Haskara, Ibrahim (GM Research & Development), Hegde, Bharatkumar (General Motors Company), Chang, ChenFang (GM R&D Center)

Reinforcement Learning Based EV Energy Management for Integrated Traction and Cabin Thermal Management Considering Battery Aging

Scheduled for presentation during the Regular Session "Onboard Energy Management in Electrified Powertrains " (WeAT3), Wednesday, August 31, 2022, 10:40−11:00, Ballroom

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 26, 2024

Abstract

Energy management in electric vehicles plays a significant role in both reducing energy consumption and limiting the rate of battery capacity degradation. The work summarized in this paper was aimed at exploring of the potential of AI/ML techniques in electrified propulsion control development in designing energy management (EM) controllers. The specific role of the EM strategy was to coordinate delivery of multiple power requests from a modular battery of an Electric Vehicle (EV) to improve range and battery longevity w/o compromising individual load objectives. Within this overall framework, particular application example was integrated EV traction and HVAC controls, where reinforcement learning techniques were adopted within an add-on supervisory scheme to augment existing EV traction and HVAC controls. The proposed supervisory EM controller was structured to monitor current drive parameters and adjust internal HVAC control parameters accordingly to improve energy efficiency and battery SoH w/o affecting driver demand and desired cabin comfort. An empirical battery aging model was incorporated in the problem formulation to address the effect of energy management on long-term battery capacity degradation. Reduced energy consumption and battery aging w/ coordinated controls were demonstrated with several control formulations.