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The Precise Battery—towards Digital Twins for Advanced Batteries

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A special issue of Batteries (ISSN 2313-0105).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 32062

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Prof. Dr. Kai Peter Birke

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Guest Editor

Chair for Electrical Energy Storage Systems, Institute for Photovoltaics, University of Stuttgart, Pfaffenwaldring 47, 70569 Stuttgart, Germany
Interests: battery cell research; battery system technology; battery block building kits; modeling of battery cells and battery systems; battery state estimation (state of charge, state of health, state of function); implementation of artificial intelligence for detrmination of battery cell parameters with enhanced accuracy; digital twins for battery cells and battery systems; high boiling point safety enhanced electrolytes; double layer capacitors; pseudo 3D-capacitors; power to X (X = gas, liquid, solid)
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Dr. Alexander Fill

E-Mail Website
Guest Editor

Chair for Electrical Energy Storage Systems, Institute for Photovoltaics, University of Stuttgart, Pfaffenwaldring 47, 70569 Stuttgart, Germany
Interests: battery cell research; novel battery diagnostics; battery modeling; battery characterization; battery cell state estimation; cell-to-cell variances; local cell state imbalances
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electromobility has led to high demands with respect to energy and power density, durability and safety. Thus, there have been rapid and varied developments in battery technology, especially for Li-Ion and post Li-Ion battery cells.

To meet these stringent requirements (cell, module, system) and to set new benchmarks, research efforts in various areas are in progress. On the one hand, new candidates for negative electrodes are being investigated, such as lithium metal or silicon. Research is also constantly ongoing on solid electrolytes to make said anode materials feasible. At the positive electrode, the Ni content is steadily increased to reduce the amount of cobalt and nickel. Furthermore, abundant and safe electrodes such as lithium iron phosphate (LFP) have gained enhanced interest.

In parallel to new material developments at the cellular level, the optimization of cell design and operating strategy are in focus. Important factors include the tab design, the geometric cell and battery formats, the cell size (in terms of Ah) and the energy content. The challenges o material selection and cell design are doubtless important trade-offs among different KPIs.

However, without a precise battery model and advanced calculation methods, all the aforementioned mentioned attempts fail. A precise battery model is a digital twin including electrical, thermal, mechanical and aging models as well as new approaches employing artificial intelligence. Additionally, the digital twin should show real-time ability.

Consequently, we want to promote and address a new Special Issue ‘The precise battery battery - towards digital twins for advanced batteries’.

Research on the enhancement of the accuracy of models, their smart combination and sophisticated stand-alone calculation methods in terms of materials, cells, modules and battery level are highly welcome. All these contributions are necessary to build up digital twins for batteries.

Potential topics include but are not limited to:

Digital twins of battery cells;
Digital twins for battery systems;
Equivalent circuit and equation-based battery modeling;
Electrical, thermal and mechanical battery modeling;
Lifetime estimation of battery cells and modules;
Accuracy enhancement methods for battery states and lifetime estimation;
Calculation-based cell size optimization for advanced Li-ion batteries;
Calculation aspects of battery cell and pack design;
Quantifying the impact of solid electrolytes;
Quantifying the impact of new electrode materials.

Prof. Dr. Kai Peter Birke
Dr. Alexander Fill
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Batteries is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

battery algorithms
modeling of battery cells and battery systems
battery state estimation (state of charge, state of health, state of function)
electrochemical models
electrical models
thermal models
aging models
mechanical models
thermal propagation models
artificial intelligence in battery models
digital twins for battery cells and battery systems
battery management systems

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Published Papers (14 papers)

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Research

13 pages, 985 KiB

Open AccessArticle

Comprehensive Study of Failure Mechanisms of Field-Aged Automotive Lead Batteries

by Rafael Conradt, Philipp Schröer, Martin Dazer, Jonathan Wirth, Florian Jöris, Dominik Schulte and Kai Peter Birke

Batteries 2023, 9(11), 553; https://doi.org/10.3390/batteries9110553 - 13 Nov 2023

