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Dirk Uwe Sauer
Electrochemical Energy Conversion and Storage Systems, Institute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jaegerstrasse 17/19, 52066, Aachen, Germany

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Journal article
Published: 26 June 2021 in Journal of Power Sources
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An accurate estimation of the internal states of lithium-ion batteries is critical to improving the reliability and durability of battery systems. Data-driven methods have exhibited enormous potential for precisely capturing electric and thermal cell dynamics with a low computational cost. However, challenges remain regarding accurate and low-cost data acquisition as electrode-level states are unmeasurable with conventional sensors. This paper presents a hybrid state estimation method for lithium-ion batteries integrating physics-based and machine learning models to leverage their respective strengths. An electrochemical-thermal model is developed and experimentally verified, which is employed to generate a large quantity of data, i.e., voltage, current, temperature and internal electrochemical states, under a comprehensive operating condition matrix including various load profiles and temperatures. These data are fed to train a deep neural network to estimate the internal concentrations and potentials in the electrodes and the electrolyte at different spatial positions. The results show that the proposed approach is capable of bridging spatial, temporal and chemical complexity and achieves a maximum error of 2.93% for all the estimated states under new ambient temperatures, indicating high reliability and generalization ability with solid robustness to input noises and outperforming the one-dimensional network under both normal and noisy conditions.

ACS Style

Weihan Li; Jiawei Zhang; Florian Ringbeck; Dominik Jöst; Lei Zhang; Zhongbao Wei; Dirk Uwe Sauer. Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries. Journal of Power Sources 2021, 506, 230034 .

AMA Style

Weihan Li, Jiawei Zhang, Florian Ringbeck, Dominik Jöst, Lei Zhang, Zhongbao Wei, Dirk Uwe Sauer. Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries. Journal of Power Sources. 2021; 506 ():230034.

Chicago/Turabian Style

Weihan Li; Jiawei Zhang; Florian Ringbeck; Dominik Jöst; Lei Zhang; Zhongbao Wei; Dirk Uwe Sauer. 2021. "Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries." Journal of Power Sources 506, no. : 230034.

Journal article
Published: 10 June 2021 in Journal of Power Sources
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The degradation of batteries is complex and dependent on several internal mechanisms. Variations arising from manufacturing uncertainties and real-world operating conditions make battery lifetime prediction challenging. Here, we introduce a deep learning-based battery health prognostics approach to predict the future degradation trajectory in one shot without iteration or feature extraction. We also predict the end-of-life point and the knee-point. The model correctly learns about intrinsic variability caused by manufacturing differences, and is able to make accurate cell-specific predictions from just 100 cycles of data, and the performance improves over time as more data become available. Validation in an embedded device is demonstrated with the best-case median prediction error over the lifetime being 1.1% with normal data and 1.3% with noisy data. Compared to state-of-the-art approaches, the one-shot approach shows an increase in accuracy as well as in computing speed by up to 15 times. This work further highlights the effectiveness of data-driven approaches in the domain of health prognostics.

ACS Style

Weihan Li; Neil Sengupta; Philipp Dechent; David Howey; Anuradha Annaswamy; Dirk Uwe Sauer. One-shot battery degradation trajectory prediction with deep learning. Journal of Power Sources 2021, 230024 .

AMA Style

Weihan Li, Neil Sengupta, Philipp Dechent, David Howey, Anuradha Annaswamy, Dirk Uwe Sauer. One-shot battery degradation trajectory prediction with deep learning. Journal of Power Sources. 2021; ():230024.

Chicago/Turabian Style

Weihan Li; Neil Sengupta; Philipp Dechent; David Howey; Anuradha Annaswamy; Dirk Uwe Sauer. 2021. "One-shot battery degradation trajectory prediction with deep learning." Journal of Power Sources , no. : 230024.

Review
Published: 03 June 2021 in Energies
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Battery degradation is a fundamental concern in battery research, with the biggest challenge being to maintain performance and safety upon usage. From the microstructure of the materials to the design of the cell connectors in modules and their assembly in packs, it is impossible to achieve perfect reproducibility. Small manufacturing or environmental variations will compound big repercussions on pack performance and reliability. This review covers the origins of cell-to-cell variations and inhomogeneities on a multiscale level, their impact on electrochemical performance, as well as their characterization and tracking methods, ranging from the use of large-scale equipment to in operando studies.

ACS Style

David Beck; Philipp Dechent; Mark Junker; Dirk Sauer; Matthieu Dubarry. Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review. Energies 2021, 14, 3276 .

AMA Style

David Beck, Philipp Dechent, Mark Junker, Dirk Sauer, Matthieu Dubarry. Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review. Energies. 2021; 14 (11):3276.

