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Simulation results on a high fidelity lithium-ion battery model demonstrate that the observer is effective in estimating SOC and SOH despite a range of common errors due to model order reductions, linearisation, initialisation and noisy measurement.
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Figure 13 Simulation results after Model Order Reduction.
Hence, we develop a mathematical model order reduction scheme.
Our methodology involves physical simplifications and model order reduction.
It combines two scales of model order reduction.
Model Order Reduction was successfully applied to coupled EM-heat problems.
In order to alleviate these problems, model order reduction techniques have become indispensable.
This paper presents a new, analytically driven frequency weighted model order reduction method.
With model order reduction (MOR) the computational time is reduced to enable the process parallel simulation.
We combine regression tools techniques and trade off analysis with recent techniques from Model Order Reduction.
Model order reduction technique is adopted to reduce the computational time in the wave dispersion analysis.
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