Battery management system, method and airborne vehicle

Abstract

The present invention relates to a battery management system, a method and an aerial vehicle for observing battery health parameters, in particular cell impedance, using two redundant, independent and dissimilar channels. Specifically, the impedance observation of the first channel of the channels is based on electrochemical impedance spectroscopy (EIS). The other channel uses an algorithm different from EIS. In an embodiment, the battery status observation is further performed independently by two channels, wherein the first channel also uses EIS and the other channel uses a different (dissimilar) algorithm. Based on the status observation and health observation, the state (functional state) of the battery system can be predicted to determine the flight range according to a predetermined flight profile.

Description

Technical Field

[0001] The present invention relates to a battery management system, and more particularly to a battery management system for health monitoring of an energy storage system for an electric aerial vehicle. Background Art

[0002] In recent years, electricity has become increasingly important as a form of energy for powering aerial vehicles, particularly electric vertical take-off and landing (eVTOL) vehicles.

[0003] A key component of an electric (i.e., electrically driven / electrically propulsed) aircraft, including eVTOLs, is an appropriate energy storage system (ESS). The energy storage system can be implemented in the form of a battery system of rechargeable batteries, which can be constructed into a plurality of individual battery cells. The individual battery cells can be combined together to form one or more battery modules of the aircraft battery system that serve as the energy storage system. An example of a battery type suitable for use within the framework of the present invention is a lithium (Li) ion battery, although the present invention is not limited thereto.

[0004] Generally speaking, the function of an energy storage system is to provide an electrically powered aircraft with sufficient available energy for safe flight and landing. As this is typically the case with air traffic, the highest safety standards apply to components of an air vehicle, including an ESS. To ensure safe operation, and in particular, to ensure safe landing when sufficient remaining available energy is available, ESS parameters defining critical states that limit the amount of available energy must be monitored and communicated to the operator.

[0005] Such parameters include, but are not limited to, the cell temperature or state of charge (SOC) of individual battery cells of an ESS. Since some of these parameters are not directly measurable quantities but rather are related to internal states, the design of the ESS must foresee appropriate equipment for observing the corresponding states. State observation is based on measurements of physical data, including, but not limited to, terminal cell voltage, cell surface temperature, or current. Furthermore, possible residuals in the state determination must be considered, as these potentially limit the available energy and, therefore, the range of the air vehicle. Such residuals typically occur given the finite accuracy of any model used to describe the ESS and its states. Furthermore, the available energy of the battery is highly dependent on the flight profile. Therefore, the energy management system must also perform state predictions for the planned flight profile until the electric-powered aircraft achieves a safe landing. Specifically, within the framework of the present disclosure, terms like "monitoring" or "observing" are used to indicate not only determining the corresponding data (functional state, health parameter) at a specific point in time, but also repeating its determination in order to gather information about its evolution over time, particularly before and during a specific flight. The intervals for updating the respective determinations can be set depending on the circumstances and, in particular, can be set so short that a quasi-permanent observation is possible.

[0006] More generally, it distinguishes between two types of relevant time-dependent variables that characterize battery systems. On the one hand, the (battery cell) state is defined by system variables that evolve rapidly over time (i.e., in seconds depending on system inputs). Examples of cell state are cell state of charge, cell core temperature, or cell die temperature. On the other hand, health parameters are system variables that evolve slowly over time (i.e., in days depending on system inputs).

[0007] The importance of health parameter monitoring lies in that, in addition to specific variables reflecting the current state of charge, the overall state of the battery also depends on additional factors. These additional factors may reflect changes on a larger time scale during the battery life cycle, such as aging, and are summarized under the term "health parameters." In particular, battery health parameters may include, but are not limited to, at least one of cell capacity and cell impedance.

[0008] Therefore, a key task during the ESS design phase is to ensure that the maximum errors in the state and health predictions for the planned flights are known and can be considered both during the planning of individual flights and during the flights themselves, when determining the current state of the ESS, and in particular, the remaining available energy to define the remaining flight range. The planned profile defines the power consumed over a period of time and should strictly comply with the operational requirements of the aircraft. The maximum error measure in the state predictions over the entire lifetime of the ESS should be considered as a safety margin. This ensures that residual errors in state observations and state predictions do not significantly affect the utilization of the ESS within its known physical limits.

