Method and device for determining a state variable of a vehicle battery

Abstract

Method for determining a state variable of a vehicle battery using a battery model, wherein the determined state variable (xn) correlates with a current state of charge of the vehicle battery (1), wherein the state variable (xn) of the vehicle battery (1) is determined using the battery model, wherein the battery model is generated as a nonlinear state-space model using input variables, wherein the input variables of the state-space model include a measured battery voltage (yv) and / or a battery current (u) and / or a temperature, wherein the nonlinear state-space model is transformed into a parameterized linear system (a*) with a set of discrete time-constant parameter vectors (a1, a2, ak) by specifying known time constants, wherein for each parameter vector (a1, a2, ak) a state vector (x1, x2, xk) is estimated and weighted based on a conditional probability,wherein each estimated state vector includes a value for the state variable (xn) to be determined, wherein a correct parameter vector (a1, a2, ak) and a corresponding estimated state vector (x1, x2, xk) are identified based on a maximum probability calculation, and the state variable (xn) to be determined is read from the identified state vector (x1, x2, xk).

Description

State of the art

[0001] The invention relates to a method for determining a state variable of a vehicle battery using a battery model according to the preamble of independent claim 1, as well as to an associated device, a computer program product and a data processing program for carrying out the method for determining a state variable of a vehicle battery using a battery model.

[0002] Methods for determining a battery's state of charge (SOC) are known from the prior art, in which the battery's state of charge is determined using a battery sensor in contact with the battery. Methods for estimating the battery's open-circuit voltage are frequently used in battery state determination. The open-circuit voltage is defined as the steady-state voltage that an unloaded battery reaches at 25°C after a very long period of time. Since the open-circuit voltage is subject to certain boundary conditions, it cannot be directly measured in certain applications, such as in a motor vehicle, due to polarization effects from existing quiescent currents, disturbances from temperature changes, and an insufficient rest period for the open-circuit voltage to stabilize.

[0003] Established methods for estimating the open-circuit voltage typically evaluate voltage, current, and temperature measurements repeatedly recorded at intervals of one or more hours during a vehicle's rest period. To determine the measured battery voltage behavior, these measurements are then compared with predefined reference curves to identify the most suitable curve for calculating the open-circuit voltage. A disadvantage of these methods is that the rest-time constants can vary due to operating point shifts or different battery technologies.

[0004] For example, German patent application DE 10 2007 031 303 A1 describes a method and a device for determining a state variable of a motor vehicle battery that correlates with the battery's state of charge. The described method determines the state variable using a self-learning battery model. The state variable correlated with the battery's state of charge is, in particular, the open-circuit voltage of the motor vehicle battery.

[0005] Since batteries from different manufacturers can vary greatly in their resting time constants due to different manufacturing processes, it is difficult to develop a universally valid self-learning battery model that covers all possible uncertainties. Disclosure of the invention

[0006] The inventive method for determining a state variable of a vehicle battery, or the inventive device for determining a state variable of a vehicle battery with the features of independent claims 1 and 5 respectively, have the advantage that the state variable can be determined within a time period that is significantly shorter than the settling time of the vehicle battery. Advantageously, embodiments of the present invention are robust against variations in the recovery time constant, since this can be specified as a known value over a wide range. Furthermore, the history of the battery load profiles, as well as dynamic excitation currents and temperature changes during the active phase of the vehicle battery and the aging of the vehicle battery, can advantageously be taken into account for determining the state variable of a vehicle battery.Thus, embodiments of the present invention are advantageously able to identify the time constants for the rest processes.

[0007] In an inventive method for determining a state variable of a vehicle battery, which correlates with the current state of charge of the vehicle battery, a battery model is generated as a nonlinear state-space model and transformed into a parameterized linear system with a set of discrete time-constant parameter vectors by specifying known time constants. Here, a state vector is estimated for each parameter vector and weighted based on a conditional probability, with each estimated state vector including a value for the state variable to be determined. Furthermore, a correct parameter vector and a corresponding estimated state vector are identified based on a maximum probability calculation, and the state variable to be determined is read from the identified state vector.

