Method and device for providing an optimized charging curve of a device battery by efficient parameter estimation

Abstract

The invention relates to a computer-implemented method for providing an optimized charging curve in a battery-operated technical device (1) with a device battery (11), comprising the following steps: - Recording (S2) a temporal course of company sizes, - Determination (S3) of operating points for parameterizing a battery model; - Parameterization (S5) of model parameters of the battery model using a fitting procedure with the temporal operating variable curves at the determined operating points; - determining (S6) a capacity-related aging state (SOH-C) and a resistance-related aging state (SOH-R) from the model parameters of the battery model; - selecting (S7) a charging curve which defines a charging current for a charging process of the device battery (11) depending on a state of charge, depending on the capacity-related aging state and a resistance-related aging state using a predetermined charging curve assignment table; - Use (S8) the charging curve for at least one subsequent charging process.

Description

Technical area

[0001] The invention relates to the provision of a charging curve adapted to the aging state of a device battery. Technical background

[0002] In off-grid technical devices, such as electric vehicles, powerful portable batteries, especially lithium-ion batteries, are used as energy storage devices. The electrical behavior of such portable batteries is often described using battery models. Electrochemical P2D battery models are particularly suitable for lithium-ion batteries, allowing them to describe battery behavior during use in a particularly efficient manner.

[0003] The P2D (pseudo-two-dimensional) battery model is a mathematical model for describing the electrochemical processes in a lithium-ion battery. The P2D battery model is capable of predicting the performance and behavior of batteries under various operating conditions, and in particular, of modeling the terminal voltage of a device battery.

[0004] The P2D model considers various physical and chemical processes in a battery, such as lithium ion transport (transport of lithium ions between and within the anode and cathode materials), electron transport (transport of electrons through the anode, electrolyte, and cathode), electrochemical reactions (reactions that occur during charging and discharging of the battery), and heat generation and dissipation during battery operation.

[0005] The P2D model uses partial differential equations to describe these processes and predict how the battery will behave under different operating conditions. This model can be used in computer simulations to optimize battery design.

[0006] For example, the P2D battery model can be used to determine the internal battery states of the device battery, from which the aging state of the device battery can be derived. Furthermore, the internal states can be used to generate charging curves, allowing charging processes to be carried out in aging-friendly operating ranges. An example of an aging-critical internal battery state is the anode overpotential, which must be maintained at a positive level.

[0007] The parameterization of the model parameters of a P2D battery model is based on time series of operating variables of the device battery using numerical optimization methods.

[0008] The document DE 10 2021 211 146 A1 discloses a method for determining a charging profile for charging a device battery of a battery-operated device.

[0009] The document DE 10 2021 208 340 A1 discloses a computer-implemented method for providing an aging state model for determining a modeled aging state (SOH) of an electrical energy storage device with at least one electrochemical unit, in particular a battery cell, in a technical device.

[0010] The document DE 10 2016 007 479 A1 discloses a device and a method for charging a battery cell having an anode and a cathode, wherein a charging current (I) is fed into the battery cell during the charging process, wherein at least in a predetermined charging phase the anode potential of the anode is indirectly regulated via the charging current (I) to a predeterminable value.

[0011] The document DE 10 2018 203 824 A1 discloses a method for operating an electrical energy storage device, control for an electrical energy storage device.

[0012] Document GB 2600757 A discloses a battery management system and a method for optimising battery performance, particularly with regard to rapid charging and aging. Disclosure of the invention

[0013] According to the invention, a method for providing an optimized charging curve in a technical device according to claim 1 and a corresponding device according to the independent claim are provided.

[0014] Further embodiments are specified in the dependent claims.

