Multi-range vehicle speed prediction using vehicle connectivity for enhanced energy efficiency of vehicles
US11960298B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 8, 2021 |
| Grant date | Apr 16, 2024 |
| Priority date | — |
| Expiry date | Mar 10, 2042 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02D30/70
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.