Chicago/Turabian Style

David Beck; Philipp Dechent; Mark Junker; Dirk Sauer; Matthieu Dubarry. 2021. "Inhomogeneities and Cell-to-Cell Variations in Lithium-Ion Batteries, a Review." Energies 14, no. 11: 3276.

Journal article
Published: 01 May 2021 in Journal of The Electrochemical Society
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ACS Style

Paul Wulfert-Holzmann; Moritz Huck; Lukas Gold; Jochen Settelein; Dirk Uwe Sauer; Guinevere A. Giffin. A New In Situ and Operando Measurement Method to Determine the Electrical Conductivity of the Negative Active Material in Lead-Acid Batteries during Operation. Journal of The Electrochemical Society 2021, 168, 050537 .

AMA Style

Paul Wulfert-Holzmann, Moritz Huck, Lukas Gold, Jochen Settelein, Dirk Uwe Sauer, Guinevere A. Giffin. A New In Situ and Operando Measurement Method to Determine the Electrical Conductivity of the Negative Active Material in Lead-Acid Batteries during Operation. Journal of The Electrochemical Society. 2021; 168 (5):050537.

Chicago/Turabian Style

Paul Wulfert-Holzmann; Moritz Huck; Lukas Gold; Jochen Settelein; Dirk Uwe Sauer; Guinevere A. Giffin. 2021. "A New In Situ and Operando Measurement Method to Determine the Electrical Conductivity of the Negative Active Material in Lead-Acid Batteries during Operation." Journal of The Electrochemical Society 168, no. 5: 050537.

Journal article
Published: 24 April 2021 in Applied Energy
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In order to fulfill the energy and power demand of battery electric vehicles, a hybrid battery system with a high-energy and a high-power battery pack can be implemented as the energy source. This paper explores a cloud-based multi-objective energy management strategy for the hybrid architecture with a deep deterministic policy gradient, which increases the electrical and thermal safety, and meanwhile minimizes the system’s energy loss and aging cost. In order to simulate the electro-thermal dynamics and aging behaviors of the batteries, models are built for both high-energy and high-power cells based on the characterization and aging tests. A cloud-based training approach is proposed for energy management with real-world vehicle data collected from various road conditions. Results show the improvement of electrical and thermal safety, as well as the reduction of energy loss and aging cost of the whole system with the proposed strategy based on the collected real-world driving data. Furthermore, processor-in-the-loop tests verify that the proposed strategy can achieve a much higher convergence rate and a better performance in terms of the minimization of both energy loss and aging cost compared with state-of-the-art learning-based strategies.

ACS Style

Weihan Li; Han Cui; Thomas Nemeth; Jonathan Jansen; Cem Ünlübayir; Zhongbao Wei; Xuning Feng; Xuebing Han; Minggao Ouyang; Haifeng Dai; Xuezhe Wei; Dirk Uwe Sauer. Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning. Applied Energy 2021, 293, 116977 .

AMA Style

Weihan Li, Han Cui, Thomas Nemeth, Jonathan Jansen, Cem Ünlübayir, Zhongbao Wei, Xuning Feng, Xuebing Han, Minggao Ouyang, Haifeng Dai, Xuezhe Wei, Dirk Uwe Sauer. Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning. Applied Energy. 2021; 293 ():116977.

Chicago/Turabian Style

Weihan Li; Han Cui; Thomas Nemeth; Jonathan Jansen; Cem Ünlübayir; Zhongbao Wei; Xuning Feng; Xuebing Han; Minggao Ouyang; Haifeng Dai; Xuezhe Wei; Dirk Uwe Sauer. 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning." Applied Energy 293, no. : 116977.

Journal article
Published: 21 April 2021 in IEEE Transactions on Control Systems Technology
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Electrochemical mechanisms in lithium-ion batteries (LIBs) pose a significant challenge in deriving models that are highly accurate, have low computational complexity, and enable real-time state and parameter estimation. In this article, we propose a machine learning model as an important building block of a physics-based ANCF-e model that was recently proposed for LIBs. This machine learning model is used to estimate nonlinear potentials, including the open-circuit potential, electrolyte potential, and lithium-intercalation overpotential. Such an estimation is shown to result in a much smaller computational complexity and therefore can enable real-time state and parameter estimation. Three different machine learning architectures are explored, including multilayer perceptron, radial basis function (RBF)-based neural networks, and support vector machines. The training of these machine learning models is carried out using current profiles obtained with an electric vehicle model from driving cycles as inputs and ANCF-e model-based outputs. The underlying ANCF-e model is validated both through a high-fidelity numerical approach, including COMSOL and an experimental test using commercial LIBs. Both validations are carried out under both constant current discharging and dynamic load cycles. The resulting performance using these machine learning models is compared using different metrics, including estimation errors, convergence rates, training time, and computational time. The results indicate that an RBF-based neural network leads to better estimation of the underlying potentials in LIBs and that all machine learning models require a computational time that is 95% smaller than a physics-based approach for this estimation.

