SOC correction

Abstract

The invention relates to a battery system (1) comprising: a plurality of battery modules (2), which each have sensors (3) for the sensor-based recording of measurement series of physical quantities of battery cells (5) located in the battery modules (2) and have a battery management system, BMS (4); and an energy management system, EMS (7), which is assigned to the plurality of battery modules (2) and is configured to obtain charge states, SOCs, of each battery module (2-1, 2-n) of the plurality of battery modules (2), which charge states are estimated by the particular BMS (4) and are based on the measurement series, and to operate the plurality of battery modules (2) depending on the estimated SOCs, wherein the battery system (1) is configured to adapt control of the plurality of battery modules (2) using the EMS (7) on the basis of corrected SOCs.

Description

[0001] SOC CORRECTION

[0002] TECHNICAL FIELD

[0003] The invention relates to a method for remote diagnosis of a battery system having the features of the preamble of claim 1 as well as a remote diagnosis device and the battery system.

[0004] The following background is intended only to provide information necessary to understand the context of the inventive ideas and concepts disclosed herein. Therefore, this background section may contain patentable subject matter and should not, per se, be considered prior art.

[0005] BACKGROUND

[0006] Currently, the state-of-charge (SOC) of a battery storage system is calculated using a battery management system (BMS). However, BMS SOC estimators are often inaccurate, especially for lithium iron phosphate (LFP) batteries. This is due to the lack of historical data available for analysis and the limited local computing resources. Furthermore, the fact that battery systems consist of many individual battery modules complicates the situation. In some cases, significant differences occur between the SOCs of individual battery modules. This occurs because BMS SOC estimators only analyze a single battery module and do not consider the entire battery system.

[0007] To overcome these problems and improve the accuracy of SOC estimates, new technologies and approaches have been developed. One promising solution involves analyzing battery sensor data in the cloud. This sensor data monitors various parameters such as voltage, temperature, and current, and is then analyzed in the cloud to obtain a more accurate picture of the battery module's condition. Furthermore, advanced algorithms and machine learning are used to analyze the sensor data and generate more precise SOC estimates. These algorithms also take into account the individual characteristics of the battery modules and can better compensate for variations between them.

[0008] Another focus in the development of battery management systems is the integration of cloud computing and artificial intelligence. Connecting to the cloud allows battery systems to access historical data and external computing power to perform more accurate SOC estimation. This is particularly relevant in large-scale applications where numerous batteries are interconnected.

[0009] In summary, improving SOC calculations in battery storage systems is an important step toward optimizing energy storage systems. This is made possible by the use of external sensors, advanced algorithms, and cloud integration to obtain accurate SOC data and increase battery efficiency and lifetime.

[0010] The invention is based on the object of increasing the accuracy of SOC estimation. In particular, improved control of the battery system (when should charging / discharging occur? How much?) can be considered, as well as the reduction of capacity reserves due to state-of-charge estimation errors. This can also include fast and efficient communication or data processing for efficient SOC estimation.

[0011] SUMMARY

[0012] This summary is intended to introduce a selection of features and concepts of the invention that are explained further in the description. This summary is not intended to identify important or essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter.

[0013] According to the invention, the above-mentioned object is achieved by the features of the independent claims.

[0014] Specifically, the problem is solved by a method for remote diagnosis of a battery system. The battery system has a plurality of battery modules. The battery modules each have sensors for sensing measurement series of physical variables of battery cells located in the battery modules. The battery modules each have a battery management system (BMS). The battery system further has an energy management system (EMS). The EMS is assigned to the plurality of battery modules. The EMS is configured to obtain estimated states of charge (SOCs) of each battery module of the plurality of battery modules based on the measurement series. The states of charge are estimated by the respective BMS based on the measurement series. The EMS is configured to operate the plurality of battery modules depending on the estimated SOCs. The method includes receiving data at or through a remote diagnostic device.The data is based on the measurement series. The measurement series or data relate to a corresponding battery module of the plurality of battery modules. The method comprises determining individual correction factors at or by the remote diagnostic device. Each correction factor relates to an individual battery module of the plurality of battery modules. The determination includes comparing the data with reference battery data from reference batteries of the same type according to the corresponding battery modules of the plurality of battery modules. The method comprises correcting the estimated SOCs using the individual correction factors, preferably at the remote diagnostic device or at the EMS or by the remote diagnostic device or by the EMS. The method comprises adapting a control of the plurality of battery modules based on the corrected SOCs by the EMS.Alternatively, or in addition to increasing redundancy, the method can include correcting the estimated SOCs using individual correction factors external to the EMS. Thus, the EMS can directly replace the estimated SOC, and the adjustment of the control of the plurality of battery modules can be based directly on the corrected SOCs. The invention has the advantage of providing faster and more precise control of the battery system.

[0015] The physical quantities may include electrical quantities that represent, for example, electrical properties, in particular current and / or voltage. The physical quantities may also include temperature.

[0016] The sensors may include current sensors, voltage sensors and / or temperature sensors.

[0017] The current sensors can each measure the current flowing through a single battery module. The current sensor can operate based on the Hall effect or shunt resistance principle and continuously measure the electrical current flowing through the battery module. The measured current information can be transmitted to the EMS in real time, enabling the EMS to track the current operating status of the battery and respond to deviations from, for example, an expected current level. This not only enables accurate performance monitoring but also the identification of overcurrent conditions or undesirable changes in current flow, which can be crucial for ensuring battery module safety and achieving efficient performance.As explained below, such a case (power error message) can be taken into account by means of an operation information and / or tag in a data frame related to the battery module in question.

[0018] The voltage sensors, which are integrated, for example, in the BMS of the respective battery module, can each be used to precisely monitor the voltage in a battery module. In particular, direct data exchange can be provided between the BMS of the respective battery modules and the EMS. Here, the measurement series can be sent from the respective BMS to the EMS. Preferably, the BMS can estimate the SOCs based on the measurement series and then forward them to the EMS. This can be forwarded directly or pre-buffered by the BMS. The BMS and the EMS can be connected via a serial bus. The EMS can store the estimated SOCs in a buffer upon receipt, with time resolution, before forwarding them. The voltage sensor is based on the principle of electrical isolation and continuously measures the electrical voltage in the battery module.The recorded voltage information is delivered to the BMS in real time, allowing the BMS to track the current state of the battery and respond to voltage deviations. The voltage information is crucial for monitoring battery health, identifying cell imbalances, and early detection of potential problems such as overcharging or deep discharge. As explained below, such a case (voltage error message) can be addressed using operational information and / or a tag in a data frame related to the affected battery module. The voltage sensor can thus play a central role in ensuring the performance and safety of battery systems in various applications, from electric vehicles to renewable energy storage.

