Estimating the Internal Resistance of Li-Ion Battery with Joule's Law to find the Open Circuit Voltage

2021 ◽  
Author(s):  
Venkata Nagarjun PM ◽  
Hirshik Ram S ◽  
Pratik Uthan ◽  
Veeramani V ◽  
Senthilkumar Subramaniam
Keyword(s):  
Open Circuit Voltage ◽  
Internal Resistance ◽  
Open Circuit ◽  
Li Ion Battery ◽  
Li Ion

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.

scholarly journals Experimental assessment of the lumped lithium–ion battery model

2019 ◽  
Vol 128 ◽  
pp. 01022
Author(s):  
Tanilay Özdemir ◽  
Ali Amini ◽  
Özgür Ekici ◽  
Murat Köksal
Keyword(s):  
Open Circuit Voltage ◽  
Effective Properties ◽  
Lithium Ion ◽  
Open Circuit ◽  
Computational Effort ◽  
Li Ion Battery ◽  
Electrical Behavior ◽  
Lumped Model ◽  
Battery Model ◽  
Li Ion

In this study, an axisymmetric computational lumped model is used to investigate the thermal and electrical behavior of a cylindrical Lithium-ion (Li–ion) battery during various discharging processes at 0-20-50°C operating temperatures. A typical cylindrical Li–ion cell consists ofmultiple spiral layers, on the other hand, the model employs the lumped battery interface approach in COMSOL Multiphysics to reduce the computational effort. In the lumped approach, layers of different materials are approximated as a uniform material with effective properties. Among other parameters, the Open Circuit Voltage (OCV) as a function of the State of Charge (SOC) is determined experimentally andused as input to the model. The model is then used to analyze the effects of the correlations of various parameters on the transient electrical and thermal responses of the battery and validated by comparing predicted results with experimental data.

scholarly journals Estimation of State of Charge (SoC) Using Modified Coulomb Counting Method With Open Circuit Compensation For Battery Management System (BMS)

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Puspita Ningrum ◽  
Novie Ayub Windarko ◽  
Suhariningsih Suhariningsih
Keyword(s):  
Management System ◽  
Voltage Divider ◽  
Open Circuit ◽  
Voltage Sensor ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
Li Ion Battery ◽  
Control Center ◽  
Li Ion

Abstract— Battery is one of the important components in the development of renewable energy technology. This paper presents a method for estimating the State of Charge (SoC) for a 4Ah Li-ion battery. State of Charge (SoC) is the status of the capacity in the battery in the form of a percentage which makes it easier to monitor the battery during use. Coulomb calculations are widely used, but this method still contains errors during integration. In this paper, SoC measurement using Open Circuit Voltage Compensation is used for the determination of the initial SoC, so that the initial SoC reading is more precise, because if the initial SoC reading only uses a voltage sensor, the initial SoC reading is less precise which affects the next n second SoC reading. In this paper, we present a battery management system design or commonly known as BMS (Battery Management System) which focuses on the monitoring function. BMS uses a voltage sensor in the form of a voltage divider circuit and an ACS 712 current sensor to send information about the battery condition to the microcontroller as the control center. Besides, BMS is equipped with a protection relay to protect the battery. The estimation results of the 12volt 4Ah Li-ion battery SoC with the actual reading show an error of less than 1%.Keywords—Battery Management System, Modified Coulomb Counting, State of Charge.

Internal Resistance Based Assessment Model for the Degradation of Li-Ion Battery Pack

2020 ◽  
Author(s):  
Iffandya Popy Wulandari ◽  
Min-Chun Pan
Keyword(s):  
Internal Resistance ◽  
Remaining Useful Life ◽  
Assessment Model ◽  
Battery Pack ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Battery Management System ◽  
Li Ion Battery ◽  
Battery Packs ◽  
Li Ion

Abstract As one pioneer means for energy storage, Li-ion battery packs have a complex and critical issue about degradation monitoring and remaining useful life estimation. It induces challenges on condition characterization of Li-ion battery packs such as internal resistance (IR). The IR is an essential parameter of a Li-ion battery pack, relating to the energy efficiency, power performance, degradation, and physical life of the li-ion battery pack. This study aims to obtain reliable IR through applying an evaluation test that acquires data such as voltage, current, and temperature provided by the battery management system (BMS). Additionally, this paper proposes an approach to predict the degradation of Li-ion battery pack using support vector regression (SVR) with RBF kernel. The modeling approach using the relationship between internal resistance, different SOC levels 20%–100%, and cycle at the beginning of life 1 cycle until cycle 500. The data-driven method is used here to achieve battery life prediction.based on internal resistance behavior in every period using supervised machine learning, SVR. Our experiment result shows that the internal resistance was increasing non-linear, approximately 0.24%, and it happened if the cycle rise until 500 cycles. Besides, using SVR algorithm, the quality of the fitting was evaluated using coefficient determination R2, and the score is 0.96. In the proposed modeling process of the battery pack, the value of MSE is 0.000035.

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