Methods for determining the health status and quantifying health-related differences of batteries

Abstract

The invention relates to a method for determining the health status of batteries, wherein a number (n) of distributions is determined from a plurality of measured variables and a plurality of aging variables influencing the batteries, wherein for each distribution a mean value (µ), two difference values (σ) and a difference (s i ) between a maximum value ( yimax ) and a minimum value ( yimin ) is determined, whereby the state variable (SOHI) is calculated using the following formula: SOHI : = 1 n ∑ i = 1 n ( max k σ i , k min k σ i , ksi ) 2 , SOHI E [0; ∞), provided that the mean value (µ) is not equal to the maximum value ( yimax ) and the minimum value ( yimin ) where the state variable (SOHI) takes the value 0 if all maximum values ( yimax ) and minimum values ( yimin ) a distribution, and where the state variable (SOHI) takes the value ∞ if any of the difference values (σ) of a distribution is equal to their difference (s i ) is.

Description

[0001] The invention relates to a method for determining the state of health and related differences of interconnected batteries according to the preamble of claim 1.

[0002] Over time, different aging states can develop among the packs in battery systems with multiple packs. In addition to the fact that SOH determination is often error-prone, SOH is not a comprehensive indicator of the battery's state of health, as it only reflects capacity loss. As a result, certain anomalies that provide information about the pack's proper function are not detectable and can easily be overlooked during maintenance. Furthermore, anomalies are often determined on a pack-by-pack basis and therefore do not utilize a holistic view of the battery system to assess pack functionality / health.

[0003] DE 10 2022 122 597 A1 describes a system for monitoring a battery of a vehicle, comprising a processor and a memory storing instructions that, when executed by the processor, configure the processor to receive first characteristics containing statistics about internal resistances of a plurality of cell groups in a battery pack of the battery, calculate second characteristics for the battery pack based on the first characteristics, determine whether the battery pack is faulty depending on one or more of the second characteristics, and in response to the battery pack being faulty, determine whether one or more of the cell groups are faulty depending on one or more of the first characteristics.

[0004] The invention is based on the object of providing a novel method for determining the health status of batteries.

[0005] The object is achieved according to the invention by a method for determining the health status of batteries having the features of claim 1.

[0006] Advantageous embodiments of the invention are the subject of the subclaims.

[0007] A method for determining differences in the state of health of batteries is proposed, wherein a number n of distributions, including a distribution of a relaxation time after simultaneous shutdown of several battery packs, battery modules or battery cells in a system, a voltage distribution after simultaneous shutdown plus a relaxation time, a distribution of the voltage change to the current change, a current distribution of connected batteries at low total current, a current distribution of connected batteries at high total current and a distribution of the temperature change at high total current, is determined from a plurality of measured variables, including time, voltage, current and temperature of several batteries, and a plurality of aging variables influencing the batteries, including an impedance, an internal resistance, a capacity and an open circuit voltage of the batteries.According to the invention, a mean value, two difference values σ and a difference s between a maximum value and a minimum value are determined for each distribution, whereby a state variable SOHl is calculated using the following formula:. SOHI:=1n∑i=1n(maxkσi,kminkσi,ksi)2, SOHI ∈ [0; ∞), provided that the mean value is not equal to the maximum value and the minimum value, where the state variable SOHI takes the value 0 if all maximum and minimum values of a distribution are equal, and where the state variable SOHI takes the value ∞ if any of the difference values σ of a distribution is equal to their difference s.

[0008] The present invention introduces a new state variable (“State of Health Inconsistency” - SOHI)), which is calculated exclusively based on measured variables and can be defined at the system level or pack level, i.e., it describes, for example, the SOHI of a pack with multiple cells or a vehicle with multiple packs. For this purpose, distributions are determined from various measured variables in different situations, which provide information about differences in the health of the packs. The parameters of these distributions (variance, mean, etc.) are then used in a formula to calculate the SOHI. The SOHI thus describes the extent of the variance in the aging state (age-relevant variables: capacitance, impedance, OCV, etc.) among the packs of a system. It can be assumed that the SOHI naturally increases somewhat with the progress of aging; however, above a certain SOHI threshold, this can also be an indicator of a system anomaly.

