TY - GEN
T1 - Optimizing fuel economy of hybrid electric vehicles using a Markov decision process model
AU - Lin, Xue
AU - Wang, Yanzhi
AU - Bogdan, Paul
AU - Chang, Naehyuck
AU - Pedram, Massoud
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/8/26
Y1 - 2015/8/26
N2 - In contrast to conventional internal combustion engine (ICE) propelled vehicles, hybrid electric vehicles (HEVs) can achieve both higher fuel economy and lower pollutant emissions. The HEV features a hybrid propulsion system consisting of one ICE and one or more electric motors (EMs). The use of both ICE and EM increases the complexity of HEV power management, and so advanced power management policy is required for achieving higher performance and lower fuel consumption. This work aims at minimizing the HEV fuel consumption over any driving cycles, about which no complete information is available to the HEV controller in advance. Therefore, this work proposes to model the HEV power management problem as a Markov decision process (MDP) and derives the optimal power management policy using the policy iteration technique. Simulation results over real-world and testing driving cycles demonstrate that the proposed optimal power management policy improves HEV fuel economy by 23.9% on average compared to the rule-based policy.
AB - In contrast to conventional internal combustion engine (ICE) propelled vehicles, hybrid electric vehicles (HEVs) can achieve both higher fuel economy and lower pollutant emissions. The HEV features a hybrid propulsion system consisting of one ICE and one or more electric motors (EMs). The use of both ICE and EM increases the complexity of HEV power management, and so advanced power management policy is required for achieving higher performance and lower fuel consumption. This work aims at minimizing the HEV fuel consumption over any driving cycles, about which no complete information is available to the HEV controller in advance. Therefore, this work proposes to model the HEV power management problem as a Markov decision process (MDP) and derives the optimal power management policy using the policy iteration technique. Simulation results over real-world and testing driving cycles demonstrate that the proposed optimal power management policy improves HEV fuel economy by 23.9% on average compared to the rule-based policy.
UR - http://www.scopus.com/inward/record.url?scp=84951123872&partnerID=8YFLogxK
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U2 - 10.1109/IVS.2015.7225769
DO - 10.1109/IVS.2015.7225769
M3 - Conference contribution
AN - SCOPUS:84951123872
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 718
EP - 723
BT - IV 2015 - 2015 IEEE Intelligent Vehicles Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Intelligent Vehicles Symposium, IV 2015
Y2 - 28 June 2015 through 1 July 2015
ER -