Battery Performance Prediction

Abstract

A test system (110) for determining battery performance during development of a battery configuration in a test environment is proposed. The test system (110) comprises at least one communication interface (114) and at least one processing device (122). The test system (110) is configured for receiving operating data indicative of at least one test protocol via the communication interface (114). The test system (110) is configured for receiving battery performance input data via the communication interface (114). The processing device (122) is configured for determining at least one predicted time series of at least one state variable indicative of battery performance based on the battery performance input data and on the operating data using at least one data driven model. The test system (110) is configured for providing at least parts of the predicted time series of the state variable.

Claims

1. A test system for determining battery performance during development of a battery configuration in a test environment, the test system comprising at least one communication interface and at least one processing device, wherein the test system is configured for receiving operating data indicative of at least one test protocol via the communication interface, wherein the test system is configured for receiving battery performance input data via the communication interface, wherein the processing device is configured for determining at least one predicted time series of at least one state variable indicative of battery performance based on the battery performance input data and on the operating data using at least one data driven model, and wherein the test system is configured for providing at least parts of the predicted time series of the state variable.

2. The test system according to claim 1, wherein the data driven model comprises at least one recurrent neural network such as at least one echo state network.

3. The test system according to claim 1, wherein the state variable is derivable from at least one charge-discharge-curve, wherein the state variable is at least one variable selected from the group consisting of: discharge capacity; charge capacity; shape of charge-discharge curve; average voltage; open circuit voltage; differential capacity; coulombic efficiency; and internal resistance.

4. The test system according to claim 1, wherein the operating data indicative of at least one test protocol comprises at least one sequence of different charge cycles and/or discharge cycles.

5. The test system according to claim 1, wherein the data driven model was trained on at least one training dataset, wherein the training dataset comprises time series of historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol.

6. The test system according to claim 1, wherein the processing device is configured for using the test protocol as an input parameter for determining the predicted time series of the state variable with the data driven model, and/or wherein the processing device comprises a plurality of data driven models, wherein the processing device is configured for selecting one of the data driven models for determining the predicted time series of the state variable depending on the test protocol.

7. The test system according to claim 1, wherein the processing device is configured for using the battery performance input data as input parameter for determining the predicted time series of the state variable with the data driven model.

8. The test system according to claim 1, wherein the processing device comprises a plurality of data driven models, wherein the processing device is configured for analyzing the battery performance input data, wherein the analyzing comprises determining at least one material characteristic, wherein at least one of the data driven models is selected based on the material characteristic.

9. The test system according to claim 1, wherein the battery performance input data comprises data generated in response to the test protocol.

10. The test system according to claim 1, wherein the test protocol is predefined.

11. The test system according to claim 1, wherein the data driven model was parametrized based on operating data indicative of the at least one test protocol and battery performance input data.

12. The test system according to claim 11, wherein the data driven model uses knowledge of past and future charge-discharge-cycles following the at least one test protocol to predict future battery performance.

13. The test system according to claim 1, wherein the data driven model has a time memory and/or the data driven model is a time dependent model

14. The test system according to claim 1, wherein the test protocol comprises information about at least one battery performance test, wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles, wherein in the battery performance test discharge-charge curves are determined for each cycle.

15. (canceled)

16. The test system according to claim 1, wherein the battery performance input data comprises one or more of information about discharge capacity; information charge capacity; information about shape of charge-discharge curve; information about average voltage; information about open circuit voltage; information about differential capacity; information about coulombic efficiency; and information about internal resistance.

17. (canceled)

18. A test rig configured for performing at least one battery performance test on at least one battery based on at least one test protocol, wherein the battery performance test comprises at least one sequence of different charge cycles and/or discharge cycles, wherein the battery performance test comprises determining of discharge-charge curves for each cycle, wherein the test rig comprises at least one communication interface configured for providing operating data indicative of the test protocol and battery performance input data to at least one test system according to claim 1.

19. A method for determining at least one data driven model for determining battery performance during development of a battery configuration in a test environment, wherein the method comprises training the data driven model with at least one training data set, wherein the training data set comprises historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol.