[0008] An inventive device for determining a state variable of a vehicle battery using a battery model comprises a state estimation device and a battery sensor for detecting the current battery voltage, wherein the determined state variable correlates with the current state of charge of the vehicle battery. According to the invention, the state estimation device generates the battery model as a nonlinear state-space model and, based on known time constants, transforms it into a parameterized linear system with a set of discrete time-constant parameter vectors. Furthermore, the state estimation device estimates a state vector for each parameter vector, which an evaluation and control unit weights based on a conditional probability, wherein each estimated state vector includes a value for the state variable to be determined.Based on a maximum probability calculation, the evaluation and control unit identifies a correct parameter vector and a corresponding estimated state vector. Furthermore, the evaluation and control unit reads the state variable to be determined from the identified state vector and outputs the read state variable.

[0009] In this context, the term "evaluation and control unit" refers to an electrical device, such as a component of a control unit, particularly a battery control unit, which processes or evaluates acquired sensor signals. The evaluation and control unit can have at least one interface, which may be implemented in hardware and / or software. In the case of a hardware-based interface, the interfaces may, for example, be part of a so-called system ASIC, which incorporates various functions of the evaluation and control unit. However, it is also possible for the interfaces to be separate integrated circuits or at least partially composed of discrete components. In the case of a software-based interface, the interfaces may be software modules, such as those found on a microcontroller alongside other software modules.

[0010] The device according to the invention is designed to execute steps of the aforementioned method and to control a computer program for these steps when the computer program is executed by the device. The device according to the invention can be understood to be an electrical device, such as a control unit, which processes or evaluates acquired sensor signals. The device can have at least one interface, which can be implemented in hardware and / or software. In a hardware-based implementation, the interfaces can, for example, be part of a so-called system ASIC, which incorporates various functions of the device. However, it is also possible for the interfaces to be separate integrated circuits or to consist at least partially of discrete components.In software-based training, the interfaces can be software modules, such as those found on a microcontroller alongside other software modules. A computer program product with program code stored on a machine-readable medium, such as semiconductor memory, hard disk storage, or optical memory, is also advantageous. This program is used to carry out the method according to one of the described embodiments when executed on the device.

[0011] The measures and further developments listed in the dependent claims enable advantageous improvements to the method specified in independent claim 1 for determining a state variable of a vehicle battery and to the device specified in independent claim 5 for determining a state variable of a vehicle battery.

[0012] A particular advantage is that the maximum probability calculation for identifying the correct parameter vector and the corresponding state vector interpolates the calculated conditional probabilities for the weighted state vectors. This makes it advantageously possible to identify the correct parameter vector and the corresponding state vector and use them to determine the state variable.

[0013] In an advantageous embodiment of the method according to the invention, the nonlinear state-space model for modeling the battery voltage behavior is generated as the sum of several exponential functions with variable scaling factors and time constants, wherein input variables of the state-space model include a measured battery voltage and / or a battery current and / or a temperature, and the resulting state space is estimated by a Kalman filter arrangement. This advantageously enables simple and accurate modeling of the battery voltage behavior. The battery model is preferably optimized to determine the open-circuit voltage of the vehicle battery as a state variable.

[0014] In an advantageous embodiment of the device according to the invention, the state estimation device for generating the nonlinear state-space model and for modeling the battery voltage behavior as a sum of several exponential functions with variable scaling factors and time constants can comprise a Kalman filter arrangement with an evaluation and control unit and several Kalman filter elements, wherein input variables of the state-space model include a measured battery voltage and / or a battery current and / or a temperature. Furthermore, each Kalman filter element can estimate a corresponding state vector for an assigned parameter vector.Although the parameter space for the time constants is infinitely large, in practice it can be restricted to a limited number, so that 6 to 10 Kalman filter elements are sufficient to determine the state variable, preferably the open-circuit voltage of the vehicle battery, with sufficient accuracy and manageable computational effort.