ACS Style

Weihan Li; Damas W. Limoge; Jiawei Zhang; Dirk Uwe Sauer; Anuradha M. Annaswamy. Estimation of Potentials in Lithium-Ion Batteries Using Machine Learning Models. IEEE Transactions on Control Systems Technology 2021, PP, 1 -16.

AMA Style

Weihan Li, Damas W. Limoge, Jiawei Zhang, Dirk Uwe Sauer, Anuradha M. Annaswamy. Estimation of Potentials in Lithium-Ion Batteries Using Machine Learning Models. IEEE Transactions on Control Systems Technology. 2021; PP (99):1-16.

Chicago/Turabian Style

Weihan Li; Damas W. Limoge; Jiawei Zhang; Dirk Uwe Sauer; Anuradha M. Annaswamy. 2021. "Estimation of Potentials in Lithium-Ion Batteries Using Machine Learning Models." IEEE Transactions on Control Systems Technology PP, no. 99: 1-16.

Journal article
Published: 27 March 2021 in Journal of Energy Chemistry
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Internal short circuit (ISC) is the major failure problem for the safe application of lithium-ion batteries, especially for the batteries with high energy density. However, how to quantify the hazard aroused by the ISC, and what kinds of ISC will lead to thermal runaway are still unclear. This paper investigates the thermal-electrical coupled behaviors of ISC, using batteries with Li(Ni1/3Co1/3Mn1/3)O2 cathode and composite separator. The electrochemical impedance spectroscopy of customized battery that has no LiPF6 salt is utilized to standardize the resistance of ISC. Furthermore, this paper compares the thermal-electrical coupled behaviors of the above four types of ISC at different states-of-charge. There is an area expansion phenomenon for the aluminum-anode type of ISC. The expansion effect of the failure area directly links to the melting and collapse of separator, and plays an important role in further evolution of thermal runaway. This work provides guidance to the development of the ISC models, detection algorithms, and correlated countermeasures.

ACS Style

Lishuo Liu; Xuning Feng; Christiane Rahe; Weihan Li; Languang Lu; Xiangming He; Dirk Uwe Sauer; Minggao Ouyang. Internal short circuit evaluation and corresponding failure mode analysis for lithium-ion batteries. Journal of Energy Chemistry 2021, 61, 269 -280.

AMA Style

Lishuo Liu, Xuning Feng, Christiane Rahe, Weihan Li, Languang Lu, Xiangming He, Dirk Uwe Sauer, Minggao Ouyang. Internal short circuit evaluation and corresponding failure mode analysis for lithium-ion batteries. Journal of Energy Chemistry. 2021; 61 ():269-280.

Chicago/Turabian Style

Lishuo Liu; Xuning Feng; Christiane Rahe; Weihan Li; Languang Lu; Xiangming He; Dirk Uwe Sauer; Minggao Ouyang. 2021. "Internal short circuit evaluation and corresponding failure mode analysis for lithium-ion batteries." Journal of Energy Chemistry 61, no. : 269-280.

Preprint content
Published: 05 March 2021
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Lithium-Ion battery lifetimes from cyclic and calendar aging tests of more than 1000 cells were compared employing novel plots termed ENPOLITE (energy-power-lifetime-temperature). Battery cell data from in-house measurements and published data were combined into a uniform database; the total dataset size exceeds 1000 GB. At a glance, ENPOLITE plots inform about the nominal capacity, cell format, cell chemistry, average aging test duration, measurement temperature, specific power employed for testing, energy density, and the achieved lifetime for every cell. A battery lifetime coefficient was derived, allowing the comparison of lithium-ion batteries with different weights or volumes, capacities, and cell chemistries. The combination of multiple parameters in ENPOLITE facilitated a thorough comparison of various batteries' respective lifetimes. In addition to the cell-specific parameters during cycling, the specific stored energy and the storage temperature were depicted in a calendar ENPOLITE-Plot.

ACS Style

Philipp Dechent; Alexander Epp; Dominik Jöst; Yuliya Preger; Peter Attia; Weihan Li; Dirk Uwe Sauer. ENPOLITE: Comparing Lithium-Ion Cells across Energy, Power, Lifetime, and Temperature. 2021, 1 .

AMA Style

Philipp Dechent, Alexander Epp, Dominik Jöst, Yuliya Preger, Peter Attia, Weihan Li, Dirk Uwe Sauer. ENPOLITE: Comparing Lithium-Ion Cells across Energy, Power, Lifetime, and Temperature. . 2021; ():1.

