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Hierarchical Optimal Energy Management of Electric Vehicles

Project description

Smart energy management system for electric vehicles

Electro-mobility forms a central component of the EU’s ambition to decarbonise its economy. This would also mean a shift towards electrical control of existing and new vehicle functions. As electric architecture becomes more complex, efficient control technology will play a crucial role in increasing the market share and the overall efficiency of electric vehicles. In view of this, the HOEMEV project is developing a novel adaptive control framework for intelligent energy management of electric vehicles. The project team will also develop effective fast optimisation methods based on quadratic programming and advanced process control. These will help achieve maximum fuel economy, emission reduction, drivers’ performance and battery life extension.

Objective

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.

Fields of science (EuroSciVoc)

Programme(s)

Topic(s)

Funding Scheme

MSCA-IF-EF-ST - Standard EF

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Call for proposal

(opens in new window) H2020-MSCA-IF-2018

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Coordinator

QUEEN MARY UNIVERSITY OF LONDON
Net EU contribution
€ 224 933,76
Address
327 MILE END ROAD
E1 4NS LONDON
United Kingdom
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 224 933,76