Method and system for determining a charging current limit for a charging process of a rechargeable battery

Abstract

The invention relates to a method (100), a computer program product, a control system (10) and a battery charging system (90) for determining a charging current limit for a charging process of a rechargeable battery device (1000). In this case, measurement parameters (MP) are recorded at the battery device (1000). Furthermore, battery parameters (BP) are determined by means of a process physics-based battery model on the basis of the recorded measurement parameters (MP). Furthermore, prediction parameters (VP) for the onset of metal plating at an electrode (1001, 1002) of the battery device (1000) are determined with the aid of a prediction model, in particular a data-driven prediction model, wherein at least one forecast onset time of metal plating is determined as prediction parameter(s) (VP) on the basis of at least the battery parameters (BP) as input parameters of the prediction model. Control parameters (KP) for controlling the charging process are determined with the aid of a control model, which is likewise in particular a data-driven control model, wherein on the basis of the measurement parameters (MP), the battery parameters (BP) and the prediction parameters (VP), the charging current limit is determined as the at least one control parameter (KP) and the determined charging current limit for specifying the charging current is output to a battery charging system (90).

Description

[0001] Method and system for determining a charging current limit for a charging process of a rechargeable battery

[0002] The present invention relates to a method for determining a charging current limit for a charging process of a rechargeable battery device, particularly at low operating temperatures. The invention also relates to a computer program product for computer-based execution of the steps of the inventive method and further to a control system for controlling a charging process of a rechargeable battery device. Furthermore, the invention relates to a battery charging system with the inventive control system.

[0003] Fast charging of rechargeable batteries is becoming increasingly important for a wide range of applications. In the automotive industry in particular, fast charging of vehicle batteries is intended to enable even purely electric vehicles to travel long distances. To achieve this, the charging process must be completed within an acceptable time period.

[0004] State-of-the-art solutions allow for the rapid charging of batteries under certain conditions. One challenge with fast charging of batteries is that a large amount of charge must be transported within the battery within a short period of time through electrochemical processes. These processes are temperature-dependent, which can lead to degradation of the battery being charged at temperatures below 15° Celsius and at high charging currents.

[0005] One of the underlying principles of degradation in lithium-ion batteries is so-called lithium plating.

[0006] Lithium plating is the formation of metallic lithium on the anode of lithium-ion batteries during the charging process. This deposition on the anode reduces the amount of lithium ions available in the battery's electrolyte. At the same time, a barrier layer is formed at the anode that prevents the free diffusion of lithium ions into the anode. Consequently, fewer lithium ions can diffuse into the anode and become embedded there. This type of deposition can also be referred to as intercalation.

[0007] One cause of lithium plating is that high charging currents can lead to an accumulation of lithium ions on the surface of the anode, meaning there are more lithium ions at the anode than can be stored in it. This can cause the lithium ions to react to form metallic lithium and deposit on the anode. This effect is further aggravated by low charging temperatures, as this slows the diffusion rate of the lithium ions into the anode.

[0008] For example, the state of the art attempts to address the problem of lithium plating using parameter monitoring, as demonstrated, for example, in US 2017 / 203667 A1. Certain battery parameters, such as the discharge voltage, are monitored, and the onset of lithium plating is detected when a specified threshold is reached. However, such methods are relatively imprecise and only attempt to influence the charging process once lithium plating has already occurred.

[0009] Other devices known from the prior art address the problem of lithium plating by switching on an external heater for the battery at low temperatures to prevent or at least delay the onset of lithium plating. The disadvantage of such solutions is that heating the battery is a relatively energy-intensive process, thus prolonging the charging process. Furthermore, due to the complexity of the processes taking place in the battery, simply switching on a heater across the board cannot enable a situation-adapted control process for the charging process to prevent or at least counteract the onset of lithium plating. Furthermore, this approach requires a certain residual charge in the battery to enable heating.Especially in the case of particularly heavily discharged batteries, such a procedure cannot be carried out, so that either rapid charging must be carried out despite adverse conditions or rapid charging is not possible.

[0010] The object of the present invention is to at least partially remedy the disadvantages described above. In particular, the object of the present invention is to provide methods and systems by means of which a charging process, in particular a rapid charging process, can be controlled at low operating temperatures in an energy-efficient manner and carried out as quickly as possible while avoiding metal plating.

[0011] The above object is achieved by a method having the features of claim 1, a computer program product having the features of claim 13, a control system having the features of claim 14 and a battery charging system having the features of claim 17.

[0012] Further advantages and features of the invention emerge from the dependent claims, the description, and the drawings. Features and details described in connection with the method according to the invention naturally also apply in connection with the computer program product according to the invention, with the control system according to the invention, and with the battery charging system according to the invention, and vice versa, so that with regard to the disclosure of the individual aspects of the invention, reference is always made to each other and can be made to each other.

