Equivalent lap time minimization strategies for a hybrid electric race car

Mauro Salazar, Camillo Balerna, Eugenio Chisari, Carlo Bussi, Christopher Onder

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)

Abstract

The powertrain of the Formula 1 car is composed of an electrically turbocharged internal combustion engine and an electric motor used for boosting and regenerative braking. The energy management system that controls this hybrid electric power unit strongly influences the achievable lap time, as well as the fuel and battery consumption. Therefore, it is important to design robust feedback control algorithms that can run on the ECU in compliance with the sporting regulations, and are able to follow lap time optimal strategies while properly reacting to external disturbances. In this paper, we design feedback control algorithms inspired by equivalent consumption minimization strategies (ECMS) that adapt the optimal control policy implemented on the car in real-time. This way, we are able to track energy management strategies computed offline in a lap time optimal way using three PID controllers. We validate the presented control structure with numerical simulations and compare it to a previously designed model predictive control scheme.
Original languageEnglish
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers
Pages6125-6131
Number of pages7
ISBN (Electronic)9781538613955
DOIs
Publication statusPublished - Dec 2018
Externally publishedYes
Event57th IEEE Conference on Decision and Control, (CDC2018) - Miami, United States
Duration: 17 Dec 201819 Dec 2018
Conference number: 57

Conference

Conference57th IEEE Conference on Decision and Control, (CDC2018)
Abbreviated titleCDC 2018
CountryUnited States
CityMiami
Period17/12/1819/12/18

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