6,727 research outputs found
Smart Procurement of Naturally Generated Energy (SPONGE) for Plug-in Hybrid Electric Buses
We discuss a recently introduced ECO-driving concept known as SPONGE in the
context of Plug-in Hybrid Electric Buses (PHEB)'s.Examples are given to
illustrate the benefits of this approach to ECO-driving. Finally, distributed
algorithms to realise SPONGE are discussed, paying attention to the privacy
implications of the underlying optimisation problems.Comment: This paper is recently submitted to the IEEE Transactions on
Automation Science and Engineerin
Numerical Analysis of National Travel Data to Assess the Impact of UK Fleet Electrification
Accurately predicting the future power demand of electric vehicles is
important for developing policy and industrial strategy. Here we propose a
method to create a representative set of electricity demand profiles using
survey data from conventional vehicles. This is achieved by developing a model
which maps journey and vehicle parameters to an energy consumption, and
applying it individually to the entire data set. As a case study the National
Travel Survey was used to create a set of profiles representing an entirely
electric UK fleet of vehicles. This allowed prediction of the required
electricity demand and sizing of the necessary vehicle batteries. Also, by
inferring location information from the data, the effectiveness of various
charging strategies was assessed. These results will be useful in both National
planning, and as the inputs to further research on the impact of electric
vehicles
Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles
Abstract—This paper describes the application of state-estimation
techniques for the real-time prediction of the state-of-charge
(SoC) and state-of-health (SoH) of lead-acid cells. Specifically,
approaches based on the well-known Kalman Filter (KF) and
Extended Kalman Filter (EKF), are presented, using a generic
cell model, to provide correction for offset, drift, and long-term
state divergence—an unfortunate feature of more traditional
coulomb-counting techniques. The underlying dynamic behavior
of each cell is modeled using two capacitors (bulk and surface) and
three resistors (terminal, surface, and end), from which the SoC
is determined from the voltage present on the bulk capacitor. Although
the structure of the model has been previously reported for
describing the characteristics of lithium-ion cells, here it is shown
to also provide an alternative to commonly employed models of
lead-acid cells when used in conjunction with a KF to estimate
SoC and an EKF to predict state-of-health (SoH). Measurements
using real-time road data are used to compare the performance
of conventional integration-based methods for estimating SoC
with those predicted from the presented state estimation schemes.
Results show that the proposed methodologies are superior to
more traditional techniques, with accuracy in determining the
SoC within 2% being demonstrated. Moreover, by accounting
for the nonlinearities present within the dynamic cell model, the
application of an EKF is shown to provide verifiable indications of
SoH of the cell pack
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