[0015] According to a first aspect, a computer-implemented method for providing an optimized charging curve in a battery-operated technical device with a device battery is provided, comprising the following steps: - Recording a temporal development of company sizes, - Determination of operating points for parameterizing a battery model; - Parameterizing model parameters of the battery model using a fitting procedure; - Determining a capacity-related aging state and a resistance-related aging state from the model parameters of the battery model; - Selecting a charging curve that defines a charging current for a charging process of the device battery depending on a state of charge, depending on the capacity-related aging state and a resistance-related aging state using a predefined charging curve assignment table; - Use the charging curve for at least one subsequent charging process.

[0016] A state of health (SOH) is the key parameter for indicating the remaining battery capacity or remaining battery charge as a battery health. The state of health of a device battery is not typically measured directly. This would require a series of sensors inside the device battery, making the production of such a device battery costly and complex and increasing the space required.

[0017] The state of age represents a measure of the ageing of the device battery. In the case of a device battery, a battery module, or a battery cell, the state of age can be specified as the capacity retention rate (SOH-C). The capacity retention rate SOH-C, i.e. the capacity-related state of age, is specified as the ratio of the measured instantaneous capacity to the initial capacity of the fully charged battery and decreases with increasing age. Alternatively, the state of age can be specified as the increase in internal resistance (SOH-R) relative to the internal resistance at the beginning of the device battery's service life. The relative change in internal resistance SOH-R increases with increasing battery age.

[0018] The aging state of a portable battery is therefore informative regarding the battery condition, which indicates its future usability. Knowledge of other battery conditions that indicate internal electrochemical battery conditions is also useful, for example, when an anomaly or fault in the portable battery needs to be detected early. Such internal electrochemical battery conditions can include, for example, a layer thickness (e.g., SEI thickness), an indication of a change in cyclable lithium due to anode / cathode side reactions, an indication of rapid electrolyte consumption, an indication of slow electrolyte consumption, an indication of a loss of active material in the anode, an indication of a loss of active material in the cathode, an anode potential, etc.The aging state as well as the internal electrochemical states of the device battery as battery states can be determined using an electrochemical battery model with the aid of an evaluation of nonlinear differential equations depending on the operating variables.

[0019] Furthermore, the battery model can correspond to a P2D battery model, where the fitting procedure uses a least squares method or a maximum likelihood approach.

[0020] The electrochemical battery model comprises a system of differential equations that, based on differential equations parameterized by model parameters, models internal battery states, in particular equilibrium states and, if applicable, kinetic states, using a time integration method. It provides a relationship between the operating variables of the battery cells of the device battery, namely a battery current, a battery voltage, a battery temperature, and a state of charge of the device battery. Such electrochemical battery models are known, for example, from the publications US 2016 / 023,566, US 2016 / 023,567, and US 2020 / 150,185.

[0021] The aging state of a portable battery, both in the form of the capacity-related aging state SOH-C and in the form of the resistance change-related aging state SOH-R, can be approximately determined, for example, as a linear combination of the internal electrochemical battery states.

[0022] The degradation of a portable battery is largely determined by the charging power during a charging process. To ensure gentle charging, the charging process is controlled using a charging curve that specifies a maximum charging current depending on the charge level or charging time. The charging current generally decreases with increasing charge level.

[0023] The charging curves of a battery are determined depending on internal battery conditions, which can be determined using the P2D battery model. In particular, charging curves are determined such that an anode overpotential is always maintained positive. The charging curves, which depend on the aging state, can each be provided in the form of a charging matrix and, depending on the aging state, implemented in the form of a charging curve assignment table in a control unit of a battery-operated technical device. This allows a charging curve optimized for the current internal battery state to be selected and used, depending on a specific capacity-related and resistance-change-related aging state SOH-C, SOH-R. This reduces the aging of the device battery during charging processes and thus the limitations regarding its usability, while keeping the charging time as short as possible.

