It is imperative to effectively utilize the limited measurement data available at any life stage to address the challenge of predicting the degradation trajectory of lithium-ion batteries under various operating conditions throughout their lifespan. This study presents a method for incorporating base points (points obtained that can be interpolated to predict the degradation trajectories) by applying a convolutional neural network, which learns the mapping relationships from charging data from any stage, even down to a single cycle. The objective is to accurately determine the specific locations of these base points appropriate for interpolation and extrapolation of degradation trajectories. This study utilizes three metaheuristic algorithms to optimize the base point locations and model hyperparameters using different input data to achieve optimal model performance. The proposed method is validated on two independent datasets: A public dataset containing 95 LFP cells and a private dataset comprising 14 NCA cells. The experimental conditions for the NCA test set fluctuate with aging, thereby simulating real-world scenarios. In addition, selection criteria for the input data and specific optimization algorithms are proposed to maximize the potential for achieving optimal accuracy. The results indicate that the minimum RMSE (and MAE) for LFP degradation trajectory predictions can attain 0.67 % (0.51 %) across various base point locations and hyperparameter combinations. These values can improve for the NCA test set to as low as 0.30 % (0.25 %). Furthermore, the optimal strategy in a relatively stable testing environment, such as a laboratory environment, involves the application of the differential evolution (DE) algorithm utilizing a limited number of base points. In addition, the present method is not sensitive to measurement quantity and the life stage at which the input data is located
The incremental capacity analysis (ICA) technique is notably limited by its sensitivity to variations in charging conditions, which constrains its practical applicability in real-world scenarios. This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health (SOH) of batteries based on ICA that is applicable under differing charging conditions. This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile. This approach’s efficacy is contingent upon precisely acquiring the equivalent impedance. To obtain the equivalent impedance throughout the batteries’ lifespan while minimizing testing costs, this study employs a current interrupt technique in conjunction with a long short-term memory (LSTM) network to develop a predictive model for equivalent impedance. Following the derivation of ICA curves using voltage profiles under quasi-static conditions, the research explores two scenarios for SOH estimation: one utilizing only incremental capacity (IC) features and the other incorporating both IC features and IC sampling. A genetic algorithm-optimized backpropagation neural network (GA-BPNN) is employed for the SOH estimation. The proposed generalized framework is validated using independent training and test datasets. Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions. These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04% for RMSE and 0.90% for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0% and 70%, which constitutes a major advancement compared to established ICA methods. It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.
Accurate estimation of the battery state is a crucial requirement for advanced battery management systems (BMS). Model-based state estimation methods represent the most promising option to meet BMS requirements, where the equivalent circuit model (ECM) is an effective balance between modelling complexity and accuracy. ECM's accuracy is influenced by the combination of chosen model type and parameter identification method. In this paper, batteries are aged under various conditions. Both frequency and time domain measurements are performed on batteries in a variety of aging states. These measurements are employed for comparing all combinations of 7 existing models with 7 common identification methods. In addition, the accuracy of SOH models based on ECM parameters is investigated. The experimental results indicate that for frequency and time domain measurements, the same identification algorithm may exhibit distinct performances. Overall, PSO, GWO and LSQ are ideal candidates. Among them, PSO and GWO perform optimally in the frequency domain environment, while LSQ is superior in the time domain environment. Furthermore, this conclusion does not change with battery aging. Meanwhile, a simpler model structure is even beneficial for efficiently monitoring SOH when utilizing the aforementioned superior identification methods.
Accurate state-of-health (SOH) estimation is an essential prerequisite for a battery management system (BMS) to improve battery utilization efficiency. The impedance information can be utilized to reflect the SOH. However, the traditional electrochemical impedance spectroscopy (EIS) method suffers from time-consuming measurements and specialized equipment. This study aims to establish a connection between EIS and the current interrupt method, which reduces the difficulty of obtaining impedance information through its utilization. This method can be applied in real time during charging, enabling it to be incorporated into a BMS. A genetic algorithm optimized back propagation neural network (GA-BPNN) is developed to estimate the SOH based on the impedance information obtained in the current interrupt method as inputs. The genetic algorithm improves the weights and thresholds of the neural network, which solves the parameter calibration problem. In this study, besides utilizing measurement data from different aging conditions as training set data, hybrid tests comparable to the actual usage environment are employed as validation set data. The experimental results show that a combination of the proposed current interrupt method and GA-BPNN can estimate SOH accurately with the root mean square error (RMSE) as low as 0.77 % in a complex hybrid test environment.
