Implementation and Analyses of an Eco-Driving Algorithm for Different Battery Electric Powertrain Topologies Based on a Split Loss Integration Approach

Alexander Koch, Lorenzo Nicoletti, Thomas Herrmann and Markus Lienkamp
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Alexander Koch: Institute of Automotive Technology, Department of Mobility Systems Engineering, School of Engineering & Design, Technical University of Munich (TUM), 85748 Garching bei München, Germany
Lorenzo Nicoletti: Institute of Automotive Technology, Department of Mobility Systems Engineering, School of Engineering & Design, Technical University of Munich (TUM), 85748 Garching bei München, Germany
Thomas Herrmann: Institute of Automotive Technology, Department of Mobility Systems Engineering, School of Engineering & Design, Technical University of Munich (TUM), 85748 Garching bei München, Germany
Markus Lienkamp: Institute of Automotive Technology, Department of Mobility Systems Engineering, School of Engineering & Design, Technical University of Munich (TUM), 85748 Garching bei München, Germany

Energies, 2022, vol. 15, issue 15, 1-29

Abstract: Eco-driving algorithms optimize the speed profile to reduce the energy consumption of a vehicle. This paper presents an eco-driving algorithm for battery electric powertrains that applies a split loss integration approach to incorporate the component losses. The algorithm consistently uses loss models to overcome the drawbacks of efficiency maps, which cannot represent no-load losses at zero torque. The use of loss models is crucial since the optimal solution includes gliding, during which there are no-load losses. An analysis shows, that state-of-the-art nonlinear programming algorithms cannot represent these no-load losses at zero torque with a small modeling error. To effectively compute the powertrain losses with only a small error in comparison to the measurement data, we introduce a tailored combination of nonlinear inequality constraints that interleave two polynomial fits. This approach can properly represent reality. We parameterize the algorithm and validate the vehicle model used with real-world measurement data. Furthermore, we investigate the influence of the proposed interleaved fits by comparing them to a single continuous high-order polynomial fit and to the state of the art. The algorithm is published open source.

Keywords: eco-driving; energy-efficient driving; nonlinear programming; battery electric vehicles; open source; eco-acc; powertrain topologies (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: 2022
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