Reinforcement Learning in Optimizing the Electric Vehicle Battery System Coupling with Driving Behaviors Temple University

Format:

Book

Conference/Event

Author/Creator:

Altiner, Irem, author.

Contributor:

Ou, Shiqi (Shawn)

Conference Name:

WCX SAE World Congress Experience (2024-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2024

Summary:

Battery Run-down under the Electric Vehicle Operation (BREVO) model is a model that links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. This study implements reinforcement learning (RL) to the simplified BREVO model to optimize drivers' decisions on charging such as charging rate, charging time, and charging capacity needed. This is done by a reward function that considers both the driver's daily travel demands and the minimization of battery degradation over a year. It shows that using appropriate charger type (No Charge, Level 1, Level 2, direct-current Fast Charge [DCFC], extreme Fast Charging [xFC]) with an appropriate charging time can reduce battery degradation and total charging cost at the end of the year while satisfying driver's daily travel demand. Using the Level 2 charging every day for night charging can reduce the battery capacity by 1.3819 whereas following the charger type and charging time suggestions of the RL will bring this number down to the level of 0.8037 over a one-year timespan. This gap between degradation rates gets bigger when one prefers using DC FC or xFC only respectively. Based on their daily travel demands, this RL model provides valuable strategic guidance to drivers to increase the battery lifetime and minimize the total cost of owning an electric vehicle

Notes:

Vendor supplied data

Publisher Number:

2024-01-2006

Access Restriction:

Restricted for use by site license