Development of a Genetic Algorithm-Based Control Strategy for Fuel Consumption Optimization in a Mild Hybrid Electrified Vehicle’s Electrified Propulsion System

Roberto H. Q. Filho, Rodrigo P. M. Ruiz, Eisenhawer de M. Fernandes (), Rosalvo B. Filho and Felipe C. Pimenta
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Roberto H. Q. Filho: Robert Bosch GmbH, Campinas 13065-900, Brazil
Rodrigo P. M. Ruiz: Robert Bosch GmbH, Campinas 13065-900, Brazil
Eisenhawer de M. Fernandes: Laboratory of Electronic Instrumentation and Control (LIEC), Department of Electrical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil
Rosalvo B. Filho: Robert Bosch GmbH, Campinas 13065-900, Brazil
Felipe C. Pimenta: Laboratory of Electronic Instrumentation and Control (LIEC), Department of Electrical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil

Energies, 2024, vol. 17, issue 9, 1-19

Abstract: Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs’ development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development.

Keywords: hybrid vehicles; virtualization; vehicle simulation; machine learning; genetic algorithm; tuning automation/automatic calibration; fuel consumption optimization; gear shift control; Matlab/Simulink (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: 2024
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