A distributed optimization approach to energy management for a heavy-duty truck

Energy management systems (EMS) aim at minimizing the
vehicle fuel consumption and tailpipe emissions under the
wide range of driving conditions. Classical energy management
systems for hybrid vehicles control the powersplit between
the internal combustion engine (ICE) and the electric
motor (EM) [1]. In the last decade, this research topic is
extended towards integrating (thermal) battery management
systems and engine aftertreatment systems (EAS). The next
challenge is to extend energy management to incorporate all
energy flows present in the truck [2]. Figure 1 shows the energy
flows schematically for a Hybrid Truck. In this figure,
there is one primary energy source, the ICE, and multiple energy
converters and energy buffers. The goal is to minimize
the fuel consumption whilst meeting the minimum power request
of each individual energy consumer. Distributed control
is a promising technique for this problem, as it will enhance
modularity of the system in the sense that components
can be removed or added to the system without affecting the
optimality and complexity of the system. The main research
questions on this topic are i) what fuel consumption reduction
can be achieved by increasing the number of controlled
energy flows and ii) can we develop a complete vehicle energy
management system (CVEMS) that enables this with
manageable complexity resulting in an acceptable development
time.