Method and apparatus for parameterizing an electrochemical battery model for a battery based on data from a plurality of batteries

Abstract

Computer-implemented method for parameterizing an electrochemical battery model of a specific battery (41) of a device (4), comprising the following steps: - Providing (S5) a current operating characteristic point of the specific battery (41) and operating characteristic points for further batteries of a plurality of further devices (4) for different evaluation periods, wherein the operating characteristic points characterise the operation of the respective battery (41) within one or more evaluation periods on the basis of several operating characteristics (M), wherein the operating characteristics (M) result depending on the courses of operating variables (F) of the respective battery (41); - selecting (S6) operating characteristic points of the further batteries (41) that are similar to the current operating characteristic point of the specific battery, wherein the selection of operating characteristic points of the further batteries (41) that are similar to the current operating characteristic point of the specific battery (41) is carried out using a clustering method, in particular k-means; - Providing model parameters of the battery model associated with the current operating characteristic point and the selected operating characteristic points; - determining (S7) at least one fused model parameter depending on the model parameters of the current operating characteristic point of the specific battery (41) and the selected operating characteristic points of the further batteries; - Signaling (S8) the at least one fused model parameter or updating the battery model with the at least one fused model parameter.

Description

Technical area

[0001] The invention relates to battery-operated electrical devices that operate off-grid and also to measures for determining the state of health (SOH) of the electrical energy storage devices. Furthermore, the invention relates to both mobile and stationary electrical energy storage devices. Technical background

[0002] The energy supply of electrical devices and machines that operate independently of the mains, such as electrically powered vehicles, is often provided by batteries.

[0003] The aging state of a battery decreases noticeably over its service life, resulting in a decreasing maximum storage capacity. The degree of battery aging depends on the individual load on the battery, i.e., the usage behavior of a battery-powered device, external environmental conditions, and the vehicle battery type.

[0004] Although an empirical aging model can be used to determine the current aging state of the energy storage system based on historical operating conditions, this model is inaccurate in certain situations. This inaccuracy of the conventional aging model makes it difficult to predict the aging state progression. However, predicting the aging state progression of the energy storage system is an important technical parameter, as it enables an economic assessment of the residual value of the energy storage system.

[0005] An electrochemical battery model can be used to determine the aging state of a battery. This electrochemical battery model is parameterized by a variety of model parameters and is based on differential equations that model internal equilibrium states.

[0006] To adapt the model parameters of the electrochemical battery model, the operating parameters of a battery with a known or measured aging state can be used to characterize the battery's operation, such as battery current, battery voltage (terminal voltage), battery temperature, and state of charge. Since the electrochemical battery model depends significantly on the battery's aging state, the battery model is reparameterized or the model parameters updated at regular intervals.

[0007] Document EP 3 224 632 B1 discloses a wireless network-based battery management system (BMS) for hybrid or electric vehicles, comprising an off-board subsystem and an on-board subsystem, wherein the on-board subsystem is an on-board battery system, wherein the off-board subsystem comprises off-board data storage for storing historical data of the on-board subsystem and data from other battery systems, and comprises off-board data processing means configured to process the stored data and to create and validate accurate and complex off-board battery models. Disclosure of the invention

[0008] According to the invention, a method for parameterizing an electrochemical battery model for a battery of a battery-operated machine according to claim 1 and a device according to the independent claim are provided.

[0009] Further embodiments are specified in the dependent claims.

[0010] According to a first aspect, there is provided a computer-implemented method for parameterizing an electrochemical battery model of a particular battery of a device, comprising the following steps: - Providing a current operating characteristic point of the specific battery and operating characteristic points for other batteries of a plurality of other devices for different evaluation periods, wherein the operating characteristic points characterise the operation of the respective battery within one or more evaluation periods on the basis of several operating characteristics, wherein the operating characteristics result depending on the course of operating variables of the respective battery; - selecting operating characteristic points of the further batteries that are similar to the current operating characteristic point of the specific battery, wherein the selection of operating characteristic points of the further batteries (41) that are similar to the current operating characteristic point of the specific battery (41) is carried out using a clustering method, in particular k-means; - Providing model parameters of the battery model associated with the current operating characteristic point and the selected operating characteristic points; - Determining at least one fused model parameter depending on the model parameters of the current operating characteristic point of the specific battery and the selected operating characteristic points of the other batteries; - Signaling the at least one fused model parameter or updating the battery model with the at least one fused model parameter.