[0009] By collecting a large number of SOHls from different systems over time, it is possible to determine which SOHI most systems are located at, depending on the system age. The frequency distributions can be used to calculate the limits for anomaly intervals, i.e., the SOHl above which an anomaly is diagnosed for the system. This information can be used, for example, to provide a note for reviewing this system after a sale. Furthermore, the distributions underlying the SOHI can be used to draw conclusions about which pack(s) are conspicuous. The method proposed here does not determine pack anomalies based on individual pack observations, but uses the aggregate of all packs to identify outliers or significant differences between packs, and can therefore be more reliable than pack-specific anomaly detection.Because the SOHI is based on measured variables and thus defined independently of SOH, it can also be used as a corrective in the SOH determination and also contributes to obtaining a more comprehensive picture of the health status of the system.

[0010] Embodiments of the invention are explained in more detail below with reference to drawings.

[0011] Showing: Fig. 1 a schematic view of a method for determining a state variable to describe the state of health or an inconsistency in the state of health of batteries, Fig. 2 schematically shows a distribution of a quantity, Fig. 3 a schematic diagram of the state variable and a difference of a maximum health state over time in a simulation, Fig.4 a schematic diagram of a distribution of a frequency of the state variable in a vehicle fleet, and Fig. 5 schematically shows the tracking of the state variable of a vehicle over time.

[0012] Corresponding parts are provided with the same reference numerals in all figures.

[0013] Fig. 1 is a schematic view of a method for determining a state variable SOHI to describe the state of health or an inconsistency in the state of health of batteries.

[0014] From a plurality of measured variables, including time t, voltage V, current I and temperature T of high-voltage batteries, and a plurality of influencing aging variables, including an impedance Z (comprising an internal resistance IR) of the battery, a capacity Q of the battery and an open circuit voltage OCV of the battery, a number n of components of the state variable SOHl is determined, including a distribution of a relaxation period VR after simultaneous shutdown of several battery packs, battery modules or battery cells in a system, a voltage distribution SV after simultaneous shutdown (plus a relaxation time), a distribution of the voltage change to the current change VSS, a current distribution of connected high-voltage batteries at low total current SVHVBL, a current distribution of connected high-voltage batteries at high total current SVHVBH and a distribution of the temperature change VT of the high-voltage batteries at high total current.

[0015] The impedance Z describes the internal resistance IR of the battery and its diffusion kinetic reactance.

[0016] The system may, for example, be a vehicle, in particular a commercial vehicle, a bus or a passenger car.

[0017] In particular, the distribution of the relaxation time VR after simultaneous shutdown can be determined based on the time t, influenced by the impedance Z. The voltage distribution SV after simultaneous shutdown (+relaxation time) describes the open circuit voltages OCV and can thus be determined based on the voltage V, which is influenced by the state of charge SOC, the course of which is influenced by the capacitance Q. The distribution of the voltage change to the current change VSS can be determined based on the voltage V and the current I, which represent the internal resistance IR. The current distribution of connected high-voltage batteries with a low total current SVHVBL can be determined based on the current I, influenced by the open circuit voltage OCV and by the state of charge SOC, the course of which is influenced by the capacitance Q.The current distribution of connected high-voltage batteries at high total current SVHVBH can be determined using the current I, which is influenced by the internal resistance IR. The distribution of the temperature change of the high-voltage batteries VT can be determined using the temperature T, which is influenced by the internal resistance IR.

[0018] The distribution can contain the median or mean µ i and a measure of the deviation from the mean µ i , for example a difference s i from a maximum value yimax and a minimum value yimin of the samples and / or the difference values σ i,1 , σ i,2 and / or variance.

[0019] This results in a number n of distributions, each with a mean value µ i or median and a measure of the deviation from the mean µ i .

