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@PHDTHESIS{Jin:998435,
author = {Jin, Limei},
othercontributors = {Granwehr, Josef and Ponci, Ferdinanda and Lüchow, Arne},
title = {{AI}-based simulation of battery system combined with
advanced spectroscopy},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-11389},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2025; Dissertation, RWTH Aachen University, 2024},
abstract = {The surge in electric vehicle adoption necessitates the
development of high-performance and reliable battery
systems. To optimize battery performance and ensure their
safe and efficient operation, the Battery Management System
(BMS) plays a crucial role. While BMS is commonly utilized
at the pack level to oversee the overall health and
performance of a battery pack, the recognition of each
individual cell with unique operational characteristics
within the pack needs more granular and precise management
and monitoring at the cell level. Therefore, battery digital
twin, which is a real-time, multi-scale virtual
representation of the physical battery cell, comes into
view. We aim to develop a simplest possible equivalent
circuit model-based digital twin. It is designed to
accurately reflect the behavior of the actual battery cell
under various load conditions. The virtual battery model
serves to simulate these conditions and to obtain a wealth
of simulated data accordingly. These large amount of data
can then be employed by machine learning algorithms to
predict the battery's state, providing optimization feedback
to enhance the real battery's operation.Equivalent Circuit
Modeling (ECM) of Electrochemical Impedance Spectroscopy
(EIS) data is a common technique to describe the current
state of batteries and can be used as a virtual battery. To
characterize ECM parameters in a data supported way, the
Gaussian Process Regression (GPR) based Distribution of
Relaxation Times (DRT) technique, which simplify EIS data by
deconvolution with a suitable kernel, is used to provide the
number of distinguishable features based solely on an
individual EIS data set. Here a weighted DRT is employed,
where weights are tailored based on the frequency-dependent
sensitivity of the data, and GPR is utilized to accurately
determine these weights. GPR is capable of estimating an
suitable weighting matrix from a single data set,
potentially enabling automatized DRT inversion without user
intervention. The obtained DRT spectrum is then used for the
selection of an equivalent circuit model, its initial
parametrization, and setting of constraints. Thereby, the
ECM parameters that are fitted to experimental EIS data for
a single cycle, vary by the State of Charge (SOC).
Eventually, by means of the investigated battery it is
discussed that using a combination of DRT and ECM, a more
physically relevant description of processes in an
electrochemical system can be achieved.Unlike SOC,
developing a robust and continuous State of Health (SOH)
estimation using EIS to build battery digital twins poses a
formidable challenge. To bridge the gap between the
experiment with data driven techniques that do not rely on
fitting of experimental data using a priori models, ECM
parameters over a single cycle are expanded in a
high-dimensional Chebyshev space. It facilitates not only a
mapping of the SOC dependence with robust boundary
conditions, but also an extension towards a more abstract
SOH description is possible. Due to the long impedance
measurement time, the Quasi Monte Carlo (QMC) method can be
employed to generate differently aged battery models with
limited experimental impedance data. As data becomes
available, the space spanning the possible states of a
battery can be gradually refined. The developed framework,
therefore, allows for the training of big data models
starting with very little experimental information and
assuming random fluctuations of the model parameters
consistent with available data. Battery data, whether
obtained through direct measurement or simulation, is
inherently complex and high-dimensional. Therefore,
detecting anomalies or deciphering the aging pathways from
raw data poses significant challenges. To harness effective
battery state management, we propose to extract
characteristic information from autoencoder latent spaces.
First of all, we undertake a comparative analysis of the
latent spaces derived from frequency-based impedance data,
and from time-based voltage and current data. This
comparison provides confidence in the applicability of
methods traditionally used for EIS in laboratory
environments to the analysis of raw time-series data.
Furthermore, in order to estimate realistic battery usage
simulation results by incorporating random noise and
variations into the data, which is essential for developing
robust battery management systems, we compare the latent
space trained from ideal sinusoidal data and that from
permuted noisy data. The extracted battery features in the
latent space were analyzed using Support Vector Machine
(SVM) classifiers with both linear and non-linear kernels to
segment batteries into three distinct age groups: fresh,
aged and damaged cells. The consistent and observable robust
aging motion, as depicted by the distribution of classes in
the latent space, remains unaffected by the choice of using
time-based or frequency-based data, as well as whether the
data is ideal or noisy. It underscores the potential and
reliability for the development of pseudo-random complex
pulse current excitation that can be used for estimating the
battery's state, which can then potentially be utilized to
inform the development of optimized cell-based load profiles
in the future.While EIS offers valuable insights into a
battery’s state, real-world battery operation during
driving scenarios involves dynamic state changes, where
current and voltage signals are far from ideally sinusoidal.
To bridge the gap between EIS and real-world driving cycle
analysis, we introduce the concept of a stochastic pulse
design compatible with the load profile. This approach
starts with frequency-based impedance data as a reference
and transitions into stochastic pulse signals that act as a
weak perturbation in the time domain, thereby providing
optimal contrast regrading the battery's state. The
systematic generation of stochastic pulse signals is
inspired by fractal curves and is limited by constraints of
experimental devices. These signals are designed to mimic
the variability and unpredictability of real-world battery
usage more closely. The current pulse signals are then
simulated through the battery models at different SOC/SOH
combinations, yielding the corresponding voltage responses.
The analysis of this pairwise current/voltage data is also
conducted in the latent space of an Autoencoder, which
comprises essential features extracted from the input data.
Here, traditional EIS data is computed as a reference
standard to compare the effects of different pulse sequences
against a well-established benchmark in battery analysis.
For a visual and quantitative comparison between two latent
spaces, Quantile-Quantile (Q-Q) plots are employed to
estimate performance among different pulse sequences when
replicating the distribution of the EIS-derived latent
space. Additionally, their performance can also be visual by
latent space segmentation. By correlating the segmentation
of latent space with battery aging indicators, we validate
the representativeness of our identified best and worst
performed pulse signals against conventional EIS data.
Furthermore, at certain stages of a battery's aging process,
altering the pulse sequence can lead to enhanced performance
in terms of age group separation. This insight underscores
the potential for adaptive strategies in battery management,
where pulse sequences can be dynamically adjusted based on
the battery's stage of aging to optimize performance and
possibly extend its lifespan.},
cin = {155520 / 150000},
ddc = {540},
cid = {$I:(DE-82)155520_20160614$ / $I:(DE-82)150000_20140620$},
pnm = {Helmholtz AI - Helmholtz Artificial Intelligence
Coordination Unit – Local Unit FZJ (E.40401.62) / Impuls-
und Vernetzungsfonds},
pid = {G:(DE-Juel-1)E.40401.62 / G:(DE-HGF)IVF-20140101},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2024-11389},
url = {https://publications.rwth-aachen.de/record/998435},
}
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