[0011] The aging state of a rechargeable electrical energy storage device, especially a portable battery, is not typically measured directly. This would require a series of sensors near the energy storage device, making the production of such an energy storage device costly and complex and increasing the installation space. Furthermore, practical measurement methods for determining the aging state in the devices are not yet commercially available. Therefore, the current aging state is usually determined using a physical aging model in the devices. This physical aging state model is inaccurate in certain situations and typically exhibits model deviations of up to 5%.

[0012] The physical aging model is based on an electrochemical battery model, which has a series of differential equations for determining equilibrium parameters and is parameterized via model parameters. The model parameters of the electrochemical battery model are typically parameterized by measuring the battery's total capacity or internal resistance in order to determine the battery's aging state. Using an optimizer, the model parameters of the electrochemical battery model can be determined in a conventional manner based on the behavior of operating variables such as battery voltage (terminal voltage), battery current, battery temperature, and state of charge, as well as the specific information on the aging state.

[0013] Due to the inaccuracy of the physical aging state model, it can only indicate the current aging state of the energy storage device. A prediction of the aging state, which depends particularly on the operating mode of the energy storage device, such as the level and quantity of charge inflow and outflow in a portable battery, and thus on usage behavior and usage parameters, leads to very high fluctuations and inaccuracies in the predictions and is therefore not usable.

[0014] The state of aging (SOH) is the key parameter for indicating the remaining battery capacity or remaining battery charge in portable batteries as electrical energy storage devices. The state of aging represents a measure of the aging of the electrical energy storage device. In the case of a portable battery, a battery module, or a battery cell, the state of aging can be expressed as the capacity retention rate (SOH-C) or as the increase in internal resistance (SOH-R). The capacity retention rate SOH-C is expressed as the ratio of the measured instantaneous capacity to the initial capacity of the fully charged battery. The relative change in internal resistance SOH-R increases with increasing battery age.

[0015] The above method provides for determining the model parameters of the electrochemical battery model for a specific battery based on data from a large number of additional batteries. For this purpose, similar operating characteristic points of additional batteries are selected using a clustering method, in particular k-means, to a current operating characteristic point of the specific battery. The associated model parameters of the electrochemical battery model are determined for each of the selected operating characteristic points. Using a fusion method, the model parameters of the current operating characteristic point of the specific battery and the selected operating characteristic points of the additional batteries are combined to obtain fused model parameters for the battery model of the specific battery.

[0016] In this way, the model parameters of the electrochemical battery model can be determined more accurately, even in the presence of measurement inaccuracies, for example, in the aging state information obtained through measurement or in the operating parameters of specific or additional batteries. The model parameters of the electrochemical battery model are now suitable for determining the aging state in an electrochemical aging state model or for using this model in an open-circuit characteristic model.

[0017] Furthermore, it can be provided that aging states are provided for at least a part of the current operating characteristic point of the specific battery and the operating characteristic points for the further batteries and that operating characteristic points similar to the current operating characteristic point of the specific battery are selected depending on the aging states of the respective operating characteristic point.

[0018] It can be provided that the aging states are each determined based on a measurement, in particular based on a Coulomb counting method, wherein the model parameters of the battery of the relevant operating characteristic point are updated depending on the measurement and on the courses of operating variables of the relevant battery.

[0019] Alternatively, an aging state model may be provided based on the battery model to model a respective aging state to the current operating characteristic point of the specific battery and to the operating characteristic points of the further batteries, wherein the selection of operating characteristic points similar to the current operating characteristic point of the specific battery is performed depending on the aging states of the respective operating characteristic point.

[0020] According to one embodiment, the method can be executed in a central unit, wherein the fused model parameters are transmitted to the device with the battery in order to update the battery model there.