[0019] The temperature sensors can each contain a thermocouple or resistance thermometer and be mounted at predefined locations within the battery module to ensure comprehensive temperature detection. The respective temperature sensor can continuously measure the temperature in the form of electrical signals, convert them into digital temperature information, and transmit it to the BMS. The BMS can use the temperature information to create a detailed temperature map of the battery module and detect hot spots or unusual temperature changes. This enables targeted thermal regulation that optimizes the performance and service life of the battery module. Furthermore, the temperature sensor can be capable of monitoring temperature limits and initiating safety measures such as cooling or shutdown if necessary.As explained below, such a case (temperature error message) can be addressed using operational information and / or a tag in a data frame related to the battery module in question. Overall, the temperature sensor can play an essential role in ensuring the operational reliability and efficiency of the battery system in a variety of applications.

[0020] The measurement series can be understood as a sequence of measurements of the respective physical quantity, which are carried out at regular intervals or over a certain period of time in order to form the data and to send it, for example in a bundle (for example in a data frame or data packet), to the remote diagnostic device.

[0021] Assigning the EMS to the plurality of battery modules can mean that the battery modules are dedicated to the EMS. Thus, the EMS and the plurality of battery modules can form a single unit. Likewise, information about the single unit can be part of a test code in the data frame to increase operational reliability.

[0022] The remote diagnostic device can be a server or cloud system that can provide the computing power to efficiently determine correction factors.

[0023] In addition to the measurement series of current information, voltage information and / or temperature information, the data may also contain calculated values ​​and measurement series of SOCs and / or states of health (SOH) calculated by the BMS or EMS.

[0024] Reference battery data can be a collection of measurements conducted on batteries under laboratory conditions that characterize the performance and / or operating parameters of a specific battery technology or battery model. The reference battery data can include information such as capacity, voltage, current, charge and discharge rates, service life, internal resistance, temperature behavior, and other key metrics, such as SOCs and SOHs. The reference battery relating to a corresponding battery module of the plurality of battery modules can be of the same type as the corresponding battery module.

[0025] Correcting the estimated SOCs using the individual correction factors can be performed by recalibrating the corresponding sensors. This can reset any drift in the measured physical quantity. Correcting the estimated SOCs may also involve overwriting the corresponding estimated SOCs.

[0026] Adjusting the control of the majority of battery modules may involve the use or execution of charging and / or discharging processes of the battery modules. Since, in addition to the SOC correction, the corresponding sensors are also recalibrated using the individual correction factors, an even more accurate subsequent estimation (subsequent cycle) can lead to a consistently higher energy yield of the battery modules.

[0027] The number of battery modules can be greater than 50 (or 100 or 200 or 1000).

[0028] Advantageous embodiments of the invention are specified in the subclaims.

[0029] The remote diagnostic device can transmit the individual correction factors directly to the EMS. For example, the individual correction factors can be transmitted as a single packet or as individual packets corresponding to the number of individual correction factors, according to specified channels (time slots or frequency slots), for example, separately from each other.

[0030] In addition, the remote diagnostic device can receive priority information from the EMS. The priority information can be sent together with the data from the EMS to the remote diagnostic device. For example, the data can be accompanied by the priority information. This way, the data can be differentiated according to the battery modules. During transmission, the data from battery modules to be treated with priority among the plurality of battery modules can be sent from the EMS to the remote diagnostic device before battery modules to be treated with lower priority among the plurality of battery modules. In this case, the priority information can be provided as part of a data preamble to the data. This way, priority information and data together can form respective data frames. This makes it possible to set a calculation priority before the data processing itself, thus optimizing the battery system as needed.

[0031] In other words, a first part of the data associated with a first battery module of the plurality of battery modules can be accompanied by first priority information. A second part of the data associated with a second battery module of the plurality of battery modules can be accompanied by second priority information. Upon receiving the data, the remote diagnostic device can determine that the (parts of) the data are processed according to their priority information. Thus, the part of the data accompanied by the first priority information can be further processed directly (for example, to determine the correction factors). The part of the data accompanied by the second priority information can be temporarily stored in a buffer memory for further processing at a later time.

[0032] More efficient data processing can lead to better battery system performance.

[0033] The individual correction factors can each indicate a difference to the current state of charge of the corresponding battery module of the plurality of battery modules. The individual correction factors can each specify a floating-point value that is intended to replace the current state of charge of the corresponding battery module of the plurality of battery modules. This allows the EMS to change or replace the estimated SOC. The adjustment can be performed using a simple logical AND circuit.

[0034] This allows a simple SOC correction to be implemented.

[0035] Correcting may involve recalibrating each of the estimated SOCs. Adjusting the control may involve the EMS using the recalibrated SOCs in relation to each other to deploy the plurality of battery modules according to a preset intended use of the battery system. This allows different states of charge of the battery modules to be used to equalize them. This equalization may be achieved by the EMS calculating an assumed average of a desired state of charge and sequentially switching the battery modules off and on.

[0036] This allows battery systems to operate for a longer period of time.

[0037] To determine the correction factor, any offset error and / or gain error caused by the current sensor can first be corrected. The current can then be integrated. Since the correction is not perfect, the integrated current does not accurately reflect the SOC. Therefore, a further correction via the OCV can be used, as described below.

[0038] The correction factors can be determined by comparing the slope values ​​of an open-circuit voltage (OCV) curve calculated from the data with the slope values ​​of an OCV curve specified by the reference battery data. The OCV curves to be calculated can each be derived from composite pairs of voltage and current values ​​contained in the series of measurements of physical quantities.

[0039] Thus, a simple mathematical function can contribute to improving the battery system. This allows a new, better SOC to be calculated. Overvoltage models can be used to derive the OCV curve from the measured voltage. Hysteresis models can be used, especially for LFP batteries.