[0013] A first aspect of the invention relates to a method for determining a charging current limit for a charging process of a rechargeable battery device. The method comprises a step in which measurement parameters are determined at the battery device. The measurement parameters include at least an operating temperature, a battery voltage, and a battery current of the battery device. Furthermore, battery parameters are determined from a physics-based battery model for mapping physical processes occurring in the battery device. At least an expected temperature progression of the operating temperature and an electrode voltage are determined as the battery parameters based on the recorded measurement parameters as input parameters of the battery model.In a further step, prediction parameters for the onset of metal plating at an electrode of the battery device are determined from a, in particular data-driven, prediction model. At least one predicted onset time of metal plating is determined as the prediction parameters based on at least the determined battery parameters as input parameters of the prediction model. Furthermore, at least one control parameter for controlling a charging process is determined from a, in particular data-driven, control model. Based on the measurement parameters, the battery parameters, and the prediction parameters as input parameters of the control model, at least the charging current limit is determined as the at least one control parameter, and the determined charging current limit is output.

[0014] In this case, outputting can be understood in particular as providing at least one value. This can be understood, for example, as simply providing at least one value, but also as outputting at least one value for further display and / or processing, in particular for control purposes.

[0015] The output charging current limit forms an upper limit of the charging current.

[0016] In other words, the invention can provide a method by which control parameters for a charging process of a rechargeable battery device can be determined. The control parameters can, in particular, be parameters that allow the control and / or regulation of a battery charging process. A rechargeable battery device can, in particular, be understood as a rechargeable, electrochemical power storage device.

[0017] In the method according to the invention, measurement parameters are determined on the battery device. The measurement parameters are recorded in particular by physical measurements on the battery device. For example, the operating temperature can be determined as a temperature inside the battery device. The operating temperature can also be, for example, an average of several cell temperatures, a housing temperature, or the like. The battery voltage can, for example, be tapped as the total voltage at the connection contacts of the battery device and / or recorded by determining individual cell voltages. The battery current can, for example, be understood as the total current that can currently be provided by the battery device.

[0018] Furthermore, battery parameters are determined from a physics-based battery model to represent the physical processes occurring in the battery device. The physics-based battery model can represent the physical processes occurring in the battery device, for example, using equations, constant specifications, or other relationships. Using the recorded measurement parameters, this physics-based battery model can determine battery parameters that characterize the state of the battery device or the state of its components from a physical perspective.

[0019] For the purposes of the present invention, the determined operating parameters include at least an expected temperature progression of the operating temperature and an electrode voltage. The expected temperature progression is understood, in particular, to be a forecast of the temperature progression over a future period. For example, the operating temperature and its progression over the next 5 seconds can be determined as operating parameters using the physics-based battery model. The forecast period preferably begins directly after the acquisition time of the measurement parameters.

[0020] In particular, the electrode voltage can be an estimate of a current value. However, it is also conceivable that the electrode voltage can be understood as a predicted value for a future period.

[0021] The battery parameters are used in a, in particular data-driven, prediction model to determine a predicted onset time for the occurrence of metal plating on at least one of the electrodes of the battery device. The onset time can be configured, for example, as an absolute date in the future, as the remaining operating time until metal plating occurs, as the remaining charging cycles, or similar. The predicted onset time of metal plating can be determined from a prediction model based on at least the determined battery parameters and the recorded measurement parameters as input parameters of the prediction model.

[0022] Metal plating can specifically be understood as the formation of metal deposits on one of the electrodes of a metal-ion battery. For example, sodium deposits can occur in a sodium-ion battery or calcium deposits in a calcium-ion battery.

[0023] In contrast to the battery parameters, the prediction parameters are determined using a model, particularly a data-driven one. A data-driven model can be understood in particular as a model in which relationships between input and output variables are determined and / or modeled using data.

[0024] In addition, at least one control parameter for controlling a charging process is determined from a control model, in particular a data-driven one. At least the charging current limit is determined as the at least one control parameter based on the measurement parameters, the battery parameters, and the prediction parameters as input parameters of the control model.

[0025] Weighting within the meaning of the invention can be understood, in particular, as a combination of the measurement parameters, the battery parameters, and the prediction parameters, in which a weight is assigned to the respective parameters. The weights of the respective parameters can differ from one another. The weights can, for example, reflect the influence of the individual parameters on the occurrence of metal plating.

[0026] This makes it possible to control the battery charging process in such a way that metal plating on one of the electrodes of the battery device can be reliably prevented without slowing down the charging process. This is based in particular on the fact that in the method the battery parameters, which are good indicators of the onset of metal plating, can be determined relatively accurately from measured values ​​and by means of analytical methods. According to the invention, despite the high complexity of the processes taking place in the battery, a prediction of the onset time can be made using the battery parameters. This is possible because the complexity is managed by a model, in particular a data-driven one. The determination of suitable control parameters using the onset time can also be carried out reliably and accurately.This is possible even though highly complex relationships exist between control actions, such as limiting the charging current, and resulting changes in the electrical and chemical states in the battery. According to the invention, this complexity is also managed here by a model, in particular a data-driven model. Unnecessary limitations of the charging current during a rapid charging phase are therefore no longer necessary. A further advantage is that the method can be implemented using conventional battery infrastructure. Preferably, the at least one control parameter can be determined for controlling a charging process, in particular for a rapid charging process at operating temperatures below 15° Celsius, or 10° Celsius, or 5° Celsius, or 0° Celsius, or -5° Celsius.

