Method for estimating the state of charge of a battery cell, computer programme product, and battery management system

Abstract

The invention relates to a method for estimating a current state of charge (22) of at least one battery cell (14) of an electrical energy storage device (12) of an at least partially electrically operated motor vehicle (10) by means of a battery management system (16) of the electrical energy storage device (12), said method comprising the steps of: - specifying an estimation model (24) for states of charge of the battery cell (14) by means of an electronic computing device (20) of the battery management system (16), wherein modal coordinates of the states of charge are predefined in the estimation model (24); - detecting at least one current impedance spectrum of the battery cell (14) by means of a detection device (18) of the battery management system (16); - determining current modal coordinates of the detected impedance spectrum by means of the electronic computing device (20); and - estimating the current state of charge (22) depending on the estimation model (24) and the current modal coordinates. The invention also relates to a computer programme product and to a battery management system (16).

Description

[0001] Method for estimating a charge state of a battery cell, computer program product, and battery management system

[0002] The invention relates to a method for estimating a current state of charge of at least one battery cell of an electrical energy storage device of an at least partially electrically operated motor vehicle by means of a battery management system of the electrical energy storage device according to the applicable patent claim 1. Furthermore, the invention relates to a computer program product and a battery management system.

[0003] Due to their high energy and power density, lithium-ion battery cells play a crucial role in the implementation and success of electromobility concepts. The battery packs used in mobile applications are monitored by a battery management system (BMS) to ensure their reliable and optimal use. Safe operation by the battery management system requires compliance with battery operating limits and accurate condition assessment for various aging and temperature ranges.

[0004] The battery management system relies on an accurate estimate of the state of charge (SoC) and the remaining energy (SoE) to ensure the reliability of the vehicle system. Cell temperature estimation is relevant for maintaining the operating limits of the battery cell and calculating the temperature dependencies of battery states, such as internal cell resistance.

[0005] EP 3 008 771 B1 describes that the internal temperature of an electrochemical device can be measured without a thermocouple, an infrared detector, or other auxiliary temperature measuring device. Some methods include exciting an electrochemical device with a drive profile; acquiring voltage and current data from the electrochemical device in response to the drive profile; calculating an impulse response from the current and voltage data; calculating an impedance spectrum of the electrochemical device from the impulse response; calculating a state of charge of the electrochemical device; and then estimating the internal temperature of the electrochemical device based on a temperature-impedance-state of charge relationship. The electrochemical device can be, for example, a battery, a fuel cell, an electrolytic cell, or a capacitor.

[0006] The object of the present invention is to provide a method, a computer program product and a battery management system by means of which a current state of charge of a battery cell can be estimated in an improved manner.

[0007] This object is achieved by a method, a computer program product, and a battery management system according to the independent patent claims. Advantageous embodiments are specified in the subclaims.

[0008] One aspect of the invention relates to a method for estimating a current state of charge of at least one battery cell of an electrical energy storage device of an at least partially electrically powered motor vehicle using a battery management system of the electrical energy storage device. An estimation model for the state of charge of the battery cell is specified by an electronic computing device of the battery management system, wherein modal coordinates of the state of charge are specified in the estimation model.

[0009] At least one current impedance spectrum of the battery cell is recorded using a recording device of the battery management system. Current modal coordinates of the recorded impedance spectrum are determined using the electronic computing device. The current state of charge is estimated based on the estimation model and the current modal coordinates.

[0010] In particular, a method for estimating the state of charge (SoC), in particular the state of charge (SoC), and, for example, also the temperature of battery cells, particularly silicon-ion battery cells, is proposed based on electrochemical impedance spectroscopy. The proposed algorithm is based on data-driven modal reduction through orthogonal eigenmode decomposition. Insights from measured temperatures, estimated states of charge, and the associated impedance spectra are encoded, particularly offline, in so-called low-rank patterns suitable for on-board application. A routine is proposed with which each impedance spectrum can be represented with approximately ten modal coordinates of the dominant dynamics of the impedance spectra as a function of temperature and state of charge.Cell temperature and state of charge are estimated on-board using a parameterized polynomial function of the modal coordinates. This enables both more accurate and faster state of charge and temperature estimation compared to state-of-the-art technology.

[0011] According to an advantageous embodiment, the current temperature of the battery cell is also determined and taken into account when estimating the current state of charge. In particular, the state of charge is also temperature-dependent. By estimating the temperature, especially in relation to the modal coordinates, the current state of charge of the battery cell can thus be reliably determined.