[0009] Therefore, state prediction based on condition monitoring and health parameter monitoring can ensure that the planned profile does not violate any safety margins until a safe landing is achieved. This allows the operator to confirm the available energy and range for a given mission during the flight according to a specific profile and at any time before landing.

[0010] The following will refer to Figure 1 Schematic diagram depicting tasks to be performed by an energy management system for state observation and state prediction during flight according to a particular flight profile.

[0011] exist Figure 1 The upper portion of the diagram shows the power required over time during the flight according to the flight profile. As can be seen from the diagram, the power required is particularly high immediately after takeoff and in the final phase of the flight (i.e., before landing). In the example shown in the diagram, it is assumed that the current time, indicated by the symbol for the aircraft in flight, is between the start of the takeoff and landing phases. Therefore, at this current time, the previous flight phase has already passed, and the upcoming flight phase should be executed according to the planned flight profile. As further indicated by the shaded box at the end of the flight, for safety reasons, a certain amount of energy should remain available at the destination. Therefore, the end point on the time scale is defined by the condition under which a predetermined residual energy level is still available (the "end point condition"). In other words, to account for uncertainties in the state prediction, the indicated later time point (the "physical limit") that is still achievable based on the residual available energy should not be considered operationally reachable.

[0012] During flight, the state of the ESS is permanently monitored ("state observation"). This includes, but is not limited to, physical measurements, model-based estimations, observations using neural networks, and model-based correction / calibration of measurement data. State observation specifically monitors multiple functional states (SOF). These functional states may include, but are not limited to, cell state of charge (SOC), cell core temperature, cell current connector temperature, cell current, and HV (high voltage) cable temperature.

[0013] Based on the state observations and health parameter observations before and during the flight phases before (in the past) the current time, the state prediction is performed for a future time point. In particular, the state prediction may include, but is not limited to, the use of lookup tables, model-based predictions, and predictions using neural networks. By taking into account any predetermined safety margins and residuals, this enables the prediction of the state until the end of the planned flight profile (e.g., the SOF listed above and indicated in the lower part of the figure). In particular, as soon as the remaining available energy upon landing according to the planned flight profile falls below a predefined "remaining energy at the destination," an alert must be immediately issued to the operator to ensure a safe landing at the nearest accessible airport.

[0014] An erroneous determination of available energy could result in a catastrophic failure condition. This classification stems from the assumption that an erroneous display of available energy would lead the pilot to perform flight maneuvers (particularly flight distance) that the battery could not sustain with sufficient energy to continue flight and landing.

[0015] As mentioned above, parameters that determine the battery state or health, in particular the state of charge or impedance of a battery cell, are generally not directly measurable quantities. Therefore, the problem arises of how to reliably determine the battery state of charge or health in a manner that complies with the highest safety requirements applicable in air traffic, in particular for electric aerial vehicles. Summary of the Invention

[0016] The present invention aims to provide a battery management system and a corresponding method, which can reliably determine and monitor battery cell impedance as a battery health parameter of an ESS of an electric aerial vehicle, thereby complying with the high safety requirements applicable in air traffic.

[0017] This is achieved by the features of the independent claims.

[0018] According to a first aspect of the present invention, a battery management system for an electric aerial vehicle is provided, the battery management system being adapted to observe the current battery health of a battery system forming an energy storage system of the aerial vehicle, the battery management system comprising: two redundant and dissimilar channels for battery cell measurements, wherein a first of the two channels comprises a device for determining a cell impedance as a battery health parameter for each individual battery cell of a plurality of battery cells of the battery system by using electrochemical impedance spectroscopy (EIS), and a second of the two channels comprises a device for performing the cell impedance determination using an algorithm different from EIS.