Cited by 1 | Viewed by 2200

Abstract

Modern vehicles have increasing safety requirements and a need for reliable low-voltage power supply in their on-board power supply systems. Understanding the causes and probabilities of failures in a 12 V power supply is crucial. Field analyses of aged and failed 12 V lead batteries can provide valuable insights regarding this topic. In a previous study, non-invasive electrical testing was used to objectively determine the reasons for failure and the lifetime of individual batteries. By identifying all of the potential failure mechanisms, the Latin hypercube sampling method was found to effectively reduce the required sample size. To ensure sufficient confidence in validating diagnostic algorithms and calculating time-dependent failure rates, all identified aging phenomena must be considered. This study presents a probability distribution of the failure mechanisms that occur in the field, as well as provides insights into potential opportunities, but it also challenges diagnostic approaches for current and future vehicles. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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29 pages, 1320 KiB

Open AccessArticle

Analytic Free-Energy Expression for the 2D-Ising Model and Perspectives for Battery Modeling

by Daniel Markthaler and Kai Peter Birke

Batteries 2023, 9(10), 489; https://doi.org/10.3390/batteries9100489 - 25 Sep 2023

Viewed by 2660

Abstract

Although originally developed to describe the magnetic behavior of matter, the Ising model represents one of the most widely used physical models, with applications in almost all scientific areas. Even after 100 years, the model still poses challenges and is the subject of active research. In this work, we address the question of whether it is possible to describe the free energy A of a finite-size 2D-Ising model of arbitrary size, based on a couple of analytically solvable 1D-Ising chains. The presented novel approach is based on rigorous statistical-thermodynamic principles and involves modeling the free energy contribution of an added inter-chain bond

Δ A_{bond} (β, N)

as function of inverse temperature

β

and lattice size N. The identified simple analytic expression for

Δ A_{bond}

is fitted to exact results of a series of finite-size quadratic

N \times N

-systems and enables straightforward and instantaneous calculation of thermodynamic quantities of interest, such as free energy and heat capacity for systems of an arbitrary size. This approach is not only interesting from a fundamental perspective with respect to the possible transfer to a 3D-Ising model, but also from an application-driven viewpoint in the context of (Li-ion) batteries where it could be applied to describe intercalation mechanisms. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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26 pages, 2634 KiB

Open AccessArticle

Modeling and Experimental Investigation of the Interaction between Pressure-Dependent Aging and Pressure Development Due to the Aging of Lithium-Ion Cells

by Arber Avdyli, Alexander Fill and Kai Peter Birke

Batteries 2023, 9(10), 484; https://doi.org/10.3390/batteries9100484 - 22 Sep 2023

Cited by 1 | Viewed by 2530

Abstract

In order to meet the increasing demands of the battery in terms of range, safety and performance, it is necessary to ensure optimal operation conditions of a lithium-ion cell. In this thesis, the influence of mechanical boundary conditions on the cell is investigated theoretically and experimentally. First, fundamental equations are derived that lead to coupled models that can be parameterized based on specific cell measurements and predict the pressure evolution due to capacity aging and vice versa. The model is used to derive optimal operating points of the cell, which can be considered in the module design. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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16 pages, 1148 KiB

Open AccessArticle

Temperature Estimation in Lithium-Ion Cells Assembled in Series-Parallel Circuits Using an Artificial Neural Network Based on Impedance Data

by Marco Ströbel, Vikneshwara Kumar and Kai Peter Birke

Batteries 2023, 9(9), 458; https://doi.org/10.3390/batteries9090458 - 9 Sep 2023

Cited by 3 | Viewed by 2648

Abstract

Lithium-ion cells are widely used in various applications. For optimal performance and safety, it is crucial to have accurate knowledge of the temperature of each cell. However, determining the temperature for individual cells is challenging as the core temperature may significantly differ from the surface temperature, leading to the need for further research in this field. This study presents the first sensorless temperature estimation method for determining the core temperature of each cell within a battery module. The accuracy of temperature estimation is in the range of

Δ T \approx 1

K. The cell temperature is determined using an artificial neural network (ANN) based on electrochemical impedance spectroscopy (EIS) data. Additionally, by optimizing the frequency range, the number of measurement points, input neurons, measurement time, and computational effort are significantly reduced, while maintaining or even improving the accuracy of temperature estimation. The required time for the EIS measurement can be reduced to 0.5 s, and the temperature calculation takes place within a few milliseconds. The setup consists of cylindrical 18,650 lithium-ion cells assembled into modules with a 3s2p configuration. The core temperature of the cells was measured using sensors placed inside each cell. For the EIS measurement, alternating current excitation was applied across the entire module, and voltage was measured individually for each cell. Various State of Charge (SoC), ambient temperatures, and DC loads were investigated. Compared to other methods for temperature determination, the advantages of the presented study lie in the simplicity of the approach. Only one impedance chip per module is required as additional hardware to apply the AC current. The ANN consists of a simple feedforward network with only one layer in the hidden layer, resulting in minimal computational effort, making this approach attractive for real-world applications. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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15 pages, 6975 KiB

