Summary
It has been widely recognized that vehicle electrification provides a potential way for the EU to move towards a more decarbonized transport system and sustainable circular economy. To increase the market share of electric vehicles (EVs), control technology plays an indispensable role in improving the overall efficiency of EVs; however, EV control problem is very challenging because EVs exhibit complex dynamics with uncertainties and nonlinearities, and strong physical couplings among different subsystems. The overarching objective of this project is to develop a novel computationally efficient hierarchical adaptive optimal control framework incorporating transportation information and drivers’ habits suitable for energy management of EVs. To enhance the computational power of the framework, an effective fast optimisation method based on quadratic programming (QP), a novel velocity predictor with varying-prediction-horizon calibrator, and a fast MPC controller will be developed and embedded into the hierarchical control framework, so as to achieve multi-objective optimal control targets, i.e. maximum fuel economy, reduction of emissions, improvement of drivability and battery life extension. Moreover, a hardware-in-the-loop (HIL) test platform will be built up for real-time experiments to validate the efficacy of the proposed approaches. The project will contribute to both control theory and applications in EVs with promising extensions to other engineering problems. This project will sufficiently merge the critical information of human, road and vehicle with a hierarchical control framework to facilitate the energy management of EVs.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/845102 |
| Start date: | 01-08-2019 |
| End date: | 31-07-2021 |
| Total budget - Public funding: | 224 933,76 Euro - 224 933,00 Euro |
Cordis data
Original description
It has been widely recognized that vehicle electrification provides a potential way for the EU to move towards a more decarbonized transport system and sustainable circular economy. To increase the market share of electric vehicles (EVs), control technology plays an indispensable role in improving the overall efficiency of EVs; however, EV control problem is very challenging because EVs exhibit complex dynamics with uncertainties and nonlinearities, and strong physical couplings among different subsystems. The overarching objective of this project is to develop a novel computationally efficient hierarchical adaptive optimal control framework incorporating transportation information and drivers’ habits suitable for energy management of EVs. To enhance the computational power of the framework, an effective fast optimisation method based on quadratic programming (QP), a novel velocity predictor with varying-prediction-horizon calibrator, and a fast MPC controller will be developed and embedded into the hierarchical control framework, so as to achieve multi-objective optimal control targets, i.e. maximum fuel economy, reduction of emissions, improvement of drivability and battery life extension. Moreover, a hardware-in-the-loop (HIL) test platform will be built up for real-time experiments to validate the efficacy of the proposed approaches. The project will contribute to both control theory and applications in EVs with promising extensions to other engineering problems. This project will sufficiently merge the critical information of human, road and vehicle with a hierarchical control framework to facilitate the energy management of EVs.Status
CLOSEDCall topic
MSCA-IF-2018Update Date
28-04-2024
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