20. The method according to claim 19, wherein the method comprises: generating at least one random dynamical reservoir; determining at least one input unit and at least one output unit; generating input-to-reservoir connections and output-to-reservoir connections; selecting at least one input weight matrix and at least one reservoir weight matrix; determining of output weights by training the data driven model with the training data, wherein the output weights are determined using regression analysis.

21. A computer implemented method for determining battery performance during development of a battery configuration in a test environment, wherein in the method at least one test system according to claim 1 is used, the method comprising: a) retrieving operating data indicative of at least one test protocol via at least one communication interface; b) retrieving battery performance input data via the communication interface; c) determining a predicted time series of a state variable indicative of battery performance based on the battery performance input data and on the operating data using a data driven model by using a processing device; d) providing at least parts of the predicted time series of the state variable.

22. A computer program for determining battery performance during development of a battery configuration in a test environment, configured for causing a computer or computer network to perform the method for determining battery performance during development of a battery configuration in a test environment according to the preceding claim, when executed on the computer or computer network, wherein the computer program is configured to perform at least steps a) to d) of the method for determining battery performance during development of a battery configuration in a test environment according to claim 21 the preceding claim.

23. (canceled)

24. (canceled)

Description

In the Figures

[0120] FIG. 1 shows schematically an embodiment of a test system and test rig according to the present invention;

[0121] FIG. 2 shows schematically an embodiment of a method for determining battery performance during development of a battery configuration in a test environment according to the present invention;

[0122] FIG. 3 shows a comparison of experimental results and prediction using the method according to the present invention; and

[0123] FIGS. 4A and 4B shows an exemplary charge-discharge curve and extraction of battery performance input data.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0124] FIG. 1 shows highly schematically an embodiment of a test system 110 for determining battery performance during development of a battery configuration in a test. Exemplary a battery 112 is shown. The battery 112 may be a rechargeable battery. The battery 112 may be selected from the group consisting of: lithium-ion battery (Li—Ion); nickel-cadmium (Ni—Cd); nickel metal-hydride (Ni—MH). For example, the battery may comprises at least one cathode material selected from the group consisting of: LiCoO2 (lithium cobalt oxide); LiNixMnyCozO2 (lithium Nickel-Manganese-Cobalt-oxide) and LiFePO4 (lithium iron phosphate). For example, the battery may comprises at least one anode material selected from the group consisting of: graphite, silicon. For example, the battery may comprises at least one electrolyte selected from the group consisting of: LiPF6, LiBF4 or LiClO4 in an organic solvent, such as ethylene carbonate, dimethyl carbonate, and diethyl carbonate.

[0125] The test system 110 comprises at least one communication interface 114. The test system 110 is configured for receiving battery performance input data of the battery 112 via the communication interface 114. The communication interface 114 may comprise the at least one data storage device configured to store the battery performance input data. The communication interface 114 may specifically provide means for transferring or exchanging information. In particular, the communication interface 114 may provide a data transfer connection, e.g. Bluetooth, NFC, inductive coupling or the like. As an example, the communication interface 114 may be or may comprise at least one port comprising one or more of a network or internet port, a USB-port and a disk drive. The communication interface 114 may be at least one web interface. The communication interface 114 may be or may comprise at least one database selected from the group consisting of: at least one server, at least one server system comprising a plurality of servers, at least one cloud server or cloud computing infrastructure. The communication interface 114 may comprise at least one storage unit configured to store received battery performance input data.

[0126] The battery performance input data may be data comprising information about behavior and/or performance of the battery generated in response to the test protocol. The battery performance input data may comprises data generated in response to at least one test protocol. The battery performance input data may be or may comprise raw data and/or preprocessed data. The battery performance input data may be determined by performing the at least one test program. The test program may comprise determining at least one discharge-charge curve of the battery 112. The test program may be performed in a test rig 116. The battery performance input data may be transferred to the test system 110 via the communication interface 114 in real-time or delayed in a bulk transfer.

