Simulation of a performance of an energy storage

Abstract

A simulation system and a method for simulating a performance of at least one storage unit of an energy storage. The simulation system includes at least one respective model for a respective storage unit of the energy storage. The encoder-decoder model includes at least one recurrent neural network or at least one neural network having a transformer architecture. The encoder processes an encoder input sequence that describes a measured temporal course of current and voltage or of power and voltage of the storage unit assigned to the model. The encoder generates an initial state of the model. The decoder processes a decoder input sequence describing a temporal course to be simulated of the current or of the power of the storage unit. The decoder generates a decoder output sequence that describes a simulated temporal course of the voltage of the storage unit assigned to the model.

Claims

1. A simulation system for simulating a performance of at least one storage unit of an energy storage, wherein the simulation system comprises: at least one respective model for a respective storage unit of the energy storage, the storage unit being assigned to the model, wherein the model includes an encoder-decoder model having an encoder and a decoder, wherein the encoder-decoder model includes at least one recurrent neural network or at least one neural network having a transformer architecture, wherein the encoder is configured for processing an encoder input sequence that describes a measured temporal course of current and voltage, or of power and voltage of the storage unit that is assigned to the model, wherein the processing of the encoder input sequence includes generating an initial state of the model from the encoder input sequence, and wherein the decoder is configured for processing a decoder input sequence while starting from the initial state of the model, the decoder input sequence describing a temporal course that is to be simulated of the current or of the power of the storage unit that is assigned to the model, wherein the processing of the decoder input sequence includes generating a decoder output sequence from the decoder input sequence while starting from the initial state of the model, the decoder output sequence describing a simulated temporal course of the voltage of the storage unit that is assigned to the model.

2. The simulation system of claim 1, wherein the encoder input sequence describes the measured temporal course of current and voltage or of power and voltage of the storage unit assigned to the model and a measured temporal course of a temperature, and wherein the decoder input sequence describes the temporal course to be simulated of the current or of the power of the storage unit assigned to the model and a temporal course to be simulated of a temperature.

3. The simulation system of claim 1, wherein at least one of the respective encoder or the respective decoder is a recurrent neural network having an Long Short-Term Memory Network (LSTM) architecture or a Gated Recurrent Unit (GRU) architecture, or is a Convolutional Neural Network (CNN).

4. The simulation system of claim 1, wherein the simulation system is configured for predicting whether the respective storage unit of the energy storage fulfills a load scenario, wherein the predicting comprises: processing a respective encoder input sequence by the encoder of the model to which the respective storage unit is assigned, wherein the encoder input sequence describes a last measured temporal course of current and voltage or of power and voltage of the storage unit assigned to the model, wherein the processing of the encoder input sequence comprises generating an initial state of the model from the encoder input sequence; and, starting from the initial state of the model, processing a respective decoder input sequence by the decoder of the model, to which the respective storage unit is assigned, wherein the decoder input sequence in accordance with the load scenario describes a temporal course to be simulated of the current or of the power of the storage unit assigned to the model, wherein the processing of the decoder input sequence comprises, while starting from the initial state of the model, generating a decoder output sequence from the decoder input sequence, wherein the decoder output sequence describes a simulated temporal course of the voltage of the storage unit assigned to the model; and checking whether the generated decoder output sequence fulfills the load scenario, and if the decoder output sequence fulfills the load scenario, predicting that the load scenario can be fulfilled, and if the decoder output sequence does not fulfill the load scenario, predicting that the load scenario cannot be fulfilled.

5. The simulation system of claim 1, wherein the simulation system is configured for estimating a state parameter that indicates a state of the respective storage unit of the energy storage, wherein the estimating of the state parameter includes: processing a respective encoder input sequence by the encoder of the model to which the respective storage unit is assigned, wherein the encoder input sequence describes a last measured temporal course of current and voltage or of power and voltage of the storage unit assigned to the model, wherein the processing of the encoder input sequence includes generating an initial state of the model from the encoder input sequence; and, starting from the initial state of the model, processing a respective decoder input sequence by the decoder of the model to which the respective storage is assigned, wherein the decoder input sequence describes a temporal course to be simulated of the current or of the power of the storage unit assigned to the model, wherein the processing of the decoder input sequence includes, while starting from the initial state of the model, generating a decoder output sequence from the decoder input sequence, wherein the decoder output sequence describes a simulated temporal course of the voltage of the storage unit assigned to the model; and determining an estimated value of the state parameter based on the temporal course to be simulated of the current or of the power and based on the associated simulated course of the voltage.

6. The simulation system of claim 1, wherein the simulation system is configured for comparing the simulated temporal course of the voltage to a further measured temporal course of the voltage of the storage unit assigned to the model, and for determining an indicator of a rating of the model from the result of the comparison.