[0040] The above-mentioned object is also achieved by a remote diagnostic method. The remote diagnostic method is carried out by a remote diagnostic device. The remote diagnostic method is intended for a battery system with a plurality of battery modules. The battery modules each have sensors for sensing measurement series of physical quantities of battery cells located in the battery modules and a battery management system (BMS). The battery system has an EMS assigned to the plurality of battery modules. The EMS is configured to receive SOCs of each battery module of the plurality of battery modules estimated by the respective BMS based on the measurement series and to operate the plurality of battery modules depending on the estimated SOCs. The remote diagnostic method comprises receiving data based on the measurement series, each related to a corresponding battery module of the plurality of battery modules.The remote diagnostic method comprises determining individual correction factors related to the individual battery modules of the plurality of battery modules by comparing the data with reference battery data from reference batteries of the same type corresponding to the corresponding battery modules of the plurality of battery modules. The remote diagnostic method comprises providing the individual correction factors to cause the EMS to correct the estimated SOCs, for example, using the individual correction factors, and to adjust a control of the plurality of battery modules based on the corrected SOCs.

[0041] The individual correction factors can each be accompanied by command information. The command information can indicate whether or not a SOC correction or control adjustment is being performed. To avoid interrupting the sequential processing of the correction factors (or the data frames in which they are contained), erroneous individual correction factors can be excluded from a SOC correction or control adjustment by the EMS using the command information. The command information can be a simple bit, which is, for example, either 1 or 0 and indicates whether the SOC correction or control adjustment should be performed. The command information can be contained in a preamble, similar to the priority information, and the command information, together with the correction factor, can form a data frame.

[0042] This ensures simple, safe and fast processing between the remote diagnostic device and the battery system.

[0043] This ensures simple processing of data and information, with no latency caused by errors. The error can be corrected in the next cycle. A cycle can begin with the provision of data from the EMS to the remote diagnostic device and end with the adjustment of the control system. The interval between cycles can be less than 1 hour (or 0.5 hours).

[0044] The above-mentioned object is also achieved by a computer program. The computer program comprises instructions which, when the computer program is executed by a computer, cause the computer to execute or initiate the above-described method / remote diagnostic method or at least one of the steps thereof. The computer program can, for example, be a module for starting / operating the battery system, the BMS, the EMS and / or the remote diagnostic device as described herein. The above-mentioned object is also achieved by a data carrier. The computer program can be stored on the machine-, processor-, or computer-readable data carrier, such as on a permanent or rewritable storage medium. This also includes the possibility of making the computer program available for download on a server or a cloud server, e.g.over a data network such as the Internet or a communications connection such as a wireless connection.

[0045] The above-mentioned object is also achieved by a remote diagnostic device for remotely diagnosing a battery system. The battery system has a plurality of battery modules, each of which has sensors for sensing measurement series of physical quantities of battery cells located in the battery modules, a battery management system (BMS), and an EMS assigned to the plurality of battery modules. The EMS is configured to obtain SOCs of each battery module of the plurality of battery modules, estimated by the respective BMS based on the measurement series, and to operate the plurality of battery modules depending on the estimated SOCs. The remote diagnostic device is configured to receive data based on or containing the measurement series, each related to a corresponding battery module of the plurality of battery modules.The remote diagnostic device is configured to determine individual correction factors related to the individual battery modules of the plurality of battery modules by comparing the data with reference battery data from reference batteries of the same type corresponding to the corresponding battery modules of the plurality of battery modules. The remote diagnostic device is configured to provide the individual correction factors, for example, by sending them to the EMS, to cause the EMS to correct or replace the estimated SOCs, preferably based on the individual correction factors, and to adjust a control of the plurality of battery modules based on the corrected SOCs.

[0046] The above-mentioned object is also achieved by a battery system with a plurality of battery modules. The battery modules each have sensors for sensing measurement series of physical quantities of battery cells located in the battery modules. The battery modules each have a BMS. The battery system further has an EMS assigned to the plurality of battery modules. This assignment can be physical and / or logical in nature. The EMS is configured to receive SOCs of each battery module of the plurality of battery modules, estimated by the respective BMS based on the measurement series, and to operate the plurality of battery modules depending on the estimated SOCs. The battery system is configured to send data to a remote diagnostic device. The data is based on the measurement series and each relates to a corresponding battery module of the plurality of battery modules.The transmission causes the remote diagnostic device to determine individual correction factors related to the individual battery modules of the plurality of battery modules. The battery system is configured to receive the correction factors from the remote diagnostic device. The correction factors were determined by comparing the data with reference battery data from reference batteries of the same type according to the corresponding battery modules of the plurality of battery modules. The battery system is configured to correct the estimated SOCs using the individual correction factors. The battery system is configured to adjust control of the plurality of battery modules by the EMS based on the corrected SOCs.

[0047] Each data item can be provided with operational information. The operational information can indicate whether or not the correction factor should be calculated for the corresponding battery module. As indicated above, the operational information can be dependent on an error message from the sensors, for example, the current sensor, voltage sensor, and / or temperature sensor. A single error message (current error message, voltage error message, or temperature error message) would be sufficient. The operational information can be contained in a tag of the data frame described above. Using the operational information, the remote diagnostic device can determine that the determination step should be omitted. The correction factor can then, for example, contain a predetermined number of 0s and / or 1s as the payload of the data frame.The payload can be further reduced by representing the correction factor as a floating-point number within a predefined range. This allows a typical payload (also called user data) of, for example, 1518 or 1522 bytes to be reduced to less than half (or less than a fifth, or less than a tenth). The total size of the data frame can be a minimum of 84 bytes to avoid collisions with other transmitting EMSs.

[0048] In other words, the invention relates to a cloud-based SOC estimator to significantly increase the accuracy of the estimation. This can significantly improve the control of the battery system (when should charging / discharging take place? How much?). BMS sensor data in the cloud can be used to improve the SOC estimator of existing (legacy) BMS systems. For this purpose, correction factors can be calculated in the cloud and fed back to the battery system. Using the correction factors, the SOCs estimated by the BMS can be improved, thus improving the battery system control. The sensor data from all modules (not just individual modules) of a battery system can be brought into the cloud. In the cloud, by analyzing the entire time series and all modules, a better SOC can be calculated than in the individual BMS. The SOC can be calculated both for each module and for the entire system.The individual SOCs estimated by the BMS and the SOC of the entire battery system can then be compared with the results in the cloud. From the resulting deviation, an overall correction factor and / or correction factors for the individual modules can be calculated. These can then be fed back to the battery system to correct and improve the SOCs estimated by the EMS. The recipient of the correction factors can be the EMS (which can also be understood as the overall energy management system of the battery system) or even the marketer / operator of the battery system (the one that controls the storage system).

[0049] Although some of the aspects described above relate to the method or remote diagnostic method, the remote diagnostic device or the battery system, these aspects may also apply to the other aspects thereof in a corresponding manner.