Maximilian Frankl,
Georg Huber,
Edgar Remmele,
Josef Kainz
Emissionen von Ethanolkraftstoffen im Straßenverkehr – E5-, E10- und E85-Kraftstoff im Flexible-Fuel-Vehicle (2018) GIT-Laborzeitschrift 12 , S. 22-23.
Book chapters
Johannes Beer,
Josef Kainz,
A. Ketterer
Diagnosis Status of a Modern Engine Management System in View of Failure Isolation of Engine Misfire Events (2006) VDI-Berichte 1931 , S. 493.
Conference contributions
A. Jmili,
R. Bouallegui,
Josef Kainz,
E. Znouda,
C. Bouden
Oliver Greil,
Josef Kainz,
Michael Kain,
Alfons Haber
Optimierungsmodell für unterschiedliche Lade- und Entladestrategien von PV-Speichersystemen (2019) Beitrag zur 11. Internationale Energiewirtschaftstagung (IEWT) vom 13.-15. Februar 2019 an der TU Wien, Österreich .
Oliver Bänfer,
Oliver Nelles,
Josef Kainz,
Johannes Beer
Local Model Networks with Modified Parabolic Membership Functions (2009) 2009 International Conference on Artificial Intelligence and Computational Intelligence (AICI), 7-8 Nov. 2009 , S. 179-183.
DOI: 10.1109/AICI.2009.477
Sergey Ganichev,
Petra Schneider,
Vassilij Belkov,
Josef Kainz,
Ulrich Rössler,
Leonid E. Golub,
Dieter Weiss,
Werner Wegscheider,
Dieter Schuh,
Wilhelm Prettl
Spin-sensitive saturation of heavy-hole-light-hole absorption in p-type GaAs QWs (2003) Nano-2003, St. Petersburg, Russland .
Josef Kainz,
Petra Schneider,
Sergey Ganichev,
Ulrich Rössler,
Werner Wegscheider,
Dieter Weiss,
Wilhelm Prettl,
Vassilij Belkov,
Leonid E. Golub,
Dieter Schuh
Hole spin relaxation in Quantum Wells from Saturation of Inter-Subband Absorption (2003) EP2DS-15, July 14th to 18th, 2003 in Nara, Japan .
Josef Kainz,
Ulrich Rössler,
R. Winkler
Intersubband absorption in p-type semiconductor quantum wells – influence of light polarization (2003) DPG-Frühjahrstagung Dresden, 24.-29. März 2003 .
Josef Kainz,
Ulrich Rössler,
R. Winkler
Spin splitting and spin-relaxation rates in semiconductor quantum structures (2002) PASPS, Würzburg, 2002 .
Josef Kainz,
Ulrich Rössler,
R. Winkler
Spin splitting and spin-relaxation time in semiconductor heterostructures (2002) 12th International Winter School on New Developments in Solid State Physics, Mauterndorf, Österreich .
Josef Kainz,
Ulrich Rössler,
R. Winkler
Spin splitting and spin-relaxation time in semiconductor heterostructures (2002) DPG-Frühjahrstagung Regensburg, 11.-15. März 2002 .
Josef Kainz,
Sergey Mikhailov,
Andreas Wensauer,
Ulrich Rössler
Ground-state energies of quantum dots in high magnetic fields: A new approach (2001) EP2DS - 14, 30. Juli - 3. August 2001 in Prag, Tschechien .
Präsentation des wissenschaftlichen Projektes „Leitfaden zur Rentabilität eines Kleinstpumpspeichersystems mit Pumpe als Turbine“ (2021) Vortrag beim 2. VWB und LVBW Wasserkraftseminar am 05. Oktober 2021 .
