State Estimation for an Electrochemical Model of Multiple-Material Lithium-Ion Batteries

2016 ◽  
Author(s):  
Leobardo Camacho-Solorio ◽  
Miroslav Krstic ◽  
Reinhardt Klein ◽  
Anahita Mirtabatabaei ◽  
Scott J. Moura
Keyword(s):  
State Estimation ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Particle Model ◽  
Battery Management ◽  
Active Materials ◽  
Energy Characteristics ◽  
Battery Management Systems ◽  
Power And Energy ◽  
Electrochemical Model

This paper presents state estimation for a system of diffusion equations coupled in the boundary appearing in reduced electrochemical models of lithium-ion batteries with multiple active materials in single electrodes. The observer is synthesized from a single particle model and is based on the backstepping method for partial differential equations. The observer is suitable for state of charge estimation in battery management systems and is an extension of existing backstepping observers which were derived only for cells with electrodes of single active materials. Observer gains still can be computed analytically in terms of Bessel and modified Bessel functions. This extension is motivated by the trend in cell manufacturing to use multiple active materials to combine power and energy characteristics or reduce degradation.

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.

scholarly journals Perspective on State-of-Health Determination in Lithium-Ion Batteries

2020 ◽  
Vol 17 (4) ◽  
Author(s):  
Matthieu Dubarry ◽  
George Baure ◽  
David Anseán
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Battery Management ◽  
State Of Health ◽  
Second Stage ◽  
Capacity Loss ◽  
Battery Management Systems ◽  
Essential Parameter ◽  
Battery Packs ◽  
Deployed Systems

Abstract State-of-health (SOH) is an essential parameter for the proper functioning of large battery packs. A wide array of methodologies has been proposed in the literature to track state of health, but they often lack the proper validation that needed to be universally adaptable to large deployed systems. This is likely induced by the lack of knowledge bridge between scientists, who understand batteries, and engineers, who understand controls. In this work, we will attempt to bridge this gap by providing definitions, concepts, and tools to apply necessary material science knowledge to advanced battery management systems (BMS). We will address SOH determination and prediction, as well as BMS implementation and validation using the mechanistic framework developed around electrochemical voltage spectroscopies. Particular focus will be set on the onset and the prediction of the second stage of accelerating capacity loss that is commonly observed in commercial lithium-ion batteries.

scholarly journals Online SOC Estimation Based on Simplified Electrochemical Model for Lithium-Ion Batteries Considering Current Bias

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5265
Author(s):  
Longxing Wu ◽  
Kai Liu ◽  
Hui Pang ◽  
Jiamin Jin
Keyword(s):  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Operating Conditions ◽  
Estimation Methods ◽  
Current Sensor ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Electrochemical Model ◽  
The Impact

State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS.

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.

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