Real Time State Estimation of Reformulated Lithium-Ion Battery Model for Advanced Battery Management Systems (BMS)

2013 ◽  
Keyword(s):  
State Estimation ◽  
Lithium Ion Battery ◽  
Real Time ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Battery Model ◽  
Battery Management Systems

scholarly journals A Data-Driven Framework to Predict Lithium-Ion Battery Cell Imbalance for Real-Time Battery Management Systems

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8492
Author(s):  
Chao Li ◽  
Assimina A. Pelegri
Keyword(s):  
Real Time ◽  
Lithium Ion ◽  
Training Data ◽  
Data Driven ◽  
Management Systems ◽  
Battery Management ◽  
Battery Cell ◽  
Operational Conditions ◽  
Battery Management Systems ◽  
Battery Cells

Models that can predict battery cells’ thermal and electrical behaviors are necessary for real-time battery management systems to regulate the imbalance within battery cells. This work introduces a Gaussian Process Regression (GPR)-based data-driven framework that succeeds the Multi-Scale Multi-Dimensional (MSMD) modeling structure. The framework can make highly accurate predictions at the same level as full-order full-distribution simulations based on MSMD. A pseudo-2D model is used to generate training data and is combined with a process that shifts computation burdens from real-time battery management systems to lab data preparation. The testing results highlight the reliability of the GPR-based data-driven framework in terms of accuracy and stability under various operational conditions.

scholarly journals Comparative Analysis of Lithium-Ion Battery Resistance Estimation Techniques for Battery Management Systems

Energies ◽  
2018 ◽  
Vol 11 (6) ◽  
pp. 1490 ◽  
Author(s):  
Manoj Mathew ◽  
Stefan Janhunen ◽  
Mahir Rashid ◽  
Frank Long ◽  
Michael Fowler
Keyword(s):  
Comparative Analysis ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Estimation Techniques ◽  
Battery Management Systems

Embedded Fiber Optic Sensing for Accurate State Estimation in Advanced Battery Management Systems

2014 ◽  
Vol 1681 ◽  
Author(s):  
Lars Wilko Sommer ◽  
Ajay Raghavan ◽  
Peter Kiesel ◽  
Bhaskar Saha ◽  
Tobias Staudt ◽  
...  
Keyword(s):  
State Estimation ◽  
Fiber Optic ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Cell State ◽  
Battery Management Systems ◽  
Fbg Sensors ◽  
Selective Sensitivity ◽  
Interesting Alternative

ABSTRACTThe problems of using performance parameters such as voltage, current and temperature measured with electrical sensors in today’s battery management systems (BMS) are well known. These parameters can be weakly informative about cell state, particularly as cells age, and contribute to over-conservative utilization and oversizing of a battery pack. Fiber optic (FO) sensors can offer an interesting alternative to conventional electrical sensors, with several advantages such as high selective sensitivity to various parameters, light weight, robustness to EMI, and multiplexing capabilities. In this study, a particular type of FO sensors, fiber Bragg grating (FBG) sensors were externally attached to lithium ion pouch cells for monitoring additional informative cell parameter such as strain and temperature. Multiple charge and discharge cycle were performed to examine the qualification of these signals for cell state estimation in BMS. In comparison to corresponding measurements using conventional electrical sensors, the FBG signals showed very promising results for utilization in effective BMS.

scholarly journals Development of an Experimental Testbed for Research in Lithium-Ion Battery Management Systems

Energies ◽  
2013 ◽  
Vol 6 (10) ◽  
pp. 5231-5258 ◽  
Author(s):  
Nima Lotfi ◽  
Poria Fajri ◽  
Samuel Novosad ◽  
Jack Savage ◽  
Robert Landers ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Management Systems ◽  
Battery Management ◽  
Battery Management Systems ◽  
Experimental Testbed

An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management

2020 ◽  
Vol 257 ◽  
pp. 114019 ◽  
Author(s):  
Xiaosong Hu ◽  
Haifu Jiang ◽  
Fei Feng ◽  
Bo Liu
Keyword(s):  
State Estimation ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Battery Management

Performance Evaluation of an Extended Kalman Filter for State Estimation of a Pseudo-2D Thermal-Electrochemical Lithium-Ion Battery Model

2015 ◽  
Author(s):  
Shi Zhao ◽  
Adrien M. Bizeray ◽  
Stephen R. Duncan ◽  
David A. Howey
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Lithium Ion Battery ◽  
Extended Kalman Filter ◽  
Lithium Ion ◽  
Low Noise ◽  
Particle Model ◽  
Battery Management System ◽  
Battery Management ◽  
Voltage Measurement

Fast and accurate state estimation is one of the major challenges for designing an advanced battery management system based on high-fidelity physics-based model. This paper evaluates the performance of a modified extended Kalman filter (EKF) for on-line state estimation of a pseudo-2D thermal-electrochemical model of a lithium-ion battery under a highly dynamic load with 16C peak current. The EKF estimation on the full model is shown to be significantly more accurate (< 1% error on SOC) than that on the single-particle model (10% error on SOC). The efficiency of the EKF can be improved by reducing the order of the discretised model while maintaining a high level of accuracy. It is also shown that low noise level in the voltage measurement is critical for accurate state estimation.

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