Optimizing Extreme Fast Charging Strategy for Lithium-Ion Batteries: A Model Predictive Control Approach

Abstract

With the increasing demand for extreme fast charging (XFC) in electric vehicles (EVs), there is an urgent need to develop health-conscious fast charging strategies for lithium-ion batteries. However, simply increasing the charging current can disproportionately accelerate battery aging, resulting in severe capacity and performance loss, while posing unacceptable safety risks during operation. In addition, the battery management system (BMS) needs to know the state of each cell for reliable and accurate management, but this involves significant computational burden. In other words, new fast charging strategies need to be developed to solve the dilemma between charging rate, battery aging, and computational complexity.In this work, we propose a novel framework for modelling, estimating, and balancing different types of state heterogeneities in a multi-cell battery module. This framework facilitates the development of optimal fast charging strategies based on battery electrochemical models and model predictive control (MPC). First, an electrochemical and thermal coupling model is formulated at the unit cell level, and a module model is formulated at the system level, taking into account various factors such as connection resistances and system terminals. Then, a non-linear MPC is used to achieve fast charging while considering the health of the battery module. Battery performance is tracked by accurately estimating the battery's internal state, which can support the balancing of all state heterogeneity types represented in the model by controlling the charging current according to the internal state. The proposed framework has three main advantages: (1) The modelling strategy has a flexible and configurable structure that can be applied to battery modules of any size. Using this, (2) the internal state of the battery can be estimated with reduced computational complexity. (3) MPC can be applied to cell-to-cell balancing and XFC strategy. The results of the proposed method provide important insights for achieving XFC and show promise for real-time online estimation and control. Figure 1