[0021] Furthermore, the determination of fused model parameters can be carried out by combining the model parameters, each associated with the current operating characteristic point of the battery and the selected operating characteristic points of the other batteries, with a Kalman filter or a Luenberger observer, or by averaging them, or by averaging them in a manner weighted depending on the similarities.

[0022] According to a further aspect, a device is provided for parameterizing an electrochemical battery model of a specific battery of a device, in particular in a central unit that is in communication with a plurality of batteries, comprising the following steps: - Providing a current operating characteristic point of the specific battery and operating characteristic points for other batteries of a plurality of other devices for different evaluation periods, wherein the operating characteristic points characterise the operation of the respective battery within one or more evaluation periods on the basis of several operating characteristics, wherein the operating characteristics result depending on the course of operating variables of the respective battery; - Selecting operating characteristic points of the additional batteries that are similar to the current operating characteristic point of the specific battery. The selection of operating characteristic points of the additional batteries (41) that are similar to the current operating characteristic point of the specific battery (41) is carried out using a clustering method, in particular k-means; - Providing model parameters of the battery model associated with the current operating characteristic point and the selected operating characteristic points; - Determining at least one fused model parameter depending on the model parameters of the current operating characteristic point of the specific battery and the selected operating characteristic points of the other batteries; - Signaling the at least one fused model parameter or updating the battery model with the at least one fused model parameter.

[0023] Furthermore, the device with the specific battery can correspond to a motor vehicle, a pedelec, an aircraft, in particular a drone, a machine tool, an autonomous robot and / or a household appliance. Brief description of the drawings

[0024] Embodiments are explained in more detail below with reference to the attached drawings. They show: Fig. 1 a schematic representation of a system for providing driver- and vehicle-specific operating variables for determining an ageing state of a vehicle battery in a central unit; Fig. 2 a flowchart illustrating a method for providing an aging state model in the central unit; and Fig. 3 a diagram illustrating an operating characteristic and the aging states of a variety of vehicle batteries. Description of embodiments

[0025] The method according to the invention is described below using vehicle batteries as electrical energy storage devices in a large number of motor vehicles as similar battery-operated devices. A physical aging state model for the respective vehicle battery can be implemented in a control unit in the motor vehicles. The aging state model can be continuously or regularly updated or retrained in a central unit based on operating parameters of the vehicle batteries from the vehicle fleet.

[0026] The above example represents a variety of stationary or mobile devices with grid-independent energy supply, such as vehicles (electric vehicles, pedelecs, etc.), systems, machine tools, household appliances, IoT devices and the like, which are connected to a central unit (cloud) via a corresponding communication connection (e.g. LAN, Internet).

[0027] Fig. Figure 1 shows a system 1 for collecting fleet data in a central unit 2 for the creation, operation, and evaluation of an aging state model. The aging state model is used to determine the aging state of an electrical energy storage device, such as a vehicle battery in a motor vehicle. Fig. 1 shows a vehicle fleet 3 with several motor vehicles 4.

[0028] One of the motor vehicles 4 is in Fig. 1. The motor vehicles 4 each have a vehicle battery 41 as a rechargeable electrical energy storage device, an electric drive motor 42, and a control unit 43. The control unit 43 is connected to a communications module 44, which is suitable for transmitting data between the respective motor vehicle 4 and a central unit 2 (cloud).

[0029] The central unit 2 has a data processing unit 21 in which the method described below can be carried out, and a database 22 for storing aging status profiles of batteries of a plurality of vehicles 4 of the vehicle fleet 3.

[0030] The motor vehicles 4 send the operating variables F to the central unit 2, which at least indicate variables on which the aging state of the vehicle battery depends. In the case of a vehicle battery, the operating variables F can indicate an instantaneous battery current, an instantaneous battery voltage, an instantaneous battery temperature and an instantaneous state of charge (SOC) at the pack, module and / or cell level. The operating variables F are recorded in a fast time grid of 2 Hz to 100 Hz and can be regularly transmitted to the central unit 2 in uncompressed and / or compressed form. For example, the time series can be transmitted to the central unit 2 in blocks at intervals of 10 minutes up to several hours.

