Power Management Strategy of a Parallel Hybrid Three-Wheeler for Fuel and Emission Reduction

Waruna Maddumage, Malika Perera, Rahula Attalage and Patrick Kelly
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Waruna Maddumage: Faculty of Engineering, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka
Malika Perera: Faculty of Engineering, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka
Rahula Attalage: Faculty of Graduate Studies and Research, Sri Lanka Institute of Information Technology, Malabe 10115, Sri Lanka
Patrick Kelly: Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK

Energies, 2021, vol. 14, issue 7, 1-30

Abstract: Millions of three-wheelers in large cities of Asia and Africa contribute to the already increasing urban air pollutants. An emerging method to reduce adverse effects of the growing three-wheeler fleet is hybrid-electric technology. The overall efficiency of a hybrid electric vehicle heavily depends on the power management strategy used in controlling the main powertrain components of the vehicle. Recent studies highlight the need for a comprehensive report on developing an easy-to-implement and efficient control strategy for hybrid electric three-wheelers. Thus, in the present study, a design methodology for a rule-based supervisory controller of a pre-transmission parallel hybrid three-wheeler based on an optimal control strategy (i.e., dynamic programming) is proposed. The optimal control problem for minimizing fuel, emissions (i.e., HC, CO and NOx) and gear shift frequency are solved using dynamic programming (DP). Numerical issues of DP are analyzed and trade-offs between optimizing objectives are presented. Since DP strategy cannot be implemented as a real-time controller, useful strategies are extracted to develop the proposed rule-based strategy. The developed rule-based strategy show performance within 10% of the DP results on WLTC and UDC-NEDC drive cycles and has the clear advantage of being near-optimal, easy-to-implement and computationally less demanding.

Keywords: hybrid electric vehicle; auto-rickshaw; energy management strategy; multi-objective optimization; rule-based control; dynamic programming; fuel economy; backward-facing model; forward-facing model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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