7. The simulation system of claim 1, wherein the simulation system is configured for training the respective model based on training data, wherein training data for the respective model include a plurality of training sequences, wherein a respective training sequence describes a measured temporal course of current and voltage or of power and voltage of the storage unit assigned to the model, wherein the training of the respective model comprises, for a respective training sequence: processing an encoder input sequence by the encoder, wherein the encoder input sequence corresponds to a first section of the training sequence and describes a measured temporal course of current and voltage or of power and voltage of the storage unit assigned to the model, wherein the processing of the encoder input sequence includes generating an initial state of the model from the encoder input sequence; starting from the initial state of the model, processing a decoder input sequence by the decoder, wherein the decoder input sequence corresponds to a second section of the training sequence and describes a measured temporal course of the current or of the power of the storage unit assigned to the model, wherein the processing of the decoder input sequence includes, while starting from the initial state of the model, generating a decoder output sequence from the decoder input sequence, wherein the decoder output sequence describes a simulated temporal course of the voltage of the storage unit assigned to the model; and adapting the model based on deviations of the generated decoder output sequence from a temporal course of the voltage according to the second section of the training sequence.

8. An energy storage management system for an energy storage having at least one storage unit, the system comprising a data storage for storing a respective measured temporal course of current and voltage or of power and voltage of the respective storage unit, characterized in that the energy storage management system further comprises a simulation system according to claim 1 for simulating a performance of the respective storage unit of the energy storage.

9. A method for simulating a performance of at least one storage unit of an energy storage by a simulation system that includes at least one respective model for a respective storage unit of the energy storage, the storage unit being assigned to the model, wherein the model includes an encoder-decoder model having an encoder and a decoder, wherein the encoder-decoder model includes at least one recurrent neural network or at least one neural network having a transformer architecture, wherein the method includes the steps of: processing an encoder input sequence by the encoder and generating an initial state of the model, wherein the encoder input sequence describes a measured temporal course of current and voltage or of power and voltage of the storage unit assigned to the model; and processing a decoder input sequence by the decoder and generating a decoder output sequence, wherein the decoder input sequence describes a temporal course to be simulated of the current or of the power of the storage unit assigned to the model, and wherein the decoder output sequence describes a simulated temporal course of the voltage of the storage unit assigned to the model.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In the following, preferred embodiment examples will be further explained on the basis of the Figures.

[0051] FIG. 1 shows a schematical representation of an energy storage management system having a simulation system according to an embodiment.

[0052] FIG. 2 shows a schematic representation of the using of training sequences for training the simulation system.

[0053] FIG. 3 shows a schematic representation of current-voltage courses.

[0054] FIG. 4 shows a schematic representation of current-voltage courses and of a simulation accuracy (rating of the simulation).

DETAILED DESCRIPTION

[0055] FIG. 1 schematically shows an energy storage management system 100 and an electrochemical energy storage 200 having a plurality of storage units 210. For example, the storage units 210 may be storage cells or storage modules. The energy storage management system 100 includes a memory (data storage) 110 for storing measured values of a voltage U, a current I, a temperature T and, if applicable, a pressure p of the respective storage units 210. In the following, the usage of values of the current I is described, for example, for input sequences and for courses to be simulated. However, the invention is not limited to this. For example, instead of the current I, the power P may be used.

[0056] The energy storage management system 100 includes a simulation system 300, which includes for each storage unit 210 a respective assigned model 310. For reasons of clarity, one model 310 is exemplarily shown in FIG. 1.

[0057] The model 310 includes an encoder-decoder model 312 for processing input sequences 320 or input vectors, which are also called an input or input features. The encoder-decoder model 312 generates output sequences 340 or output vectors, which are also called an output or output features. The encoder-decoder model 312 includes a first encoder layer 330 and a second encoder layer 332, which generate a model state 334 by processing an encoder input sequence 322. The model 310 further comprises a first decoder layer 336 and a second decoder layer 338. The first decoder layer 336 processes a decoder input sequence 324. The second decoder layer 338 generates a decoder output sequence 342. For example, the layers 330, 332, 336, 338 may be recurrent neural networks.

[0058] Each of the encoder input sequences 322 respectively describes a measured temporal course of current I and voltage U of the storage unit 210 assigned to the model 310. The input sequences 322 may be selected from the measured values stored in the memory 110 and/or may be interpolated from the stored measured values. An initial model state 334 is generated by processing the encoder input sequence 322. While stepwise processing the encoder input sequence 322, the encoder-decoder model 312 stepwise updates its state. The model 310 is brought into the initial state 334, which corresponds to a state of the storage unit 210, which storage unit is to be simulated.

[0059] Depending on the application, the decoder input sequences 324 describe a measured temporal course of the current I of the storage unit 210 assigned to the model 310, or a course 400 to be simulated of the current I of the storage unit 210 assigned to the model 310. For example, the course 400 of the current Ito be simulated may be defined by a load scenario 410 which is input into the energy storage management system 100 as an input.