[0019] According to a second aspect of the present invention, a battery management method for observing the current battery health of a battery system forming an energy storage system of an electric aerial vehicle comprises determining a cell impedance as a battery health parameter for each individual battery cell of a plurality of battery cells of the battery system by using electrochemical impedance spectroscopy (EIS), and independently determining the cell impedance as a battery health parameter for each individual battery cell of the plurality of battery cells based on an algorithm different from the EIS.

[0020] A specific method of the present invention is to determine the cell impedance of an electric aerial vehicle battery system as a battery health parameter, and optionally the state of charge, by means of two redundant and dissimilar channels of a battery cell measurement component (battery cell measurement device), wherein one of the channels uses electrochemical impedance spectroscopy (EIS). The fact that the two channels are redundant means that each channel can provide a comprehensive health parameter observation of the ESS at any time without relying on any decisions made by the other channel. In other words, the measurements of the two channels are completely independent of each other. The fact that the two channels are dissimilar means that the algorithms used by the two channels for determining the cell impedance and optionally the state of charge and other state variables are different from each other. In particular, the other of the two channels uses an algorithm different from that of electrochemical impedance spectroscopy.

[0021] A fundamental advantage of the method of the present invention using EIS is that EIS is highly accurate and fast in observing unit impedance.

[0022] Electric vehicles, particularly eVTOL applications, rely heavily on low cell impedance to deliver high hovering power requirements. Increased cell impedance is both a primary mechanism of cell aging and failure. Consequently, increased cell impedance significantly impacts the available energy, and therefore the range and safety of electric vehicles (eVTOLs).

[0023] For certification reasons, eVTOLs rely on two redundant and dissimilar cell impedance observation components (channels). Traditionally, an aging model (e.g., an empirical aging model) is used in one of the channels, running on the backend and fed with aircraft usage data. However, this aging model cannot observe the impedance inhomogeneity of multiple cells in a module, nor can it observe nonlinear aging and cell failures.

[0024] The benefit of EIS is that it allows for accurate, rapid, and on-demand physical measurement of the impedance of each individual cell. This overcomes all of the aforementioned shortcomings of aging models, resulting in a high degree of confidence in the available energy. This effectively increases the range of electric VTOL vehicles (eVTOLs) by unlocking a safety buffer against uncertainty in the available energy calculation.

[0025] Another benefit of EIS is that it allows monitoring and updating of battery health, in particular cell impedance, even during flight. Thus, state predictions during flight can be based not only on the latest state but also on updated health information.

[0026] According to an embodiment, the battery management system is also adapted for battery state monitoring. Each channel further comprises a device for determining the state of charge and / or cell core temperature of each individual battery cell. The first channel performs this determination based on EIS. The second channel performs this determination based on an algorithm different from EIS.

[0027] According to an embodiment, the amount of energy available is determined based on the health observations and, optionally, the SOC observations in each of the two channels to determine the flight range based on a model-based state prediction for the planned flight profile. In particular, this is done when no errors are detected in the determination using data from the two channels.

[0028] In an embodiment, the second channel uses a model-based cell impedance estimation algorithm and an optional model-based cell SOC and / or cell core temperature estimation algorithm. Each model-based algorithm is distinct and completely independent of the EIS used by the first channel. More specifically, the model-based algorithm used by the second channel is based on the use of a dual Kalman filter.

[0029] Another benefit of the embodiment combining EIS and model-based estimation in two redundant and independent channels is that the two algorithms are inherently different, which avoids common cause failures in both channels. This reduces the certification risk of the method.

[0030] However, the algorithm employed by the second channel is not limited to a model-based algorithm. Any other suitable algorithm known or subsequently known to a skilled person is equally applicable within the framework of the present disclosure. This includes, for example, determining SOC by coulomb counting. Coulomb counting is a simple and widespread method for determining cell state of charge. It is based on measuring the total charge current and total load current of each individual battery cell and integrating the measured currents over time.

[0031] Initially, the state of charge can be determined (reset) by OCV (open circuit voltage) measurement. Alternatively, other methods of resetting or recalibrating the SOC can be used, such as by resetting the SOC to 100% when the charger determines that the battery is fully charged. In the case of a coulomb counting algorithm, periodic recalibration is necessary to avoid erroneous results due to long-term drift in the observations.