[0127] The battery performance input data may comprise discharge-charge cycle data. The discharge-charge cycle data may comprises at least one charge-discharge curve. The battery performance input data may comprise one or more of information about discharge capacity; information charge capacity; information about shape of charge-discharge curve; information about average voltage; information about open circuit voltage; information about differential capacity; information about coulombic efficiency; and information about internal resistance. The battery performance input data may comprise metadata relating to one or more of cathode material and cell set-up. The metadata may be used to select an appropriate trained model, e.g. considering the cathode material and/or cell set-up.

[0128] The test system 110 is configured for receiving operating data indicative of the test protocol via the communication interface 114. The operating data may comprise information about at least one test program performed on the battery 112 in the test rig 116, in particular one or more of operating conditions, order of tests, processes and the like. The test rig 116 may comprise a battery storage device 118 configured to accommodate batteries during test and a cycle machine 120 configured to apply a plurality of charge-discharge cycles to the batteries. The cycle machine 120 may be configured to record the battery performance input data. The operating data indicative of at least one test protocol comprises, for example, at least one sequence of different charge cycles and/or discharge cycles. The test protocol may define an order of test programs and/or sequence of test programs and/or duration of each of the test programs. Each of the test programs may comprise at least one charging-discharging cycle, wherein the charging-discharging cycles of at least two of the test programs differ. The test protocol may comprise information about at least one battery performance test. The battery performance test may comprises at least one sequence of different charge cycles and/or discharge cycles. In the battery performance test discharge-charge curves may be determined for each cycle. The battery performance test may be performed by a customer. The customer may provide the battery performance input data to the test system 110 via the communication interface 114.

[0129] The test system 110 comprises at least one processing device 122. The processing device 122 may be configured for validating the battery performance input data. The processing device 122 is configured for determining at least one predicted time series of at least one state variable indicative of battery performance based on the battery performance input data and on the operating data using at least one data driven model, denoted with reference number 124 in FIG. 1. The validating may comprise determining if the retrieved battery performance input data is complete and/or comprises enough cycles for the determining of the battery performance. The validating may comprises one or more of reviewing, inspecting or testing the battery performance input data. Specifically, the validating comprises determining if the retrieved battery performance input data is complete and/or comprises enough cycles for the determining of the battery performance. For example, in case the validating reveals that the retrieved battery performance input data is not complete and/or comprises not enough cycles for the determining of the battery performance a request may be issued to the customer to provide additional data by using the communication interface 114.

[0130] The state variable may be derived or may be derivable from at least one charge-discharge-curve. The state variable may be at least one variable selected from the group consisting of: discharge capacity; charge capacity; shape of charge-discharge curve; average voltage; open circuit voltage; differential capacity; coulombic efficiency; and internal resistance. The state variables can be used simultaneously.

[0131] The processing device 122 may be configured for selecting information from the battery performance input data depending on the state variable whose future development is to be predicted. The processing device 122 may be configured for ranking the battery performance input data depending on the state variable whose future development is to be predicted. For example, the state variable may be the discharge capacity. The ranking may be as follows: The charge capacity from previous cycles and/or discharge capacity from previous cycles may be considered to be most relevant since they are very close to the state variable which is to be predicted. The shape of charge and/or discharge curves and the internal resistance from previous cycles may be considered to be less relevant than the charge capacity from previous cycles and/or discharge capacity from previous cycles. For other state variables such as the internal resistance the ranking may be different.

[0132] The predicted time series of the state variable may be an expected time series of the state variable determined using the at least one data driven model. The battery performance input data of the battery may be experimental data. The predicted time series of the state variable may be determined, in particular predicted, by using the data driven model, wherein the battery performance input data is used as input for the data driven model. The battery performance input data may comprise experimental data based on which the predicted time series of the state variable is predicted. Moreover, the predicted time series is determined on the operating data. The test system 110 may comprise the at least one storage device, wherein a plurality of different data driven models may be stored. For example, the storage device may comprise different data driven models for different test protocols. In particular, the processing device 122 may be configured for selecting the data driven model based on the test protocol used for the battery test. Additionally or alternatively, the data storage device may comprise different data driven models depending on the state variable to be predicted. The processing device 122 may be configured to determining a predicted time series for one state variable or a plurality of state variables. Time series of any combinations of state variables can also be predicted at the same time.