[0027] In this case, a fast charging process can be understood as a charging process with a charging power of more than 50kW.

[0028] This allows fast charging even at low temperatures. There's no need to worry about an increased risk of metal plating, as the process can provide an appropriate limitation of the charging current.

[0029] According to a preferred embodiment, several of the control parameters can be determined for controlling the charging process, in particular, they can comprise heating control parameters for controlling heating of the battery device. In particular, the control parameters can comprise heating control parameters for controlling preheating of the battery device. The heating control parameters can preferably be provided for internal pulse rate heating of the battery device.Preferably, the heating control parameters may include at least one of activating and / or deactivating an external or internal heater, activating heating, deactivating heating, a preheating time, a preheating amplitude, a charging current frequency, a pulse width of a heating charging current, a discharging current frequency, a pulse width of the discharging current, a battery target temperature, an operating temperature limit for activating preheating of the battery device, or an operating temperature limit for deactivating preheating of the battery device.

[0030] To prevent the occurrence of metal plating, not only can the charging current limit be adjusted, but it also becomes possible to actively influence the charging process and / or the battery device. For this purpose, an internal or external heater of the battery device can be activated or deactivated. By determining the likely time of occurrence of metal plating, it is possible to switch on the heater only when needed. This saves energy and reduces the duration of the charging process. In particular, the invention also makes it possible to consider whether an overall higher charging current can be achieved after heating, which can compensate for time losses during an initial preheating or preheating phase.

[0031] According to a further preferred embodiment, the battery model can be provided as an analytical model for determining an electrochemical state of the battery device, a Kalman filter, a Doyle-Fuller-Newman model, and / or a single-particle model. An analytical model can be understood, in particular, as a model in which relationships between input and output variables are determined and / or modeled using mathematical relationships.

[0032] The above-mentioned battery model configurations allow for an analytically precise description of the current and, if applicable, future state of the battery device. In particular, other indicators of the onset of metal plating can also be determined more precisely analytically. Examples of such indicators include a gradually decreasing discharge voltage, an increase in electrode resistance, an increase in electrode overpotential, or a change in electrolyte polarization.

[0033] According to a preferred embodiment, the control model, the prediction model, or the control model and the prediction model can be based on a machine learning method, preferably on a reinforcement learning method.

[0034] With the above-mentioned configurations, highly complex or unknown relationships can be represented by data and simulated on a model-based basis, thus enabling an accurate determination of the control parameters.

[0035] According to a further preferred embodiment, the charging current limit can be determined as one of the control parameters based on a weighting of the measurement parameter, the battery parameters, and the prediction parameters as input parameters of the control model. In particular, the weighting of the input parameters of the control model can be determined using machine learning in at least two steps. Thus, in a learning step, an initial weighting can be determined as the weighting. In a re-evaluation step, the weighting can be determined during the charging process, wherein in this step the weighting is adjusted based on at least the measurement parameters. Preferably, the weighting can be adjusted continuously in the re-evaluation step.

[0036] Alternatively or additionally, a weighting of the input parameters of the prediction model can be determined using machine learning in at least two steps. Thus, in a learning step, an initial weighting can be determined as the weighting, and in a re-evaluation step, the weighting can be determined during the loading process, in which the weighting can be adjusted based on at least the measurement parameters. Preferably, the weighting can be adjusted continuously in the re-evaluation step.

[0037] This makes it possible to initially train the respective models, especially data-driven ones, by populating them with data and to validate the weightings thus determined. This allows the method to be provided with high accuracy and adapted to the respective process or battery model. Due to the configuration, according to which the weighting can be continuously adjusted, continuous learning of the models, especially data-driven ones, is possible. This also allows for unavoidable aging processes of the battery device or changes due to damage to be taken into account.

[0038] According to a preferred embodiment, the weighting of the input parameters of the control model can further be determined from a time-varying prioritization of the input parameters and a time-varying prioritization of the output control parameters. The input parameters of the control model can preferably be prioritized to assess the risk of metal plating. The prioritization of the output control parameters can preferably be performed to assess the counteractability of the occurrence of metal plating with the respective control parameters. Prioritization can be determined based on a comparison of current values ​​of at least the measurement parameters and / or the battery parameters with relative historical values ​​of the output control parameters.

[0039] This allows the model to consider and learn not only the relationship between output and input values, but also their relative importance. This allows the control behavior to be optimized and, if possible, determined as a compromise between conflicting requirements.

[0040] According to a preferred embodiment, the measurement parameters may further comprise at least one cell voltage of one or more cells of the battery device as the battery voltage, an occurring charging current, or several locally different operating temperatures.

[0041] By measuring additional physical parameters of the battery device, the accuracy of the model results and thus of the determined control parameters can be further increased. Furthermore, the influence of measurement inaccuracies or outliers in the measured data can be reduced.