[0050] In one example, the battery system and the remote diagnostic device can be implemented using hardware circuits, software means, or a combination thereof. Thus, multiple units of the battery system and the remote diagnostic device can each be realized in a single physical unit, for example, when multiple functions are implemented in software. The units of the battery system and the remote diagnostic device can also be implemented in hardware components. The units of the battery system and the remote diagnostic device are each to be understood as functional units that are not necessarily physically separated from one another. Thus, the battery system and the remote diagnostic device can be implemented at least partially as a computer, field-programmable logic array (FPLA), field-programmable gate array (FPGA), microcontroller, CPU (e.g.with multiple cores), graphics processing unit (GPU), application-specific integrated circuit (ASIC) and / or digital signal processor (DSP).

[0051] In the battery system or the remote diagnostic device, for example, methods can be used which are related to pipelining the data or correction factors. In this case, instead of an entire instruction in one clock cycle of the processor used in the battery system or the remote diagnostic device, only a sub-task of it, e.g. a part of the data / correction factors, is processed. The various sub-tasks of several instructions are processed simultaneously. Furthermore, methods in the sense of multithreading on the data / correction factors and further developments thereof can be applied, for example simultaneous multithreading of the data / correction factors. This allows better utilization of the processors due to the parallel use of several processor cores. In this case, the battery system or the remote diagnostic device can be scalar or superscalar. The processor core used in the battery system orThe processor contained in the remote diagnostic device can be connected to a buffer memory of the battery system or the remote diagnostic device, which can temporarily store the data / correction factors before and / or after the processing of the data / correction factors or a portion thereof. The buffer memory can be integrated into a volatile memory of the battery system or the remote diagnostic device, e.g., a (D)RAM, or into a permanent memory of the battery system or the remote diagnostic device, e.g., a non-volatile storage device such as an SSD. This can increase the performance of the battery system or the remote diagnostic device.

[0052] All technical and scientific terms used herein have the meaning generally understood by those skilled in the art in the technical field of battery technology, with a special focus on lithium-ion batteries. They are to be interpreted based on the definitions found in the glossary or the technical jargon of this technical field. If technical terms are used incorrectly and thus do not express the technical idea of ​​the present invention, they shall be replaced by technical terms that provide a correct understanding to those skilled in the art.

[0053] If it is stated here that a component is "connected" or "communicates" with another component, this may mean, for the purposes of the present disclosure, that these components may also be directly connected or communicate with each other. The term "directly" indicates that no further component is present in between.

[0054] The process steps described herein should not be interpreted as requiring them to be performed in a particular order, unless expressly or implicitly stated otherwise, for example, if these process steps cannot be interchanged for technical reasons. The process steps may also be performed directly one after the other (without any further intervening steps) and / or continuously.

[0055] BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Further objects, features, advantages, and possible applications will become apparent from the following description of non-limiting embodiments with reference to the accompanying drawings, in which:

[0057] Fig. 1 is a schematic representation of the battery system and the remote diagnostic device; and

[0058] Fig. 2 is a schematic representation of the method for remote diagnosis of the battery system using the remote diagnosis device.

[0059] The reference symbols used in the drawings and their meanings are summarized in the list of reference symbols at the end of this description. Detailed explanations of known functions and structures are omitted if they detract from the scope of the invention.

[0060] DETAILED DESCRIPTION

[0061] The battery system and the remote diagnostic device, as well as the associated method, will now be described with reference to the embodiments. Without being limited thereto, specific details are explained to provide a deeper understanding of the invention.

[0062] Fig. 1 shows a schematic representation of the battery system 1 and the remote diagnostic device 8. In the description of Fig. 1, reference is also made directly to Fig. 2, which shows a schematic representation of the method SO for remote diagnosis of the battery system 1 with the aid of the remote diagnostic device 8.

[0063] The battery system 1 has a plurality of battery modules 2-1 to 2-n and an EMS 7. n can be a natural number > 1 (or > 5 or > 10 or > 15). The battery modules 2-1 to 2-n have corresponding battery cells 5-1 to 5-n with sensors 3-1 to 3-n connected thereto and a respective BMS 4-1 to 4-n. The sensors 3-1 to 3-n record corresponding measured values ​​of physical quantities such as current, voltage, and temperature from the battery cells 5-1 to 5-n of the respective battery modules 2-1 to 2-n dedicated to the corresponding sensors 3-1 to 3-n. An SOC estimation unit 6 can be provided in each of the BMS 4-1 to 4-n. The respective BMS 4-1 is connected to the individual sensors 3-1 of the corresponding battery module 2-1 to record the measured values ​​directly from the sensors 3-1 or to create the corresponding measurement series. The SOC estimation unit 6 then estimates the current state of charge (SOC) of the battery module 2-1 based on the measured values ​​or the corresponding measurement series.The EMS 7 then controls the battery modules 2 based on the estimated SOCs thus collected. From the estimated SOCs, the EMS 7 creates data relating to a corresponding battery module 2-1 to 2-n of the plurality of battery modules 2. The data based on the measurement series are transmitted to the remote diagnostic device 8, for example, in parallel with the adjustment of the control of the battery system 1 in step S1. The remote diagnostic device 8 can contain an SOC calculation unit 9, which can perform the calculation steps on the remote diagnostic device 8 in method S0. The SOC calculation unit 9 determines, in step S2, individual correction factors relating to the individual battery modules 2-1 to 2-n of the plurality of battery modules 2 by comparing the transmitted data with reference battery data from reference batteries of the same design as the corresponding individual battery modules 2-1 to 2-n of the plurality of battery modules 2.The remote diagnostic device 8 then transmits the correction factors to the EMS 7. A direct communication connection can be provided, for example, via the communication interface 14 described below. In step S3, the EMS 7 then corrects the estimated SOCs using the transmitted correction factors. In step S4, the EMS 7 adjusts the control of the plurality of battery modules 2 based on the corrected SOCs.

[0064] This can provide faster and more precise control of the battery system 1.

[0065] The battery system 1 is shown schematically as a block diagram in Fig. 1.

