Taesic’s Dissertation

Model-Based Condition Monitoring and Power Management for Rechargeable Electrochemical Batteries

Dr. Wei Qiao and Dr. Liyan Qu (Advisors)

 Dr. Steve Goddard and Dr. Jerry Hudgins

Friday, April 10, 2015; 10:00am|205N Scott Engineering Center


 Rechargeable multicell batteries have been used in various electrical and electronic systems, e.g., renewable energy systems, electric-drive vehicles, commercial electronics, etc. However, there are still concerns on the reliability and performance degradation of rechargeable batteries caused by low thermal stability and aging process. A properly designed battery management system (BMS) is required for condition monitoring and control of the multicell batteries to ensure the safety, reliability and optimal performance of the batteries. The goal of this dissertation research was to develop a novel BMS for condition monitoring and power management of rechargeable multicell batteries. This goal was achieved through the execution of the following three objectives.

First, this research developed high-fidelity battery models for BMS simulation and online condition monitoring of battery cells.  The battery models were capable of capturing the dynamic circuit characteristics, nonlinear capacity and nonlinear open-circuit voltage effects, hysteresis effect, and temperature effect of the battery cells.

Second, this research developed a novel power electronics-enabled, self-X, multicell battery design. The proposed multicell battery automatically configures itself according to the dynamic load/storage demand and the condition of each cell. The proposed battery can self-heal from failure or abnormal operation of single or multiple cells, self-balance from cell state imbalances, and self-optimize to achieve optimal energy conversion efficiency. These features were achieved by a highly efficient cell switching circuit and a high-performance condition monitoring and control system.

Moreover, condition monitoring is essential for a BMS. The condition monitoring of a battery involves tracking the changes in the parameters, such as maximum capacity and internal impedance, and states, such as state of charge (SOC), state of health (SOH), state of power (SOP), state of function (SOF), related to the operating and health conditions of the battery. Since the values of these parameters and states cannot be directly measured by using sensors, they are commonly obtained from model-based estimation algorithms. This dissertation research proposed several model-based condition monitoring algorithms based on the proposed battery models. First, a computational intelligence (CI) method, particle swarm optimization (PSO) algorithm-based battery parameter identification method was developed to estimate the impedance and SOC of batteries using the proposed discrete-time hybrid battery model. Second, an algorithm combining a regression method for parameter identification, a sliding-mode observer (SMO) for SOC estimation, and a two-point capacity estimation method were proposed. In addition, an electrical circuit model incorporating hysteresis model-based condition monitoring algorithm was proposed. It systematically integrates 1) a fast upper-triangular and diagonal recursive least square (FUDRLS) online parameter identification, 2) a smooth variable structure filter (SVSF) for SOC estimation, and 3) a recursive total least square (RTLS) for maximum capacity estimation, which is indicates the SOH of the battery. These algorithms leaded to accurate, robust condition monitoring for lithium-ion batteries. Due to the low complexity, it turns out that the proposed second and third algorithms are suitable for the embedded BMS applications.

2015-04-10 10.00.51