Toward Data‐Driven Applications in Lithium‐Ion Battery Cell Manufacturing

2019 ◽  
Vol 8 (2) ◽  
pp. 1900136 ◽  
Author(s):  
Artem Turetskyy ◽  
Sebastian Thiede ◽  
Matthias Thomitzek ◽  
Nicolas von Drachenfels ◽  
Till Pape ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Data Driven ◽  
Battery Cell ◽  
Cell Manufacturing

scholarly journals Data-driven cyber-physical System for Quality Gates in Lithium-ion Battery Cell Manufacturing

Procedia CIRP ◽  
2020 ◽  
Vol 93 ◽  
pp. 168-173 ◽  
Author(s):  
Artem Turetskyy ◽  
Jacob Wessel ◽  
Christoph Herrmann ◽  
Sebastian Thiede
Keyword(s):  
Lithium Ion Battery ◽  
Physical System ◽  
Lithium Ion ◽  
Data Driven ◽  
Cyber Physical System ◽  
Battery Cell ◽  
Cell Manufacturing

scholarly journals Integrated Material-Energy-Quality Assessment for Lithium-ion Battery Cell Manufacturing

Procedia CIRP ◽  
2021 ◽  
Vol 98 ◽  
pp. 388-393
Author(s):  
Jacob Wessel ◽  
Artem Turetskyy ◽  
Felipe Cerdas ◽  
Christoph Herrmann
Keyword(s):  
Quality Assessment ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Cell ◽  
Cell Manufacturing ◽  
Material Energy

scholarly journals Effect of dynamic loads and vibrations on lithium-ion batteries

2021 ◽  
pp. 146134842110081
Author(s):  
Xia Hua ◽  
Alan Thomas
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Experimental Studies ◽  
Lithium Ion ◽  
Battery Pack ◽  
Dynamic Loads ◽  
Electrical Performance ◽  
Mechanical Degradation ◽  
Energy Storage Devices ◽  
Battery Cell

Lithium-ion batteries are being increasingly used as the main energy storage devices in modern mobile applications, including modern spacecrafts, satellites, and electric vehicles, in which consistent and severe vibrations exist. As the lithium-ion battery market share grows, so must our understanding of the effect of mechanical vibrations and shocks on the electrical performance and mechanical properties of such batteries. Only a few recent studies investigated the effect of vibrations on the degradation and fatigue of battery cell materials as well as the effect of vibrations on the battery pack structure. This review focused on the recent progress in determining the effect of dynamic loads and vibrations on lithium-ion batteries to advance the understanding of lithium-ion battery systems. Theoretical, computational, and experimental studies conducted in both academia and industry in the past few years are reviewed herein. Although the effect of dynamic loads and random vibrations on the mechanical behavior of battery pack structures has been investigated and the correlation between vibration and the battery cell electrical performance has been determined to support the development of more robust electrical systems, it is still necessary to clarify the mechanical degradation mechanisms that affect the electrical performance and safety of battery cells.

Post-lithium-ion battery cell production and its compatibility with lithium-ion cell production infrastructure

Nature Energy ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 123-134
Author(s):  
Fabian Duffner ◽  
Niklas Kronemeyer ◽  
Jens Tübke ◽  
Jens Leker ◽  
Martin Winter ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Cell ◽  
Cell Production ◽  
Lithium Ion Cell

scholarly journals Box–Jenkins Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycle Data

2021 ◽  
Vol 12 (3) ◽  
pp. 102
Author(s):  
Jaouad Khalfi ◽  
Najib Boumaaz ◽  
Abdallah Soulmani ◽  
El Mehdi Laadissi
Keyword(s):  
Lithium Ion Battery ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Lithium Ion ◽  
Drive Cycle ◽  
Cell Dynamics ◽  
Battery Cell ◽  
Parameter Identifications

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.

An Active Equalizer for Serially Connected Lithium-Ion Battery Cells

2013 ◽  
Vol 732-733 ◽  
pp. 809-812 ◽  
Author(s):  
Hong Rui Liu ◽  
Chao Ying Xia
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Experimental Results ◽  
Battery Cell ◽  
Charging Current ◽  
In Series ◽  
Battery Cells

This paper proposes an equalizer for serially connected Lithium-ion battery cells. The battery cell with the lowest state of charge (SOC) is charged by the equalizer during the process of charging and discharging, and the balancing current is constant and controllable. Three unbalanced lithium-ion battery cells in series are selected as the experimental object by this paper. The discharging current under a certain UDDS and 20A charging current are used to complete respectively one time balancing experiment of discharging and charging to the three lithium-ion battery cells. The validity of the balancing strategy is confirmed in this paper according to the experimental results.

A generalized additive model-based data-driven solution for lithium-ion battery capacity prediction and local effects analysis

2021 ◽  
pp. 014233122110579
Author(s):  
Tao Chen ◽  
Ciwei Gao ◽  
Hongxun Hui ◽  
Qiushi Cui ◽  
Huan Long
Keyword(s):  
Energy Storage ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Additive Models ◽  
Data Driven ◽  
Local Effects ◽  
Interaction Terms ◽  
Battery Capacity ◽  
Formulation Parameters ◽  
Capacity Prediction

Lithium-ion battery-based energy storage systems have been widely utilized in many applications such as transportation electrification and smart grids. As a key health status indicator, battery performance would highly rely on its capacity, which is easily influenced by various electrode formulation parameters within a battery. Due to the strongly coupled electrical, chemical, thermal dynamics, predicting battery capacity, and analysing the local effects of interested parameters within battery is significantly important but challenging. This article proposes an effective data-driven method to achieve effective battery capacity prediction, as well as local effects analysis. The solution is derived by using generalized additive models (GAM) with different interaction terms. Comparison study illustrate that the proposed GAM-based solution is capable of not only performing satisfactory battery capacity predictions but also quantifying the local effects of five important battery electrode formulation parameters as well as their interaction terms. Due to data-driven nature and explainability, the proposed method could benefit battery capacity prediction in an efficient manner and facilitate battery control for many other energy storage system applications.

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