[0066] The battery system 1 as a (NR) UE can be deployed in Narrow Band (NB) Internet of Things (IoT) applications where only occasional and small amounts of data are sent in the uplink (UL), such as the data. For example, the data can be sent when the battery system 1 is in a Radio Resource Control (RRC) CONNECTED state. However, since the amount of NB-IoT data is small, the data can be sent less frequently and more efficiently. In particular, the different RRC states of the battery system 1 consume different amounts of resources, and therefore, transitioning between the RRC states can efficiently reduce network resources. The battery system 1 can be in one of the following states at any one time: (NR) RRC CONNECTED state, (NR) RRC INACTIVE state, and (NR) RRC IDLE state.

[0067] When the battery system 1 is turned off (e.g., when no electrical power is being supplied), the battery system 1 is in a disconnected state and is not in any of the three RRC states. After the battery system 1 is turned on, the battery system 1 may initially transition to the RRC IDLE state. In the RRC IDLE state, the battery system 1 may attempt to establish a wireless connection with a serving base station (e.g., gNB - not shown) and transition to the RRC CONNECTED state. After the battery system 1 transitions, the battery system 1 may also be released from the RRC CONNECTED state to return to the RRC IDLE state. However, after the initial transition to the RRC CONNECTED state, the battery system 1 may transition to the RRC INACTIVE state to more efficiently utilize grid resources.The RRC INACTIVE state of Battery System 1 can be released, resumed, or suspended to transition back to the RRC CONNECTED state. Furthermore, Battery System 1 can be released from the RRC INACTIVE state and transition back to the RRC IDLE state. The RRC INACTIVE state minimizes latency and reduces signaling load, thereby using network resources more efficiently and reducing the power consumption of Battery System 1 during data transmission.

[0068] For example, battery system 1 may be part of a 4-stage Random Access Channel (RACH) transmission procedure, which includes the transmission of four messages (Msgl, Msg2, Msg3, and Msg4) before transmitting the data to the serving base station. Here, battery system 1 may perform a random access by sending a RACH preamble—e.g., Msgl—on a RACH resource. The serving base station may respond with a Random Access Response (RAR)—e.g., Msg2. Battery system 1 may then send a Radio Resource Control (RRC) connection request—e.g., Msg3—on the Physical Uplink Shared Channel (PUSCH) (e.g., NR-PUSCH). The serving base station may then respond with an RRC Connection Setup—e.g., Msg4—that completes battery system 1's initial access process.This RACH mode is an inefficient way of transmitting data, since data transmission between battery system 1 and the serving base station only takes place after the four messages.

[0069] Another example: Battery system 1 can be part of an Early Data Transmission (EDT) procedure that includes the transmission of two messages (Msgl and Msg2) before transmitting the data to the serving base station. This means that in this EDT mode, battery system 1 can send the data in message 3 (Msg3), and the serving base station can send the downlink (DL) data in message 4 (Msg4) of the (legacy) 4-step RACH mode. This type of data transmission is more efficient than the 4-step RACH mode. Battery system 1 can continue to send / receive UL / DL data packets in EDT mode after Msg4 in the RRC IDLE state or RRC INACTIVE state.

[0070] Another example: Battery System 1 may be part of a transmission procedure in which no data packets from Msgl to Msg4 are transmitted prior to data transmission. In this Preconfigured UL Resources (PUR) mode, Battery System 1 immediately sends the data to the serving base station using preconfigured resources. This type of UL data packet transmission is more efficient than the two aforementioned RACH and EDT modes. This allows for the transmission of UL data packets in the RRC INACTIVE state. Accordingly, the data contained in the UL data packets can be sent periodically or sporadically.

[0071] As described below, the (NR-)PUR parameters can be adjusted to transmit data more efficiently to better accommodate changing radio conditions and traffic patterns and to provide Battery System 1 with improved functionality for transmitting small UL data packets in the RRC INACTIVE state.

[0072] Battery system 1 transmits the UL data packets containing the data in the RRC INACTIVE state to the serving base station based on an (initial) PUR configuration. The (initial) PUR configuration is defined according to a set of PUR parameters (e.g., in connection with 5G NR) that include one or more of the following elements: a control resource set configuration (CORESET) including an aggregate level and repetition types, a PUSCH configuration including a frequency hopping pattern, a UL narrow beam direction for frequency range 2 (FR2), or a transmit-receive point (TRP) assignment.The CORESET configuration comprises a UE-specific CORESET configuration or a general CORESET configuration to enable the battery system 1 to monitor and decode Downlink Control Indicator (DCI) information received by the battery system 1 from the serving base station in order to reconfigure the PUR configuration in the RRC INACTIVE state. At least one of the PUR parameters in the RRC INACTIVE state is updated, activated, or deactivated based on the received DCI information. The DCI information may include an acknowledgment (ACK) / negative ACK (NACK) of the PUR transmissions as well as the UL grant for a Hybrid Automatic Repeat Request (HARQ) retransmission in case of NACK. The ACK / NACK and the UL grant for the HARQ retransmission may be sent over a UE-specific or common search space.

[0073] Once the PUR is assigned, the PUR configurations can be used for a period of time. However, the radio channel and radio traffic can change continuously, so the (initial) PUR configurations may no longer be suitable for Battery System 1 after a certain period of time. Therefore, a PUR reconfiguration mechanism can be considered. Reconfiguration can be performed via the PUR response message. The PUR response message can contain ACK / NACK of the PUR transmissions (if not included in DCI) and information for reconfiguring the PUR parameters. The PUR response message can be a UE-specific RRC signaling or a Broadcast System Information Block to reconfigure the (initial) PUR configuration and create a new PUR configuration.

[0074] The battery system 1 may receive a message from the serving base station indicating that the (initial or newly created) PUR configuration may be released if no UL data packets intended to contain at least part of the data are sent for more than a predetermined number of continuous PUR transmissions.

[0075] The battery system 1 may receive a message from the serving base station indicating to the battery system 1 that the battery system 1 should fall back to EDT mode or RACH mode in the event of a UL transmission failure. The occurrence of the UL transmission failure may be determined based on at least one of the following: timing mismatch (TA), low transmit power, deep channel fading, or beam dropout. The battery system 1 may receive a signal from the serving base station to reconfigure the battery system 1 for transmitting the UL data packets containing at least a portion of the data in the RRC INACTIVE state based on one or more PUR parameters after the battery system 1 has returned to EDT mode or RACH mode. The one or more PUR parameters may be reconfigured based on a request from the battery system 1, radio network utilization, or radio link performance.

