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Xiaopeng TANG

Doctor Candidate in Chemical and Biomolecular Engineering

Supervisor: Prof. Furong GAO

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Research Topic

Battery Management System (BMS): State Estimation and Fault Diagnosis

Abstract

Electric vehicles (EVs) help reduce emissions and reliance on petroleum. With increasing demand for covering longer distance, thousands of battery cells are interconnected in a vehicle to provide sufficient power. A battery management system (BMS) is required to manage these batteries for safety and efficiency.
Typically, BMS manages the battery cells and packs by monitoring the battery current and voltage, protecting the battery, estimating the battery states, balancing the cells, recording the data, etc. State estimation and fault diagnosis belong to the key issues for BMS among this long list of tasks, and they are chosen as the focus for this Ph.D. research as they remain as academic challenges with engineering importance.
To improve the quality of the state estimation, in this research, firstly, an enhanced BMS battery model will be developed to study the battery performance under different aging stages, different temperatures and large discharging rate; secondly, signal processing and state estimation tools, such as extended Kalman Filter and particle filter, will be developed and applied to address nonlinear state estimation issues encountered. BMS has to deal with many batteries at a time using an embedded micro controller unit (MCU) with limited computational ability, therefore, these nonlinear state estimation methods will need to be simplified to reduce computational complexity so that they can be implemented in MCU. Thirdly, a comprehensive study of battery state estimation will be carried out because the battery states such as state of charge (SOC), state of health (SOH) and state of power (SOP) are interrelated, a composite estimation of them will be investigated. Lastly, many kinds of faults, such as battery connection fault, temperature sensor failure and voltage sensor malfunction may happen, pattern recognition based methods for fault diagnosis will be developed to improve the robustness of the battery state estimation.
These models and methods developed will be tested and verified with the data obtained from experiments.

Journal Publication


Tang, Xiaopeng; Yao, Ke; Liu, Boyang; Hu, Wengui; Gao, Furong, "Long-term battery voltage, power, and surface temperature prediction using a model-based extreme learning machine", (2018)
 
Tang, Xiaopeng; Liu, Boyang; Lv, Zhou; Gao, F., "Observer Based Battery SOC Estimation: Using Multi-gain-switching Approach", (2017)
 
Wang, Yujie; Pan, Rui; Yang, Duo; Tang, Xiaopeng; Chen, Zonghai, "Remaining Useful Life Prediction of Lithium-ion Battery Based on Discrete Wavelet Transform", (2017)
 
Tang, Xiaopeng; Liu, Boyang; Gao, Furong, "State of Charge Estimation of LiFePO4 Battery Based on a Gain-classifier Observer", (2017)
 
Tang, Xiaopeng; Liu, Boyang; Gao, Furong; Lv, Zhou, "State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer", (2016)