Open AccessEditor’s ChoiceArticle

Thermal Propagation Test Bench with Multi Pouch Cell Setup for Reproducibility Investigations

by Björn Mulder, Jan Schöberl and Kai Peter Birke

Batteries 2023, 9(9), 447; https://doi.org/10.3390/batteries9090447 - 31 Aug 2023

Cited by 1 | Viewed by 1875

Abstract

Thermal propagation events of the traction batteries in electric vehicles are rare. However, their impact on the passengers in form of fire, smoke and heat can be severe. Current data on the dependencies and the reproducibility of thermal propagation is limited despite these major implications. Therefore, a thermal propagation test bench was developed for custom multi pouch experiments. This setup includes a multitude of temperature sensors throughout the module, voltage monitoring and a mass flow sensor. Two distinct experiments were initiated by nail penetration. These show a high degree of reproducibility thus allowing for future experiments regarding the dependencies of initial module temperatures and State of Charge (SoC) variations. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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17 pages, 12775 KiB

Open AccessEditor’s ChoiceArticle

Data-Driven Diagnosis of PV-Connected Batteries: Analysis of Two Years of Observed Irradiance

by Matthieu Dubarry, Fahim Yasir, Nahuel Costa and Dax Matthews

Batteries 2023, 9(8), 395; https://doi.org/10.3390/batteries9080395 - 29 Jul 2023

Cited by 3 | Viewed by 1625

Abstract

The diagnosis and prognosis of PV-connected batteries are complicated because cells might never experience controlled conditions during operation as both the charge and discharge duty cycles are sporadic. This work presents the application of a new methodology that enables diagnosis without the need for any maintenance cycle. It uses a 1-dimensional convolutional neural network trained on the output from a clear sky irradiance model and validated on the observed irradiances for 720 days of synthetic battery data generated from pyranometer irradiance observations. The analysis was performed from three angles: the impact of sky conditions, degradation composition, and degradation extent. Our results indicate that for days with over 50% clear sky or with an average irradiance over 650 W/m², diagnosis with an average RMSE of 1.75% is obtainable independent of the composition of the degradation and of its extent. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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20 pages, 7995 KiB

Open AccessArticle

Design, Properties, and Manufacturing of Cylindrical Li-Ion Battery Cells—A Generic Overview

by Sabri Baazouzi, Niklas Feistel, Johannes Wanner, Inga Landwehr, Alexander Fill and Kai Peter Birke

Batteries 2023, 9(6), 309; https://doi.org/10.3390/batteries9060309 - 3 Jun 2023

Cited by 29 | Viewed by 20534

Abstract

Battery cells are the main components of a battery system for electric vehicle batteries. Depending on the manufacturer, three different cell formats are used in the automotive sector (pouch, prismatic, and cylindrical). In the last 3 years, cylindrical cells have gained strong relevance and popularity among automotive manufacturers, mainly driven by innovative cell designs, such as the Tesla tabless design. This paper investigates 19 Li-ion cylindrical battery cells from four cell manufacturers in four formats (18650, 20700, 21700, and 4680). We aim to systematically capture the design features, such as tab design and quality parameters, such as manufacturing tolerances and generically describe cylindrical cells. We identified the basic designs and assigned example cells to them. In addition, we show a comprehensive definition of a tabless design considering the current and heat transport paths. Our findings show that the Tesla 4680 design is quasi-tabless. In addition, we found that 25% of the cathode and 30% of the anode are not notched, resulting in long electrical and thermal transport paths. Based on CT and post-mortem analyses, we show that jelly rolls can be approximated very well with the Archimedean spiral. Furthermore, we compare the gravimetric and volumetric energy density, the impedance, and the heating behavior at the surface and in the center of the jelly rolls. From the generic description, we present and discuss production processes focusing on format and design flexible manufacturing of jelly rolls. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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19 pages, 5478 KiB