[0076] The battery system 1 can also transmit the data at a predefined event. This predefined event can be the successful completion of a communication connection to a network. In this case, the battery system 1 can retrieve the data stored in a volatile memory and forward it in packets via the mobile radio system. In the event of a handover, for example, when the motor vehicle containing the battery system 1 moves between two cells of the mobile radio system, the data can be stored in packets in the battery system 1 such that a first part of the packets is transferred in a transferring cell and a second part of the packets is transferred in a serving cell containing the serving base station. Ultimately, the data consisting of the first and second parts of the packets are combined in the network underlying the mobile radio system itself.

[0077] The remote diagnostic device 8 is shown schematically as a block diagram in Fig. 1 and has the functionalities of a computer.

[0078] The remote diagnostic device 8 implements one or more steps of the method SO, as illustrated in Fig. 2. In particular, the remote diagnostic device 8 provides functionality, such as computer software, that runs on the remote diagnostic device 8 and performs one or more steps of the method SO. In particular, the remote diagnostic device 8 can execute instructions related to the data contained in the computer program described herein and cause the remote diagnostic device 8 to perform the one or more steps of the method SO.

[0079] The data described herein may be compact data and may specifically include one or more of the following information: series of measurements on voltage, current, temperature, and SOCs estimated by the BMS 4, estimated SOCs and states of health (SOHs) of the individual battery modules 2 aggregated by the EMS 7. These elements are called compact data because they are summarized in small, time-divided batches and preferably sent in frames or packets.

[0080] It is contemplated herein that the remote diagnostic device 8 may take any suitable physical form. By way of example, the remote diagnostic device 8 may be embodied at least partially as an embedded computer, system-on-chip (SOC), single-board computer (SBC), server, and / or user equipment (UE). The remote diagnostic device 8 may be unified or distributed; span one or more locations; span one or more machines or data centers; or be located in a cloud, which may include cloud components in a network. The remote diagnostic device 8 may perform one or more steps of the method SO without significant spatial or temporal limitations. By way of example, the remote diagnostic device 8 may perform one or more steps of the method SO in real time, in parallel, or in batch mode. The remote diagnostic device 8 may perform step(s) of the method SO at different times or at different locations.

[0081] The remote diagnostic device 8 has at least one or more of the following additional components: a volatile memory 10, a persistent memory 11, a bus 12, an arbiter 13, and a communication interface 14. The components of the remote diagnostic device 8 can be implemented at least partially in hardware and / or software. The interconnection of the components of the remote diagnostic device 8 is structured as shown in Fig. 1 merely for the sake of simplicity. In particular, the interconnection and connection can differ in implementation due to signal processing and signaling. The SOC computing unit 9 has means for executing instructions related to the data, e.g., of the computer program described herein. For example, the SOC computing unit 9 can execute the instructions related to the data contained in the computer program described herein, e.g.,from the volatile memory 10 and / or the persistent memory 11 and then execute the instructions, which in turn causes the SOC computing unit 9 to perform the one or more steps of the method SO, as shown, for example, in Fig. 2. The SOC computing unit 9 may have an internal register / cache for the data, for the instructions associated with the data, and / or for associated addresses. The SOC computing unit 9 may have an FPGA, ASIC, DSP, microcontroller, a CPU, and / or GPU for accessing the internal register / cache. As an example, to execute the instructions associated with the data, the SOC computing unit 9 may retrieve them from the internal register / cache of the SOC computing unit 9, the volatile memory 10, or the persistent memory 11; decrypt and execute them; and then write a result to the internal register / cache of the SOC arithmetic unit 9, the volatile memory 10 or the persistent memory 11.

[0082] As an example, the SOC 9 may include an instruction cache, a data cache, and / or a translation buffer (TLB). The data-related instructions in the instruction cache may be copies of instructions in the volatile memory 10 and / or persistent storage 11, and the instruction cache may accelerate the retrieval of these data-related instructions by the SOC 9. The data in the data cache may be copies of data for the data-related instructions currently executing on the SOC 9 in the volatile memory 10 and / or persistent storage 11. The results of previous data-related instructions executed on the SOC 9 may be provided for access by subsequent data-related instructions to be executed on the SOC 9, or for writing to the volatile memory 10 and / or persistent storage 11.The data cache can accelerate the read or write operations of the SOC computing unit 9. The addresses associated with the data in the TLB can be address references to addresses in the volatile memory 10 and / or persistent memory 11 in order to accelerate the virtual address translation for the SOC computing unit 9. The volatile memory 10 can be a dynamic RAM (DRAM) or a static RAM (SRAM). The volatile memory 10 can in particular be embodied as the data storage medium described herein, on which the computer program described herein can be at least temporarily stored. Furthermore, the volatile memory 10 can be a single-channel or multi-channel RAM.The volatile memory 10 may include main memory for storing data-related instructions for the SOC computing unit 9, which then executes these instructions; or may include the data for the SOC computing unit 9, which the SOC computing unit 9 uses to operate on it. For example, the remote diagnostic device 8 may load these instructions into the volatile memory 10 from the persistent memory 11 or another source (such as another computer, another remote diagnostic device, the network, or the cloud). The SOC computing unit 9 may then load these instructions from the volatile memory 10 into the internal register / cache of the SOC computing unit 9. To execute these instructions, the SOC computing unit 9 may retrieve and decrypt these instructions from the corresponding internal register / cache.During or after executing these instructions, the SOC computing unit 9 can write a result (which can be intermediate or final results) to the internal register / cache. The SOC computing unit 9 can then write the result to the volatile memory 10.

[0083] For example, the SOC computing unit 9 only executes the instructions related to the data in the internal register / cache of the SOC computing unit 9 or in the volatile memory 10 (as opposed to the persistent memory 11), and only operates on the data in the internal register / cache of the SOC computing unit 9 or in the volatile memory 10 (as opposed to the persistent memory 11).

[0084] The persistent memory 11 has a mass storage device, e.g., a non-volatile mass storage device (NVM), for the data or the instructions associated with the data. The persistent memory 11 can be configured, in particular, as the data storage device described herein, on which the computer program described herein can be stored. For example, the persistent memory 11 can be a flash memory, in particular an SSD or eMMC. The persistent memory 11 can store the data in an erasable or non-erasable manner. The persistent memory 11 can be located in the remote diagnostic device 8, i.e., internally, or externally.

