Miller, Cory (The Ohio State University), Goutham, Mithun (The Ohio State University), Chen, Xiaoling (The Ohio State University), Stockar, Stephanie (The Ohio State University)

DC Fast Charging Optimization for Capacity Fade Minimization

Scheduled for presentation during the Regular Session "Energy Storage and Charging Systems" (TuAT3), Tuesday, August 30, 2022, 11:40−12:00, Pfahl Hall 140

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

DC fast charging is a critical step to support the recharging demands of electric vehicles and increase their penetration in the market. However, compared to normal Level 1 or 2 charging, DC fast charging imposes additional battery capacity fade which can result in premature aging of the battery, reducing its useful life. This paper proposes a computationally efficient, meta-heuristic approach to optimize the charging C-Rate profile while considering battery degradation associated with not only the charging but also the expected drive cycle following charging. The battery and its degradation are modeled with a semi-empirical, physics-based approach which yields a high accuracy and is computationally efficient. The meta-heuristic approach to optimize the charge profile is first validated for a simplified case with Dynamic Programming. To demonstrate the effectiveness of the approach in attenuating the battery aging, a benchmark case with 15 minutes of constant current charging of a Lithium Iron Phosphate battery is set. A nearly 1% capacity fade improvement is obtained for a single charge-discharge cycle after charging C-Rate optimization, which would generate significant benefit over the electric vehicle life.