[0015] Exemplary embodiments of the invention are shown in the drawings and are explained in more detail in the following description. Brief description of the drawings

[0016] Fig. Figure 1 shows a schematic block diagram of an embodiment of a device according to the invention for determining a state variable of a motor vehicle battery.

[0017] Fig. Figure 2 shows a schematic flowchart of an embodiment of a method according to the invention for determining a state variable of a motor vehicle battery.

[0018] Fig. Figure 3 shows a more detailed block diagram of an embodiment of a device according to the invention for determining a state variable of a motor vehicle battery. Fig. 1.

[0019] Fig. Figure 4 shows a diagram of a parameter grid for a device according to the invention for determining a state variable of a vehicle battery. Embodiments of the invention

[0020] Fig. Figure 1 shows a device according to the invention for determining a state variable x n a motor vehicle battery 1 , Fig. Figure 2 shows a method according to the invention for determining a state variable of a motor vehicle battery and Fig. Figure 3 shows a more detailed representation of the device according to the invention. Fig. 1.

[0021] As from Fig. 1 and Fig. As can be seen in section 3, it includes a device for determining a state variable of a vehicle battery. 1using a battery model, a state estimation device 10 and a battery sensor 5 to detect a current battery voltage y, where one is determined by the battery sensor 5 measured voltage y v In addition to the actual battery voltage y, the measurement also includes a noise component v. This fact is described in Fig. 1 and Fig. 3 through a summation point 5.1 The diagram shows the current battery voltage y and the noise component v relative to the measured battery voltage y. v In summary, in addition to the measured battery voltage y-, a battery current u is also used as an input variable for the state estimation device. 10 The essential components of the battery model for modeling the voltage behavior are shown in Tables 1 and 2. In the illustrated embodiment, the state variable to be determined corresponds to a resting voltage x. n the vehicle battery1 and correlates with the current state of charge (SOC) of the vehicle battery 1 According to the invention, the state estimation device generates 10 The battery model is treated as a nonlinear state-space model and transformed according to known time constants λ1, λ2, ..., λ. n-1 into a parameterized linear system a* with a set of discrete time-constant parameter vectors a1, a2, a k um. The condition assessment device 10 estimates for each parameter vector a1, a2, a k a state vector x1, x2, x k An evaluation and control unit 20 weights the estimated state vectors x1, x2, x k based on a conditional probability. Here, each estimated state vector includes x1, x2, x k a value for the state variable x to be determined n Furthermore, the evaluation and control unit identifies 20based on a maximum probability calculation, a correct parameter vector a1, a2, a k and a corresponding estimated state vector x1, x2, x k and reads the state variable x to be determined n from the identified state vector x1, x2, x k out. As an optimized estimation result 22 The identified state vector x1, x2, x k , (x^, x^1, x^2, x^ k ) the read state variable x n and an estimated battery voltage y (ŷ, ŷ1, ŷ2, ŷ k ) issued. Time range State space x1 = A1exp(–λ1·t) x2 = A1exp(–λ2·t) ... x n-1 = A n-1 exp(–λ n-1 ·t) x n = c2 – ∫c1·u1(t)dt ẋ1 = –λ1·x1 ẋ1 = –λ2·x2 ... ẋ n-1 = –λ n-1 ·x n-1 ẋ n = c t ·u1 y = x1 + ... + x n +u2 y = x1 + ... + x n +u2 Table 1 symbol Unit Description [x1, x2, ..., x n-1 ] [V] Polarization voltage x n [V] Resting voltage u1 [A] Battery power u2 [V] Static compensation y [V] Battery voltage A1, A2, ..., A n-1 [–] Scaling factor λ1, λ2, ..., λ n-1 [s] Recovery time constant C1 [F] Acid capacity C2 [V] Initial resting voltage Table 2

[0022] As can be seen from Tables 1 and 2, the nonlinear state-space model for modeling the battery voltage behavior y is used as the sum of several exponential functions x1 to x. n-1 with variable scaling factors A1 to A n-1 and time constants λ1, λ2, ..., λ n-1generated, where the input variables of the state-space model are a measured battery voltage y v and / or include a battery current u and / or a temperature. Additionally, process noise w is taken into account.