[0031] From the operating variables F, operating characteristics can be generated in the central unit 2 or, in other embodiments, already in the respective motor vehicles 4, which relate to an evaluation period. The evaluation period for determining the aging state can range from a few hours (e.g., 6 hours) to several weeks (e.g., one month). A typical value for the evaluation period is one week.

[0032] The operating characteristics can, for example, include characteristics related to the evaluation period and / or accumulated characteristics and / or statistical variables determined over the entire service life to date. In particular, the operating characteristics can include, for example: histogram data on the state of charge curve, the temperature, the battery voltage, the battery current, in particular histogram data regarding the battery temperature distribution over the state of charge, regarding the charging current distribution over the temperature and / or regarding the discharging current distribution over the temperature, accumulated total charge (Ah), an average capacity increase during a charging process (in particular for charging processes in which the charge increase exceeds a threshold proportion (e.g., 20%) of the total battery capacity), a maximum of the differential capacity (dQ / dU: charge change divided by change in battery voltage), and others.If known, electrochemical conditions (layer thicknesses, concentrations, cyclable lithium, ...) can also be used as operating characteristics.

[0033] Further information can be obtained from the operating characteristics: a temporal load pattern such as charging and driving cycles, determined by usage patterns (such as rapid charging at high currents or strong acceleration or braking with recuperation), a usage time of the vehicle battery, a charge accumulated over the running time and a discharge accumulated over the running time, a maximum charging current, a maximum discharging current, a charging frequency, an average charging current, an average discharging current, a power throughput during charging and discharging, a (particularly average) charging temperature, a (particularly average) spread of the state of charge and the like.

[0034] A physical aging state model is implemented in each of the motor vehicles 4. The aging state model can be used regularly, i.e., after the respective evaluation period has elapsed, to determine the current aging state of the vehicle battery 41 based on the operating characteristics. In other words, it is possible to determine the aging state of the respective vehicle battery 41 based on the operating characteristics resulting from the operating parameter profiles of one of the motor vehicles 4 in the fleet 3.

[0035] The aging state model is based on an electrochemical battery model of the battery cell and its cell chemistry. The battery model is formed by a series of differential equations that incorporate the behavior of the battery characterization, and in particular, electrochemical equilibrium states, as model parameters. The model parameters can include, for example, layer thicknesses such as the SEI thickness, a change in cyclable lithium due to anode-cathode side reactions, rapid electrolyte consumption, slow electrolyte consumption, a loss of the active material in the anode, a loss of the active material in the cathode, and the like. This battery model determines internal physical battery states depending on the behavior of the operating variables F in order to provide a physically based aging state in the form of a capacity retention rate (SOH-C) and / or an internal resistance increase rate (SOH-R).

[0036] The electrochemical battery model can be used in the aging state model or in a state of charge model based on an open-circuit characteristic determined by the battery model.

[0037] In Fig. Figure 2 shows a flowchart describing the sequence of a method for determining model parameters for an electrochemical battery model based on fleet data from batteries in a plurality of motor vehicles 4. The method can be executed partially or completely in the central unit 2.

[0038] The method is implemented as software and / or as hardware in the control unit 21 of the central unit 2.

[0039] In step S1, the operating variables F are continuously received by the central unit 2 from the plurality of motor vehicles 4, so that the profiles of the operating variables are available in the central unit 2. Furthermore, physical aging models based on an electrochemical battery model are implemented in each of the motor vehicles 4. The electrochemical battery model is initially parameterized with model parameters.

[0040] In addition, in step S2, complete charge or discharge cycles are carried out in the vehicles 4, either triggered by a specific operating state or randomly, using a Coulomb counting method. These cycles make it possible to provide information about the aging state SOH in the form of a maximum capacity (capacity maintenance rate SOH-C) or as an internal resistance increase rate (SOH-R). This information essentially makes it possible to update the parameterization of the electrochemical battery model using known methods based on an electrochemical battery model used by an aging state model. The aging state SOH is also transmitted to the central unit 2.