[0042] According to a further preferred embodiment, the battery parameters may further comprise at least a state of charge of the battery device, an electrode overpotential, or a state of health of the battery device.

[0043] The expected temperature progression of the operating temperature may preferably comprise a future temporal progression of an operating temperature at one or different sections of the battery device.

[0044] Thus, further indicators for the occurrence of metal plating can be identified.

[0045] According to a preferred embodiment, the prediction parameters may further comprise a predicted remaining charging time.

[0046] Thus, further information for controlling the charging process can be determined, which can be used by the control model and / or by a user of the process.

[0047] According to a further preferred embodiment, the control parameters may further comprise at least one activation of the charging process, one deactivation of the charging process, a pulse width of a charging current, or a duration of a charging current.

[0048] By additionally determining further control parameters, it becomes possible to influence the charging process in other ways. This provides a possibility to counteract the onset of metal plating with measures other than limiting the charging current or heating the battery device. According to a preferred embodiment, the battery model can have a battery temperature model for mapping a temporal progression and / or a local profile of the at least one operating temperature of the battery device. Alternatively or additionally, the battery model can have a battery state model for the preferably numerical mapping of chemical and / or electrical processes in the battery device.In this case, the battery state model can preferably have at least an initial ion concentration in an electrolyte of the battery device, a diffusion rate of ions into one of the electrodes, a reaction coefficient of the chemical reactions taking place in the battery device, or an electrical conductivity of an electrolyte provided in the battery device as the input parameters.

[0049] Thus, the accuracy of the analytically determined battery parameters can be increased, so that an overall improvement in the determination of the control parameters can be achieved.

[0050] According to a further preferred embodiment, the battery device can be a lithium-ion battery, preferably with one or more battery cells. Thus, the metal plating can be, for example, lithium plating. The electrode can be an anode. The electrode voltage can be an anode voltage. The anode can preferably comprise graphite and / or metal.

[0051] This provides a determination of the charging current limit for lithium-ion batteries.

[0052] A further aspect of the present invention relates to a computer program product which has instructions which, when the program is executed by a computer, cause the computer to carry out one or more of the steps of the method described above.

[0053] A further aspect of the present invention relates to a control system for determining a charging current limit of a rechargeable battery device. The control system has a measuring module for recording measurement parameters on the battery device. The measurement parameters include at least an operating temperature, a battery voltage, and a battery current of the battery device. The control system further has a battery condition module for determining battery parameters from a physics-based battery model for mapping physical processes occurring in the battery device. The battery model has at least the measurement parameters as input parameters. The battery parameters include at least an expected temperature progression of the operating temperature and an electrode voltage.The control system additionally comprises a prediction module for determining prediction parameters for the occurrence of metal plating on an electrode of the battery device from a, in particular data-driven, prediction model. The prediction model has at least the battery parameters as input parameters. The prediction parameters have at least one predicted occurrence time for the metal plating. The control system further comprises a control determination module for determining at least one control parameter for controlling the charging process from a, in particular data-driven, control model. The control model has the measurement parameters, the battery parameters, and the prediction parameters as input parameters, wherein the at least one control parameter is based in particular on a weighting of these input parameters. The at least one control parameter has at least one charging current limit.The charging current limit can be determined as a time profile. The control system also has an output module for outputting the determined charging current limit, in order to control the charging process, in particular based on the at least one control parameter.

[0054] Preferably, the measuring module may comprise at least one current sensor, one voltage sensor and / or one temperature sensor.

[0055] The control system and the computer program product can achieve the same technical effects and advantages already described for the aforementioned method. In particular, the control parameters for a fast-charging process of a battery device at low temperatures can be precisely determined. Furthermore, precise control of the charging process is enabled.

[0056] According to a preferred embodiment, the measuring module and / or the battery status module can be provided on a first computing unit. For example, the measuring module and / or the battery status module can be provided on a battery control unit of the battery device. Alternatively or additionally, the prediction module and / or the control determination module can be provided on a second computing unit. Preferably, the second computing unit can be provided separately from the first computing unit. For example, the prediction module and / or the control determination module can be provided on an external server or cloud server.

[0057] This makes it possible, for example, to collect and process measurement data directly on the battery control unit of a battery module.

[0058] This allows, for example, the measurement parameters to be processed. However, the models, especially data-driven ones, can be run on an external computer, which may have higher computing power. This can increase the speed of determining the charging current limit. This makes it possible, for example, to configure the control system as a real-time capable system.

[0059] Another aspect of the invention relates to a battery charging system. The battery charging system comprises a rechargeable battery device with at least one battery cell. The battery charging system also comprises charging terminals for coupling electrodes of the battery device to an electrical charging device in order to electrically charge the battery device with a charging current. Furthermore, the battery charging system comprises the aforementioned control system. The output charging current limit is specified by the control system as an upper limit of the charging current.

[0060] With the aforementioned battery charging system, the same technical effects and advantages can be achieved that have already been described for the aforementioned method, the aforementioned computer program product, and the aforementioned control system. In particular, a rapid charging process of a battery device at low temperatures can be precisely controlled, thereby reducing the risk of metal plating.