[0024] The aim of the above method is to adapt the charging curve in a control unit of the technical device depending on the determined capacity-related and resistance change-related aging state SOH-C, SOH-R, even with limited computing capacity, so that an optimal charging curve can be applied. For this purpose, a P2D battery model is first parameterized in the beginning-of-life state, i.e. in a brand-new condition of the device battery. This is usually done by measuring the device battery in question on a test bench, whereby the model parameters of the battery model for the initial battery state are determined depending on operating parameter curves, such as battery current and battery temperature, using a suitable parameterization method, such as a fitting method based on an RMS approach (least squares method) or a maximum likelihood approach.

[0025] Based on the model parameters of the parameterized battery model, a parameter space of the battery model's model parameters can then be estimated up to the end of the device battery's service life. This parameter space specifies the value ranges within which the relevant model parameters may vary. The model's parameter space corresponds to the physically permissible states of the electrochemical model, for example, a value range for cyclable lithium. The capacity-related and resistance-change-related aging states SOH-C, SOH-R, and an optimal charging curve are then assigned to the parameter combinations of the parameter space.

[0026] Subsequently, a battery model is parameterized for each varying model parameter within the parameter space. By evaluating the battery model, the capacity-related and resistance-change-related aging states SOH-C and SOH-R are determined, and a charging curve is calculated based on the internal battery states determined by the respective battery model. A charging curve assignment table is provided that assigns the capacity-related and resistance-change-related aging states SOH-C and SOH-R to the determined charging curve for the variously parameterized battery models.

[0027] This charging curve assignment table is determined offline before commissioning the device battery and stored in the control unit of the technical device. Based on the determined parameter space of the model parameters, the charging curve assignment table can thus be appropriately implemented in a control unit of the technical device. The appropriate charging curve is then selected depending on the capacity-related and resistance-change-related aging state SOH-C, SOH-R determined by the control unit. This allows the charging curve to be continuously adjusted without requiring a communication connection to a central unit remote from the device. The implementation of the charging curve assignment table thus represents a cost-effective option for gentle operation of the device battery.

[0028] The capacity-related and resistance-change-related aging state SOH-C, SOH-R can be determined by performing a fitting procedure for the battery model in the control unit.

[0029] It can be provided that the charging curve is selected depending on the capacity-related aging state, in particular with regard to the capacity-related aging state, of the battery cell that is most aged, and depending on a resistance-related aging state of the battery cell that is most aged, in particular with regard to the resistance-related aging state.

[0030] During operation of the technical device, the model parameters of the battery model are determined based on characteristic operating points or operating periods. From these, the capacity-related aging state and the resistance-related aging state are determined, and the charging curve is selected accordingly. In particular, the operating points can be determined for each battery cell, so that the capacity-related aging state (SOH-C) of the most aged battery cell can be determined using an equilibrium model.

[0031] The equilibrium model describes the part of the electrochemical model that can be estimated using the battery's resting-phase voltages. Using the resting-phase voltages and the capacity throughput between the individual points, a fitting process and comparison with the beginning-of-life OCV-SOC curve can be used to directly estimate the capacity-related aging state (SOHC) and the remaining capacity. This is achieved by estimating at least three equilibrium parameters (possibly more depending on the cell chemistry). These equilibrium parameters are the anode volume fraction, the cathode volume fraction, and the cyclable lithium.

[0032] The operating points for the evaluation of the equilibrium model can in particular be operating variables after a relaxation period after a discharge phase or a charging phase.

[0033] Furthermore, operating points can be determined for determining kinetic parameters, such as current pulses under defined conditions, such as at a specific battery temperature, a specific state of charge (range), with a specific pulse duration, and the like. This allows the resistance-related aging state to be calculated. Here, too, the resistance-related aging state can be calculated for the most aged battery cell, and the battery current for the respective battery cell can be monitored accordingly.

[0034] Determining the capacity-related aging state using the equilibrium model requires only minimal computing power and can therefore be performed in the control unit of the technical device. The so-called equilibrium parameters are used to determine the capacity-related aging state of the battery cell.

