Title: A near-optimal rule-based energy management strategy for medium duty hybrid truck

Authors: Riccardo Biasini; Simona Onori; Giorgio Rizzoni

Addresses: Tesla Motors Inc., Palo Alto, CA, 94304, USA ' Centre for Automotive Research, The Ohio State University, Columbus, OH, 43212, USA ' Centre for Automotive Research, The Ohio State University, Columbus, OH, 43212, USA; Mechanical and Aerospace Engineering Department, The Ohio State University, Columbus, OH, 43212, USA

Abstract: This paper covers the design and implementation of a rule-based energy management strategy for a medium duty hybrid truck. In this paper, a procedure for the design of a near-optimal energy management strategy is presented. The procedure utilises the dynamic programming (DP) algorithm to find the optimal control strategy that minimises the fuel consumption over a given driving mission. Through the analysis of the behaviour of DP control actions, near-optimal rules are extracted and tuned to design a rule-based strategy for charge sustaining operation which, unlike DP control signals, is implementable on-board of the vehicle. Drivability metrics such as frequent clutching and engine on/off behaviour are also included in the control design based on the implementation of the DP under different drivability scenarios. The performance of the proposed energy management control strategy is studied by using a proposed longitudinal vehicle model of a pre-transmission parallel medium duty hybrid truck with a clutch. The proposed near-optimal rule-based strategy, benchmarked against the optimal DP solution, shows performance within 3% of the global optimal one.

Keywords: hybrid trucks; hybrid electric vehicles; HEVs; dynamic programming; powertrain modelling; rule-based control strategy; heavy vehicles; energy management strategy; control design.

DOI: 10.1504/IJPT.2013.054151

International Journal of Powertrains, 2013 Vol.2 No.2/3, pp.232 - 261

Available online: 26 May 2013

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