Chicago/Turabian Style

Philipp Dechent; Alexander Epp; Dominik Jöst; Yuliya Preger; Peter Attia; Weihan Li; Dirk Uwe Sauer. 2021. "ENPOLITE: Comparing Lithium-Ion Cells across Energy, Power, Lifetime, and Temperature." , no. : 1.

Journal article
Published: 20 February 2021 in Journal of Energy Storage
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For vehicle electrical systems, high-power optimized lithium-ion batteries offer superior cycle stability, compactness and weight compared to conventional lead–acid batteries. To identify lithium-ion cell candidates during early concept and development phases, both performance characteristics and a comparison of commercialized lithium-ion cells covering different cell chemistries are needed. Since the market share of high-power lithium ion cells is limited, scientific studies and extensive characterizations are rare. This study closes the gap by benchmarking state-of-the-art high-power cells considering the requirements of 12 V/48 V applications. The sensible begin-of-life parameters OCV, internal resistance, and capacity were investigated by stepwise OCV measurement, pulse power characterization and capacity measurement regarding the dimensions: SOC (0% to 100%), temperature (−25 °C to +55 °C) and current rate (up to 30C). All cells exhibit temperature dependent OCV curves, with ambient temperatures above zero hardly affecting the OCV hysteresis. A SOC dependency of the internal resistance of the tested lithium titanate oxide cell reduces the power capability, available cell capacity and energy efficiency. This cell, in contrast to the graphite-based cells, enables a neglection of a Butler–Volmer dependency and offers high charge acceptance at negative temperatures. The internal resistance of the lithium iron phosphate cell is less affected by SOC which allows for constant power output. Above 25 °C and up to 15C, energy efficiencies of the graphite-based cells exceed 95%. We conclude that the lithium iron phosphate cell is best suited for 12 V applications due to its voltage band and discharge characteristics. None of the cells stand out for use in 48 V applications. Our findings from benchmarking among different cell chemistries are beneficial to other research areas such as battery simulation, battery management systems, or cell/system design.

ACS Style

Thomas Bank; Sebastian Klamor; Nicholas Löffler; Dirk Uwe Sauer. Performance benchmark of state-of-the-art high-power lithium-ion cells and implications for their usability in low-voltage applications. Journal of Energy Storage 2021, 36, 102383 .

AMA Style

Thomas Bank, Sebastian Klamor, Nicholas Löffler, Dirk Uwe Sauer. Performance benchmark of state-of-the-art high-power lithium-ion cells and implications for their usability in low-voltage applications. Journal of Energy Storage. 2021; 36 ():102383.

Chicago/Turabian Style

Thomas Bank; Sebastian Klamor; Nicholas Löffler; Dirk Uwe Sauer. 2021. "Performance benchmark of state-of-the-art high-power lithium-ion cells and implications for their usability in low-voltage applications." Journal of Energy Storage 36, no. : 102383.

Journal article
Published: 12 February 2021 in Journal of Energy Storage
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In this paper, we propose an energy management strategy based on deep reinforcement learning for a hybrid battery system in electric vehicles consisting of a high-energy and a high-power battery pack. The energy management strategy of the hybrid battery system was developed based on the electrical and thermal characterization of the battery cells, aiming at minimizing the energy loss and increasing both the electrical and thermal safety level of the whole system. Primarily, we designed a novel reward term to explore the optimal operating range of the high-power pack without imposing a rigid constraint of state of charge. Furthermore, various load profiles were randomly combined to train the deep Q-learning model, which avoided the overfitting problem. The training and validation results showed both the effectiveness and reliability of the proposed strategy in loss reduction and safety enhancement. The proposed energy management strategy has demonstrated its superiority over the reinforcement learning-based methods in both computation time and energy loss reduction of the hybrid battery system, highlighting the use of such an approach in future energy management systems.

ACS Style

Weihan Li; Han Cui; Thomas Nemeth; Jonathan Jansen; Cem Ünlübayir; Zhongbao Wei; Lei Zhang; Zhenpo Wang; Jiageng Ruan; Haifeng Dai; Xuezhe Wei; Dirk Uwe Sauer. Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles. Journal of Energy Storage 2021, 36, 102355 .

AMA Style

Weihan Li, Han Cui, Thomas Nemeth, Jonathan Jansen, Cem Ünlübayir, Zhongbao Wei, Lei Zhang, Zhenpo Wang, Jiageng Ruan, Haifeng Dai, Xuezhe Wei, Dirk Uwe Sauer. Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles. Journal of Energy Storage. 2021; 36 ():102355.

