TY - GEN
T1 - Reinforcement learning based power management for hybrid electric vehicles
AU - Lin, Xue
AU - Wang, Yanzhi
AU - Bogdan, Paul
AU - Chang, Naehyuck
AU - Pedram, Massoud
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/1/5
Y1 - 2015/1/5
N2 - Compared to conventional internal combustion engine (ICE) propelled vehicles, hybrid electric vehicles (HEVs) can achieve both higher fuel economy and lower pollution emissions. The HEV consists of a hybrid propulsion system containing one ICE and one or more electric motors (EMs). The use of both ICE and EM increases the complexity of HEV power management, and therefore requires advanced power management policies to achieve higher performance and lower fuel consumption. Towards this end, our work aims at minimizing the HEV fuel consumption over any driving cycle (without prior knowledge of the cycle) by using a reinforcement learning technique. This is in clear contrast to prior work, which requires deterministic or stochastic knowledge of the driving cycles. In addition, the proposed reinforcement learning technique enables us to (partially) avoid reliance on complex HEV modeling while coping with driver specific behaviors. To our knowledge, this is the first work that applies the reinforcement learning technique to the HEV power management problem. Simulation results over real-world and testing driving cycles demonstrate the proposed HEV power management policy can improve fuel economy by 42%.
AB - Compared to conventional internal combustion engine (ICE) propelled vehicles, hybrid electric vehicles (HEVs) can achieve both higher fuel economy and lower pollution emissions. The HEV consists of a hybrid propulsion system containing one ICE and one or more electric motors (EMs). The use of both ICE and EM increases the complexity of HEV power management, and therefore requires advanced power management policies to achieve higher performance and lower fuel consumption. Towards this end, our work aims at minimizing the HEV fuel consumption over any driving cycle (without prior knowledge of the cycle) by using a reinforcement learning technique. This is in clear contrast to prior work, which requires deterministic or stochastic knowledge of the driving cycles. In addition, the proposed reinforcement learning technique enables us to (partially) avoid reliance on complex HEV modeling while coping with driver specific behaviors. To our knowledge, this is the first work that applies the reinforcement learning technique to the HEV power management problem. Simulation results over real-world and testing driving cycles demonstrate the proposed HEV power management policy can improve fuel economy by 42%.
KW - Hybrid electric vehicle (HEV)
KW - power management
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=84936873024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84936873024&partnerID=8YFLogxK
U2 - 10.1109/ICCAD.2014.7001326
DO - 10.1109/ICCAD.2014.7001326
M3 - Conference contribution
AN - SCOPUS:84936873024
T3 - IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
SP - 32
EP - 38
BT - 2014 IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014 - Digest of Technical Papers
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 33rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014
Y2 - 2 November 2014 through 6 November 2014
ER -