[0023] As from Fig. As can be seen further in section 3, the state estimation device is a Kalman filter arrangement. 10 with the evaluation and control unit 20 and several Kalman filter elements 10.1 , 10.2 , 10.k executed. Here, each Kalman filter element estimates 10.1 , 10.2 , 10.k for an assigned parameter vector a1, a2, a k of the parameterized linear system a* a corresponding state vector x1, x2, x k with x = [x1, x2 ... x n ] where each parameter vector is a1, a2, a k a set of predefined time constants λ1, λ2, ..., λ n-1 , i.e. a n= [λ1, λ2, ..., λ1] comprises. The individual Kalman filter elements 10.1 , 10.2 , 10.k work in parallel and the evaluation and control unit 20 weights the values ​​determined by the Kalman filter elements 10.1 , 10.2 , 10.k estimated state vectors x1, x2, x k .

[0024] As from Fig. As can be seen in Figure 2, the inventive method for determining a state variable of a vehicle battery generates a battery model as a nonlinear state-space model in a first step S10. By specifying known time constants λ1, λ2, ..., λ n-1 In step S20, the nonlinear state-space model is converted into a parameterized linear system a* with a set of discrete time-constant parameter vectors a1, a2, a k , converted, where for each parameter vector a1, a2, a k a state vector x1, x2, x kis estimated, which represents a value for the state variable x to be determined. n This includes [the following]. In step S30, the estimated state vectors x1, x2, x are [the following]. k weighted based on a conditional probability, and in step S40 a correct parameter vector a1, a2, a k and a corresponding estimated state vector x1, x2, x k Identified based on a maximum probability calculation. In step S50, the state variable x to be determined is defined. n from the identified state vector x1, x2, x k Read and output. The maximum probability calculation for identifying the correct parameter vector a1, a2, a k and the corresponding state vector x1, x2, x k interpolates the calculated conditional probabilities for the weighted state vectors x1, x2, x k .

[0025] The parameter space for the time constants λ1, λ2, ..., λ n-1 Although infinitely large, in practice it can be limited to a finite number, so that 6 to 10 Kalman filter elements are used. 10.1 , 10.2 , 10.k are sufficient to determine the state variable x n to determine with sufficient accuracy and manageable computational effort.

[0026] Fig. Figure 4 shows an example diagram of a parameter grid for two different time constants λ1 and λ2. The parameter grid can be classified according to various aspects. For example, vertically hatched points represent... 36 for example, impossible behavior, while unhatched points 30 For example, they represent normal operating behavior. Cross-hatched dots. 32represent behavior with a high state of charge (SOC) or low battery temperature, while diagonally hatched points 34 represent a low state of charge (SOC) or a high battery temperature.

[0027] Embodiments of the present invention can be implemented as a circuit, device, method, data processing program using program code, and / or as a computer program product. Accordingly, the present invention can be implemented entirely as hardware and / or as software and / or as a combination of hardware and / or software components. Furthermore, the present invention can be implemented as a computer program product on a computer-accessible storage medium with computer-readable program code, whereby various computer-readable storage media such as hard disks, CD-ROMs, optical or magnetic storage elements, etc., can be used.