[0133] The data driven model may be derived from analysis of experimental data. The data driven model may be a machine-learning tool. The data driven model may comprise at least one trained model. For example, the data driven model was trained on at least one training dataset, wherein the training dataset comprises time series of historical data of charge and discharge cycles of at least one known battery configuration and at least one known test protocol. The training dataset may comprise a plurality of experimental results of battery tests, e.g. for one or more of the parameters characterizing the battery performance. The training dataset may comprise a plurality of experimental results of battery tests determined at at least two time points, such as for different cycles. For training of the model a plurality of training datasets may be used, for example relating to different batteries under test.

[0134] The data driven model may be a feature based model, wherein the features or a subset of features are used to predict battery performance. The training data may be used to select the features for the trained model. The feature selection may comprise selecting a subset of relevant features, in particular variables and predictors, for use in the model construction. The feature selection may comprise deriving electrochemical aspects at certain time points such as relevant time points of charge or discharge curve, internal resistance, open circuit voltage, differential capacity. The features may be selected considering irregularities due to material class. The training data may comprise raw data. Feature selection may comprise data aggregation such as per step to per cycle. Features may be selected using electrochemistry knowledge and/or traditional feature-selection methods such as retain features with high correlation with the response. Electrochemistry knowledge may comprise, for example, information about voltage intervals in which interesting phase transitions are expected and thus regions of the curve from which features should be extracted.

[0135] The data driven model may comprise at least one recurrent neural network such as at least one echo state network.

[0136] The processing device 122 may be configured for using the battery performance input data as input parameter for determining the predicted time series of the state variable with the data driven model. For determining the battery performance of a battery 112 under test, the battery performance input data may be processed and applied as input to the data driven model. The processing may comprise data aggregation. The processing may comprise selecting of information relating to at least one of the parameters characterizing the battery performance whose time series is predicted by the data driven model, also denoted as target state variable. The output generated by the data driven model in this case may be the predicted time series of the target state variable or matrix of state variables.

[0137] The processing device 122 may be configured for using the test protocol as an input parameter for determining the predicted time series of the state variable with the data driven model. Specifically, the processing device may be configured for selecting at least one suitable data driven model based on the received battery performance input data such as based on the received information about the test protocol and/or the sequence of different charge cycles and/or discharge cycles. The processing device 122 may comprise a plurality of data driven models, wherein the processing device 122 is configured for selecting one of the data driven models for determining the predicted time series of the state variable depending on the test protocol. For example, each of the plurality of data driven models may be trained on data derived by using a different test protocol. The models may have different fitting parameters and different hyperparameters, which control the complexity. However, the general structure may be the same for all models. Other criteria for selecting the most suitable data driven model can be possible, too. For example, the processing device 122 may comprise a plurality of data driven models, e.g. depending on material characteristic. The processing device 122 may be configured for analyzing the battery performance input data, wherein the analyzing comprises determining at least one material characteristic. The processing device 122 may be configured for selecting at least one of the data driven model based on the material characteristic. The information about the material characteristic may be determined from the battery performance input data such as from the metadata.

[0138] The processing device 122 may be configured for performing at least one confidence test, wherein the determined state variable is compared to experimental test results. In the confidence test the predicted time series of the state variable may be compared to experimental test results. Experimental results for different battery types or material may be stored in at least one data storage and may be used for comparison in the confidence test.

[0139] The test system 110 is configured for providing at least parts of the predicted time series of the state variable. The test system 110 may comprise at least one output interface 126 configured for outputting at least one output comprising information about at least parts of the predicted time series of the state variable. In FIG. 1, the communication interface 114 and the output interface 126 are identical. The output may comprise one or more of at least one histogram showing a development of the state variable in time. The output furthermore may comprise information about at least one prediction of battery lifetime and/or at least one categorization. The test system 110 may be configured for performing at least one measure depending on the predicted time series of the state variable, wherein the measure comprises one or more of: issuing a recommendation; issuing of a warning; issuing an indication that modification of cathode material of the battery is demanded. The test system 110 may be configured for providing at least one information about the determined battery performance to the customer of the battery 112. For example, the information may be provided by the output interface 126 to the customer. The information furthermore may comprise one or more of ranking on batteries in the test rig 116, classification of batteries, classification of cathode material, or at least one recommendation. The output may comprise a ranking on batteries in the test rig 116 and/or recommendations which measurement of batteries can be stopped. The communication interface 114 may be configured to allow monitoring a status of the test rig 116. This may allow preparing and designing next experiment based on the result of the determination of the battery performance.