[0035] Using the resting voltages and the capacity throughput between the individual points, the capacity-related aging state (SOHC) and the remaining capacity can be directly estimated through a fitting process and comparison with the beginning-of-life OCV-SOC curve. This is achieved by estimating at least three equilibrium parameters. The equilibrium parameters determine the shape of the OCV curve; consequently, the equilibrium parameters change over the aging of the cell in a strong correlation with the capacity-related aging state (SOHC). These equilibrium parameters can be estimated through a fitting process using resting voltage points and the corresponding states of charge or current throughput between the points.

[0036] The kinetic parameters are determined during dynamic operation (driving or charging). They are primarily related to the resistance change-related aging state (SoHR). Calculating the kinetic parameters is typically computationally and data-intensive. Therefore, we avoid this in this approach.

[0037] The equilibrium parameters have a dominant influence on the charging curve for a large part of the relevant aging state range, whereas a change in the kinetic parameters, which influence the resistance change-related aging state, only has a stronger influence on the optimized charging curve at lower aging states, such as at approximately 85% SOH-C.

[0038] According to a further aspect, there is provided a computer-implemented method for providing a charging curve mapping table for use with the above method for an individual device battery, comprising the steps of: - Recording a temporal operating variable profile of operating variables at the beginning of the commissioning of the device battery, - Determining model parameters of a battery model that describes the device battery, whereby a capacity-related aging state and a resistance-related aging state can be determined from a combination of model parameters; - Providing parameter spaces of the model parameters of the battery model, which specify value ranges for the model parameters; - Creating the charging curve assignment table, which assigns several different charging curves to a combination of a capacity-related aging state and a resistance-related aging state, whereby a charging curve, the assigned capacity-related aging state and the resistance-related aging state each result from a specific combination of values for the model parameters.

[0039] According to a further aspect, a device for providing an optimized charging curve in a battery-operated technical device with a device battery is provided, wherein the device is designed to: - Recording a temporal development of company sizes, - Determination of operating points for parameterizing a battery model; - Parameterizing model parameters of the battery model using a fitting procedure; - Determining a capacity-related aging state and a resistance-related aging state from the model parameters of the battery model; - Selecting a charging curve that defines a charging current for a charging process of the device battery depending on a state of charge, depending on the capacity-related aging state and a resistance-related aging state using a predefined charging curve assignment table; - Use the charging curve for at least one subsequent charging process. Brief description of the drawings

[0040] Embodiments are explained in more detail below with reference to the attached drawings. They show: Fig. 1 a schematic representation of a battery-powered vehicle with implemented charging curve generation; Fig. 2 a representation of an exemplary charging curve; Fig. 3 a method for operating the vehicle and the vehicle battery; and Fig. 4 a method for determining a charging curve assignment table for use in the control unit of the vehicle. Description of embodiments

[0041] The method according to the invention is described below using a vehicle battery in a motor vehicle. The method for determining the charging curve is carried out in the vehicle and enables the selection of an optimized charging curve generated remotely according to the battery condition.

[0042] Fig. Figure 1 shows an electrically powered vehicle 1 comprising a vehicle battery 11 as a rechargeable electrical energy storage device, an electric drive motor 12, and a control unit 13. The control unit 13 can be connected to a communication module 14 capable of receiving data from a central unit 2 (a so-called cloud).

[0043] A charging curve assignment table can be implemented in the control unit 13, which provides a charging curve for a charging process depending on an aging state of the vehicle battery 11.

[0044] The charging curve for a vehicle battery 11 indicates a maximum charging current I depending on a state of charge SOC [%], ie the stored usable charge of the vehicle battery 11, or over the charging time. charg Typical charging curves are step-like, as in Fig. 2. The charging curves are specified and determined such that the anode potential does not exceed a limit value related to an anode overpotential.

[0045] The anode overpotential can be determined from the parameterized electrochemical model. An optimization is then applied that calculates the maximum charging current for each combination of state of charge and temperature, such that the anode potential at the electrode surface is greater than or equal to 0 compared to the potential of metallic lithium, plus an optional safety margin to account for model or measurement inaccuracies.