[0012] Furthermore, it has proven advantageous to consider a variety of temperatures and / or aging states and / or states of charge in the estimation model. In particular, impedance spectra are measured at different states of charge and temperatures. Battery cell impedance spectra are measured offline, if possible, at different states of charge, temperatures, and aging states. This data is necessary for training the data-driven estimation method.

[0013] It is also advantageous if the estimation model interpolates impedance spectra to a uniform reference frequency support point and plots them vectorially. Specifically, the impedance spectra are interpolated and plotted on a uniform frequency axis. All measured impedance spectra should be interpolated to the same frequency support points. These impedance spectra are plotted vectorially. Vectors then contain the real part of the impedance followed by its imaginary part.

[0014] Furthermore, it has proven advantageous to decompose impedance spectra using singular value decomposition in the estimation model. In particular, the proposed approach is based on proper orthogonal decomposition (POD). The data-driven method uses singular value decomposition (SVD) as a stable matrix decomposition that always exists. The approach thus describes a method that encodes the complex nonlinear dependence of cell impedance on state of charge and temperature into its dominant low-rank patterns.

[0015] It is also advantageous if the current impedance spectrum is interpolated to a uniform reference frequency support point and set up vector-wise. This allows a reliable comparison of the current impedance spectra with the impedance spectra of the estimation model.

[0016] A further advantageous embodiment provides that the estimate is determined from an orthogonal eigenmode decomposition and reconstruction of the cell impedance. In particular, the temperature and state of charge of the lithium-ion battery cells can thus be determined from the orthogonal eigenmode decomposition and reconstruction of the cell impedance.

[0017] Furthermore, it has proven advantageous to actively and / or passively record the current impedance spectrum. This allows the impedance spectrum to be recorded in different ways. This allows for a simple yet reliable determination of the current impedance spectrum.

[0018] The method presented is, in particular, a computer-implemented method. Therefore, a further aspect of the invention relates to a computer program product with program code means that, when the program code means are processed by the electronic computing device, cause an electronic computing device to perform a method according to the preceding aspect.

[0019] Furthermore, the invention therefore also relates to a computer-readable storage medium with the computer program product.

[0020] Yet another aspect of the invention relates to a battery management system for an electrical energy storage device for estimating a current state of charge of at least one battery cell of the electrical energy storage device of an at least partially electrically powered motor vehicle, comprising at least one detection device and an electronic computing device, wherein the battery management system is designed to carry out a method according to the preceding aspect. In particular, the method is carried out by means of the battery management system.

[0021] The electronic computing device comprises, for example, processors, circuits, in particular integrated circuits, as well as other electronic components in order to be able to carry out corresponding process steps.

[0022] Furthermore, the invention also relates to an electrical energy storage device with at least one battery management system according to the preceding aspect and with at least one battery cell.

[0023] Yet another aspect of the invention also relates to a motor vehicle with an electrical energy storage device according to the preceding aspect.

[0024] Advantageous embodiments of the method are to be regarded as advantageous embodiments of the battery management system, the electrical energy storage device and the motor vehicle.

[0025] The motor vehicle is designed in particular as an at least partially electrically powered motor vehicle or as a fully electrically powered motor vehicle.

[0026] Further advantages, features, and details of the invention will become apparent from the following description of a preferred embodiment and from the drawings. The features and combinations of features mentioned above in the description, as well as the features and combinations of features mentioned below in the description of the figures and / or shown alone in the single figure, can be used not only in the respective combinations specified, but also in other combinations or on their own, without departing from the scope of the invention.

[0027] Showing:

[0028] Fig. 1 shows a schematic block diagram according to an embodiment of a motor vehicle with an embodiment of an electrical energy storage device with an embodiment of a battery management system; and Fig. 2 shows a schematic flow diagram according to an embodiment of the method.

[0029] In the figures, identical or functionally identical elements are provided with the same reference symbols.

[0030] Fig. 1 shows a schematic block diagram according to an embodiment of a motor vehicle 10. The motor vehicle 10 is designed in particular as an at least partially electrically operated motor vehicle or as a fully electrically operated motor vehicle 10. The motor vehicle 10 has at least one electrical energy store 12, wherein the electrical energy store 12 has at least one battery cell 14, in particular in the form of a lithium-ion battery cell, and at least one battery management system 16. The battery management system 16 has at least one detection device 18 and an electronic computing device 20.