[0032] The advantages of Coulomb Counting (CC) are its low complexity, high determinism, and therefore low computational effort. This saves weight and cost, and reduces certification risk.

[0033] In channels using CC for SOC monitoring, aging models can be used for battery state of health (SOH) monitoring. This model estimates battery health based on observed utilization. Examples of parameters that characterize battery system utilization are charge throughput (Ah), average temperature, and depth of discharge. The inputs to the aging model for battery health observations are the current, voltage, and temperature of each individual cell. The outputs are the corresponding health parameters (cell impedance, cell capacity) for each individual cell.

[0034] Algorithms for battery condition monitoring using aging models in channels can additionally be supported by dedicated maintenance procedures, including dedicated charging procedures or predefined pulse power tests.

[0035] According to an embodiment, a first channel includes a device for exciting battery cells of a battery system with a sinusoidal current having a variable frequency and a device for measuring the voltage response of each cell. More specifically, the first channel also includes processing circuitry for calculating the system impedance spectrum based on the ratio between the input excitation current and the voltage response.

[0036] In an embodiment, the second channel includes a battery cell measurement device for measuring at least one of a voltage (e.g., cell terminal voltage), a current (e.g., input or output current), and a temperature (e.g., surface temperature or cell die temperature). These parameters can be obtained through measurement and can form the basis for calculating parameters indicative of the battery state of charge and battery health based on known algorithms (including model-based algorithms) that take into account the specific hardware structure of the battery system and cells.

[0037] In an embodiment, the aerial vehicle is an electric vertical take-off and landing vehicle, or eVTOL.

[0038] According to another specific aspect of the present invention, there is provided an aerial vehicle including a battery management system according to the above aspects or embodiments.

[0039] Further features and advantages of the invention are set out in the dependent claims.

[0040] The embodiments and features of the invention described herein or recited in the appended claims may be combined unless it is clear from the context that such a combination is not possible for a particular embodiment or feature. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Additional features and advantages of the present invention will become apparent from the following more particular description, as illustrated in the accompanying drawings, in which:

[0042] Figure 1 is an overview showing state observation and state prediction for an energy storage system of an aerial vehicle flying according to a predetermined flight profile;

[0043] Figure 2 is a diagram illustrating the operation of a dual-channel battery management system for status observation and health observation in pre-flight and during-flight stages according to an embodiment of the present invention;

[0044] Figure 3 is a diagram showing details of state prediction during flight according to a planned flight profile using state of charge observation and battery health observation according to an embodiment of the present invention; and

[0045] Figure 4 is a flow chart illustrating the basic steps of an exemplary battery management method. DETAILED DESCRIPTION

[0046] The present invention relates to a battery management system for an electrically powered aerial vehicle, in particular an eVTOL, for determining the battery health and optionally the state of charge of a battery system forming an energy storage system (ESS). This enables the amount of energy available in the ESS to be determined in order to determine (predict) the flight range according to a predetermined flight profile, in particular at any time during the flight.

[0047] To this end, the present invention utilizes an innovative two-channel battery management system architecture that provides two redundant and dissimilar battery health parameter and status observation channels, one of which is based on electrochemical impedance spectroscopy (EIS).

[0048] According to the present invention, the first of the two channels operates based on EIS. The second of the two channels operates based on a different algorithm. In an embodiment, this algorithm is a model-based state (particularly SOC) estimation algorithm for state (particularly state of charge) observation, for example, using a dual Kalman filter. Similarly, according to an embodiment, a model-based cell parameter estimation algorithm, for example, using a dual Kalman filter, is used for battery health parameter observation. In particular, cell impedance is used as the battery health parameter.

[0049] In each channel, the battery state can be predicted via a model-based state prediction during the planned flight profile based on the observed state and health parameters. Specifically, the battery state determines the amount of energy available for the flight according to the planned flight profile. The thus-determined available energy, including a safety margin based on a predetermined maximum error in the state prediction, determines the maximum range of the flight and can therefore be confirmed as safe to reach the planned destination.