Chicago/Turabian Style

Weihan Li; Han Cui; Thomas Nemeth; Jonathan Jansen; Cem Ünlübayir; Zhongbao Wei; Lei Zhang; Zhenpo Wang; Jiageng Ruan; Haifeng Dai; Xuezhe Wei; Dirk Uwe Sauer. 2021. "Deep reinforcement learning-based energy management of hybrid battery systems in electric vehicles." Journal of Energy Storage 36, no. : 102355.

Journal article
Published: 26 October 2020 in Journal of Power Sources
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There is an increasing demand for modern diagnostic systems for batteries under real-world operation, specifically for the estimation of their state of health, for example, via their remaining capacity. The online estimation of the capacity of a cell is challenging due to the dynamic nature of cell aging and the limited variety of inputs available from a cell under operation. The scope of this work is the development of a data-driven capacity estimation model for cells under real-world working conditions with recurrent neural networks having long short-term memory capability. Voltage-time sensor data from the partial constant current phase charging curve is used as input, reflecting input availability in the real world. The network achieves a best-case mean absolute percentage error of 0.76% and is extremely robust while handling input noise. It also has the ability to handle variations in the length of the input time series and can generate a viable estimation even with an incomplete collection of input due to sensor errors. The model validation with several scenarios is done in a local embedded device, highlighting the use case of such models in future battery management systems.

ACS Style

Weihan Li; Neil Sengupta; Philipp Dechent; David Howey; Anuradha Annaswamy; Dirk Uwe Sauer. Online capacity estimation of lithium-ion batteries with deep long short-term memory networks. Journal of Power Sources 2020, 482, 228863 .

AMA Style

Weihan Li, Neil Sengupta, Philipp Dechent, David Howey, Anuradha Annaswamy, Dirk Uwe Sauer. Online capacity estimation of lithium-ion batteries with deep long short-term memory networks. Journal of Power Sources. 2020; 482 ():228863.

Chicago/Turabian Style

Weihan Li; Neil Sengupta; Philipp Dechent; David Howey; Anuradha Annaswamy; Dirk Uwe Sauer. 2020. "Online capacity estimation of lithium-ion batteries with deep long short-term memory networks." Journal of Power Sources 482, no. : 228863.

Journal article
Published: 26 August 2020 in Journal of Power Sources
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The use of reduced-order electrochemical models creates opportunities for battery management systems to control the battery behavior by monitoring the internal states in electrochemical processes, which are critical for safety enhancement and degradation mitigation. This paper explores a state observer for lithium-ion batteries based on an extended single-particle model, which results in a trade-off between high accuracy and low computational burden, thus enables the real-time application. An adaptive unscented Kalman filter based on this model is developed to estimate not only the state of charge but also lithium-ion concentrations and potentials, which precisely describe battery internal behaviors to avoid lithium plating. Experimental tests are carried out with a lithium-ion battery cell for both model and state estimation validations. Furthermore, the estimation accuracies of the unmeasurable states are also verified by numerical validation tests with a high-fidelity electrochemical model. All estimated states present fast convergence, robustness, and high accuracy despite a 20% initial state-of-charge error. Additionally, the enhancement in the state estimation accuracy and robustness by the new noise adaption step is demonstrated by an application-relevant evaluation framework, considering sensor noise, state uncertainty, parameter uncertainty, and computation time.

ACS Style

Weihan Li; Yue Fan; Florian Ringbeck; Dominik Jöst; Xuebing Han; Minggao Ouyang; Dirk Uwe Sauer. Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter. Journal of Power Sources 2020, 476, 228534 .

AMA Style

Weihan Li, Yue Fan, Florian Ringbeck, Dominik Jöst, Xuebing Han, Minggao Ouyang, Dirk Uwe Sauer. Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter. Journal of Power Sources. 2020; 476 ():228534.

Chicago/Turabian Style

Weihan Li; Yue Fan; Florian Ringbeck; Dominik Jöst; Xuebing Han; Minggao Ouyang; Dirk Uwe Sauer. 2020. "Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter." Journal of Power Sources 476, no. : 228534.

Journal article
Published: 25 August 2020 in eTransportation
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Electric vehicle (EV) charging infrastructure is a new type of consumer in the power grid. Oftentimes, theoretical models have to be used to understand the impact of these new assets since little empirical data of charging station usage is available. This paper aims to increase understanding of charging station usage by providing empirical data collected from 26,951 charging station connectors in Germany. We demonstrate that currently the usage intensity of stations is overall between 15% and 20% and therefore relatively low, but depends strongly on weekday and hour of the day. Fast-chargers are generally occupied less time compared to slower chargers while each charging event also takes significantly less time. A key challenge in optimizing real-world asset usage are EVs which are parked significantly longer at charging stations than the actual charging process takes. We show that an unexpectedly high share of charging events requires between 8 and 10 h indicating that people park their EVs before going to work and then picking them up after they finished working.

