iHorizon-Enabled Energy Management for Electrified Vehicles
Nonfiction, Science & Nature, Technology, Engineering, Automotive
| Author: | Clara Marina Martinez, Dongpu Cao | ISBN: | 9780128150115 |
| Publisher: | Elsevier Science | Publication: | September 11, 2018 |
| Imprint: | Butterworth-Heinemann | Language: | English |
| Author: | Clara Marina Martinez, Dongpu Cao |
| ISBN: | 9780128150115 |
| Publisher: | Elsevier Science |
| Publication: | September 11, 2018 |
| Imprint: | Butterworth-Heinemann |
| Language: | English |
iHorizon-Enabled Energy Management for Electrified Vehicles proposes a realistic solution that assumes only scarce information is available prior to the start of a journey and that limited computational capability can be allocated for energy management. This type of framework exploits the available resources and closely emulates optimal results that are generated with an offline global optimal algorithm. In addition, the authors consider the present and future of the automotive industry and the move towards increasing levels of automation. Driver vehicle-infrastructure is integrated to address the high level of interdependence of hybrid powertrains and to comply with connected vehicle infrastructure.
This book targets upper-division undergraduate students and graduate students interested in control applied to the automotive sector, including electrified powertrains, ADAS features, and vehicle automation.
- Addresses the level of integration of electrified powertrains
- Presents the state-of-the-art of electrified vehicle energy control
- Offers a novel concept able to perform dynamic speed profile and energy demand prediction
iHorizon-Enabled Energy Management for Electrified Vehicles proposes a realistic solution that assumes only scarce information is available prior to the start of a journey and that limited computational capability can be allocated for energy management. This type of framework exploits the available resources and closely emulates optimal results that are generated with an offline global optimal algorithm. In addition, the authors consider the present and future of the automotive industry and the move towards increasing levels of automation. Driver vehicle-infrastructure is integrated to address the high level of interdependence of hybrid powertrains and to comply with connected vehicle infrastructure.
This book targets upper-division undergraduate students and graduate students interested in control applied to the automotive sector, including electrified powertrains, ADAS features, and vehicle automation.
- Addresses the level of integration of electrified powertrains
- Presents the state-of-the-art of electrified vehicle energy control
- Offers a novel concept able to perform dynamic speed profile and energy demand prediction