[0028] Computer-usable or computer-readable media can include, for example, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, equipment, or distribution media. Furthermore, computer-readable media can include an electrical connection with one or more wires, a portable computer disk, direct-access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), an optical cable, and a portable CD-ROM. The computer-usable or computer-readable medium can even be paper or another suitable medium on which the program is written and from which it can be electrically detected, for example, by optical scanning of the paper or other medium, then compiled, interpreted, or, if necessary, otherwise processed, and then stored in computer memory. QUOTES INCLUDED IN THE DESCRIPTION

[0029] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0030] DE 102007031303 A1

[0004]

Claims

[1] Method for determining a state variable of a vehicle battery using a battery model, wherein the determined state variable (x n ) with a current charge level of the vehicle battery ( 1 ) correlates, characterized by , that the battery model is generated as a nonlinear state-space model, which is transformed into a parameterized linear system (a*) with a set of discrete time-constant parameter vectors (a1, a2, a) by specifying known time constants k ) is converted, where for each parameter vector (a1, a2, a k ) a state vector (x1, x2, x k ) is estimated and weighted based on a conditional probability, where each estimated state vector has a value for the state variable to be determined (x) n ) includes, where a correct parameter vector (a1, a2, a k ) and a corresponding estimated state vector (x1, x2, x k) are identified based on a maximum probability calculation and the state variable to be determined (x) n ) from the identified state vector (x1, x2, x k ) is read out. [2] Method according to claim 1, characterized in that the maximum probability calculation for identifying the correct parameter vector (a1, a2, a k ) and the corresponding state vector (x1, x2, x k ) the calculated conditional probabilities for the weighted state vectors (x1, x2, x k ) interpolated. [3] Method according to claim 1 or 2, characterized in that the nonlinear state-space model for modeling the battery voltage behavior is generated as a sum of several exponential functions with variable scaling factors and time constants, wherein input variables of the state-space model are a measured battery voltage (y v) and / or a battery current (u) and / or a temperature, wherein the resulting state space is defined by a Kalman filter arrangement ( 10 ) is estimated. [4] Method according to one of claims 1 to 3, characterized in that the battery model for determining the open-circuit voltage (x n ) the vehicle battery ( 1 ) is optimized as a state variable. [5] Device for determining a state variable of a vehicle battery using a battery model with a state estimation device ( 10 ) and a battery sensor ( 5 ) to detect a current battery voltage (y), where the determined state variable (x) n ) with a current charge level of the vehicle battery ( 1 ) correlated, characterized in that the state estimation device ( 10) the battery model is generated as a nonlinear state-space model and, according to known time constants, transformed into a parameterized linear system (a*) with a set of discrete time-constant parameter vectors (a1, a2, a k ) converts, whereby the state estimation device ( 10 ) for each parameter vector (a1, a2, a k ) a state vector (x1, x2, x k ) estimates and with an evaluation and control unit ( 20 ) weighted based on a conditional probability, where each estimated state vector has a value for the state variable to be determined (x) n ) includes the evaluation and control unit ( 20 ) a correct parameter vector (a1, a2, a k ) and a corresponding estimated state vector (x1, x2, x k ) based on a maximum probability calculation, the state variable to be determined (x) is identified. n) from the identified state vector (x1, x2, x k ) reads and outputs. [6] Device according to claim 5, characterized in that the state estimation device ( 10 ) as a Kalman filter array ( 10 ) with the evaluation and control unit ( 20 ) and several Kalman filter elements ( 10.1 , 10.2 , 10.k ) is executed, which generates the nonlinear state-space model for modeling the battery voltage behavior as a sum of several exponential functions with variable scaling factors and time constants, where input variables of the state-space model are a measured battery voltage (y n ) and / or a battery current (u) and / or a temperature. [7] Device according to claim 6, characterized in that each Kalman filter element ( 10.1 , 10.2 , 10.k ) for an assigned parameter vector (a1, a2, a k) a corresponding state vector (x1, x2, x k ) estimates. [8] Device according to one of claims 5 to 7, characterized in that the battery model for determining the open-circuit voltage (x n ) the vehicle battery ( 1 ) is optimized as a state variable. [9] Computer program product with program code stored on a machine-readable medium for carrying out the method according to any one of claims 1 to 4, when the program is carried out by a state estimation device ( 10 ) is executed. [10] Data processing program with program code means for executing the method according to one of claims 1 to 4 by an evaluation and control unit ( 20 ), if the data processing program is routed through the state estimation device ( 10 ) is being processed.