[0085] The SOC computing unit 9 can be connected to the persistent memory 11 directly or indirectly, e.g., via the arbiter 13. The connection can be implemented via a clock bus, command bus, and data bus. This is shown only schematically using bus 12 in Fig. 1. The persistent memory 11 receives commands associated with the data and the data in conjunction with a clock signal provided by the SOC computing unit 9 on the clock bus. The clock signal clocks the reception of the commands associated with the data and the data. The SOC computing unit 9 sends a command associated with the data to the persistent memory 11 via the command bus. Furthermore, the SOC computing unit 9 sends the data corresponding to the command via the data bus to the persistent memory 11 or receives the data from the persistent memory 11 via the data bus.

[0086] In one example, persistent memory 11 may have a clock pin through which the clock signal is received at persistent memory 11. The clock signal may be a write enable signal and / or a read enable signal. Persistent memory 11 may further have first and second input / output (I / O) pins. Data is received at persistent memory 11 via the first I / O pin in synchronization with the clock signal. Persistent memory 11 may further have a command / address buffer, control logic, and an I / O buffer. The command / address buffer operates at a first operating speed and, synchronously with the clock signal, buffers the command and corresponding address received via the second I / O pin and associated with the data. One I / O buffer operates at the first operating speed and buffers the data as read data from the NVM or writes the data as write data to the NVM. The first and second I / O pins may coincide.Here, the clock signal can be formed by a first and second clock signal, in which the first clock signal only switches during a period in which the command and the address (both related to the data) are received by persistent memory 11, and the second clock signal only switches during a period in which the data is received by persistent memory 11. The first operating speed corresponds to a data input speed or data output speed between persistent memory 11 and SOC computing unit 9. The control logic controls an operation with respect to the NVM based on the buffered command and the buffered address (both related to the data). Here, the control logic operates at a second operating speed, which is lower than the first operating speed and corresponds to an internal operating speed of persistent memory 11.

[0087] The bus 12 can be understood herein as a subsystem of the remote diagnostic device 8, which transmits the data and / or electrical power between the components of the remote diagnostic device 8. The (one) bus 12 can connect the components of the remote diagnostic device 8 to one another via the same set of lines. The bus 12 can be designed for dedicated data communication between two or more of the components of the remote diagnostic device 8. The bus 12 can be a system bus via which the SOC computing unit 9 is connected to the other components of the remote diagnostic device 8. In this case, the bus 12 can be synchronous - the data is transferred bidirectionally with a clock edge of a clocking of the bus 12 - and / or asynchronous - no clocking, but a handshake takes place to transfer the data.In such a semi-synchronous system bus, the bus 12 is clocked, but control lines allow wait cycles to also use slow components, such as the persistent memory 11, via the bus 12.

[0088] The arbiter 13 can be provided for at least partial control over the bus 12. The arbiter 13 can be understood as a coprocessor subordinate to the SOC computing unit 9. The arbiter 13 regulates the data-related access to the bus 12 based on a two-way handshake or three-way handshake. For this purpose, the three signals Bus Request (BREQ) are used for forwarding the data, Bus Grant (BGRT) for confirming and approving the forwarding, and Bus Grant Acknowledge (BGA) for optional forwarding confirmation. The arbiter 13 simultaneously receives several BREQs from different components of the remote diagnostic device 8 via the bus 12. The arbiter 13 sorts the BREQs by priority and forwards them sequentially – in a pipeline – to the SOC computing unit 9.As soon as the SOC computing unit 9 has received the BREQ, the SOC computing unit 9 sends the BGRT to the arbiter 13 or directly to the component of the remote diagnostic device 8 that sent the BREQ. A subordinate BREQ of the BREQs in the pipeline - e.g., from another component of the remote diagnostic device 8 - is forwarded to the SOC computing unit 9 in response to a BGRT sent by the SOC computing unit 9 relating to the BREQ that has priority in the pipeline and is related to at least part of the data. The BGRT relating to the subordinate BREQ is sent from the SOC computing unit 9 to the arbiter 13 after at least part of the data has been processed. The arbiter 13 can, for example, in turn, in response to the BGRT relating to the subordinate BREQ, send a BREQ that is further subordinate in the pipeline - e.g., B. refers to another part of the data - send the BREQs to the SOC computing unit 9.Likewise, in response to each BGRT from the SOC computing unit 9, the arbiter 13 can send a corresponding BGA to the SOC computing unit 9. With the procedure described herein, a BGA can also be omitted entirely. This saves overhead in the communication between the components of the remote diagnostic device 8. That is, a two-way handshake is provided instead of a three-way handshake.

[0089] The bus 12 can also comprise a data bus, address bus, and control bus. Data is transmitted bidirectionally between the components of the remote diagnostic device 8 via the data bus. The address bus is operated solely by the SOC computing unit 9 and unidirectionally transmits memory addresses associated with the data. The control bus is controlled solely by the arbiter 13, e.g., in the sense of a watchdog, and transfers control of it to the SOC computing unit 9 in the pipelined manner described above to control the data transmission.

[0090] The communication interface 14 enables the remote diagnostic device 8 to communicate with the battery system 1. Alternatively or additionally, a direct communication connection can be established between the remote diagnostic device 8 and the battery system 1 via the communication interface 14. For example, it can be an ad hoc network, wireless personal area network ((W)PAN), e.g., a Bluetooth WPAN, local area network (LAN), and / or Wi-Fi network consisting of the battery system 1 and the remote diagnostic device 8. The communication interface 14 also enables the remote diagnostic device 8 to communicate with a network, e.g., Bluetooth, WLAN, a mobile radio system, and / or at least part of the Internet.The communication interface 14 provides means for communicating (such as packet-based communication) the data and correction factors between the battery system 1 and the remote diagnostic device 8 (wired - via cable, e.g. optical connection - and / or wireless - via antenna).

[0091] The method steps represented as blocks of the block diagram in Fig. 2 can, for example, be substantially mapped into a machine-, processor- or computer-readable data carrier and thus executed by a computer, such as the EMS 7 or the remote diagnostic device 8, or a processor, such as the SOC computing unit 9, as described above with reference to Fig. 1. Examples can further be or relate to a computer program containing program code for executing at least some of the method steps from Fig. 2 when the computer program is executed on the computer or processor. An example can also include a volatile memory 10 or persistent memory 11, as also described above with reference to Fig.1, which is machine-, processor- or computer-readable and encodes machine-executable, processor-executable or computer-executable programs with instructions that cause some or all of the method steps to be carried out.

[0092] At this point, it should be noted that all parts described above, viewed individually and in any combination, particularly the details shown in the drawings, are claimed as essential to the invention. Modifications to these are familiar to those skilled in the art.

