349 research outputs found
The automatic validation tool for PDDL2.1
The 3rd International Planning Competition [1] was a great success and a cornerstone to this success was the initial definition of a semantics for the language used in the competition, PDDL2.1. This created a general understanding of the semantics of the domains defined using this language and therefore a general understanding of what constitutes a valid plan. With this consensus on what a valid plan is it was possible to implement an automatic plan validator, VAL. This tool conveys what is a valid plan in PDDL2.1 to anyone developing a planner using this language, as well as providing extra information in a L ATEX report featuring graphs of changing numerical values and a Gantt chart (see figure 2). Actions With Continuous Effects A numerical quantity that can be changed, a function in PDDL, is called a primitive numerical expression (PNE). These PNEs can have continuous change initiated with changes made to the values of their (time) derivatives by durative actions. The effect starts at the beginning of the durative action and ends at the end of the durative action. The introduction of continuous change creates two further complications to the discrete temporal model: 1) Continuous changes can interact with one another, and 2) Invariant conditions may depend on values that are continuously changing. The key extension to the discrete temporal model is that interactin
Validating plans with exogenous events
We are concerned with the problem of deciding the validity of a complex plan involving interacting continuous activity. In these situations there is a need to model and reason about the continuous processes and events that arise as a consequence of the behaviour of the physical world in which the plan is expected to execute. In this paper we describe how events, which occur as the outcome of uncontrolled physical processes, can be taken into account in determining whether a plan is valid with respect to the domain model. We do not consider plan generation issues in this paper but focus instead on issues in domain modelling and plan validation
VAL : automatic plan validation, continuous effects and mixed initiative planning using PDDL
This paper describes aspects of our plan validation tool, VAL. The tool was initially developed to support the 3rd International Planning Competition, but has subsequently been extended in order to exploit its capabilities in plan validation and development. In particular, the tool has been extended to include advanced features of PDDL2.1 which have proved important in mixed-initiative planning in a space operations project. Amongst these features, treatment of continuous effects is the most significant, with important effects on the semantic interpretation of plans. The tool has also been extended to keep abreast of developments in PDDL, providing critical support to participants and organisers of the 4th IPC
Sensorless Battery Internal Temperature Estimation using a Kalman Filter with Impedance Measurement
This study presents a method of estimating battery cell core and surface
temperature using a thermal model coupled with electrical impedance
measurement, rather than using direct surface temperature measurements. This is
advantageous over previous methods of estimating temperature from impedance,
which only estimate the average internal temperature. The performance of the
method is demonstrated experimentally on a 2.3 Ah lithium-ion iron phosphate
cell fitted with surface and core thermocouples for validation. An extended
Kalman filter, consisting of a reduced order thermal model coupled with
current, voltage and impedance measurements, is shown to accurately predict
core and surface temperatures for a current excitation profile based on a
vehicle drive cycle. A dual extended Kalman filter (DEKF) based on the same
thermal model and impedance measurement input is capable of estimating the
convection coefficient at the cell surface when the latter is unknown. The
performance of the DEKF using impedance as the measurement input is comparable
to an equivalent dual Kalman filter using a conventional surface temperature
sensor as measurement input.Comment: 10 pages, 9 figures, accepted for publication in IEEE Transactions on
Sustainable Energy, 201
On-board monitoring of 2-D spatially-resolved temperatures in cylindrical lithium-ion batteries: Part II. State estimation via impedance-based temperature sensing
Impedance-based temperature detection (ITD) is a promising approach for rapid
estimation of internal cell temperature based on the correlation between
temperature and electrochemical impedance. Previously, ITD was used as part of
an Extended Kalman Filter (EKF) state-estimator in conjunction with a thermal
model to enable estimation of the 1-D temperature distribution of a cylindrical
lithium-ion battery. Here, we extend this method to enable estimation of the
2-D temperature field of a battery with temperature gradients in both the
radial and axial directions.
An EKF using a parameterised 2-D spectral-Galerkin model with ITD measurement
input (the imaginary part of the impedance at 215 Hz) is shown to accurately
predict the core temperature and multiple surface temperatures of a 32113
LiFePO cell, using current excitation profiles based on an Artemis HEV
drive cycle. The method is validated experimentally on a cell fitted with a
heat sink and asymmetrically cooled via forced air convection.
A novel approach to impedance-temperature calibration is also presented,
which uses data from a single drive cycle, rather than measurements at multiple
uniform cell temperatures as in previous studies. This greatly reduces the time
required for calibration, since it overcomes the need for repeated cell thermal
equalization.Comment: 11 pages, 8 figures, submitted to the Journal of Power Source
Circuit Synthesis of Electrochemical Supercapacitor Models
This paper is concerned with the synthesis of RC electrical circuits from
physics-based supercapacitor models describing conservation and diffusion
relationships. The proposed synthesis procedure uses model discretisation,
linearisation, balanced model order reduction and passive network synthesis to
form the circuits. Circuits with different topologies are synthesized from
several physical models. This work will give greater understanding to the
physical interpretation of electrical circuits and will enable the development
of more generalised circuits, since the synthesized impedance functions are
generated by considering the physics, not from experimental fitting which may
ignore certain dynamics
Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of
batteries is necessary to ensure reliable system operation and to minimise
maintenance costs. The complex nature of battery degradation has meant that
mechanistic modelling of capacity fade has thus far remained intractable;
however, with the advent of cloud-connected devices, data from cells in various
applications is becoming increasingly available, and the feasibility of
data-driven methods for battery prognostics is increasing. Here we propose
Gaussian process (GP) regression for forecasting battery state of health, and
highlight various advantages of GPs over other data-driven and mechanistic
approaches. GPs are a type of Bayesian non-parametric method, and hence can
model complex systems whilst handling uncertainty in a principled manner. Prior
information can be exploited by GPs in a variety of ways: explicit mean
functions can be used if the functional form of the underlying degradation
model is available, and multiple-output GPs can effectively exploit
correlations between data from different cells. We demonstrate the predictive
capability of GPs for short-term and long-term (remaining useful life)
forecasting on a selection of capacity vs. cycle datasets from lithium-ion
cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201
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