Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel
Cell systems
The present paper proposes an advanced approach for Polymer
Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and
isolation through a model-based diagnostic algorithm. The considered
algorithm is developed upon a lumped parameter model simulating a
whole PEMFC system oriented towards automotive applications. This
model is inspired by other models available in the literature, with
further attention to stack thermal dynamics and water management. The
developed model is analysed by means of Structural Analysis, to
identify the correlations among involved physical variables, defined
equations and a set of faults which may occur in the system (related
to both auxiliary components malfunctions and stack degradation
phenomena). Residual generators are designed by means of Causal
Computation analysis and the maximum theoretical fault isolability,
achievable with a minimal number of installed sensors, is
investigated. The achieved results proved the capability of the
algorithm to theoretically detect and isolate almost all faults with
the only use of stack voltage and temperature sensors, with
significant advantages from an industrial point of view. The effective
fault isolability is proved through fault simulations at a specific
fault magnitude with an advanced residual evaluation technique, to
consider quantitative residual deviations from normal conditions and
achieve univocal fault isolation.
Pierpaolo Polverino, Erik Frisk, Daniel Jung, Mattias Krysander and Cesare Pianese
Journal of Power Sources,
2017
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