[0060] In a simulation run, the model 310 processes the decoder input sequence 324 and generates a decoder output sequence 342, wherein, initially, the model 310 has the initial state 334. While stepwise processing the decoder input sequence 324, the decoder output sequence 342 is stepwise generated, wherein the encoder-decoder model 312 stepwise updates its state, wherein in a respective step, the encoder-decoder model 312 processes a current (electrical current) value, the encoder-decoder model 312 updates its state, and the encoder-decoder model 312 generates a voltage value of the decoder output sequence 342. For example, this makes it possible to perform a simulation of a discharging by a defined current and to output a value for this discharging process for this discharging process (SoHic). An example thereof is shown in FIG. 3.

[0061] Depending on the application, for example, an output 340 of the model 310 may include an estimated value 420 of a state parameter of a respective storage unit 210 of the energy storage 200, or a prediction 430 about whether the load scenario 410 is fulfillable by the respective storage unit 210 or, respectively, by the energy storage 200 in its present condition. Voltage values of the output 340 that are output may also include mean values and standard deviation of the voltage. Furthermore, an output 340 may include an indicator 440 of a rating of the respective model 310 (model accuracy). It is possible to evaluate the model accuracy through correlating the simulated values and the subsequently actually measured values. For example, this may be done through a correlation, the coefficient of determination R.sup.2, or the mean deviation. By examining the error, it is furthermore possible to evaluate weaknesses of the model 310 and to indicate estimated uncertainties for the simulated values. This is exemplarily shown in the diagrams of FIG. 4. Apart from the values of the current I or the current I and voltage U, the respective input sequences 320 may additionally also describe a course of the temperature T and, if applicable, a course of the mechanical pressure p for the respective storage unit 210. Accordingly, a respective simulated course 400 or a load scenario 410 may also include values for the temperature T and/or the pressure p.

[0062] In a first step, the respective model 310 is trained for the corresponding storage unit 210 or storage cell. For this, historical data of voltage U, temperature T, current I and pressure p are used. FIG. 2 schematically shows the usage of training sequences 500, based on the stored measured data of the memory 110. Here, only voltage U and current I are exemplarily shown; however, temperature T and/or pressure p may be handled in the same manner as the current I. The respective training sequence 500 is divided into a first section 510 and a second section 520. An encoder input sequence 322 corresponds to the first section 510 and describes, in particular, the measured temporal course of current I and voltage U of the first section 510. The encoder/decoder ansatz (that is, approach) determines the initial state 334 (battery state) of the battery from, for example, the data (voltage U, current I and temperature T, and pressure p, where applicable) of the past 60 minutes, for example, corresponding to the first section 510. The initial state 434 is represented by a mathematical representation (corresponding to a matrix or a vector having a plurality of values), which cannot necessarily be physically interpreted. For example, this state 344 may be found or optimized through recursive neural networks (RNN) such as LSTM, GRU and others of the layers 330, 332.

[0063] Subsequently, this initial state 334 is used as an input value for the decoder 336, 338 (also in the form of RNN). The decoder 336 simulates the resulting voltage profile on the basis of the internal state and the expected current profile or temperature profile in accordance with an encoder input sequence 324. The decoder input sequence 324 is obtained from the second section 520 of the training sequence 500 and describes the measured temporal course of the current I in the second section 520 (as well as temperature T and pressure p, where applicable). A comparative sequence 526 is obtained from the second section 520 and describes the measured temporal course of the voltage U in the second section 520. A feedback unit 530 compares the comparative sequence to the output sequence 342 generated by the model 310, and the model 310 is adapted in accordance with the determined deviation.

[0064] For example, the trained simulation system 300 may be used for estimating a state variable of the storage unit 210. The left part of FIG. 3 exemplarily and schematically shows a temporal course of a current I (as a thin line), a measured voltage U (as dots) and a simulated voltage U (as a thick line) for a storage unit 210. In the example of FIG. 3, a discharging by a defined current of 2 A is simulated. Both the measured voltage and the simulated voltage decrease over time and finally fall below the end-point voltage. Based on this, for example, the simulation system 300 may determine a state parameter SoH.sub.1C.

[0065] It is furthermore possible to continuously (or successively) train each model 310 using the (newly) measured data of its corresponding cell or storage unit 210. Thus, a precise digital representation of this cell or storage unit 210 is obtained, and precise model predictions and a statement about the individual desired battery state may be made on the basis thereof. By the gained knowledge, a user is enabled to adapt operation strategies and maintenance intervals to the battery state and the system state, and to actively react to deviations from the expected behavior. The right part of FIG. 3 schematically shows both the measured voltage (as a continuous line) in volts and the simulated voltage (as dots) in volts over the measured voltage in volts. It is found that there is a good correspondence.

[0066] The left part of FIG. 4 schematically shows a diagram of current and voltage courses, corresponding to the diagram of FIG. 3, for an example of a load scenario having a varying current I. The right part of FIG. 4 shows in mV a temporal course of the relative error of the simulated voltage as opposed to the measured voltage. The result of the comparison allows to draw conclusions about the accuracy of the model 310 and to selectively train the model 310 in determined regions of a larger deviation. By a specific training in these regions of larger deviation, an error that is obtained in these regions can be minimized.