[0093] 1 battery system

[0094] 2 battery modules

[0095] 3 sensors

[0096] 4 BMS

[0097] 5 battery cells

[0098] 6 SOC estimation unit

[0099] 7 EMS

[0100] 8 Remote diagnostic device

[0101] 9 SOC computing unit

[0102] 10 Volatile memory

[0103] 11 Permanent storage bus

[0104] Arbiter

[0105] Communication interface

Claims

Claims 1. A method (SO) for remote diagnosis of a battery system (1) comprising a plurality of battery modules (2), each having sensors (3) for sensory recording of measurement series of physical quantities of battery cells (5) located in the battery modules (2), and a battery management system, BMS (4), and an energy management system, EMS (7) assigned to the plurality of battery modules (2), which is configured to obtain, based on the measurement series and estimated by the respective BMS (4), states of charge, SOCs, of each battery module (2-1, 2-n) of the plurality of battery modules (2), and to operate the plurality of battery modules (2) depending on the estimated SOCs, the method (SO) comprising: Receiving (Sl), at a remote diagnostic device (8), data based on the series of measurements, each related to a corresponding battery module (2-1, 2-n) of the plurality of battery modules (2); Determining (S2), at the remote diagnostic device (8), individual correction factors related to the individual battery modules (2-1, 2-n) of the plurality of battery modules (2) by comparing the data with reference battery data of reference batteries of the same type according to the corresponding battery modules (2-1, 2-n) of the plurality of battery modules (2); Correcting (S3) the estimated SOCs using the individual correction factors; and Adjusting (S4) a control of the plurality of battery modules (2) by the EMS (7) based on the corrected SOCs.

2. Method (SO) according to claim 1, characterized in that the remote diagnostic device (8) transmits the individual correction factors directly to the EMS (7).

3. Method (SO) according to claim 1 or 2, characterized in that the individual correction factors each indicate a difference to the current state of charge of the corresponding battery module (2-1, 2-n) of the plurality of battery modules (2).

4. The method (SO) according to any one of the preceding claims, characterized in that the correcting includes recalibrating each of the estimated SOCs; and the adjusting the control includes the EMS (7) using the recalibrated SOCs in relation to each other to deploy the plurality of battery modules (2) according to a preset intended use of the battery system (1).

5. Method (SO) according to one of the preceding claims, characterized in that the determination of the correction factors is carried out in each case by comparing gradient values of an open circuit voltage, OCV, curve to be calculated from the data with gradient values of an OCV curve predetermined by the reference battery data, and in that the OCV curves to be calculated in each case result from composite pairs of voltage and current values contained in the series of measurements of physical quantities.

6. Remote diagnostic method (SO), carried out by a remote diagnostic device (8), for a battery system (1) with a plurality of battery modules (2), each of which has sensors (3) for the sensory recording of measurement series of physical Sizes of battery cells (5) located in the battery modules (2) and a battery management system, BMS (4), and an energy management system, EMS (7) assigned to the plurality of battery modules (2), which is configured to obtain, based on the measurement series, estimated states of charge, SOCs, of each battery module (2-1, 2-n) of the plurality of battery modules (2) by the respective BMS (4) and to operate the plurality of battery modules (2) depending on the estimated SOCs, the remote diagnostic method (SO) comprising: Receiving (Sl) data based on the series of measurements, each related to a corresponding battery module (2-1, 2-n) of the plurality of battery modules (2); Determining (S2) individual correction factors related to the individual battery modules (2-1, 2-n) of the plurality of battery modules (2) by comparing the data with reference battery data of reference batteries of the same type according to the corresponding battery modules (2-1, 2-n) of the plurality of battery modules (2); Providing (S3) the individual correction factors to cause (S4) the EMS (7) to correct the estimated SOCs and to adjust a control of the plurality of battery modules (2) based on the corrected SOCs.

7. Computer program, characterized in that the computer program comprises instructions which, when the computer program is executed by a computer, cause the computer to execute or initiate the method (SO) according to one of the preceding claims or at least one of the steps thereof.

8. Data carrier, characterized in that on the data carrier the computer program according to claim 7 is stored.

9. Remote diagnostic device (8) for remotely diagnosing a battery system (1) with a plurality of battery modules (2), each having sensors (3) for sensory recording of measurement series of physical quantities of battery cells (5) located in the battery modules (2) and a battery management system, BMS (4), and an energy management system, EMS (7) assigned to the plurality of battery modules (2), which is configured to obtain, based on the measurement series and estimated by the respective BMS (4), states of charge, SOCs, of each battery module (2-1, 2-n) of the plurality of battery modules (2) and to operate the plurality of battery modules (2) depending on the estimated SOCs, wherein the remote diagnostic device (8) is configured to: Receiving (Sl) data based on the series of measurements, each related to a corresponding battery module (2-1, 2-n) of the plurality of battery modules (2); Determining (S2) individual correction factors related to the individual battery modules (2-1, 2-n) of the plurality of battery modules (2) by comparing the data with reference battery data of reference batteries of the same type according to the corresponding battery modules (2-1, 2-n) of the plurality of battery modules (2); Providing (S3) the individual correction factors to cause (S4) the EMS (7) to correct the estimated SOCs and to adjust a control of the plurality of battery modules (2) based on the corrected SOCs.

10. Battery system (1) with a plurality of battery modules (2) which have respective sensors (3) for sensory recording of measurement series of physical quantities of battery cells (5) located in the battery modules (2) and a battery management system, BMS (4), and an energy management system, EMS (7) assigned to the plurality of battery modules (2), which is configured to obtain, based on the measurement series, states of charge, SOCs, of each battery module (2-1, 2-n) of the plurality of battery modules (2) estimated by the respective BMS (4) and to operate the plurality of battery modules (2) depending on the estimated SOCs, wherein the battery system (1) is configured to: Sending (S1) to a remote diagnostic device (8) data based on the series of measurements, each relating to a corresponding battery module (2-1, 2-n) of the plurality of battery modules (2), in order to cause the remote diagnostic device (8) to determine individual correction factors relating to the individual battery modules (2-1, 2-n) of the plurality of battery modules (2) (S2); Receiving (S2), from the remote diagnostic device (8), the correction factors determined (S2) by comparing the data with reference battery data from reference batteries of the same type according to the corresponding battery modules (2-1, 2-n) of the plurality of battery modules (2); Correcting (S3) the estimated SOCs using the individual correction factors; and Adjusting (S4) a control of the plurality of battery modules (2) by the EMS (7) based on the corrected SOCs.

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