scholarly journals Comparative Study of two Kalman Algorithms for Estimating the State of Charge of Lithium-Ion Cells at Ambient Temperature

2018 ◽  
pp. 12360-12379
Author(s):  
Fuwu Yan ◽  
Keyword(s):  
Comparative Study ◽  
Ambient Temperature ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Lithium Ion Cells

scholarly journals Degradation Reactions in SONY-Type Li-Ion Batteries

1999 ◽  
Vol 575 ◽  
Author(s):  
E. Peter Roth ◽  
G. Nagasubramanian
Keyword(s):  
Lithium Ion ◽  
Thermal Runaway ◽  
The State ◽  
State Of Charge ◽  
Reaction Products ◽  
Exothermic Reactions ◽  
Degradation Reactions ◽  
Lithium Ion Cells ◽  
Reaction Region ◽  
Charge Degree

ABSTRACTThermal instabilities were identified in SONY-type lithium-ion cells and correlated with interactions of cell constituents and reaction products. Three temperature regions of interaction were identified and associated with the state of charge (degree of Li intercalation) of the cell. Anodes were shown to undergo exothermic reactions as low as 100°C involving the solid electrolyte interface (SEI) layer and the LiPF6 salt in the electrolyte (EC:PC:DEC/LiPF6). These reactions could account for the thermal runaway observed in these cells beginning at 100°C. Exothermic reactions were also observed in the 200°C-300°C region between the intercalated lithium anodes, the LiPF6 salt, and the PVDF. These reactions were followed by a hightemperature reaction region, 300°C-400°C, also involving the PVDF binder and the intercalated lithium anodes. The solvent was not directly involved in these reactions but served as a moderator and transport medium. Cathode exothermic reactions with the PVDF binder were observed above 200°C and increased with the state of charge (decreasing Li content). This offers an explanation for the observed lower thermal runaway temperatures for charged cells.

scholarly journals Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2749 ◽  
Author(s):  
Nicolae Tudoroiu ◽  
Mohammed Zaheeruddin ◽  
Roxana-Elena Tudoroiu
Keyword(s):  
Comparative Study ◽  
Real Time ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Li Ion Battery ◽  
Matlab Simulation ◽  
Soc Estimation ◽  
Battery Model ◽  
Li Ion

Estimating the state of charge (SOC) of Li-ion batteries is an essential task of battery management systems for hybrid and electric vehicles. Encouraged by some preliminary results from the control systems field, the goal of this work is to design and implement in a friendly real-time MATLAB simulation environment two Li-ion battery SOC estimators, using as a case study a rechargeable battery of 5.4 Ah cobalt lithium-ion type. The choice of cobalt Li-ion battery model is motivated by its promising potential for future developments in the HEV/EVs applications. The model validation is performed using the software package ADVISOR 3.2, widely spread in the automotive industry. Rigorous performance analysis of both SOC estimators is done in terms of speed convergence, estimation accuracy and robustness, based on the MATLAB simulation results. The particularity of this research work is given by the results of its comprehensive and exciting comparative study that successfully achieves all the goals proposed by the research objectives. In this scientific research study, a practical MATLAB/Simscape battery model is adopted and validated based on the results obtained from three different driving cycles tests and is in accordance with the required specifications. In the new modelling version, it is a simple and accurate model, easy to implement in real-time and offers beneficial support for the design and MATLAB implementation of both SOC estimators. Also, the adaptive extended Kalman filter SOC estimation performance is excellent and comparable to those presented in the state-of-the-art SOC estimation methods analysis.

scholarly journals Extended Kalman Filter based Estimation of the State of Charge of Lithium-Ion cells using a Switched Model

2020 ◽  
Vol 53 (2) ◽  
pp. 13922-13927
Author(s):  
Bikky Routh ◽  
Desham Mitra ◽  
Amit Patra ◽  
Siddhartha Mukhopadhyay
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
State Of Charge ◽  
Lithium Ion Cells

Theoretical prediction of state of charge of lithium ion cells

2005 ◽  
Vol 576 (1) ◽  
pp. 43-47 ◽  
Author(s):  
S. Shriram ◽  
N.G. Renganathan ◽  
M. Ganesan ◽  
M.V.T. Dhananjeyan
Keyword(s):  
Theoretical Prediction ◽  
Lithium Ion ◽  
State Of Charge ◽  
Lithium Ion Cells

scholarly journals Estimation of lithium-ion battery state-of-charge using an extended kalman filter

2021 ◽  
Vol 10 (4) ◽  
pp. 1759-1768
Author(s):  
Mouhssine Lagraoui ◽  
Ali Nejmi ◽  
Hassan Rayhane ◽  
Abderrahim Taouni
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
The State ◽  
Operating Conditions ◽  
State Of Charge ◽  
Ohmic Loss ◽  
Battery Management System ◽  
Transfer Resistance

The main goal of a battery management system (BMS) is to estimate parameters descriptive of the battery pack operating conditions in real-time. One of the most critical aspects of BMS systems is estimating the battery's state of charge (SOC). However, in the case of a lithium-ion battery, it is not easy to provide an accurate estimate of the state of charge. In the present paper we propose a mechanism based on an extended kalman filter (EKF) to improve the state-of-charge estimation accuracy on lithium-ion cells. The paper covers the cell modeling and the system parameters identification requirements, the experimental tests, and results analysis. We first established a mathematical model representing the dynamics of a cell. We adopted a model that comprehends terms that describe the dynamic parameters like SOC, open-circuit voltage, transfer resistance, ohmic loss, diffusion capacitance, and resistance. Then, we performed the appropriate battery discharge tests to identify the parameters of the model. Finally, the EKF filter applied to the cell test data has shown high precision in SOC estimation, even in a noisy system.

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