[0050] Embodiments of the present invention provide for the implementation of both battery health parameter (SOH) observation and battery state (particularly SOC) observation in a manner that satisfies certification requirements for eliminating single points of failure. This is achieved by implementing battery state observation and cell impedance observation (SOH observation) in a redundant and dissimilar manner by two respective channels that determine state in parallel.

[0051] According to an embodiment, the battery cell measurements performed by the battery cell measurement devices of each independent channel are dissimilar to each other. Therefore, the measurement of physically available parameters itself causes the channel dissimilarities. This avoids the situation where system failures occur due to any inherent defects in the measurement algorithm or principle. Examples of dissimilar measurement schemes include: a scheme using a PTC (positive temperature coefficient) element for temperature measurement and a scheme using an NTC (negative temperature coefficient) element for temperature measurement, a scheme using a shunt for current measurement and a scheme using a Hall effect sensor for current measurement, or a scheme using two different ADC (analog-to-digital converter) suppliers for voltage measurement.

[0052] The following will refer to Figure 2 A detailed description of the operation of these two redundant and dissimilar channels of battery state observation and health parameter observation during all flight phases is given.

[0053] Channel 1 (shown in the bottom row of the figure and corresponding to the second channel described in the above overview of the invention) uses a conventional model-based state estimation algorithm and health parameter estimation algorithm for state and health observation, for example, using a dual Kalman filter. The input to this algorithm is the current, voltage, and temperature of each individual unit measured by the corresponding measurement devices of channel 1. The output is an estimate of the state (such as the functional state SOF described above) and health parameters for each individual unit. To obtain the SOF and health parameter outputs from the measured parameters, corresponding model-based evaluations are performed.

[0054] Model-based estimation algorithms are well known to those skilled in the art, and therefore a detailed description thereof will be omitted here. They are used to estimate state variables of a system that cannot be directly measured, such as a cell's state of charge, cell core temperature, cell impedance, or cell capacity. Model-based algorithms generally compare the values of a measurable output variable of a system with known input variable values with the output of a system model for the same input values. At least one model parameter representing the state variable to be estimated is periodically updated as feedback based on the difference between the measured system output and the model output.

[0055] As input, the current, voltage, and temperature of each individual cell measured by the battery cell measurement device for the channel using model-based estimation are used. The estimated system variables are, for example, state and health parameters for each individual cell. In addition, as shown, if both model-based health estimation and model-based state estimation are implemented, the results can be interactively updated between the model-based state estimation and the model-based health estimation. In particular, the SOC and other variables that characterize the functional state of the battery, such as cell core temperature, can be obtained in a similar manner using the model-based algorithm.

[0056] Channel 2 (shown in the middle row of the figure and corresponding to the first channel introduced in the above overview of the invention) uses a state observation algorithm and a health parameter observation algorithm based on electrochemical impedance spectroscopy (EIS). EIS actively excites all individual cells with a sinusoidal current with a variable frequency and measures the voltage response of each cell. The ratio between the output signal and the input signal allows the calculation of the (complex) system impedance, which depends on the excitation frequency, i.e., the system impedance spectrum. Typical frequencies of the excitation current range from the Hertz (Hz) level to the kilohertz (kHz) level. The system impedance spectrum allows the calculation of the cell impedance as the main battery health parameter. The input to the algorithm is the excitation current of the EIS and the voltage of each individual cell measured by the corresponding measurement equipment of channel 2.

[0057] EIS is based on the active excitation of all individual cells with a sinusoidal current and the measurement of the voltage response of each cell. The ratio between the output signal and the input signal allows the calculation of the complex system impedance. The excitation is generated by sinusoidal currents of different frequencies. Therefore, a frequency-dependent voltage response can be obtained. This allows the determination of the frequency-dependent system impedance (system impedance spectrum). The system impedance spectrum allows the derivation of the cell impedance of each individual battery cell as a basic health parameter. In addition, the frequency-dependent system impedance allows the derivation of the SOC and the cell core temperature, which characterize the cell state of each individual cell.