[0041] In step S3, a check is performed for each vehicle battery 41 to determine whether the aging state of the vehicle battery 41 has decreased by a predetermined incremental relative amount, such as by 2%. If this is the case (alternative: yes), the method continues with step S4 for the specific vehicle battery 41 in question; otherwise, the method returns to step S1. This criterion triggers the updating of the model parameters of the electrochemical battery model.

[0042] In step S4, the corresponding model parameters of the electrochemical battery model can now be updated based on the aging states of the vehicles 4 measured in step S2. In this way, the central unit 2 always has up-to-date model parameters for the battery models of the vehicle batteries 41 of the vehicles 4 of the vehicle fleet 3.

[0043] In step S5, operating characteristics of the batteries 41 in the plurality of vehicles 4 are subsequently determined based on the profiles of the operating variables such as battery current, battery voltage, battery temperature, and state of charge. As described above, the operating characteristics are determined at the various evaluation times, each of which can be associated with a modeled or measured aging state. Thus, for each vehicle battery 41 of the vehicles, the database 22 contains the most recently determined model parameters of the electrochemical battery model, the measured or modeled aging state, and a respective operating characteristic point for each evaluation period, which indicates the entirety of the operating characteristics of the respective vehicle battery 41.

[0044] Based on the current operating characteristic point of the specific vehicle battery 41 and the operating characteristic points of the batteries 41 of the vehicles 4 of the vehicle fleet 3, and, if applicable, the respective associated aging state SOH, in step S6, using a clustering method, additional batteries with similar battery states can be selected for the specific vehicle battery 41 for which the model parameters of the battery model are to be updated. The selection is made from the set of operating characteristic points for each of the batteries 41 since the start of the respective commissioning and the model parameters available at that respective point in time or evaluation period.

[0045] In Fig.3 shows an operating characteristic M plotted against an aging state for a plurality of batteries 41. Two clusters are obtained as an example. By applying the clustering method, similar operating characteristic points can be easily determined for a current operating characteristic point of the specific vehicle battery 41, which have currently or in the past been determined for the other vehicle batteries.

[0046] By applying a clustering method, such as k-means, operating characteristic points of other vehicle batteries are identified that are similar to the operating characteristic point of the specific battery under consideration. Similar are all operating characteristic points that have a Euclidean distance to the current operating characteristic point that is smaller than a predefined threshold. Alternatively, the distance to a centroid of an identified cluster in which the current operating characteristic point is located can also be selected. Accordingly, the operating characteristic points are used to identify the associated batteries of the multitude of batteries in the vehicle fleet, and the model parameters valid for the respective electrochemical battery model during the evaluation period of the identified operating characteristic point are determined.

[0047] As an alternative to the model parameters of the electrochemical battery model for each vehicle battery being continuously updated in step S4, the model parameters can also be determined only for the selected operating characteristic points. This is done based on the operating variables associated with the assigned battery for the evaluation period identified by the operating characteristic point and the measured aging state, in particular using an optimization method. Accordingly, the determination of the model parameters of the respective electrochemical battery model for the selected operating characteristic points is triggered when the specific battery, which is similar to the selected batteries, has reached a certain change in its aging state.

[0048] One now obtains the corresponding model parameters of the electrochemical battery model for the specific battery as well as for the selected batteries.

[0049] In a subsequent step S7, the model parameters thus determined are merged to obtain the adjusted model parameters for the specific battery. For example, the individual model parameters for the operating characteristic points under consideration can each be merged using a Kalman filter or a Luenberger observer to obtain updated model parameters. Alternatively, the model parameters can each be averaged or weighted averaged. The weightings for this can result from the Euclidean distance to the centroid of the cluster determined for the specific vehicle battery or from the distance to the operating characteristic point of the specific vehicle battery 41.

[0050] In this way, it is possible to update the model parameters for a specific battery based on fleet data from a large number of batteries.

[0051] In step S8, the fused model parameters thus determined are transferred to the vehicle with the specific vehicle battery and applied there in the electrochemical battery model for determining the state of age or the state of charge.

