Optimal Real-Time Velocity Planner of a Battery Electric Vehicle in V2V Driving
| Autor: | Daniela Anna Misul, Giovanni Belingardi, Pier Giuseppe Anselma, Matteo Spano, Alessia Musa |
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| Rok vydání: | 2021 |
| Předmět: |
Consumption (economics)
Vehicular ad hoc networks Energy consumption Simulation Vehicular ad hoc networks Transportation Electric vehicles Minimization Real-time systems Electric vehicles Computer science Transportation Ranging Energy consumption Minimization Automotive engineering Field (computer science) Control theory Battery electric vehicle Minification Real-time systems Simulation Energy (signal processing) |
| Zdroj: | 2021 IEEE Transportation Electrification Conference & Expo (ITEC). |
| DOI: | 10.1109/itec51675.2021.9490121 |
| Popis: | Autonomous driving systems are among the most interesting technologies in the transportation field nowadays, ensuring a high level of safety and comfort while also enhancing energy saving. For the following case study, a Battery Electric Vehicle (BEV) is considered able to communicate with other vehicles through vehicle-to-vehicle (V2V) technology by exchanging information on position and velocity. In this framework, an innovative real-time velocity planner has been developed aiming at maximizing the battery energy savings while improving the passenger comfort as well. This controller uses the principles of the equivalent consumption minimization strategy (ECMS) when the preceding vehicle is accelerating. Simulation results demonstrate improvements in comfort ranging from 26% to 42% ca. and in energy consumption (from 0.4% to 1.3%) when performing different drive cycles in V2V automated driving mode thanks to the proposed controller. |
| Databáze: | OpenAIRE |
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