[0031] The battery management system 16 is designed to estimate a current state of charge 22 of the battery cell 14.

[0032] Fig. 2 shows a schematic flow diagram according to one embodiment of the method. The left side of Fig. 2 shows, in particular, the so-called off-board method, and the right side shows the so-called on-board method. In particular, the off-board method involves offline preparation or a one-time parameterization. In a first step S1, impedance spectra are measured at different charge states and temperatures. In particular, the impedance spectra of the battery cell 14 are measured offline, if possible, at different charge states, temperatures, and aging states. This data is necessary for training the data-driven estimation method.

[0033] In a second step S2, the impedance spectra are interpolated and aligned to a uniform frequency axis. All measured impedance spectra are interpolated to the same frequency reference point. These impedance spectra are aligned vectorially. Vectors contain the real part of the impedance followed by its imaginary part. In the third step S3, orthogonal modes are aligned. Vectorially aligned preprocessed impedance spectra are decomposed into the orthogonal modes using singular value decomposition. In the fourth step S4, modes are truncated. The termination order r of the modal reduction is determined. In a fifth step S5, the first r-dominant modes are normalized and the remaining ones are omitted accordingly. In the sixth step S6, the modal coordinates are generated.For each preprocessed impedance spectrum, the modal coordinates for the modal reconstruction are calculated, and the references for the measured temperature and the estimated states of charge are provided. In a seventh step (S7), the estimation functions are parameterized. The polynomial function of the modal coordinates for estimating the state of charge and temperature is parameterized with the corresponding modal coordinates and the reference temperatures and states of charge. The estimation model 24 is then generated.

[0034] The so-called on-board application is described in more detail below. This occurs for each impedance determination. The estimation model 24 is provided in the process. In an eighth step S8, the current impedance spectrum of the battery cell 14 is determined. In particular, the cell impedance can be measured on-board using special hardware. Alternatively, passive impedance spectroscopy can be performed from the signal analysis of the current and voltage of the battery cell 14. In a ninth step S9, the impedance spectra are interpolated and aligned to a uniform frequency axis. An on-board acquired impedance spectrum is interpolated to the same frequency points and aligned vectorially, analogous to the corresponding offline measured impedance spectra. In the tenth step S10, the corresponding current modal coordinates are generated.The modal coordinates are calculated, and optionally the reconstruction error is output as an indicator of the quality of the estimate. In an eleventh step S11, the parameterized estimation functions for the cell temperature and the state of charge are evaluated.

[0035] Lithium-ion batteries (LIBs) play a crucial role in the implementation and success of electromobility concepts due to their high energy and power density. The battery packs used in mobile applications are monitored by the battery management system 16 to ensure their reliable and optimal use. Safe operation by the battery management system 16 requires compliance with the battery operating limits and an accurate condition assessment for various aging and temperature ranges.

[0036] The battery management system 16 relies on an accurate estimate of the state of charge (SOC) and the remaining energy (SOE) to ensure the reliability of the vehicle system. The cell temperature estimate is relevant for maintaining the operating limits of the battery cell 14 and for calculating the temperature dependencies of the battery states, such as the internal cell resistance.

[0037] An on-board algorithm for cell core temperature and state-of-charge estimation is proposed using proven signal processing algorithms. The approach is based on proper orthogonal decomposition (POD). The data-driven method uses singular value decomposition (SVD) as a stable matrix decomposition that always exists. The approach describes a procedure that encodes the complex nonlinear dependence of cell impedance on state-of-charge and temperature into its dominant low-rank patterns.

[0038] For the purpose of parameterizing the estimation functions, offline impedance spectra are measured for as many aging states, temperatures, and SOCs as possible. The representation of each impedance spectrum Z(a>) in the complex plane is calculated for a frequency vector a> ref is interpolated and then rewritten into the vectorial form in Eq. 1. The vectors of the measured impedance spectra Z T S0C are placed in the matrix of snapshots Z as shown in Eq. (2).

[0039] A set of orthogonal modes U is generated, which describe the contribution of state-of-charge and temperature dependence to the impedance spectra, by performing the eigenmode decomposition using the SVD in Eq. (3).