Claims

[1] Computer-implemented method for parameterizing an electrochemical battery model of a specific battery (41) of a device (4), comprising the following steps: - Providing (S5) a current operating characteristic point of the specific battery (41) and operating characteristic points for further batteries of a plurality of further devices (4) for different evaluation periods, wherein the operating characteristic points characterise the operation of the respective battery (41) within one or more evaluation periods on the basis of several operating characteristics (M), wherein the operating characteristics (M) result depending on the courses of operating variables (F) of the respective battery (41); - selecting (S6) operating characteristic points of the further batteries (41) that are similar to the current operating characteristic point of the specific battery, wherein the selection of operating characteristic points of the further batteries (41) that are similar to the current operating characteristic point of the specific battery (41) is carried out using a clustering method, in particular k-means; - Providing model parameters of the battery model associated with the current operating characteristic point and the selected operating characteristic points; - determining (S7) at least one fused model parameter depending on the model parameters of the current operating characteristic point of the specific battery (41) and the selected operating characteristic points of the further batteries; - Signaling (S8) the at least one fused model parameter or updating the battery model with the at least one fused model parameter. [2] Method according to claim 1, comprising the further steps: - providing (S4) aging states for at least part of the current operating characteristic point of the specific battery and the operating characteristic points for the further batteries (41); - Selecting (S6) operating characteristic points similar to the current operating characteristic point of the specific battery depending on the aging states of the respective operating characteristic point. [3] Method according to claim 2, wherein the aging states are each determined based on a measurement, in particular based on a Coulomb counting method, wherein the model parameters of the battery (41) of the relevant operating characteristic point are updated depending on the measurement and on the courses of operating variables of the relevant battery (41). [4] The method according to claim 2, wherein an aging state model is provided based on the battery model to model a respective aging state to the current operating characteristic point of the specific battery (41) and to the operating characteristic points of the further batteries (41), wherein the selection of operating characteristic points similar to the current operating characteristic point of the specific battery is performed depending on the aging states of the respective operating characteristic point. [5] Method according to one of claims 1 to 4, wherein the method is carried out in a central unit (2), wherein the fused model parameters are transmitted to the device (4) with the specific battery (41) in order to update the battery model there. [6] Method according to one of claims 1 to 5, wherein the determination of fused model parameters is carried out by combining the model parameters, which are respectively associated with the current operating characteristic point of the battery (41) and the selected operating characteristic points of the further batteries (41), each with a Kalman filter or a Luenberger observer, or each averaging them, or each averaging them in a manner weighted depending on the similarities. [7] Method according to one of claims 1 to 6, wherein the device with the specific battery corresponds to a motor vehicle, a pedelec, an aircraft, in particular a drone, a machine tool, an autonomous robot and / or a household appliance. [8] Device for parameterising an electrochemical battery model of a battery (41) in a device (4), wherein the device, in particular in a central unit (2) which is in communication connection with a plurality of batteries (41), is designed to: - Providing a current operating characteristic point of the specific battery (41) and operating characteristic points for further batteries of a plurality of further devices (4) for different evaluation periods, wherein the operating characteristic points characterise the operation of the respective battery (41) within one or more evaluation periods on the basis of several operating characteristics, wherein the operating characteristics arise depending on the course of operating variables of the respective battery (41); - selecting operating characteristic points of the further batteries (41) that are similar to the current operating characteristic point of the specific battery, wherein the selection of operating characteristic points of the further batteries (41) that are similar to the current operating characteristic point of the specific battery (41) is carried out using a clustering method, in particular k-means; - Providing model parameters of the battery model associated with the current operating characteristic point and the selected operating characteristic points; - Determining at least one fused model parameter depending on the model parameters of the current operating characteristic point of the specific battery and the selected operating characteristic points of the other batteries; - Signaling the at least one fused model parameter or updating the battery model with the at least one fused model parameter. [9] Computer program product comprising instructions which, when the program is executed by at least one data processing device, cause the device to carry out the steps of the method according to one of claims 1 to 7. [10] Machine-readable storage medium comprising instructions which, when executed by at least one data processing device, cause the device to carry out the steps of the method according to one of claims 1 to 7.

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