[0061] Further advantages, features, and details of the invention will become apparent from the following description, in which exemplary embodiments of the invention are described in detail with reference to the drawings. They show schematically:

[0062] Fig. 1 shows an embodiment of a method according to the invention,

[0063] Fig. 2 shows an embodiment of a control system according to the invention,

[0064] Fig. 3 shows another embodiment of a control system according to the invention,

[0065] Fig. 4 shows another embodiment of a control system according to the invention,

[0066] Fig. 5 shows an embodiment of a battery charging system according to the invention.

[0067] Figure 1 shows exemplary steps of a method 100 according to the invention, from which a sequence of commands of a computer program product according to the invention can also be derived by way of example. Figures 2 to 5 each show exemplary different embodiments of a control system 10 according to the invention. Figure 5 shows an embodiment of a battery charging system 90 according to the invention.

[0068] The method 100 in Figure 1 is designed to determine a charging current limit for influencing a charging process of a rechargeable battery device 1000. For this purpose, it comprises a series of steps that can be performed iteratively during a charging process. Furthermore, the steps can preferably be performed repeatedly and continuously. Thus, the value of the charging current limit can change or remain the same in the next iteration.

[0069] A measurement step S20 is thus performed, in which measurement parameters MP are determined for the battery device 1000. The measurement parameters MP include at least an operating temperature, a battery voltage, and a battery current of the battery device 1000.

[0070] Furthermore, a battery state determination step S30 is performed, in which battery parameters BP are determined from a physics-based battery model. The physics-based battery model is designed to represent physical processes that occur in the battery device 1000. The battery parameters BP include an expected temperature progression of the operating temperature and an electrode voltage of the battery device 1000. In a prediction step S40, prediction parameters VP for the onset of metal plating at an electrode 1001, 1002 of the battery device 1000 are determined from a, in particular data-driven, prediction model. The prediction parameters VP include at least one predicted onset time for the metal plating at an electrode of the battery device 1000 and are determined on the basis of at least the battery parameters BP as input parameters of the prediction model.

[0071] In a control determination step S50, the desired control parameters KP for controlling the charging process are determined from a control model, in particular a data-driven one. The control parameters KP comprise at least one charging current limit. The control parameters are determined with the control model based on a weighting of the measurement parameters MP, the battery parameters BP, and the prediction parameters VP as input parameters of the control model.

[0072] In an output step S60, the charging current limit determined in this way is output to control the charging process.

[0073] Unlike the battery model, the prediction model and the control model are one, particularly data-driven, model, rather than an analytical model. The prediction model can be designed, for example, as an artificial neural network, a Markov model, logistic regression, or a decision tree-based algorithm. An input layer, in which each neuron represents the input features, can be provided. Furthermore, intermediate layers and an output layer with output features can be provided. The individual layers can be connected to each other via weights and input matrices.

[0074] The input characteristics of the prediction model can be, for example, an anode overvoltage, a state of health of the battery device 1000, a cell voltage, or temperatures belonging to different segments of the battery cell 1003. The output characteristics of the prediction model can be an estimated charging duration or the predicted onset time until a metal plating event. For example, the prediction model can output as the predicted onset time that the onset of metal plating is to be expected within the next 30 seconds. The control model can preferably simulate a controller for the operating temperature of the battery device 1000 and the charging current. For example, a suitable heating configuration for a preheating phase of the battery device 1000 can be determined from the control model. For example, the charging and discharging pulses to be set and their pulse frequency can be determined.

[0075] Preferably, the control model can be based on reinforcement learning. The requirements arising during the charging process can be defined, for example, as a Markov decision process. A Markov decision process comprises, as components, an environment, states of the environment, and executable actions from which a choice can be made. In the present case, for example, preheating and short charging times of the battery device 1000 can be defined as components of a Markov environment. The charging time and the prediction for the onset of metal plating can be defined as states of the environment. The manipulation of preheating, the configuration of the charging and discharging pulses, and the charging current limit can be defined as executable actions. As an action, for example, a last assumed value can be either kept the same, decreased, or increased.Preferably, the control model is designed in such a way that the choice of actions is not based on chance, i.e. the choice is deterministic.

[0076] In a training step S41, S51, for example, the weightings or other parameters for the models, in particular data-driven models, can be determined using a sufficient amount of training data sets. The weightings or parameters determined and trained in this way can be validated using a validation data set. It is preferred that the training and validation data sets originate from real test environments. The training step can be considered complete when the accuracy of the weightings or other parameters only increases. Alternatively, the training step can be considered complete when the absolute error between the validation data sets and the data sets calculated from the models, in particular data-driven models, has been sufficiently reduced. Continuous learning and dynamic adjustment of the weightings can be achieved, for example, in a re-evaluation step S42, S52.For example, past data and parameters are analyzed and related to the output values ​​of the prediction model or the control model.

[0077] Figure 2 shows an example of the control system 10. The control system 10 is suitable and designed for controlling a rapid charging process of a rechargeable battery device, such as the aforementioned battery device 1000.