Open AccessEditor’s ChoiceArticle

Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation

by Soumya Singh, Yvonne Eboumbou Ebongue, Shahed Rezaei and Kai Peter Birke

Batteries 2023, 9(6), 301; https://doi.org/10.3390/batteries9060301 - 30 May 2023

Cited by 15 | Viewed by 8656

Abstract

Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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13 pages, 1218 KiB

Open AccessEditor’s ChoiceArticle

How Cell Design Affects the Aging Behavior: Comparing Electrode-Individual Aging Processes of High-Energy and High-Power Lithium-Ion Batteries Using High Precision Coulometry

by Sebastian Michael Peter Jagfeld, Kai Peter Birke, Alexander Fill and Peter Keil

Batteries 2023, 9(4), 232; https://doi.org/10.3390/batteries9040232 - 18 Apr 2023

Cited by 6 | Viewed by 2534

Abstract

The aging behavior of lithium-ion batteries is crucial for the development of electric vehicles and many other battery-powered devices. The cells can be generally classified into two types: high-energy (HE) and high-power (HP) cells. The cell type used depends on the field of application. As these cells differ in their electrical behavior, this work investigates whether both cell types also show different aging behavior. More precisely, the occurring capacity loss and internal side reactions are analyzed via the charge throughput. For comparison, aging tests are carried out with a high-precision battery tester, allowing the application of High Precision Coulometry (HPC). This enables early detection of aging effects and also allows us to break down the capacity loss into electrode-individual processes. A total of two sub-studies are performed: (1) a cyclic study focusing on lithium plating; and (2) an accelerated calendar aging study. It is found that HE cells exhibit stronger cyclic aging effects (lithium plating) and HP cells exhibit stronger calendar aging effects. The higher lithium plating can be explained by the higher diffusion resistance of the lithium ions within the electrodes of HE Cell. The higher calendar aging fits to the larger electrode surfaces of the HP cell. These results give deep insights into the proceeding aging in a novel way and are interesting for the selection of the appropriate cell type in the context of battery development. In a next step, the measured capacity losses could also be used for a simple parameterization of battery aging models. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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16 pages, 7035 KiB

Open AccessArticle

Cycle Tests on the Influence of Different Charging Currents—A Case Study on Different Commercial, Cylindrical Lithium Ion Cells

by Anke Parschau, David Degler, Alexander Fill, Kai Peter Birke and Frank Allmendinger

Batteries 2023, 9(2), 83; https://doi.org/10.3390/batteries9020083 - 26 Jan 2023

Cited by 5 | Viewed by 3145

Abstract

On the way to a Precise Battery, the generation of measurement results and findings based on them play an important role. Although cycle life tests are time-consuming and expensive, they can provide support and important information. Especially in the current topic of accelerating the charging process, it is important to know how different charging currents affect different cell types. The CC CV charging method is still the most common, widely used method. Therefore, long-term cycle tests are carried out in this work in order to clarify the influence of different charging currents, as recommended by the cell manufacturers. Common high-energy and high-power cylindrical lithium ion cells are investigated and compared. In addition to the influence of the charging protocol on the aging, charging time and heating, the effects on the dispersion of the cells as well as the effects on the constant current and the constant voltage part of the charging process are considered. From the results it can be seen how different the investigated cells behave in response to increased charging currents. Even supposedly similar cells show significant differences in aging behavior. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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20 pages, 3316 KiB

Open AccessArticle

Comparison of an Experimental Electrolyte Wetting of a Lithium-Ion Battery Anode and Separator by a Lattice Boltzmann Simulation

by Johannes Wanner and Kai Peter Birke

Batteries 2022, 8(12), 277; https://doi.org/10.3390/batteries8120277 - 6 Dec 2022

Cited by 9 | Viewed by 4011

Abstract

The filling with electrolyte and the subsequent wetting of the electrodes is a quality-critical and time-intensive process in the manufacturing of lithium-ion batteries. The exact processes involved in the wetting are still under investigation due to their poor accessibility. The accurate replication of the wetting phenomena in porous media can be demonstrated in other research fields by lattice Boltzmann simulations. Therefore, this paper deals with the comparison of experimental wetting and the simulative investigation of the wetting processes of lithium-ion battery materials by a lattice Boltzmann simulation. Particular attention is paid to the interfaces between the battery materials. These effects are relevant for a simulation of the wetting properties at the cell level. The experimental results show a 43% faster wetting of the interface between an anode and a separator than with only an anode. Overall, the simulation results show a qualitatively successful reproduction of the experimental wetting phenomena. In addition, the steps for a more precise simulation and the development of the Digital Twin are shown. This extension enables simulations of the electrolyte wetting phenomena in manufacturing lithium-ion batteries and the quantification of the wetting times. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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24 pages, 3414 KiB