[0020] Table 1 describes normalization variables (situational influences on the dispersion of the distributions) as well as stopping criteria for the components of the state variable SOHI. ingredient Standardization values Stop criteria SV - Total Ah throughput since last full charge, - Percentage of high-current phases since last full charge - One or more high-voltage batteries could not be fully charged, - One or more high-voltage batteries are / were switched off VR - One or more high-voltage batteries are switched off SVHVBL - Total current, - Total Ah throughput since last full charge, - Percentage of high-current phases since last full charge - One or more high-voltage batteries could not be fully charged, - One or more high-voltage batteries are / were switched off, - one of the pack currents is greater than a limit value, for example 5A. SVHVBH Total current - One or more high-voltage batteries are switched off, - one of the pack currents is less than a limit value, for example 5A. VT - Total current, - Ambient temperature (per high-voltage battery) - One or more high-voltage batteries are switched off,

[0021] Stop criteria refer to conditions under which the respective distribution should no longer be determined. If this is the case, the respective distribution should be ignored as a component of the state variable SOHI for this determination cycle. Thus, the use of a distribution is stopped for that point in time.

[0022] Latency is the time required to capture a distribution due to the noise nature of the sensor signals. Therefore, the respective signal should be averaged over a certain period of time, for example, ten times the cycle time. The cycle time refers to the signal update frequency and is, for example, in a range of 10 ms to 1000 ms.

[0023] To determine the distribution VSS of the voltage change to the current change ΔUΔi an ohmic component R Ohm The internal resistance IR can be calculated directly from measurement signals by calculating the ratio of voltage U to current I in a time series. The result is a noisy vector that can be averaged to calculate the internal resistance IR. To increase accuracy, it is recommended to use a filter to remove data points with small current changes Δi from the calculation, since the sensitivity of the internal resistance IR increases with large current changes Δi. ROhm=U(t+1)−U(t)i(t+1)−i(t)

[0024] Alternatively, the total internal resistance IR per high-voltage battery can be determined after simultaneous shutdown. IR=U(trelaxed)−U(tloaded)i(trelaxed)−i(tloaded)

[0025] The advantage here is that the total internal resistance IR is considered.

[0026] Fig. 2 schematically illustrates a distribution of a quantity.

[0027] Each distribution can be described by its mean µ i and a measure of the deviation from the mean µ i , for example by the difference values σ i,1 , σ i,2 and / or a minimum value yimax and a maximum value yimax or their difference s i describe.

[0028] The following vectors are exemplary 5-dimensional vectors and thus describe five distributions. The index i represents the i-th distribution. μ=[μ1μ2μ3μ4μ5],s=[s1s2s3s4s5],σ=[σ1,1σ1,2σ2,1σ2,2σ3,1σ3,2σ4,1σ4,2σ5,1σ5,2],σi,k∈[0;10),si∈[0;10]

[0029] In one example, there are three battery packs and 15 min, 20 min and 25 min were recorded as relaxation times: yimin=15min; 15 min; yimax=25min, 25 min, µ i = 20 min; σ i,1 = 5 min; σ i,2 = 5 min; s i = 10 min.

[0030] The index k describes the number of columns of the σ-vector (=2), as it consists of two parts (max-µ and µ-min). The equation is formulated such that the larger of the two σ-values is in the numerator for each distribution, so the maximum (or the minimum in the denominator) is calculated using the index k. The index n describes the number n of distributions used for the SOHl calculation. The Fig. Not all of the six distributions shown in Figure 1 necessarily have to be used.

[0031] A vehicle can be uniquely defined in terms of the state variable SOHI by all elements of the s-vector and by the first column of the σ-vector (the second column results from both, since s i = σ i,1 + σ i,2 ). Therefore, the vehicle can not only be tracked via the state variable SOHI, but can also be defined per time step via an n*2 - dimensional point (in the present example it is 5*2 = 10).

[0032] This allows a clear definition of the state variable SOHI of the vehicle in time t and tracking in 10-dimensional space.

[0033] The state variable SOHI can be calculated from a number n distributions.

[0034] For µ i ≠ y min / max SOHI:=1n∑i=1n(maxkσi,kminkσi,ksi)2,SOHI∈[0;∞) with n: number of distributions

[0035] When determining the state variable SOHI, a moving average of the vehicle SOHl over time t can be performed.