[0078] The battery device 1000 may in particular be a lithium-ion battery.

[0079] The battery device 1000 is shown as an example in Figure 4.

[0080] The battery device 1000 can have one or more battery cells 1003. The voltage potential of the battery cells 1003 can be reversibly adjustable. The individual battery cells 1003 can be connected together as a voltage and / or current source. The battery device 1000 can have two electrodes 1001, 1002 for power output or power consumption. Furthermore, it is also conceivable for the battery device 1000 to have additional electrodes for potential and current measurements. It is also conceivable for the battery device 1000 to be part of the control system 10.

[0081] As further shown in Figure 2, the control system 10 comprises a measuring module 20 for detecting the measurement parameters MP at the battery device 1000. For this purpose, the measuring module 20 or the battery device 1000 can comprise sensors suitable for detecting the measurement parameters. Further details regarding the sensors are described in the following description of Figure 4.

[0082] As further shown in Figure 2, the control system 10 further comprises a battery state module 30 for determining the battery parameters BP. For this purpose, the battery state module 30 comprises a physics-based battery model. With the physics-based battery model, physical processes occurring, for example, in the battery device 1000 can be described in formulas and expressed numerically. Figure 2 makes it clear that the battery model has at least the measurement parameters MP as input parameters and the battery parameters BP as output parameters. The battery state module 30 and the measurement module 20 can both be provided on a first computing unit 11. The first computing unit 11 can, for example, be a battery control unit.

[0083] As further shown in Figure 2, the control system 10 also has a prediction module 40 for determining prediction parameters VP for the occurrence of metal plating on an electrode 1001, 1002 of the battery device 1000 from a, in particular data-driven, prediction model. Figure 2 clearly shows that the prediction model can have, in addition to the battery parameter BP, also the measurement parameters MP as input parameters. It is also conceivable to use only the measurement parameters MP as input parameters of the prediction model.

[0084] As further shown in Figure 2, the control system 10 has a control determination module 50 for determining the control parameters KP for controlling the charging process using a control model, in particular a data-driven one. Figure 2 shows, by way of example, that the control model has the measurement parameters MP, the battery parameters BP, and the prediction parameters VP as input parameters. The control determination module 50 weights these input parameters in order to determine the at least one control parameter KP.

[0085] As further shown in Figure 2, the control system 10 has an output module 60 for outputting the at least one control parameter KP in order to control the charging process based on the at least one control parameter KP. The output module 60 can be configured as a data interface or a data bus for transmitting data to other devices external to the control system. Such other devices can be, for example, the battery device 1000 or an external battery charging device.

[0086] The prediction module 40, the control determination module 50, and / or the output module 60 can each be provided on a second computing unit 12. Preferably, the second computing unit 12 can be a cloud server.

[0087] Figure 3 shows the control system 10 with a configuration similar to Figure 2. Commonalities between the embodiments will therefore not be discussed below. Figure 3 discloses a preferred embodiment of the battery condition module 30. The battery condition module 30 illustrated as an example in Figure 3 has a battery temperature model 31 and a battery condition model 32 as the battery model.

[0088] Thus, with the battery temperature model 31, an expected temporal progression of a temperature of the battery device 1000 can be determined as a temperature parameter TP. Furthermore, a local profile of a temperature of the battery device 1000 can be analytically determined as a further component of the temperature parameter TP.

[0089] With the battery state model 32, chemical and / or electrical processes in the battery device 1000 can be simulated and output as battery state parameters BSP. In this case, the battery state model 32 can also consider parameters other than the measurement parameters MP, such as an initial ion concentration or a diffusion rate of ions of the battery device 1000.

[0090] The battery condition parameters BSP and the temperature parameters TP can be output by the battery condition module 30 as the battery parameters BP by means of a signal connector 34.

[0091] Figure 4 shows the control system 10 with a configuration like that of Figures 2 and 3. Commonalities between the embodiments will therefore not be discussed below.

[0092] However, Figure 4 discloses a preferred embodiment of the measuring module 20.

[0093] Figure 4 shows the control system 10 with the measuring module 20, which has a plurality of sensors for detecting the measurement parameters MP on the battery device 1000. Alternatively, it is also conceivable for the battery device 1000 in particular to have such sensors. Figure 4 shows, by way of example, that the cell voltage of each of the battery cells 1003 is determined by a voltage sensor 21. A local temperature profile of the operating temperature of the battery device 1000 can be detected by means of a plurality of distributed temperature sensors 22. The battery current delivered by the battery device 1000 or consumed for charging can be detected by the measuring module 20 by means of the current sensor 23.The battery device 1000, the sensors 21, 22, 23 and the first computing unit 11, which may include the measuring module 20 and the battery state module 30, may be provided as a structural unit and thus form, for example, a battery module 1100.

[0094] Furthermore, Figure 4 discloses a preferred embodiment in which the prediction module 40, the control determination module 50, and the output module 60 are entirely contained in the second computing unit 12. Accordingly, the prediction module 40, the control determination module 50, and the output module 60 are not shown separately in Figure 4.

