Analytical Modeling of a Segmented Bimorph Lithium Ion Battery Actuator

2020 ◽  
Author(s):  
Cody Gonzalez ◽  
Shuhua Shan ◽  
Mary Frecker ◽  
Christopher Rahn
Keyword(s):  
Range Of Motion ◽  
Lithium Ion Battery ◽  
Shape Change ◽  
Lithium Ion ◽  
Internal Resistance ◽  
Composite Anode ◽  
Volumetric Expansion ◽  
Single Side ◽  
Battery Capacity ◽  
A Charge

Abstract Silicon composite anode-based lithium ion battery actuators have strong potential for large actuation strain, complex shape change, and large compliance tunability. This allows for actuators with potential to do work with no energy expenditure beyond parasitic losses to internal resistance. This paper studies a segmented bimorph actuator comprised of a silicon-based composite anode double-side coated on copper foil. The bimorph design allows for a nearly 360° range of motion in transverse bending and can double the capacity of the battery in comparison to a single side coated unimorph design. An Euler-Bernoulli model is developed to predict free deflection for a range of geometric configurations and states of charge of the bimorph. By modeling the charging of both active sides of the bimorph simultaneously, it is possible to achieve zero bending while still accommodating for the volumetric expansion of the silicon. The bimorph is able to achieve actuation in one given direction by charging each anode coating separately or maintaining a charge difference between the two coatings. The former method allows for the accommodated volumetric expansion of silicon while maintaining zero bending as in a conventional battery setup. The latter method allows for larger battery capacity and range of motion for a novel electrochemically-based actuation mechanism.

High-energy-density lithium-ion battery using a carbon-nanotube–Si composite anode and a compositionally graded Li[Ni0.85Co0.05Mn0.10]O2 cathode

2016 ◽  
Vol 9 (6) ◽  
pp. 2152-2158 ◽  
Author(s):  
Joo Hyeong Lee ◽  
Chong S. Yoon ◽  
Jang-Yeon Hwang ◽  
Sung-Jin Kim ◽  
Filippo Maglia ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Energy Density ◽  
Lithium Ion Battery ◽  
State Of The Art ◽  
Lithium Ion ◽  
High Energy ◽  
High Energy Density ◽  
Rechargeable Battery ◽  
Composite Anode ◽  
Battery System

A Li-rechargeable battery system based on state-of-the-art cathode and anode technologies demonstrated high energy density, meeting demands for vehicle application.

Ball-Milled Graphite-Tin Composite Anode Materials for Lithium-Ion Battery

2012 ◽  
Vol 736 ◽  
pp. 127-132
Author(s):  
Kuldeep Rana ◽  
Anjan Sil ◽  
Subrata Ray
Keyword(s):  
Lithium Ion Battery ◽  
Anode Materials ◽  
High Capacity ◽  
Lithium Ion ◽  
Composite Anode ◽  
X Ray Diffraction ◽  
Promising Alternative ◽  
X Ray ◽  
Initial Discharge ◽  
Lithium Ion Cell

Lithium alloying compounds as an anode materials have been a focused for high capacity lithium ion battery due to their highenergy capacity and safety characteristics. Here we report on the preparation of graphite-tin composite by using ball-milling in liquid media. The composite material has been characterized by scanning electron microscope, energy depressive X-ray spectroscopy, X-ray diffraction and Raman spectra. The lithium-ion cell made from graphite-tin composite presented initial discharge capacity of 1065 mAh/g and charge capacity 538 mAh/g, which becomes 528 mAh/g in the second cycle. The composite of graphite-tin with higher capacity compared to pristine graphite is a promising alternative anode material for lithium-ion battery.

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.

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

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