the temperature of the lithium-ion battery cell without using a sensor, using only the ambient temperature, current and terminal voltage collected in the current battery management system.

[0102] Another aspect of the present invention also provides a sensorless temperature estimation device for a lithium ion battery, the device comprising:

[0103] The sensorless temperature prediction module of the lithium-ion battery obtains the data set of the battery to be tested, inputs the ampere-hour integration formula and the parameterized lumped mass thermal model for processing to obtain the predicted state of charge and the predicted temperature value; the predicted state of charge and the predicted temperature value are input into the trained terminal voltage model to obtain the predicted terminal voltage;

[0104] The sensorless temperature correction output module for lithium-ion batteries combines the measured terminal voltage of the battery to be tested and the predicted terminal voltage, uses unscented Kalman filtering for feedback correction, and outputs the estimated temperature value of the battery to be tested.

[0105] The above is a detailed introduction to a sensorless temperature estimation method and device for a lithium-ion battery provided by the present invention. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person skilled in the art can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A sensorless temperature estimation method for a lithium-ion battery, characterized in that: include: Obtain the data set of the battery to be tested, input it into the ampere-hour integration formula and the parameterized lumped mass thermal model for processing to obtain the predicted state of charge and the predicted temperature value; Inputting the predicted state of charge and the predicted temperature value into a trained terminal voltage model to obtain a predicted terminal voltage; Combining the actually measured terminal voltage of the battery to be tested and the predicted terminal voltage, an unscented Kalman filter is used for feedback correction to output a temperature estimation value of the battery to be tested; The terminal voltage model is constructed based on a state-dependent model.

2. The sensorless temperature estimation method for a lithium-ion battery according to claim 1, wherein: The terminal voltage model is: Where U t is the terminal voltage, I is the current, p and q are the terminal voltage U t and the regression order of the current I, ζ0, ζ Ut,i , I,i is the functional coefficient, w(k) is the state quantity, and e is Gaussian white noise.

3. The sensorless temperature estimation method for a lithium-ion battery according to claim 2, wherein: The state quantity w(k)=[SOC(k)I(k)T(k)]; Where SOC is the state of charge, I is the current, and T is the temperature of the lithium-ion battery.

4. The sensorless temperature estimation method for a lithium-ion battery according to claim 2, wherein: The functional coefficients ζ0, ζ Ut,i , I,i Determined by radial basis function neural network: Where m is the number of neurons in the radial basis function neural network. and are the linear weights of the radial basis function neural network, is the center of the radial basis function neural network, is the scaling factor of the radial basis function neural network.

5. The sensorless temperature estimation method for a lithium-ion battery according to any one of claims 1 to 4, characterized in that: The lumped mass thermal model is: Where T is the temperature of the lithium-ion battery, h is the equivalent convection heat transfer coefficient, A is the battery surface area, m q is the battery mass, c is the battery specific heat capacity, Δt is the sampling period, is the heat generation rate, T a is the ambient temperature.

6. The sensorless temperature estimation method for lithium-ion batteries according to claim 5, characterized in that: The heat generation rate Heat generation model Calculated; wherein OCV is the open circuit voltage, and the open circuit voltage OCV is a function of the state of charge SOC.

7. The sensorless temperature estimation method for a lithium-ion battery according to claim 6, characterized in that: The open circuit voltage OCV is obtained by calculating the open circuit voltage-state of charge curve; the open circuit voltage-state of charge curve is fitted by the following steps:

001. Perform a small current charge and discharge test on the lithium-ion battery to collect small current charge and discharge data of the lithium-ion battery, wherein the small current charge and discharge data includes terminal voltage and state of charge; 002. Take 1% of the state of charge as an interval as a state of charge point, extract the terminal voltage corresponding to the state of charge point as an open circuit voltage point, and fit the state of charge point and the open circuit voltage point into an open circuit voltage-state of charge curve through a polynomial function.

8. The sensorless temperature estimation method for a lithium-ion battery according to claim 5, characterized in that: The equivalent convection heat transfer coefficient h and specific heat capacity c are optimized using a particle swarm algorithm.

9. The sensorless temperature estimation method for a lithium-ion battery according to any one of claims 1 to 4, characterized in that: The ampere-hour integration formula is: In the formula, C b is the capacity of a lithium-ion battery.

10. A sensorless temperature estimation device for a lithium-ion battery, characterized in that: The device comprises: The sensorless temperature prediction module of the lithium-ion battery obtains the data set of the battery to be tested, inputs the ampere-hour integration formula and the parameterized lumped mass thermal model for processing to obtain the predicted state of charge and the predicted temperature value; the predicted state of charge and the predicted temperature value are input into the trained terminal voltage model to obtain the predicted terminal voltage; The sensorless temperature correction output module for lithium-ion batteries combines the measured terminal voltage of the battery to be tested and the predicted terminal voltage, uses unscented Kalman filtering for feedback correction, and outputs the estimated temperature value of the battery to be tested.

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