ACS Style

Christopher Hecht; Saurav Das; Christian Bussar; Dirk Uwe Sauer. Representative, empirical, real-world charging station usage characteristics and data in Germany. eTransportation 2020, 6, 100079 .

AMA Style

Christopher Hecht, Saurav Das, Christian Bussar, Dirk Uwe Sauer. Representative, empirical, real-world charging station usage characteristics and data in Germany. eTransportation. 2020; 6 ():100079.

Chicago/Turabian Style

Christopher Hecht; Saurav Das; Christian Bussar; Dirk Uwe Sauer. 2020. "Representative, empirical, real-world charging station usage characteristics and data in Germany." eTransportation 6, no. : 100079.

Journal article
Published: 06 August 2020 in Journal of The Electrochemical Society
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ACS Style

Lisa Kathrin Willenberg; Philipp Dechent; Georg Fuchs; Moritz Teuber; Marcel Eckert; Martin Graff; Niklas Kürten; Dirk Uwe Sauer; Egbert Figgemeier. The Development of Jelly Roll Deformation in 18650 Lithium-Ion Batteries at Low State of Charge. Journal of The Electrochemical Society 2020, 167, 120502 .

AMA Style

Lisa Kathrin Willenberg, Philipp Dechent, Georg Fuchs, Moritz Teuber, Marcel Eckert, Martin Graff, Niklas Kürten, Dirk Uwe Sauer, Egbert Figgemeier. The Development of Jelly Roll Deformation in 18650 Lithium-Ion Batteries at Low State of Charge. Journal of The Electrochemical Society. 2020; 167 (12):120502.

Chicago/Turabian Style

Lisa Kathrin Willenberg; Philipp Dechent; Georg Fuchs; Moritz Teuber; Marcel Eckert; Martin Graff; Niklas Kürten; Dirk Uwe Sauer; Egbert Figgemeier. 2020. "The Development of Jelly Roll Deformation in 18650 Lithium-Ion Batteries at Low State of Charge." Journal of The Electrochemical Society 167, no. 12: 120502.

Journal article
Published: 25 July 2020 in Journal of Power Sources
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Partial electrification of vehicle drive trains, for example by the usage of 48 V systems, require high-power batteries with extreme robustness to temperatures, current rates and energy throughputs. In this study, the application-relevant lifetime performance of 33 state-of-the-art high-power lithium titanate oxide nickel manganese cobalt oxide (LTO|NMC) cells is measured under cyclic, calendar, and drive cyclic aging regimes. Regular extended check-ups reveal the cell performance in terms of capacity loss and internal resistance increase, which allows for the identification of critical operating conditions. For the first time a passive electrode effect is identified in calendar aging tests of LTO cells in which the cathode is geometrically and capacitively oversized. Passive electrode areas lead to a change in cell balancing, which can be illustrated by the shift of the half-cell voltage curves. Generally, the investigated cells show an excellent cycle stability for shallow cycles, even at high ambient temperatures and high current rates. Only large cycle depths greater than 70% at elevated temperatures reduce the battery life significantly. Furthermore, the results show that cells cycled in areas of low state of charge age faster than in areas of high state of charge. The rise in internal resistance under calendar aging has the most detrimental influence on lifetime in a 48 V battery.

ACS Style

Thomas Bank; Jan Feldmann; Sebastian Klamor; Stephan Bihn; Dirk Uwe Sauer. Extensive aging analysis of high-power lithium titanate oxide batteries: Impact of the passive electrode effect. Journal of Power Sources 2020, 473, 228566 .

AMA Style

Thomas Bank, Jan Feldmann, Sebastian Klamor, Stephan Bihn, Dirk Uwe Sauer. Extensive aging analysis of high-power lithium titanate oxide batteries: Impact of the passive electrode effect. Journal of Power Sources. 2020; 473 ():228566.

Chicago/Turabian Style

Thomas Bank; Jan Feldmann; Sebastian Klamor; Stephan Bihn; Dirk Uwe Sauer. 2020. "Extensive aging analysis of high-power lithium titanate oxide batteries: Impact of the passive electrode effect." Journal of Power Sources 473, no. : 228566.

