This paper applies advanced battery modeling and multi-objective constrained nonlinear optimization techniques to derive suitable charging patterns for lithium-ion batteries. Three important yet competing charging objectives, including battery health, charging time, and energy conversion efficiency, are taken into account simultaneously. These optimization objectives are first subject to a high-fidelity battery model that is synthesized from recently developed individual electrical, thermal, and aging models. The coupling relationship and multiple timescales among different model dynamics are identified. Furthermore, constraints are considered explicitly on the current, voltage, state-of-charge, and temperature. Such a complex charging problem is solved by using an ensemble multi-objective biogeography-based optimization (EM-BBO) approach. As a result, two charging patterns, namely the constant current-constant voltage (CC-CV) and multistage constant current-constant voltage (MCC-CV), are optimized to balance various combinations of charging objectives. Different trade-offs and sensitive elements are compared and analyzed based on the Pareto frontiers. Illustrative results demonstrate that the proposed strategy can effectively offer feasible health-conscious charging with desirable trade-offs among charging speed and energy conversion efficiency under different demand priorities.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)