[0140] An embodiment of a computer implemented method for determining battery performance during development of a battery configuration in a test environment is shown schematically in FIG. 2.

[0141] The method comprises the following steps: [0142] (denoted with reference number 128) retrieving operating data indicative of at least one test protocol via at least one communication interface and retrieving battery performance input data via the communication interface; [0143] (denoted with reference number 130) determining a predicted time series of a state variable indicative of battery performance based on the battery performance input data and on the operating data using the data driven model by using the processing device 122.

[0144] The method may furthermore comprise a step of decision making 132, wherein dependent on the output of test system 110 the customer may decide whether the battery 112 and the used cathode material is considered as “good” or as “bad”. In case the cathode material is considered as “bad” the customer may proceed with changing or amending the cathode material and to start anew with the method step a). Thus, the method steps a) and b) may be performed once or may be performed repeatedly, such as repeated once or several times, in particular for different batteries 112 and/or different cathode materials.

[0145] FIG. 3 shows a comparison of experimental results and prediction using the method according to the present invention. In particular, battery capacity c as a function of the time tin days is depicted for a training data set 134 and for the prediction 136. For the prediction shown here, as input early data 138 was used. A good agreement of the prediction and measurement can be observed. In FIG. 3, the deviation between model and experiment is approximately 2% at 200 cycles in the future, which is about 3 weeks.

[0146] FIGS. 4A shows an exemplary charge-discharge-curve, in particular voltage as a function of capacity, determined in the test rig 116. The charge-discharge-curve can be used for determining several parameters indicative of battery performance such as discharge capacity, denoted with arrow a. Moreover, the voltage drop, denoted with arrows b, is depicted which is used to calculate the internal resistance. In addition, in FIG. 4A average voltage, denoted with c, dQ/dV curve, denoted with d, discharge open circuit voltage, denoted with e, charge open circuit voltage, denoted with f, are shown. FIG. 4A shows further the charge capacity, denoted with g, wherein the thin dashed line denotes the constant charge capacity and the thick dashed line denotes the constant voltage charge capacity.

[0147] FIG. 4B shows an extraction of battery performance input data from experimental data determined from 2130 charge and discharge tests. Specifically, development over time of voltage and current is depicted. The current, thin solid line, is shown to change in a stepwise fashion as constant currents are maintained for most of each charge and discharge step. The double arrows centered on the voltage curve show some of the features that can be used as model inputs. They correspond to the change in capacity over a predefined voltage interval, given by the horizontal lines in FIG. 4B, during charge. The drops in the current correspond to constant-voltage and rest steps, where the battery is allowed to relax to an equilibrium state.

LIST OF REFERENCE NUMBERS

[0148] 110 test system [0149] 112 battery [0150] 114 communication interface [0151] 116 test rig [0152] 118 battery storage device [0153] 120 cycle machine [0154] 122 processing device [0155] 124 data driven model [0156] 126 output interface [0157] 128 retrieving data [0158] 130 determining prediction [0159] 132 decision making [0160] 134 training data set [0161] 136 prediction [0162] 138 early data

REFERENCES

[0163] Kristen A. Severson et al., “Data-driven prediction of battery cycle life before capacity degradation”, Nature Energy, https://doi.org/10.1038/s41560-019-0356-8 [0164] US 2019/0115778 A1 [0165] “A Practical Guide to Applying Echo State Networks”, Mantas Lukosevicius, Published in Neural Networks: Tricks of the Trade 2012. DOI:10.1007/978-3-642-35289-8_36; http://minds.jacobs-university.de/uploads/papers/PracticalESN.pdf [0166] WO 2019/017991 A1