Journal article
Published: 10 July 2020 in Journal of Energy Storage
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Lithium-ion batteries are widely used in transportation applications due to their outstanding performance in terms of energy and power density as well as efficiency and lifetime. Although various cell chemistries exist, most of today’s electric vehicles on the market have a high-voltage lithium-ion battery system consisting of cells with a graphite-based anode and a metal-oxide cathode. These cells offer a high specific energy density that enables long driving ranges at moderate costs. For applications where power density is the critical design criterion, cells with lithium titanate oxide-based anode materials can be an alternative. These cells offer further advantages such as improved cycle stability and good charge acceptance even at temperatures below 0∘C. This paper presents different applications for high-power batteries in electrified vehicles and compares the requirements for suitable battery cells. After an introduction to lithium titanate oxide as anode material in battery cells, electrical and thermal characteristics are presented. For this reason, measurements were performed with two cells using different cathode active materials and a lithium titanate oxide-based anode. Aging behavior is investigated with lifetime tests performed under high-current cycling conditions at two different ambient temperatures. Differences in the capacity loss rate are shown and lifetime considerations are presented. Furthermore, an incremental capacity analysis is performed at different times in the aging study for a deeper analysis of the aging effects occurring in the two cell types. Finally, cost considerations of lithium titanate oxide-based battery cells with different properties are presented. Varied production volumes are considered and production costs are compared with costs of state-of-the-art graphite-based high-energy battery cells.

ACS Style

Thomas Nemeth; Philipp Schröer; Matthias Kuipers; Dirk Uwe Sauer. Lithium titanate oxide battery cells for high-power automotive applications – Electro-thermal properties, aging behavior and cost considerations. Journal of Energy Storage 2020, 31, 101656 .

AMA Style

Thomas Nemeth, Philipp Schröer, Matthias Kuipers, Dirk Uwe Sauer. Lithium titanate oxide battery cells for high-power automotive applications – Electro-thermal properties, aging behavior and cost considerations. Journal of Energy Storage. 2020; 31 ():101656.

Chicago/Turabian Style

Thomas Nemeth; Philipp Schröer; Matthias Kuipers; Dirk Uwe Sauer. 2020. "Lithium titanate oxide battery cells for high-power automotive applications – Electro-thermal properties, aging behavior and cost considerations." Journal of Energy Storage 31, no. : 101656.

Journal article
Published: 04 July 2020 in Journal of Energy Storage
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Lithium-ion batteries undergo random vibrations in automotive applications, for example due to rough road surfaces. So far, no investigation based on random vibration has considered the influence of the inner cell design and the influence of cyclic aging on vibration durability. Therefore, in this study, 18 different 18650 cell types from seven different manufacturers are tested, using two random vibration load profiles. The applied vibration profiles are the random profile according to the standard SAE J2380 and another upscaled, more severe profile. The SAE J2380 test is carried out using both new and electrically pre-cycled cells. All cell types are analyzed by computed tomography in terms of their inner design with a focus on inner mandrel, spacer and tab design. The performance of the cells is checked in terms of capacity, electrochemical impedance spectroscopy and post-vibration computed tomography. None of the cells shows significant electrical performance degradation due to the vibration. Post-vibration computed tomography reveals mechanically damaged negative current collector tabs inside of two cell types due to a loose mandrel in the case of the upscaled profile. These cell types have the lowest ratio of mandrel diameter to inner jelly roll diameter, emphasizing the importance of the inner cell design for vibration durability.

ACS Style

Philipp Berg; Markus Spielbauer; Michael Tillinger; Matthias Merkel; Maik Schoenfuss; Oliver Bohlen; Andreas Jossen. Durability of lithium-ion 18650 cells under random vibration load with respect to the inner cell design. Journal of Energy Storage 2020, 31, 101499 .

AMA Style

Philipp Berg, Markus Spielbauer, Michael Tillinger, Matthias Merkel, Maik Schoenfuss, Oliver Bohlen, Andreas Jossen. Durability of lithium-ion 18650 cells under random vibration load with respect to the inner cell design. Journal of Energy Storage. 2020; 31 ():101499.

Chicago/Turabian Style

Philipp Berg; Markus Spielbauer; Michael Tillinger; Matthias Merkel; Maik Schoenfuss; Oliver Bohlen; Andreas Jossen. 2020. "Durability of lithium-ion 18650 cells under random vibration load with respect to the inner cell design." Journal of Energy Storage 31, no. : 101499.

Journal article
Published: 23 June 2020 in Journal of Energy Storage
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Battery management is critical to enhancing the safety, reliability, and performance of the battery systems. This paper presents a cloud battery management system for battery systems to improve the computational power and data storage capability by cloud computing. With the Internet of Things, all battery relevant data are measured and transmitted to the cloud seamlessly, building up the digital twin for the battery system, where battery diagnostic algorithms evaluate the data and open the window into battery’s charge and aging level. The application of equivalent circuit models in the digital twin for battery systems is explored with the development of cloud-suited state-of-charge and state-of-health estimation approaches. The proposed state-of-charge estimation with an adaptive extended H-infinity filter is robust and accurate for both lithium-ion and lead-acid batteries, even with a significant initialization error. Furthermore, a state-of-health estimation algorithm with particle swarm optimization is innovatively exploited to monitor both capacity fade and power fade of the battery during aging. The functionalities and stability of both hardware and software of the cloud battery management system are validated with prototypes under field operation and experimental validation for both stationary and mobile applications.

