Characterization, average and electronic structures during charge–discharge cycle in 0.6Li2MnO3–0.4Li(Co1/3Ni1/3Mn1/3)O2 solid solution of a cathode active material for Li-ion battery

2015 ◽  
Vol 273 ◽  
pp. 1023-1029 ◽  
Author(s):  
Yasushi Idemoto ◽  
Ryosuke Kawai ◽  
Naoya Ishida ◽  
Naoto Kitamura
Keyword(s):  
Solid Solution ◽  
Electronic Structures ◽  
Active Material ◽  
Li Ion Battery ◽  
Discharge Cycle ◽  
Li Ion ◽  
Charge Discharge

scholarly journals Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes

Batteries ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 54 ◽  
Author(s):  
Yoichi Takagishi ◽  
Takumi Yamanaka ◽  
Tatsuya Yamaue
Keyword(s):  
Machine Learning ◽  
Process Parameters ◽  
Volume Fraction ◽  
Specific Resistance ◽  
Active Material ◽  
Li Ion Battery ◽  
Battery Electrode ◽  
Li Ion ◽  
Simulation Results ◽  
Charge Discharge

We have proposed a data-driven approach for designing the mesoscale porous structures of Li-ion battery electrodes, using three-dimensional virtual structures and machine learning techniques. Over 2000 artificial 3D structures, assuming a positive electrode composed of randomly packed spheres as the active material particles, are generated, and the charge/discharge specific resistance has been evaluated using a simplified physico-chemical model. The specific resistance from Li diffusion in the active material particles (diffusion resistance), the transfer specific resistance of Li+ in the electrolyte (electrolyte resistance), and the reaction resistance on the interface between the active material and electrolyte are simulated, based on the mass balance of Li, Ohm’s law, and the linearized Butler–Volmer equation, respectively. Using these simulation results, regression models, using an artificial neural network (ANN), have been created in order to predict the charge/discharge specific resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder/additives volume fraction are adopted, as features associated with controllable process parameters for manufacturing the battery electrode. As a result, the predicted electrode specific resistance by the ANN regression model is in good agreement with the simulated values. Furthermore, sensitivity analyses and an optimization of the process parameters have been carried out. Although the proposed approach is based only on the simulation results, it could serve as a reference for the determination of process parameters in battery electrode manufacturing.

scholarly journals Machine Learning Approaches for Designing Meso-scale Structure of Li-ion battery Electrode

2019 ◽  
Author(s):  
Yoichi Takagishi ◽  
Takumi Yamanaka ◽  
Tatsuya Yamaue
Keyword(s):  
Machine Learning ◽  
Process Parameters ◽  
Volume Fraction ◽  
Active Material ◽  
Data Driven ◽  
Li Ion Battery ◽  
Battery Electrode ◽  
Data Driven Approach ◽  
Li Ion ◽  
Charge Discharge

We have proposed a data-driven approach for designing mesoscale porous structures of Li-ion battery electrode with three-dimensional virtual structures and machine learning techniques. Over 2,000 artificial 3D structures assuming positive electrode composed of random packed spheres as active material particles are generated, and charge/discharge resistance has been evaluated using simplified Physico-chemical model. In this model, resistance from Li diffusion in active material particles (diffusion resistance), transfer resistance of Li+ in electrolyte (electrolyte resistance) and reaction resistance on the interface between active material and electrolyte are simulated based on mass balance of Li, Ohm’s law in and linearized Butler-Volmer equation, respectively. Using these simulation results, regression models via Artificial Neural Network (ANN) have been created in order to predict charge/discharge resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder volume fraction are adopted as features, associated with controllable process parameters for manufacturing battery electrode. As results, the predicted electrode resistance by ANN regression model is good agreement with the simulated values. Furthermore, sensitivity analysis and optimization of the process parameters have been carried out. The proposed data-driven approach could be a solution as a guiding principle for manufacturing battery electrode.

scholarly journals The effect of cooling process on the structure and charge/discharge capacities of Li-rich solid-solution layered oxide cathode materials for the Li-ion battery

RSC Advances ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 1715-1728
Author(s):  
Fumihiro Nomura ◽  
Tatsuya Watanabe ◽  
Hiroya Ochiai ◽  
Takao Gunji ◽  
Takeshi Hagiwara ◽  
...  
Keyword(s):  
Solid Solution ◽  
Discharge Capacity ◽  
Li Ion Battery ◽  
Cooling Process ◽  
Slow Cooling ◽  
Oxide Cathode ◽  
Li Ion ◽  
The Difference ◽  
Discharge Capacities ◽  
Charge Discharge

The difference in discharge capacity = (discharge capacity of the LLO sample prepared by quenched cooling) − (discharge capacity of the LLO sample prepared by slow cooling). The difference in discharge capacity: > 10 mA h g−1, −10 mA h g−1 < <10 mA h g−1, < −10 mA h g−1.

MoS2/Graphene Heterostructure Anode for Li-Ion Battery Application: A First-Principles Study

2021 ◽  
Vol 896 ◽  
pp. 53-59
Author(s):  
Yi Yang Shen
Keyword(s):  
Density Of States ◽  
First Principles ◽  
Open Circuit Voltage ◽  
Discharge Rate ◽  
Open Circuit ◽  
Crystal Formation ◽  
Li Ion Battery ◽  
Li Ion ◽  
Battery Anode ◽  
Charge Discharge

The development of next generation Li ion battery has attracted many attentions of researchers due to the rapidly increasing demands to portable energy storage devices. General Li metal/alloy anodes are confronted with challenges of dendritic crystal formation and slow charge/discharge rate. Recently, the prosperity of two-dimensional materials opens a new window for the design of battery anode. In the present study, MoS2/graphene heterostructure is investigate for the anode application of Li ion battery using first-principles calculations. The Li binding energy, open-circuit voltage, and electronic band structures are acquired for various Li concentrations. We found the open-circuit voltage decreases from ~2.28 to ~0.4 V for concentration from 0 to 1. Density of states show the electrical conductivity of the intercalated heterostructures can be significantly enhanced. The charge density differences are used to explain the variations of voltage and density of states. Last, ~0.43 eV diffusion energy barrier of Li implies the possible fast charge/discharge rate. Our study indicate MoS2/graphene heterostructure is promising material as Li ion battery anode.

Sign in / Sign up

Export Citation Format

Share Document