PhD Thesis - Cranfield University

Chapter 2
by Guzzella and Sciarretta [18] for sub-optimal but implementable techniques due to the
causal control nature of the method. In addition, the authors of [18] demonstrated that non-
causal methods that strongly depend upon the precision of future power profile can lead to
an energy management strategy that causes excessive deviation to energy storage system
target state of charge.
Exploring several energy management strategies, Koot et at. [33] demonstrated that the
general concept of energy management is warranted since even the most basic of strategies
yields a reduction in net energy usage. For a fixed vehicle drive profile and subsystem
architecture, the authors of [33] evaluated five energy management strategies. Since the
outcome of their work also concurred that implementable strategies do not have the drive
profile horizon as priory knowledge, they suggested a dynamic programming approach that
uses a short horizon length rather than the complete driving cycle. Although dissimilar in
implementation method, the strategy bares fundamental similarities to the ECMS proposed
by Pisu and Rizzoni [32], which replaces a global criteria of energy expenditure with a local
criterion.
Recognising the stochastic nature of the energy management problem, Lin, Peng and Grizzle
[34] proposed a strategy using stochastic dynamic programming (SDP). Representing the
vehicle power demand as transition probabilities over an unknown mission profile, the
authors formulated the power split decision rules as a time-invariant infinite horizon SDP
problem. Although the method was intended for a HEV application, the technique is
transferable to EVs. The SDP technique was also examined by Min et.al [35]. Modelling the
vehicle driver power demand as a Markov chain, the authors of [35] developed a strategy to
split power delivery between a fuel cell and battery system. By constructing a transition
probability function based on several driving scenarios, the SDP method was used to map
the observed states to the control of power split decisions.
In a recent publication, Cacciatori et al.[36] provided a basic classification of energy
management strategies. The authors categorised energy management strategies into two
groups. Strategies that require a priori knowledge about the mission profile and those that
have no or limited knowledge in that regard. For the first group, three approaches are
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