[0095] Figure 5 shows the battery charging system 90. This comprises the battery device 1000 with at least one of the aforementioned battery cells 1003 and the control system 10. Furthermore, Figure 5 shows an example of an electrical charging device 1200, which is connected to the electrodes of the battery device 1000 via electrical lines with charging connections of the battery charging system 90 in order to electrically charge the battery device 1000 with a charging current. The charging device 1200 can be a component of the battery charging system 90. The charging current limit determined and output by the control system 10 as the at least one control parameter KP is specified by the control system 10 to the battery device 1000 or the charging device 1200 as an upper limit of the charging current.

[0096] The above explanation of the embodiments describes the present invention exclusively by way of example. Of course, individual features of the embodiments can be freely combined with one another, provided they are technically feasible, without departing from the scope of the present invention.

[0097] 10 Control system

[0098] 11 first computing unit, battery control unit

[0099] 12 second computing unit, cloud server

[0100] 20 measuring module

[0101] 21 Voltage sensor

[0102] 22 Temperature sensor

[0103] 23 Current sensor

[0104] 30 Battery status module

[0105] 31 Battery temperature model

[0106] 32 Battery health model

[0107] 34 signal connectors

[0108] 40 Forecast module

[0109] 50 Control Investigation Module

[0110] 60 Output module

[0111] 90 Battery charging system

[0112] 100 procedures

[0113] 1000 battery device

[0114] 1001 , 1002 Electrode

[0115] 1003 battery cell

[0116] 1100 battery module

[0117] 1200 loading device

[0118] KP control parameters

[0119] MP measurement parameters BP battery parameters

[0120] TP temperature parameters

[0121] BSP battery health parameters

[0122] VP prediction parameters

[0123] S20 measuring step

[0124] S30 Battery condition detection step

[0125] S40 Prediction step

[0126] S50 Control determination step

[0127] S60 Output step

[0128] 541 , S51 learning step

[0129] 542, S52 Revaluation step

Claims

Patent claims 1. A method (100) for determining a charging current limit for a charging process of a rechargeable battery device (1000), comprising the steps: - detecting measurement parameters (MP) on the battery device (1000), wherein the measurement parameters (MP) comprise at least an operating temperature, a battery voltage and a battery current of the battery device (1000); characterized by - Determining battery parameters (BP) from a physics-based battery model for mapping physical processes occurring in the battery device (1000), wherein at least one expected temperature progression of the operating temperature and an electrode voltage are determined as the battery parameters (BP) on the basis of the recorded measurement parameters (MP) as input parameters of the battery model; - determining prediction parameters (VP) for the occurrence of metal plating on an electrode (1001, 1002) of the battery device (1000) from a prediction model, wherein at least one predicted occurrence time of metal plating is determined as the prediction parameters (VP) on the basis of at least the determined battery parameters (BP) as input parameters of the prediction model; - Determining at least one control parameter (KP) for controlling a charging process from a control model, wherein at least the charging current limit is determined as the at least one control parameter (KP) based on the measurement parameters (MP), the battery parameters (BP) and the prediction parameters (VP) as input parameters of the control model; and Output the determined charging current limit.

2. The method (100) according to claim 1, wherein the predicted onset time of metal plating is determined from a prediction model based on at least the determined battery parameters (BP) and the acquired measurement parameters (MP) as input parameters of the prediction model.

3. Method (100) according to claim 1 or 2, wherein the charging current limit is determined as a time profile.

4. Method (100) according to one of the preceding claims, wherein the at least one control parameter (KP) for controlling a charging process, in particular for a rapid charging process, is determined at operating temperatures of below 15° Celsius, or 10° Celsius, or 5° Celsius, or 0° Celsius, or -5° Celsius.

5. Method (100) according to one of the preceding claims, wherein a plurality of control parameters (KP) for controlling the charging process are determined, in particular the control parameters (KP) further comprise heating control parameters for controlling heating, preferably preheating, of the battery device (1000), preferably by means of internal pulse rate heating.

6. The method (100) according to claim 5, wherein the heating control parameters comprise at least one of activating an external or internal heater, deactivating an external or internal heater, activating heating, deactivating heating, a preheating time, a preheating amplitude, a charging current frequency, a pulse width of a heating charging current, a discharging current frequency, a pulse width of the discharging current, a battery target temperature, an operating temperature limit for activating preheating of the battery device (1000), or an operating temperature limit for deactivating preheating of the battery device (1000).

7. The method (100) according to any one of the preceding claims, wherein the control model and / or the prediction model is based on a machine learning method, preferably on a reinforcement learning method, and / or comprises an artificial neural network, and / or wherein the battery model is provided as at least one analytical model for determining an electrochemical state of the battery device (1000), a Kalman filter, a Doyle-Fuller-Newman model or a single-particle model.

