Sensitivity Analysis of Optimal Energy Management in Plug-in Hybrid Heavy Vehicles
Paper in proceedings, 2017

Optimal energy management strategies of hybrid vehicles are computationally expensive when considering the entire trip ahead rather than a short upcoming horizon. Considering the entire representative trip is already needed in concept design stages of the vehicle. In order to come up with an appropriate design while minimizing the total ownership cost the energy management strategies must already be used together with early concept evaluations. To investigate the possibility of replacing the optimal energy management with simpler approaches, here, the sensitivity of optimal solution to some of vehicle parameters and traffic flow is studied. It is seen that a simpler approach, i.e. an instantaneous optimization, can be used, in case of smooth traffic flow, since the gain of optimal strategy in reduction of operational cost is less than 4% for different vehicle hardware setup and for selected representative driving cycle. Dynamic programming is used as a solution method for finding the optimal strategy.

ICE

Optimal energy management strategy

Operational cost

Hardware setup

Sensitivity analysis

Batteries

Optimal control

Energy management

Electric motors

Dynamic programming

Plug-in hybrid heavy vehicle

Cost function

Author

Toheed Ghandriz

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Leo Laine

Volvo Group

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Jonas Hellgren

Volvo Group

Bengt J H Jacobson

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE)

320-327 8056932

2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE)
Singapore, Singapore,

Driving Forces

Sustainable development

Areas of Advance

Transport

Building Futures (2010-2018)

Energy

Subject Categories

Vehicle Engineering

Control Engineering

DOI

10.1109/ICITE.2017.8056932

More information

Latest update

5/28/2018