Predictive control for complete vehicle energy management
The purpose of this project is to bridge the gap between Chalmers’ finished projects in Energy Management and an already granted doctoral project which cannot be started until the budget for Phase 3 of SHC is decided.
The project builds on Lars Johannesson Mårdh’s and Nikolce Murgovski’s research on control architecture and algorithms for predictive energy management of conventional and hybrid vehicles, made in the strategic research area (SFO) Chalmers Energy Initiative (CEI). The algorithms have so far only focused on optimizing energy management in a vehicle with information about speed limits and road gradient.
The proposed control architecture and algorithms allow extension with information about the surrounding vehicles and traffic flows, which is the first step toward autonomous driving. However, if the control system should be applied to autonomous vehicles and achieve maximum fuel savings there are many unsolved questions and challenges: which is the best distributed control system for road trains of conventional and hybrid vehicles; how can convex optimization be used to reduce the computational requirements; how can algorithms be extended from mainly motorway driving to city traffic; how should the wear models be included?
A rough plan for the project is to:
1) organize an SHC seminar on CEI control system;
2) expand Chalmers simulation model to manage multiple vehicles;
3) propose a distributed predictive control algorithm for planning kinetic, electrical and thermal buffers.
Ladda ner rapporten: Predictive control for complete vehicle energy management (139 kB)
- Vehicle independent road resistance estimation
- Interdisciplinary post-doc cluster for future hybrid vehicles
- Test bench for Optimal Design and Control of Energy Buffers for Minimizing Energy Consumption
- Modellering av hybriddrivlina och avgasefterbehandlingssystem
- Energy efficient driving using electric wheel corner functionalities
- Life-Long Battery Control
- OCEAN – Operating cycle energy management
- Testing and evaluation of fault handling strategies in the research concept vehicle
- System level evaluation of diesel engine and emission after treatment systems for hybrid drivetrain applications in dynamic drive cycles
- Model for simulation of driving behavior during failures in electrified vehicles
- Evaluation of energy efficient cornering strategies using the KTH Research Concept Vehicle
- Säkra och energieffektiva fordonskonstruktioner
- Dimensioning a plug-in hybrid using drive-cycle information
- Optimore – Optimised Modular Range Extender for every day customer usage
- Energy management of HEVs – fuel optimal control
- Over-actuated fault-tolerant hybrid electric vehicles
- Generic vehicle motion modeling and control for enhanced driving dynamics and energy management
- Overall monitoring and diagnosis of hybrid electric vehicles in realistic scenarios