Electrification alters the energy demand and environmental impacts of vehicles, which brings about new challenges for sustainability in the transport sector. To further enhance the energy economy of electric vehicles (EVs) and offer an energy-efficient driving strategy for next-generation intelligent mobility in daily synthetic traffic situations with mixed driving scenarios, the model predictive control (MPC) algorithm is exploited to develop a predictive cruise control (PCC) system for eco-driving based on a detailed driving scenario switching logic (DSSL). The proposed PCC system is designed hierarchically into three typical driving scenarios, including car-following, signal antic-ipation, and free driving scenario, using one linear MPC and two nonlinear MPC controllers, re-spectively. The performances of the proposed tri-level MPC-based PCC system for EV eco-driving were investigated by a numerical simulation using the real road and traffic data of Japan under three typical driving scenarios and an integrated traffic situation. The results showed that the proposed PCC system can not only realize driving safety and comfortability, but also harvest consider-able energy-saving rates during either car-following (16.70%), signal anticipation (12.50%), and free driving scenario (30.30%), or under the synthetic traffic situation (19.97%) in urban areas of Japan.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Renewable Energy, Sustainability and the Environment
- Environmental Science (miscellaneous)
- Energy Engineering and Power Technology
- Management, Monitoring, Policy and Law