[0036] A temporally asynchronous substitution of individual distributions may be necessary to determine SOHI.

[0037] The state variable SOHI can be used for any components connected in parallel: - Battery packs for viewing anomalies at vehicle level, - Battery modules for viewing anomalies at pack level, - Battery cells for monitoring anomalies at pack / module level (predictive thermal runaway detection).

[0038] Fig. Figure 3 is a schematic diagram of the state variable SOHI and a difference in a maximum state of health Max_SOH_Difference over time t in a simulation in which four battery packs were operated in parallel. In this setup, the state variable SOHI decreases over time t. The difference in the maximum state of health Max_SOH_Difference also decreases over time t, since the older batteries or battery packs are subjected to a lower current load due to their higher internal resistance IR. Older batteries or battery packs age more slowly than newer batteries or battery packs. The aging rate of a battery depends on many factors. One effect results from the internal resistance-related current distribution among the batteries connected in parallel. The higher the internal resistance IR, the lower its percentage share of the total current.Assuming that the internal resistance IR increases analogously with decreasing health status SOH, the model predicts that batteries with a lower health status SOH and / or higher internal resistance IR slow down the aging process due to lower current load, while batteries with a lower internal resistance IR age faster. The health statuses SOH among the packs therefore converge over time t, which is why the state variable SOHI decreases.

[0039] It is believed that battery problems at the vehicle level lead to high differences in the SOH health status among batteries. Such battery problems can include: - Connection problems (contactor, pre-charging), - Shutdowns due to software detection (faulty sensors, communication problems, etc.)

[0040] If the high-voltage batteries are switched off more frequently, this results in lower energy throughput. If there is a high self-discharge rate and / or high leakage currents, this results in higher energy throughput. If there are problems with load distribution (balancing), this results in a higher depth of discharge (DOD) in some battery packs, battery modules, or battery cells. Design defects in the cooling circuit lead to different temperatures T of the battery packs, battery modules, or battery cells. Abnormal dendrite growth leads to a higher internal resistance IR and / or lower capacity of the battery packs, battery modules, or battery cells. These aging stress factors result in different states of health SOH.

[0041] Fig. Figure 4 is a schematic diagram of a distribution of a frequency H of the state variable SOHI in a vehicle fleet over its respective history based on field data.

[0042] A first distribution VT1 describes the SOHI distribution of a vehicle fleet (one SOHI per vehicle) consisting of vehicles with relatively new batteries. Most vehicles will have a SOHI close to 0, since the aging variance among the battery packs is only caused by initial variances from the battery manufacturing process and storage conditions. However, as the vehicles are used in the field, the distribution will shift to the right, toward larger SOHIs (second distribution VT2), due to additional variances in the operating conditions and potential battery failures, which only become apparent in the aging state over time t.

[0043] An acceptance range ANB and a rejection range ALB can be defined from the distributions, allowing abnormally high SOHI to be detected. A threshold value ε(t), ε(t1), ε(t2) between the acceptance range ANB and the rejection range ALB is therefore time-dependent, as the distributions shift to the right with battery age. Thus, the threshold value ε(t), ε(t1), ε(t2) for what is considered abnormal is not constant. To increase the stability of anomaly detection, it is advisable not to diagnose an anomaly at the first SOHI detected that is greater than the threshold value ε(t), ε(t1), ε(t2). Instead, this anomaly should be confirmed based on several consecutive time points.

[0044] Furthermore, it is assumed that a chi-square distribution (mostly close to 0, few outliers in the positive direction) can be applied for new batteries, which shifts to a normal distribution with battery age.

[0045] The setting condition for an anomaly of the state variable SOHI can be: SOHI(t) > ε(t) for several consecutive determination times.