ACS Style

Weihan Li; Monika Rentemeister; Julia Badeda; Dominik Jöst; Dominik Schulte; Dirk Uwe Sauer. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. Journal of Energy Storage 2020, 30, 101557 .

AMA Style

Weihan Li, Monika Rentemeister, Julia Badeda, Dominik Jöst, Dominik Schulte, Dirk Uwe Sauer. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. Journal of Energy Storage. 2020; 30 ():101557.

Chicago/Turabian Style

Weihan Li; Monika Rentemeister; Julia Badeda; Dominik Jöst; Dominik Schulte; Dirk Uwe Sauer. 2020. "Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation." Journal of Energy Storage 30, no. : 101557.

Journal article
Published: 18 June 2020 in Journal of Energy Storage
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This paper provides a method for the systematic analysis of several power flow control strategies (PFCS) in a heterogeneous multiple battery energy storage system (BESS). Due to the difficulty of comparing different PFCS in different scenarios and system configurations, in total five static and dynamic PFCS are investigated in two distinct application-oriented scenarios and are assessed by two target indicators, namely performance and efficiency. A simulation model based on a heterogeneous multiple BESS with a hierarchical control scheme is set up, the components of the model are validated and a real test bench verifies the functionality of the PFCS. Simulations in MATLAB/Simulink are carried out to analyze the performance and the efficiency of the applied PFCS in an energy- and a load-related application scenario. This approach allows a systematic comparison of PFCS in consideration of varying system parameters and different scenarios. The results suggest that depending on the objective to be achieved and the applied application scenario, the choice of the right PFCS is crucial. Thus, this paper provides a workflow to analyze the selection of proper PFCS. Furthermore, heterogeneity plays a decisive role in analyzing different PFCS, since operating limits of the BESS become more significant.

ACS Style

Markus Mühlbauer; Oliver Bohlen; Michael A. Danzer. Analysis of power flow control strategies in heterogeneous battery energy storage systems. Journal of Energy Storage 2020, 30, 101415 .

AMA Style

Markus Mühlbauer, Oliver Bohlen, Michael A. Danzer. Analysis of power flow control strategies in heterogeneous battery energy storage systems. Journal of Energy Storage. 2020; 30 ():101415.

Chicago/Turabian Style

Markus Mühlbauer; Oliver Bohlen; Michael A. Danzer. 2020. "Analysis of power flow control strategies in heterogeneous battery energy storage systems." Journal of Energy Storage 30, no. : 101415.

Journal article
Published: 16 June 2020 in Energies
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The emergence of electric vehicles offers the opportunity to decarbonize the transportation and mobility sector. With smart charging strategies and the use of electricity generated from renewable sources, electric vehicle owners can reduce their electricity bill as well as reduce their carbon footprint. We investigated smart charging strategies for electric vehicle charging at household and workplace sites with photovoltaic systems. Furthermore, we investigated the participation of an electric vehicle in the provision of positive automatic frequency restoration reserve (aFRR) in Germany from 30 October 2018 to 31 July 2019. We find that the provision of positive aFRR in Germany returns a positive net return. The positive net return is, however, not sufficient to cover the current investment cost for a necessary control unit. For home charging, we find that self-sufficiency rates of up to 48.1% and an electricity cost reduction of 17.6% for one year can be reached with unidirectional smart charging strategies. With bidirectional strategies, self-sufficiency rates of up to 56.7% for home charging and electricity cost reductions of up to 26.1% are reached. We also find that electric vehicle (EV) owners who can charge at their workplace can reduce their electricity cost further. The impact of smart charging strategies on battery aging is also discussed.

ACS Style

Fabian Rücker; Michael Merten; Jingyu Gong; Roberto Villafáfila-Robles; Ilka Schoeneberger; Dirk Uwe Sauer. Evaluation of the Effects of Smart Charging Strategies and Frequency Restoration Reserves Market Participation of an Electric Vehicle. Energies 2020, 13, 3112 .

AMA Style

Fabian Rücker, Michael Merten, Jingyu Gong, Roberto Villafáfila-Robles, Ilka Schoeneberger, Dirk Uwe Sauer. Evaluation of the Effects of Smart Charging Strategies and Frequency Restoration Reserves Market Participation of an Electric Vehicle. Energies. 2020; 13 (12):3112.

Chicago/Turabian Style

Fabian Rücker; Michael Merten; Jingyu Gong; Roberto Villafáfila-Robles; Ilka Schoeneberger; Dirk Uwe Sauer. 2020. "Evaluation of the Effects of Smart Charging Strategies and Frequency Restoration Reserves Market Participation of an Electric Vehicle." Energies 13, no. 12: 3112.