[0058] It should also be noted that within the framework of this disclosure, all measurements and observations are performed at the level of individual battery cells. Given the high safety requirements in air traffic, the cell with the lowest capacity is always used as the basis for evaluations (such as determination of available energy or range prediction, as well as prediction of potential failures).

[0059] As further shown in the figure, in each channel, the observation results are used for state prediction. Thus, the state prediction takes into account the latest unit states and the latest health parameters. The state prediction is used to report (confirm) whether there is sufficient energy available for the planned flight. In particular, the flight profile determines the time-varying required power for the planned flight, which can be pre-calculated by the flight management computer system (FMS) during the flight planning phase based on inputs such as aircraft models, weather models, and path models.

[0060] As further shown, the available energy thus determined (based on the range of the planned flight profile) is displayed to the operator (pilot) of the aircraft. This is done separately in each independent and dissimilar channel. The operator thus has a display provided for each channel and can therefore compare the results. The operator can compare the state prediction results from the two channels, which are displayed separately, with each other. If there is a deviation between the predicted states from the two channels and if the size of the deviation exceeds a predetermined threshold, the deviation can prompt an alarm to the operator. The operator should then get as close as possible to the nearest airport (vertiport in the case of eVTOL) in order to land safely. In any case, the operator can personally compare the state prediction results and decide whether the determination is reliable or whether an emergency landing is required.

[0061] During normal operation, i.e., in the absence of significant deviations, the minimum value of the displayed range (available, accessible energy) of the two channels will be used as the basis for any decision. In this framework, "minimum value" refers to the value corresponding to the lowest value of the remaining accessible energy, i.e., the lowest range (remaining safe flight distance). The same principle is applied to the overall evaluation of the battery system, based on measurements and observations related to the individual cells. The evaluation is always based on the cell, for which the "minimum value" is determined in the aforementioned sense of the observed state or health parameter.

[0062] As further illustrated in the top line of the figure, the respective operations of both channels are performed continuously both before the flight (in particular: during the charging operation of the battery system) and during the flight between take-off and landing.

[0063] Figure 3 It is shown how the available energy for an airborne vehicle can be determined via model-based state prediction using battery state observations and health parameter observations.

[0064] Figure 3 The upper part of the middle picture is repeated Figure 1 , and shows a diagram indicating the power required during flight according to the flight profile.

[0065] The lower part of the figure shows how redundant and dissimilar channels allow the calculation of the current system functional state. Based on the observed battery health parameters, the planned flight profile can be used to predict the SOF evolution of each individual cell until the aircraft reaches the conditions for a safe landing. The planned flight profile is only valid if the state prediction excludes violations of the limits. Thus, the range of the aircraft, in particular the range of an eVTOL, can be determined based on (multiple) flight profiles, and destinations outside the aircraft's range can be safely excluded even before takeoff.

[0066] In order to comply with high safety requirements, the maximum error of the state prediction of a planned flight should be known in advance (e.g. during the planning phase of the ESS) so as to be taken into account each time during the planning of a single flight as well as during the flight itself. Figure 3 In the lower part of the graph, the maximum error in the state prediction is shown by the distance between the dotted and dashed lines in the graph showing the predicted state of charge over time. This error was measured during laboratory testing and is considered a safety margin for uncertainty in the state prediction.

[0067] More specifically, the solid line (the upper one of the two lines) labeled "'Worst Case' Error State Prediction" corresponds to the state evaluation result of the flight according to the planned flight profile assuming the presence of said maximum error. The label "worst case" refers to the fact that this prediction includes a maximum overestimation of the available resources (capacity), i.e. corresponds to the "worst case" from the pilot's point of view. The actual available ("physical") capacity may be lower than the worst case estimate by the "maximum error", which corresponds to the difference between the solid and dashed lines. As mentioned above with reference Figure 1 As mentioned above, for safety reasons there must still be some remaining available energy at the destination ("end condition"). This corresponds to Figure 3 The lower part shows a 5% safety margin for uncertainty in the state predictions.

