Abstract
To improve the cycle efficiency and peak output power density of energy storage systems in electric vehicles (EVs), supercapacitors have been proposed as auxiliary energy storage elements to complement the mainstream Lithium-ion (Li-ion) batteries. The performance of such a hybrid electrical energy storage (HEES) system is highly dependent on the implemented management policy. This paper presents a model-free reinforcement learning-based approach to dynamically manage the current flows from and into the battery and supercapacitor banks under various scenarios (combinations of EV specs and driving patterns). Experimental results demonstrate that the proposed approach achieves up to 25% higher efficiency compared to a Li-ion battery only storage system and outperforms other online HEES system control policies in all test cases.
Original language | English (US) |
---|---|
Title of host publication | Proceedings, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3142-3148 |
Number of pages | 7 |
ISBN (Print) | 9781479940325 |
DOIs | |
State | Published - Feb 24 2014 |
Externally published | Yes |
Event | 40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014 - Dallas, United States Duration: Oct 30 2014 → Nov 1 2014 |
Other
Other | 40th Annual Conference of the IEEE Industrial Electronics Society, IECON 2014 |
---|---|
Country/Territory | United States |
City | Dallas |
Period | 10/30/14 → 11/1/14 |
Keywords
- Electric vehicle
- Hybrid energy storage systems
- Reinforcement learning
ASJC Scopus subject areas
- Electrical and Electronic Engineering