Energy Management Strategies of Electrified Vehicles toward the Real-World Driving
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".
Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 4781
Special Issue Editors
Interests: powertrain control; optimization; dynamical systems; modeling
Interests: powertrain control; optimal control; hybrid electric vehicle; connected and automated vehicle
Special Issue Information
Dear Colleagues,
To maximize the potential in improving the energy efficiency of electrified vehicles, the development of energy management strategies has drawn significant attention for more than two decades. For the electrified powertrains, such as hybrid electric vehicles, energy conservation is achieved by on-board managing the electric machine and the combustion engine properly under the constraint of the driver power demand. Following the last two decades, a lot of researches on the energy management strategies or hybrid powertrain control algorithms attacked the situation with previously known driving routes. This means that at the stage of developing energy optimization strategies, power demand must be known, since the derivation of the strategy is usually formulated as an optimization problem with the constraints decided by the driving route. This is one major reason why the energy management strategies obtained in this way are still out of the real-world driving loop. The aim of this Special Issue is to bridge the gap between the theoretical development and practical application in energy optimization of the electrified vehicles. We hope this Special Issue will inspire the researchers from the fields of optimization and learning algorithm, system control, and automotive engineering to challenge the issues of energy management and powertrain control strategy targeting practical use in the real world.
Prof. Dr. Tielong Shen
Dr. Fuguo Xu
Dr. Jiangyan Zhang
Guest Editors
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Keywords
- electric vehicles
- hybrid electric vehicles
- connected vehicles
- real-world traffic data
- energy consumption prediction
- energy management strategies
- energy efficiency optimization
- real-time optimization
- powertrain control
- vehicle-to-vehicle
- vehicle-to-infrastructure
- traffic prediction
- data-based learning
- charging control
- vehicle-to-grid
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