TR2016-063

Health-Aware and User-Involved Battery Charging Management for Electric Vehicles: Linear Quadratic Strategies



This work studies control-theory-enabled intelligent charging management for battery systems in electric vehicles (EVs). Charging is crucial for the battery performance and life as well as a contributory factor to a user’s confidence in or anxiety about EVs. For existing practices and methods, many run with a lack of battery health awareness during charging, and none includes the user needs into the charging loop. To remedy such deficiencies, we propose to perform charging that, for the first time, allows the user to specify charging objectives and accomplish them through dynamic control, in addition to suppressing the charging-induced negative effects on battery health. Two charging strategies are developed using the linear quadratic control theory. Among them, one is based on control with fixed terminal charging state, and the other on tracking a reference charging path. They are computationally competitive, without requiring real-time constrained optimization needed in most charging techniques available in the literature. A simulation-based study demonstrates their effectiveness and potential. It is anticipated that charging with health awareness and user involvement guaranteed by the proposed strategies will bring major improvements to not only the battery longevity but also EV user satisfaction.