[0068] The dashed line labeled "'Physical' State Evolution" (the lower of the two lines) shows the actual remaining functional state in the case of the prediction with the maximum error according to the solid line, that is, it is lower than the solid line by a distance corresponding to the maximum error. As will be readily understood by those skilled in the art, as the flight distance increases, the uncertainty of the prediction increases, and thus the error in determining the distance between the two lines increases. As a result, at the planned end point (destination) of the flight, the maximum error in the state prediction must not exceed the planned safety margin (in this example: 5% corresponding to the maximum error at the end point). This ensures the possibility of safe flight and landing even in the case of the largest prediction error.

[0069] Figure 4is a flow chart of an exemplary method that may be performed by a battery management system according to an embodiment of the present invention.

[0070] In the upper part of the flow chart, on the left hand side, the Figure 2 The operation of channel 1 shown in the lower part of FIG. Specifically, in step S10, corresponding measurements are performed at the individual battery cells. This includes, in particular, measurements of cell voltage, current and temperature.

[0071] In a subsequent step S12, the state, in particular the SOC, is derived based on the measurements. In an embodiment, this is done using a model-based approach, wherein the state is derived from an equivalent circuit model. Optionally, the cell core temperature can also be derived from the cell temperature measurements. In an embodiment, this is also done using a model-based approach, wherein the cell core temperature is also derived from an equivalent circuit model. However, the processing of channel 1 is not limited to this, but channel 1 can also use any other method than EIS. In parallel, step S15 carries out the determination of the cell impedance as a SOH parameter. In an embodiment, this is also done by using a model-based approach. In this case, as Figure 2 As shown, the relevant parameters of the equivalent circuit model are updated online during the estimation and between the state and health estimation. Likewise, the processing of channel 1 is not limited thereto, but any other method other than EIS can also be used for SOH estimation in channel 1.

[0072] The process then proceeds to step S17, where the individual determination results obtained (estimated) for channel 1 are used as the basis for predicting the battery state, i.e., the remaining available energy for the remaining range defined according to the planned flight profile. In a subsequent step S19, a corresponding display is made to the operator on the first display (or first display section) corresponding to the prediction for channel 1. This display can be implemented in various forms (such as through a graphical representation or numerical or symbolic indicators), as long as it is suitable for easily and quickly grasping the situation, especially a possible impending emergency situation.

[0073] On the right hand side of the upper part of the flowchart, the Figure 2 The corresponding operations performed by channel 2 shown in the middle are as follows: Specifically, in step S20, EIS measurement for obtaining the impedance spectrum of each cell of the battery system is performed as described above, ie, by comparing the frequency-dependent excitation current with the corresponding voltage response.

[0074] In step S22, the battery cell state, specifically the state of charge (SOC), is derived for each cell based on the impedance spectrum obtained in step S20. Optionally, the cell core temperature can also be derived based on the impedance spectrum in step S22. In parallel, step S25 determines the cell impedance of each individual battery cell based on the measurements in step S20.

[0075] The process then proceeds to step S27, where the (estimated) individual determination result obtained for channel 2 is used as the basis for a battery state prediction, i.e., the remaining available energy for the remaining range defined according to the planned flight profile. In a subsequent step S29, a corresponding display is made to the operator on a second display (or second display section) corresponding to the prediction for channel 2. Again, this display can be implemented in various forms, such as through a graphical representation or a numerical or symbolic indicator.

[0076] In the final step, S30, the operator compares the displayed state prediction results. Specifically, if the difference between the state prediction results for Channel 1 and Channel 2 exceeds a predetermined threshold, the operator can conclude that an error exists in at least one channel and decide to initiate a landing procedure at the nearest available airport, as a reliable prediction of the remaining available energy is impossible if a failure occurs in one channel. The system can also perform a comparison and, if an error is detected due to a significant deviation between the channels, issue an alert to the operator.

[0077] In summary, the present invention relates to a battery management system and method for observing battery health parameters, in particular cell impedance, using two redundant, independent and dissimilar channels. Specifically, the cell impedance observation in the first of the channels is based on electrochemical impedance spectroscopy EIS. The other channel uses an algorithm different from EIS. In an embodiment, the battery status observation is further performed independently by two channels, wherein the first channel also uses EIS and the other channel uses a different (dissimilar) algorithm. Based on the status observation and health observation, the state (functional state) of the battery system can be predicted to determine the flight range according to a predetermined flight profile.

