Model based and intelligent monitoring and control of lithium-ion batteries

Increased concerns over the limited sources of energy and environmental impact of the petroleum-based transportation infrastructure have led to increasing interest in an electric transportation infrastructure. Thus, electrical vehicles (including electric vehicle (EV), hybrid electric vehicle (HEV), and plug-in hybrid electric vehicle (PHEV)) and related issues have gained a great deal of attention. Battery technology and battery management is a key component in this regard and has indeed remained as a central challenge in vehicle electrification. This thesis deals with monitoring and control of Lithium ion batteries. The objective is to provide novel solutions to some of the challenging issues from a control theoretic perspective. The research stream in this thesis is headed towards three general directions, i.e. monitoring, diagnostics, and control. The proposed monitoring approaches are introduced as model-based and data-based approaches. The main objective in model-based approaches is to employ the high-fidelity physics-based models of the battery for monitoring. In this thesis, two particle-filtering methods are proposed for state, and joint state and parameter estimation of such models. The data based approaches try to come up with new ideas to monitor the battery accurately but with minimum computational load. In this regard, two different approaches are considered. A Takagi-Sugeno fuzzy model is developed for Li-ion battery where by the virtue of multiple-model structure of T-S model, the non-linearities of battery dynamics and corresponding parameters can appropriately be accounted for, while keeping the local models linear and easy-to-implement control/estimation algorithms. As a completely different alternative, the "Dynamic Resistance" concept is introduced that is sensitive to the battery state of charge and aging. This parameter considers changes in states of active materials in the cell during charge and discharge as well as overall interface resistances that may develop during cell aging. It can bring a new dimension to battery monitoring by providing a new easy-to-monitor parameter where the aging of the battery is also taken into account. This parameter is modeled versus the state of charge and total power throughput of the battery using a Group Method of Data Handling (GMDH) neural network and the model is used to monitor the state of charge and state of health of the battery. A reliable fault diagnosis system for batteries can play an important role in enhanced performance and reliability of electric-based transportation. In this thesis, the physics of the problem is rather comprehensively reviewed, and some of the proposed models for failure mechanism are presented and some fault-detection algorithms for some common failure mechanism are developed. Finally, over-charge/discharge of the cells within a battery pack can affect the battery's health significantly, and would pose serious safety concerns as well. Thus, a cell balancing circuit is usually employed in battery packs in order to keep all the cells in balance. In this thesis, the control problem of a cell-balancing circuit, which is essentially a switched hybrid system, is addressed in a model-based framework by proposing a nonlinear model predictive control (NMPC) strategy.