Open AccessFeature PaperArticle

Possibilities for a Quick Onsite Safety-State Assessment of Stand-Alone Lithium-Ion Batteries

by Daniel Koch and Hans-Georg Schweiger

Batteries 2022, 8(11), 213; https://doi.org/10.3390/batteries8110213 - 3 Nov 2022

Cited by 2 | Viewed by 2978

Abstract

Electric vehicles’ high-voltage lithium-ion batteries are complex systems and can be sources of several hazards for interacting people. Sophisticated battery management systems (BMS) therefore constantly monitor their characteristics and varying states, to keep the battery within desired operational conditions and to mitigate safety risks as well as excessive degradation. However, there can be several situations where the battery is not in normal operation (e.g., a stand-alone battery) and a fully functional BMS monitoring function is not available. When necessary to interact with the system, its safety state must be deduced to ensure the safety of interactors. This can be a challenging task depending on a situation’s characteristics (time pressure, technical knowledge of involved people). Thus, this article discusses how the safety state of electric vehicle batteries can be evaluated quickly even by untrained people. To develop a solution, different scenarios, which require a battery’s state assessment, and the options for collecting relevant information are motivated and discussed, respectively. Finally, a mobile interface that can evaluate and display the safety state by using BMS-internal data is described and demonstrated. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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16 pages, 2302 KiB

Open AccessArticle

Novel Approach to Ensure Safe Power Supply for Safety-Relevant Consumers

by Lars Braun, Minh Le, Jürgen Motz and Kai Peter Birke

Batteries 2022, 8(5), 47; https://doi.org/10.3390/batteries8050047 - 19 May 2022

Cited by 1 | Viewed by 2746

Abstract

The 12 V powernet in vehicles must fulfill certain safety requirements due to the safety demand of consumers. A potential risk is undervoltage for a safety-relevant consumer, which leads to its fault. Therefore, a novel approach is presented in this study, which can predict the minimum terminal voltage for consumers. This consists of diagnostics of the wiring harness and of the lead-acid battery as well as predefined consumer currents. Using simulation, first the beginning of a drive cycle is simulated to determine the state of the powernet, and afterwards a critical driving maneuver is simulated to validate the predicted minimum terminal voltage. It demonstrates that the novel approach is able to predict a fault due to undervoltage. In addition to fulfilling safety requirements, the novel approach could be used to achieve additional availability and miniaturization of powernet components compared to the state of the art. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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16 pages, 4691 KiB

Open AccessArticle

Artificial Feature Extraction for Estimating State-of-Temperature in Lithium-Ion-Cells Using Various Long Short-Term Memory Architectures

by Mike Kopp, Marco Ströbel, Alexander Fill, Julia Pross-Brakhage and Kai Peter Birke

Batteries 2022, 8(4), 36; https://doi.org/10.3390/batteries8040036 - 15 Apr 2022

Cited by 10 | Viewed by 2927

Abstract

The temperature in each cell of a battery system should be monitored to correctly track aging behavior and ensure safety requirements. To eliminate the need for additional hardware components, a software based prediction model is needed to track the temperature behavior. This study looks at machine learning algorithms that learn physical behavior of non-linear systems based on sample data. Here, it is shown how to improve the prediction accuracy using a new method called “artificial feature extraction” compared to classical time series approaches. We show its effectiveness on tracking the temperature behavior of a Li-ion cell with limited training data at one defined ambient temperature. A custom measuring system was created capable of tracking the cell temperature, by installing a temperature sensor into the cell wrap instead of attaching it to the cell housing. Additionally, a custom early stopping algorithm was developed to eliminate the need for further hyperparameters. This study manifests that artificially training sub models that extract features with high accuracy aids models in predicting more complex physical behavior. On average, the prediction accuracy has been improved by

Δ T_{cell} = 0.01 ° C

for the training data and by

Δ T_{cell} = 0.007 ° C

for the validation data compared to the base model. In the field of electrical energy storage systems, this could reduce costs, increase safety and improve knowledge about the aging progress in an individual cell to sort out for second life applications. Full article

(This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries)

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