8. Method (100) according to one of the preceding claims, wherein the charging current limit is determined as one of the control parameters (KP) based on a weighting of the measurement parameters (MP), the battery parameters (BP) and the prediction parameters (VP) as input parameters of the control model, and in particular the weighting of the input parameters of the control model and / or a further weighting of the input parameters of the prediction model is determined by means of machine learning in at least two steps, which comprise: - a learning step (S41, S51) in which an initial weighting is determined as the weighting, and - a re-evaluation step (S42, S52) during the loading process, in which the weighting is preferably continuously adjusted based on at least the measurement parameters (MP).

9. Method (100) according to one of the preceding claims, wherein the weighting of the input parameters of the control model is determined from - a time-varying prioritization of the input parameters of the control model, preferably for assessing the risk of metal plating, and - a time-varying prioritization of the output control parameters (KP), preferably for evaluating the counteractability of the occurrence of metal plating with the respective control parameters (KP), wherein the prioritization is preferably determined in each case from a comparison of current values ​​of at least the measurement parameters (MP) and / or the battery parameters (BP) with relatively historical values ​​of the output control parameters (KP).

10. Method (100) according to one of the preceding claims, wherein - the measurement parameters (MP) further comprise at least one cell voltage of one or more cells of the battery device (1000) as the battery voltage, an occurring charging current, or several locally different operating temperatures, and / or - the battery parameters (BP) further comprise at least one electrode overpotential, a state of health of the battery device (1000), or a state of charge of the battery device (1000), wherein the expected temperature progression preferably comprises a future temporal profile of an operating temperature at one or different sections of the battery device (1000), and / or - the prediction parameters (VP) further comprise a predicted remaining charging time, and / or the control parameters (KP) further comprise at least one activation of the charging process, one deactivation of the charging process, a pulse width of a charging current, or a duration of a charging current.

11. Method (100) according to one of the preceding claims, wherein the battery model comprises - a battery temperature model (31) for mapping a temporal progression and / or a local profile of the at least one operating temperature of the battery device (1000), and - a battery state model (32) for numerically mapping chemical and / or electrical processes in the battery device (1000), wherein the battery state model (32) preferably further comprises at least one electrical conductivity of an electrolyte provided in the battery device (1000), an initial ion concentration in an electrolyte provided in the battery device (1000), a diffusion rate of ions into one of the electrodes (1001, 1002), or a reaction coefficient of the chemical reactions taking place in the battery device (1000) as input parameters.

12. The method (100) according to any one of the preceding claims, wherein the battery device (1000) is a lithium-ion battery with preferably one or more battery cells (1003), wherein the metal plating is a lithium plating, and wherein the electrode (1001, 1002) is an anode and the electrode voltage is an anode voltage, wherein the anode preferably comprises graphite and / or metal.

13. A computer program product comprising instructions which, when executed by a computer, cause the computer to carry out the steps of the method (100) according to any one of the preceding claims 1 to 12.

14. Control system (IO) for determining a charging current limit of a rechargeable battery device (1000), comprising - a measuring module (20) for detecting measuring parameters (MP) on the battery device (1000), wherein the measuring parameters (MP) are at least an operating temperature, a battery voltage and a battery current of the battery device (1000); characterized by - a battery condition module (30) for determining battery parameters (BP) from a physics-based battery model for mapping physical processes occurring in the battery device (1000), wherein the battery model has at least the measurement parameters (MP) as input parameters, and wherein the battery parameters (BP) have at least an expected temperature progression of the operating temperature and an electrode voltage; - a prediction module (40) for determining prediction parameters (VP) for the occurrence of metal plating on an electrode (1001, 1002) of the battery device (1000) from a prediction model, wherein the prediction model has at least the battery parameters (BP) as input parameters and wherein the prediction parameters (VP) have at least one predicted occurrence time for the metal plating; - a control determination module (50) for determining at least one control parameter (KP) for controlling the charging process from a control model, wherein the control model has the measurement parameters (MP), the battery parameters (BP) and the prediction parameters (VP) as input parameters, wherein the at least one control parameter (KP) is based in particular on a weighting of the input parameters and wherein the at least one control parameter (KP) has at least one charging current limit, and - an output module (60) for outputting the determined charging current limit in order to control the charging process, in particular based on the at least one control parameter (KP).

15. Control system (10) according to claim 14, wherein the measuring module (20) and the battery state module (30) are provided on a first computing unit (11), preferably on a battery control unit of the battery device (1000), and wherein the prediction module (40) and the control determination module (50) are provided on a second computing unit (12), preferably on an external server or a cloud server, wherein the second computing unit (12) is preferably different from the first computing unit (11).

16. Control system (10) according to claim 14 or 15, wherein the measuring module (20) comprises at least one voltage sensor (21), a temperature sensor (22) and / or a current sensor (23).

17. Battery charging system (90) comprising - a rechargeable battery device (1000) with at least one battery cell (1003), - Charging terminals for coupling electrodes (1001, 1002) of the battery device (1000) to an electrical charging device (1200) in order to electrically charge the battery device (1000) with a charging current, characterized by a control system (10) according to one of the preceding claims 14 to 16, wherein the output charging current limit is predetermined as an upper limit of the charging current by the control system (10).

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