Optimal control design for comfortable-driving of hybrid electric vehicles in acceleration mode

Bo Zhang, Jiangyan Zhang and Tielong Shen

Applied Energy, 2022, vol. 305, issue C, No S0306261921012010

Abstract: This paper discusses a control design for improving ride comfort while maintaining the powertrain performance and power demand of a hybrid electric vehicle in a specific acceleration scenario. A parallel hybrid powertrain system with two electric machines and a single turbo-charged engine is built. The acceleration scenario is designed under the assumption that the desired acceleration rate cannot be achieved by using an electric motor alone. Therefore, improving ride comfort while also managing the torque split is challenging. This work involves two steps. First, a black-box module that is typically used in the automotive industry is exploited to quantitatively evaluate ride comfort. A genetic algorithm (GA) is used to generate an optimal acceleration curve and gear schedule by analyzing the characteristics of the black-box module. Second, two optimization problems are formulated to provide a reasonable power split with the purpose of tracking the reference acceleration provided by the GA. The problems are solved by dynamic programming and sequential quadratic programming. Comparison of the simulation results between the two different solutions is conducted on a simulator with a practical background and demonstrates the significance and efficiency of the proposed control design.

Keywords: Ride comfort; Black-box module; Hybrid powertrain control; Acceleration mode (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2021.117885

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