Model predictive control of Lithium-ion batteries: Development of optimal charging profile for reduced intracycle capacity fade using an enhanced single particle model (SPM) with first-principled chemical/mechanical degradation mechanisms

Recently, given the high demand of electric vehicles, the implementation of a battery management system (BMS) for efficient energy use, safety, and state of health estimation has garnered significant attention. For a robust BMS, the battery model which can help the monitoring and control of battery behaviors such as voltage, temperature, stress, and capacity fade should have a high accuracy. Existing battery models like single-particle model (SPM), and pseudo-two-dimensional models have either shown a mismatch with experiments or have a large computational time, both of which are not conducive to fast control of BMS. Furthermore, since existing enhanced SPMs in conjunction with classical and even advanced control methodologies can only elucidate empirically estimated inter-cycle capacity fade, they cannot be applied to intra-cycle control of battery charging. To handle these concerns, in this work, a new battery model is constructed by integrating the enhanced SPM with the first-principled chemical/mechanical degradation physics to accurately predict dynamic intra-cycle capacity fade. Subsequently, the proposed battery model is incorporated into a model predictive control framework to manipulate the applied current to minimize the capacity fade during the charging of a battery. Overall, the developed framework (a) allowed the accurate prediction of both inter-cycle and intra-cycle chemical/mechanical degradation, and the state of the battery (i.e., voltage, temperature, and mechanical stress); (b) enabled experimental model validation at different operation conditions; and (c) yielded a superior input current profile, which minimized the intra-cycle capacity fade, as compared to the traditional constant current-constant voltage (CC-CV) charging protocol.