Charging Pattern Optimization for Lithium-Ion Batteries With an Electrothermal-Aging Model

This paper applies advanced battery modeling and multiobjective 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 imposed explicitly on the current, voltage, state-of-charge, and temperature. Such a complex charging problem is solved by using an ensemble multiobjective biogeography-based optimization approach. As a result, two charging patterns, namely the constant current-constant voltage (CC-CV) and multistage CC-CV, are optimized to balance various combinations of charging objectives. Different tradeoffs 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 tradeoffs among charging speed and energy conversion efficiency under different demand priorities.

I. Introduction

Lithium-ion (Li-ion) batteries have been preferably exploited as energy and power sources to drive electric vehicles (EVs) due to their performance, financial, and environmental superiorities over other candidates, like fuel cells, supercapacitors, lead-acid batteries, and nickel-metal-hydride batteries [1], [2]. However, if compared to internal combustion engines associated with fossil fuels, Li-ion batteries are still inferior in the upfront cost, “refueling” time, driving range, and service life [3]. Although innovations in battery technologies in materials and chemistry may solve the problems in the long run, mass deployment of EVs into the current market requires an immediate solution [4], [5]. This intuitively motivates the development of intelligent battery management systems, aiming to extract the full potential of batteries.