[0046] Fig. 3 describes a method for operating the vehicle 1. The method can be implemented as software and / or hardware in the control unit 13.

[0047] For this purpose, in step S1, a charging curve assignment table is provided that assigns a capacity-related aging state and a resistance-change-related aging state to a specific charging curve. The charging curve can be specified as a charging profile, assigning a charging current to a charging state. SoHC SoHR Ladekurve 98% 100% Temperatur °C - SOC= 0% SOC= 20% SOC= 40% SOC= 60% SOC= 80% SOC= 100% 10°C 1C 1C 1C 0.5C 0.1C 0.1C 40°C 2C 2C 2C 1C 0.1C 0.1C where C corresponds to a given current value.

[0048] In step S2, operating variables of the device battery are continuously recorded at cell level.

[0049] At suitable operating points, which correspond in particular to rest points or certain pulse excitations, the operating variable points and operating variable curves can be stored at the cell level in step S3. The rest points correspond to operating variables, in particular after a relaxation phase of, for example, between 15 and 30 minutes after a charging or discharging phase, and serve to determine equilibrium parameters of an equilibrium model as part of or the entire battery model.

[0050] The equilibrium model describes the part of the electrochemical model that can be estimated using the battery's resting-phase voltages. Using the resting-phase voltages and the capacity throughput between the individual points, a fitting process and comparison with the beginning-of-life OCV-SOC curve can be used to directly estimate the capacity-related aging state (SOHC) and the remaining capacity. This is achieved by estimating at least three equilibrium parameters (possibly more depending on the cell chemistry). These equilibrium parameters are the anode volume fraction, the cathode volume fraction, and the cyclable lithium.

[0051] Furthermore, in step S4, the operating variables can be monitored for current pulses under defined conditions, which are defined, for example, by a specific battery temperature, a specific state of charge range (e.g., between 40-60% SOC), and a specific pulse duration of, for example, 1-5 seconds. The determination and subsequent evaluation of the operating points takes place for a specific period prior to the current time. The storage of the rest points and the current pulse operating cases can be performed in the control unit 13.

[0052] The operating points can then be evaluated in step S5 using a suitable battery model to determine model parameters of the battery model.

[0053] The P2D (pseudo-two-dimensional) battery model is a mathematical model for describing the electrochemical processes in a lithium-ion battery. The P2D battery model is capable of predicting the performance and behavior of batteries under various operating conditions, and in particular, of modeling the terminal voltage of a device battery.

[0054] The common P2D model takes into account various physical and chemical processes in a battery, such as lithium ion transport (transport of lithium ions between and within the anode and cathode materials), electron transport (transport of electrons through the anode, electrolyte, and cathode), electrochemical reactions (reactions that occur during charging and discharging of the battery), and heat generation and dissipation during battery operation.

[0055] The P2D model uses partial differential equations to describe these processes. The model parameters of a P2D battery model are parameterized based on the operating points using a fitting procedure based on a least-squares approach or a maximum-likelihood approach.

[0056] For example, the P2D battery model can be used to determine internal battery states of the device battery, from which an aging state of the device battery can be derived.

[0057] In step S6, a current resistance-related aging state SOH-R and a capacity-related aging state SOH-C of each battery cell are determined from the model parameters. This can be done, for example—but not exclusively—by comparing resting voltages with the current integral for the capacity-related aging state SoHC and the voltage response to current pulses for the resistance-related aging state.

[0058] In step S7, a suitable charging curve is selected depending on the capacity-related aging state of the most aged battery cell and depending on the resistance change-related aging state of the most aged battery cell using the provided charging curve assignment table.

[0059] In step S8, the charging curve is used for subsequent charging processes. The process is cyclically and continuously adapted to the current battery state.