Simon Härtl,
Josef Kainz,
Harry Schuele,
Matthias Gaderer
Control of combustion stability and center of combustion on a lean-burn gasoline engine with pre-chamber (2021) 21th Stuttgart International Symposium "Automotive and Engine Technology" 30. – 31.03.2021 .
Josef Kainz,
Florian Lugauer
Vorstellung eines Forschungsprojektes über die Verwendung eines Kleinstpumpspeicher mit Pumpe zur Optimierung der Eigenversorgung (2019) Vortrag beim Ersten Wasserkraftseminar am TUM-Campus, 11.09.2019 .
Josef Kainz
(Bio-)gas- und Elektromobilität: Was bringt wieviel für’s Klima? (2019) C.A.R.M.E.N. Symposium, 02.07.2019, Straubing .
Josef Kainz
Carbon footprint of (bio)gas mobility (2018) 2nd Israel-TUM Winter School „Climate change and energy policy in an era of technological change“, 27.11.2018, Straubing .
Josef Kainz
Einsatz von lokalen Modell-Netzen in einer Motorsteuerung zur Modellierung von Ventiltriebsvariabilitäten (2009) Congress „Haus der Technik“ Variable Ventilsteuerung, 03-04.03.2009, Essen .
Media reports
Text Medienbeitrag,
Simon Haslauer,
Josef Kainz,
Stefan Wittkopf
Umweltschützern gelten Kachelöfen und Pelletheizungen als klimaschädlich, Waldbauern halten das Heizen mit Holz dagegen für unverzichtbar. Die Energieagentur Ebersberg-München bemüht sich um eine Schlichtung der Diskussion.
More
Patents
Josef Kainz,
Tobias Braun
Method for operation of an internal combustion engine (2018) US 2018/0355815 A1 .
Josef Kainz,
Tobias Braun
Verfahren zur Identifizierung von Ventilsteuerzeiten eines Verbrennungsmotors (2015) DE 10 2015 209 665 A1 .
Gerhard Eser,
Josef Kainz
Verfahren und Vorrichtung zur Steuerung eines variablen Ventiltriebs einer Brennkraftmaschine (2012) DE 10 2011 079 436 B3 .
Josef Kainz,
Thomas Herrmann,
Markus Teiner
Steuerung für einen Verbrennungsmotor (2012) DE 10 2012 203 934 A1 .
Markus Teiner,
Josef Kainz,
Thomas Herrmann
Verfahren und Steuervorrichtung zum Ermitteln des Umgebungsdrucks eines Kraftfahrzeugs (2010) DE 10 2010 015 646 A1 .
Johannes Beer,
Josef Kainz
Verfahren und Vorrichtung zum Betreiben einer Brennkraftmaschine (2009) DE 10 2009 032 064 B3 .
Johannes Beer,
Josef Kainz
Verfahren und Vorrichtung zur Ermittlung eines Adaptionswertes für die Einstellung eines Luft-Kraftstoff-Verhältnis eines Einspritzsystems eines Verbrennungsmotors (2008) DE 10 2008 012 607 B4 .
Johannes Beer,
Oliver Bänfer,
Josef Kainz,
Oliver Nelles
Verfahren und Vorrichtung zur neuronalen Steuerung und/oder Regelung (2008) WO 2008/101835 A1 .
Johannes Beer,
Josef Kainz
Verfahren und Vorrichtung zum Erkennen eines Verbrennungsaussetzers (2007) DE 10 2005 046 955 B3 .
Johannes Beer,
Josef Kainz
Verfahren und Vorrichtung zum Betreiben einer Verbrennungskraftmaschine (2007) DE 10 2007 023 849 A1 .
Energiespeicher sind wichtige Bausteine, um den Anteil an regenerativer Energie bei der Stromversorgung zu erhöhen. Nur so kann die Volatilität erneuerbarer Energieerzeugung ausgeglichen werden. Um …
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