Politecnico di Torino (logo)

Energy optimization based on ADAS sensors and Connectivity in electric vehicles

Carlo Fiorillo

Energy optimization based on ADAS sensors and Connectivity in electric vehicles.

Rel. Angelo Bonfitto, Vittorio Ravello. Politecnico di Torino, Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo), 2023

[img] PDF (Tesi_di_laurea) - Tesi
Restricted to: Repository staff only until 1 December 2024 (embargo date).
Licenza: Creative Commons Attribution Non-commercial No Derivatives.

Download (14MB)

The global automotive landscape is evolving rapidly, driven by increasingly stringent regulations aimed at reducing emissions and mitigating the impact of climate change. As a result, Battery Electric Vehicles (BEVs) are gaining prominence considered a cleaner and more efficient mode of transportation. However, several challenges, including limited range, energy consumption, and battery degradation, continue to hinder the widespread adoption of BEVs. This thesis addresses these challenges by focusing on the development of an optimization strategy based on Model Predictive Control (MPC) for BEVs. The primary objective is to enhance energy efficiency and extend battery life. It accomplishes this by optimizing the vehicle speed profile to minimize energy consumption and reduce battery degradation. To achieve this, multiple tests employing different approaches has been implemented so that to ensure with the highest probability, real-time implementation capability without compromising performances. A comprehensive BEV baseline model is developed, incorporating state-of-the-art battery State of Health (SOH) estimation methods, longitudinal vehicle dynamics models, and HVAC model. Notably, the HVAC system serves as the foundation for a parallel developed strategy, known as Integrated Energy and Thermal Management (IETM), which aims to reduce HVAC power demand during peak traction power commands, ensuring cabin comfort without compromising vehicle performance. The MPC strategy employs a Connected Adaptive Cruise Control (CACC) system capable of optimizing the speed trajectory based on data from a leading vehicle. By predicting the leading vehicle's speed and regulating the distance from preceding vehicle, energy consumption and battery degradation are effectively reduced. Simulations conducted under various driving conditions and noise levels demonstrate the robustness and efficacy of the proposed strategy, resulting in up to 3.7% improvement in energy consumption and 9.7% increase in battery life extent. Furthermore, the implementation of this strategy requires only software updates, making it cost??effective and easily adaptable to existing BEVs. The combination of this MPC-based strategy with the IETM strategy offers the potential for even greater benefits, as both approaches complement each other, optimizing both traction power and auxiliaries loads. In summary, this research contributes to the advancement of sustainable transportation and addresses critical issues surrounding BEVs. By alleviating range anxiety, reducing energy consumption, and promoting the longevity of battery systems, this strategy lays the foundation for a more sustainable and environmentally conscious future of mobility.

Relators: Angelo Bonfitto, Vittorio Ravello
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 119
Corso di laurea: Corso di laurea magistrale in Automotive Engineering (Ingegneria Dell'Autoveicolo)
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Ente in cotutela: McMaster University (CANADA)
Aziende collaboratrici: McMaster University
URI: http://webthesis.biblio.polito.it/id/eprint/29140
Modify record (reserved for operators) Modify record (reserved for operators)