[0040] The number of decomposed snapshots (impedance spectra) determines the maximum total number of generated modes m. The number of modes necessary to accurately represent the state-of-charge and temperature dependences is called the truncation order r. The optimal truncation order r results from the number of dominant modes with singular values ​​(diagonal values ​​of the matrix £) greater than a threshold value a to be determined. min. The latter denotes the threshold of singular values ​​that distinguish important from negligible dependence patterns or represent signal noise. The first mode dominates the dependence of the impedance on state of charge and temperature. The contribution of the remaining modes to the dependence to be described usually decreases exponentially. Therefore, only the first r modes are retained, and the remaining modes are truncated. ib.- = ~ IITPZj~ll ( v 4) '

[0041] Since the modes contribute to varying degrees in describing state-of-charge and temperature dependences, their magnitudes vary. The modal reconstruction of state-of-charge and temperature dependences in the reduced r-dimensional subspace of the dependence dynamics in Eq. (6) is based on the idea of ​​separating variables. To enable comparability of the modal coordinates, a mode normalization according to Eq. (4) is performed. For this purpose, the calculated eigenmodes are divided by their Euclidean norms. The normalized modes ^retain the dimension of an impedance. The matrix of modes has the form in Eq. (5). The reduced cell impedance Z T S0C is reconstructed as a linear combination of the orthogonal modes according to Eq. (6). The estimator in Eq. (7) calculates the modal coordinate vector k after an on-board impedance measurement. Here, i denotes the observation matrix of the estimator. The optimal value of the estimator variable k is the projection of the impedance spectrum Z. T S0C in the r-dimensional subspace of the dependence patterns of temperature and state of charge, which is spanned by the orthogonal modes ipj.

[0042] From the measured temperatures and reference states of charge, the estimation functions for states of charge and temperature are parameterized offline using an optimizer according to Eq. (8) and Eq. (9).

[0043] For the on-board application, after the calculation of the modal coordinates, only the estimation function with the appropriate parameterization is evaluated on the battery management system 16.

[0044] List of reference symbols

[0045] 10 motor vehicle

[0046] 12 electrical energy storage

[0047] 14 battery cells

[0048] 16 Battery management system

[0049] 18 Recording device

[0050] 20 electronic computing devices

[0051] 22 Charge level

[0052] 24 Estimation model

[0053] S1 to S11 steps of the procedure

Claims

Patent claims 1. A method for estimating a current state of charge (22) of at least one battery cell (14) of an electrical energy storage device (12) of an at least partially electrically operated motor vehicle (10) by means of a battery management system (16) of the electrical energy storage device (12), comprising the steps: - specifying an estimation model (24) for charge states of the battery cell (14) by means of an electronic computing device (20) of the battery management system (16), wherein modal coordinates of the charge states are specified in the estimation model (24); - detecting at least one current impedance spectrum of the battery cell (14) by means of a detection device (18) of the battery management system (16); - determining current modal coordinates of the detected impedance spectrum by means of the electronic computing device (20); and - Estimating the current state of charge (22) depending on the estimation model (24) and the current modal coordinates.

2. Method according to claim 1, characterized in that in addition a current temperature of the battery cell (14) is determined and taken into account when estimating the current state of charge (22).

3. Method according to claim 1 or 2, characterized in that a plurality of temperatures and / or aging states and / or charge states are taken into account in the estimation model (24).

4. Method according to one of the preceding claims, characterized in that in the estimation model (24) impedance spectra are interpolated to a uniform reference frequency support point and are set up vector-wise.

5. Method according to one of the preceding claims, characterized in that in the estimation model (24) impedance spectra are decomposed by means of a singular value decomposition.

6. Method according to one of the preceding claims, characterized in that the current impedance spectrum is interpolated to a uniform reference frequency support point and is set up vector-wise.

7. Method according to one of the preceding claims, characterized in that the estimate is determined from an orthogonal eigenmode decomposition and reconstruction of the cell impedance.

8. Method according to one of the preceding claims, characterized in that the current impedance spectrum is recorded actively and / or passively.

9. Computer program product with program code means which cause an electronic computing device (20) to carry out a method according to one of claims 1 to 8 when the program code means are processed by the electronic computing device (20).

10. Battery management system (16) for an electrical energy storage device (12) for estimating a current state of charge (22) of at least one battery cell (14) of the electrical energy storage device (12) of an at least partially electrically operated motor vehicle (10), with at least one detection device (18) and an electronic computing device (20), wherein the Battery management system (16) is designed to carry out a method according to one of claims 1 to 8.

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