[0060] The charging curve assignment table can be determined individually before commissioning the vehicle battery. A procedure for determining the charging curve assignment table is described using the flow chart of the Fig. 4 is explained in more detail.

[0061] In step S11, a battery model is first parameterized for the individual device battery 11. This can be done by measuring operating parameter curves on a test bench and subsequently parameterizing the battery model.

[0062] In step S12, a possible parameter space of the model parameters can be estimated until the end of the vehicle battery's service life. This applies both to equilibrium parameters, which significantly influence the capacity-related aging state, and to the dynamic (kinetic) model parameters of the battery model, which significantly influence the resistance-related aging state of the battery cell. The parameter space can be estimated using empirical values from similar cell chemistries. These empirical values can be taken from the literature or from self-conducted laboratory or field tests.

[0063] In step S13, the most gentle charging curve is determined for various combinations of the capacity-related aging state and the resistance-change-related aging state using the electrochemical battery model and assigned to the corresponding combination. Since each combination of capacity-related aging state and resistance-change-related aging state (SoHC and SoHR) can be assigned a variety of electrochemical states and thus different charging curves, the lowest current for each state of charge-temperature combination is selected from all charging curves as a conservative approach.

[0064] These can now be implemented in the vehicle's control unit in a subsequent step S14.

Claims

[1] Computer-implemented method for providing an optimized charging curve in a battery-operated technical device (1) with a device battery (11), comprising the following steps: - Recording (S2) a temporal course of company sizes, - Determination (S3) of operating points for parameterizing a battery model; - Parameterization (S5) of model parameters of the battery model using a fitting procedure with the temporal operating variable curves at the determined operating points; - determining (S6) a capacity-related aging state (SOH-C) and a resistance-related aging state (SOH-R) from the model parameters of the battery model; - selecting (S7) a charging curve that defines a charging current for a charging process of the device battery (11) depending on a state of charge, depending on the capacity-related aging state and a resistance-related aging state using a predetermined charging curve assignment table; and - Use (S8) the charging curve for at least one subsequent charging process. [2] The computer-implemented method of claim 1, wherein the battery model corresponds to a P2D battery model, wherein the fitting method uses a least squares method or a maximum likelihood approach. [3] Computer-implemented method according to claim 1 or 2, wherein the charging curve is selected as a function of the capacity-related aging state (SOH-C) of the most aged battery cell, in particular with regard to the capacity-related aging state, and as a function of a resistance-related aging state (SOH-R) of the most aged battery cell, in particular with regard to the resistance-related aging state. [4] A computer-implemented method for providing a charging curve assignment table for use with the method according to any one of claims 1 to 3 for an individual device battery (11), comprising the following steps: - Recording (S11) a temporal operating variable profile of operating variables at the start of commissioning of the device battery (11), - determining (S11) model parameters of a battery model that describes the device battery (11), wherein a capacity-related aging state (SOH-C) and a resistance-related aging state (SOH-R) can be determined from a value combination of model parameters; - Providing parameter spaces of the model parameters of the battery model, which specify value ranges for the model parameters; - Creating (S13) the charging curve assignment table which assigns several different charging curves to a combination of a capacity-related aging state and a resistance-related aging state, wherein a charging curve, the assigned capacity-related aging state and the resistance-related aging state each result from a specific combination of values for the model parameters; [5] Device for providing an optimized charging curve in a battery-operated technical device (1) with a device battery (11), wherein the device is designed to: - Recording a temporal development of company sizes, - Determination of operating points for parameterizing a battery model; - Parameterizing model parameters of the battery model using a fitting procedure; - Determining a capacity-related aging state and a resistance-related aging state from the model parameters of the battery model; - Selecting a charging curve that defines a charging current for a charging process of the device battery depending on a state of charge, depending on the capacity-related aging state and a resistance-related aging state using a predefined charging curve assignment table; - Use the charging curve for at least one subsequent charging process. [6] 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 4. [7] 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 4.

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