Claims

1. A battery management system for an electric aerial vehicle, the battery management system being adapted to observe the current battery health of a battery system forming an energy storage system of the aerial vehicle, the battery management system comprising: Two redundant and dissimilar channels for battery cell measurement, The first of the two channels comprises a device for determining a cell impedance as a battery health parameter for each individual battery cell of a plurality of battery cells of the battery system, characterized in that Each of the two redundant and dissimilar channels is capable of observing comprehensive health parameters of the energy storage system at any time without relying on any determination made by the other channel. The device of the first channel determines the cell impedance using electrochemical impedance spectroscopy (EIS), A second of the two channels includes means for performing cell impedance determination using an algorithm other than EIS, The system is also suitable for battery status observation, Each of the channels comprises a device for performing a state of charge determination, i.e., SOC determination, of each individual battery cell, The SOC determination by the first channel is based on EIS, and The SOC determination performed by the second channel is based on a coulomb counting algorithm.

2. The system according to claim 1, wherein: Each of the channels comprises a device for determining the cell core temperature of each individual battery cell, The cell core temperature determination by the first channel is based on EIS, and The cell core temperature determination by the second channel is based on an algorithm different from EIS.

3. The system according to claim 1 or 2, wherein: The second channel uses a model-based cell impedance estimation algorithm.

4. The system according to claim 1 or 2, wherein: The second path uses a model-based cell SOC estimation algorithm and / or a cell core temperature estimation algorithm.

5. The system according to claim 3, wherein: The second channel uses an aging model for health status monitoring.

6. The system according to claim 1 or 2, wherein: The second channel includes a device for measuring at least one of current, voltage, and temperature of each individual battery cell in the plurality of battery cells of the battery system.

7. The system according to claim 1 or 2, wherein: The first channel comprises means for exciting the battery cells of the battery system with a sinusoidal current having a variable frequency, and means for measuring the voltage response of each cell.

8. The system according to claim 7, wherein: The first channel also includes processing circuitry for calculating a system impedance spectrum based on a ratio between an input excitation current and a voltage response.

9. The system according to claim 1 or 2, wherein: The aerial vehicle is an electric vertical take-off and landing vehicle, or eVTOL.

10. An aerial vehicle comprising the system according to any one of claims 1 to 9.

11. A battery management method for observing the current battery health of a battery system forming an energy storage system of an electric aerial vehicle, the battery management method comprising: determining a cell impedance as a battery health parameter for each individual battery cell of the plurality of battery cells of the battery system by using electrochemical impedance spectroscopy (EIS), and independently determining the cell impedance as a battery health parameter for each individual battery cell of the plurality of battery cells based on an algorithm different from EIS, The term "independent determination" means that the determinations made by the two determination steps can be fully made by each step without relying on any determination made by the corresponding other determination step. The method is also used to observe the battery status of the battery system, and includes: Performing state of charge determination (SOC) of each individual battery cell based on EIS, and The state of charge of each individual battery cell is independently determined based on a coulomb counting algorithm.

12. The method according to claim 11, further comprising: Performing cell core temperature determination for each individual battery cell based on EIS, and The cell core temperature of each individual battery cell is independently determined based on an algorithm other than EIS.

13. The method according to claim 11 or 12, wherein: The different algorithm is a model-based cell impedance estimation algorithm.

14. The method of claim 12, wherein the method determines the cell core temperature by a model-based cell core temperature estimation algorithm.

15. The method of claim 13, further comprising performing battery health status monitoring using an aging model independent of an EIS algorithm.

16. The method according to claim 13, wherein: The model-based estimation includes measuring at least one of a current, a voltage, and a temperature of each individual battery cell of the plurality of battery cells of the battery system.

17. The method according to claim 11 or 12, wherein: EIS-based measurements involve exciting the battery cells of a battery system with a sinusoidal current of variable frequency and measuring the voltage response of each cell.

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