Driving-style-oriented fuzzy control of a plug-in hybrid electric vehicle by multi-objective optimization for minimization of exhaust emissions and equivalent fuel consumption
<p>As Plug-in Hybrid Electric Vehicles (PHEV) emerge in the pursuit of sustainable transportation solutions aiming to balance combustion engines and electric motors, there is a need for the development of their power management. Besides, several studies have shown the impact of driving style o...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , , , |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | <p>As Plug-in Hybrid Electric Vehicles (PHEV) emerge in the pursuit of sustainable transportation solutions aiming to balance combustion engines and electric motors, there is a need for the development of their power management. Besides, several studies have shown the impact of driving style on vehicle performance. In this study, a driving-style-oriented PHEV control is developed using fuzzy logic optimized by the Multi-Objective Particle Swarm Optimization. A driving style classification was defined to guide the vehicle control optimization according to each style (calm, moderated, and dynamic). The optimization defines the parameters (fuzzy rules and membership functions) for the power management and gear shifting controllers while aiming to reduce emissions and equivalent fuel consumption for each style. As a result, the optimization tradeoff solution balances well pollutant emissions and equivalent fuel consumption, while the one that minimizes equivalent fuel consumption consumes less 1.80% and the one that minimizes emission pollutes less 1.29% <i>HC</i> and less 6.38% <i>NOx</i>. The control was evaluated under standard and real-world driving cycles, showing robustness and potential when compared with a no-driving-style-oriented. This approach switches controllers’ driving modes, resulting in personalized power distribution and gear shifting.</p> |
|---|