Deep learning in the development of energy Management strategies of hybrid electric Vehicles: A hybrid modeling approach

Pedro Maroto Estrada, Daniela de Lima, Peter H. Bauer, Marco Mammetti and Joan Carles Bruno

Applied Energy, 2023, vol. 329, issue C, No S030626192201488X

Abstract: The Energy Management Strategy (EMS) in an HEV is the key for improving fuel economy and simultaneously reducing pollutant emissions. This paper presents a methodology for developing hybrid models that enable EMS testing as well as the evaluation of fuel consumption, CO2 and pollutant emissions (CO, NOx and THC). In this context, pollutant emissions are hard to quantify with static models such as the well-known map-based approach which is mainly due to the pronounced impact of transient effects. The novelty of this paper primarily comes from the characterization of pollutant emissions through Convolutional Neural Networks (CNN), providing high accuracy for both, instantaneous and cumulative values. The input parameters are classical Internal Combustion Engine (ICE) measurements such as engine speed, air mass flow, torque and exhaust temperature. The proposed CNNs are reduced to a minimum for low complexity and fast computability. These models are developed with experimental data from chassis dyno testing of a conventional turbo-charged gasoline engine vehicle. The pollutant emission models are used in conjunction with physical models of the remaining powertrain allowing for real time simulations of the complete HEV vehicle. The Double Deep-Q learning algorithm is proposed for the EMS and compared to the Dynamic programming (DP) solution.

Keywords: Hybrid Electric Vehicle; Energy Management Strategy; Spark ignition engine; Virtual vehicle simulation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2022.120231

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