[0046] Fig.Figure 5 schematically shows the tracking of the state variable SOHI of a vehicle over time t. Such a representation could, for example, be shown in a dashboard. Circles mean that the state variable SOHI of the vehicle is in the acceptance range ANB, triangles mean a certain proximity to the rejection range ALB, and diamonds indicate a diagnosed anomaly at the respective point in time, i.e., that the state variable SOHI lies in the rejection range ALB. Since the threshold ε(t), ε(t1), ε(t2) between acceptance and rejection is time-dynamic, a certain value of the state variable SOHI can be recognized as an anomaly at an earlier point in time, but no longer at a later point in time. If anomaly detections on the vehicle become frequent, it is recommended to have the vehicle inspected at the workshop. In this case, a corresponding message can be displayed in the vehicle.

[0047] For example, if a diagnosed anomaly is detected, a notification can be given to the workshop at the next scheduled inspection.

[0048] The note to the workshop may include information about which high-voltage battery needs to be checked. The following test points may be included: • Switching on and / or off behavior of the high-voltage battery, • Difference between minimum and maximum cell voltage after complete discharge, • Functionality of temperature control, • Self-discharge rate. List of reference symbols ALB rejection area ANB acceptance area H Frequency I Current IR internal resistance Max_SOH_Difference Difference of the maximum health state OCV open circuit voltage Q Capacity s i difference SOHI state variable SV voltage distribution SVHVBH Power distribution of connected high-voltage batteries at high total current SVHVBL Power distribution of connected high-voltage batteries at low total current t time T Temperature V voltage VR distribution of a relaxation duration VSS Distribution of voltage change to current change VT distribution of the temperature change of a coolant VT1 first distribution VT2 second distribution maximum value minimum value Z Impedance ε(t1), ε(t2) limit µ i mean σ i,1 , σ i,2 Distribution parameter, difference value QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] DE 10 2022 122 597 A1

[0003]

Claims

[1] Method for describing the state of health of batteries, wherein a number (n) of distributions, including a distribution of a relaxation period (VR) after simultaneous shutdown of several battery packs, battery modules or battery cells in a system, a voltage distribution (SV) after simultaneous shutdown plus a relaxation time, a distribution of the voltage change to the current change (VSS), a current distribution of connected batteries at low total current (SVHVBL), a current distribution of connected batteries at high total current (SVHVBH) and a distribution of the temperature change (VT) at high total current, are determined from a plurality of measured variables, including time (t), voltage (V), current (I) and temperature (T) of several batteries, and a plurality of aging variables influencing the batteries, including an impedance (Z), an internal resistance (IR), a capacity (Q) and an open circuit voltage (OCV) of the batteries,is determined, characterized by that for each distribution a mean (µ), two difference values (σ) and a difference (s i ) between a maximum value (yimax) and a minimum value (yimin) be determined, whereby a state variable (SOHI) is calculated using the following formula: SOHI :=1n∑i=1n(maxkσi,kminkσi,ksi)2, SOHI [0; ∞), provided that the mean value (µ) is not equal to the maximum value (yimax) and the minimum value (yimin) where the state variable (SOHI) takes the value 0 when all maximum values (yimax) and minimum values (yimin) a distribution, and where the state variable (SOHI) takes the value ∞ if any of the difference values (σ) of a distribution is equal to their difference (s i ) is. [2] Method according to claim 1, characterized bythat a moving average of the state variable (SOHI) is carried out over time (t). [3] Method according to claim 1 or 2, characterized by that the state variable (SOHI) is used for parallel connected battery packs, battery modules and / or battery cells. [4] Method according to one of the preceding claims, characterized by that an ageing-related anomaly of the battery is determined based on the state variable (SOHI). [5] Method according to one of the preceding claims, characterized by that an acceptance region (ANB) and a rejection region (ALB) with an intermediate time-dependent limit value (ε(t), ε(t1), ε(t2)) are defined for the state variable (SOHI). [6] Method according to claim 5, characterized bythat a tracking of the state variable (SOHI) of at least one high-voltage battery of the vehicle or its components is shown over time (t) on a display in a vehicle, whereby upon detection of a high-voltage battery or component in the rejection area (ALB), a note is issued to the workshop at the next upcoming inspection or a note is issued in the vehicle.

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