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A Comparison of Model Order Reduction Techniques for Real-Time Battery Thermal Modelling
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Battery temperature is known to have a critical influence on overall battery pack performance, from electrochemical behavior, charge acceptance, power availability, trip efficiency, safety, reliability and life-cycle costs. Temperature monitoring is critical to ensure safe and reliable battery pack operation.
Monitoring of cell temperatures in battery packs allows for control and estimation algorithms that can ensure homogenous pack temperature distribution, prevent excessive pack temperature rise and even infer cell core temperature, potentially allowing to both predict and mitigate onset of thermal runaway.
The increasing need for improved accuracy requires inclusion of more detail in the modelling stage, leading inevitably to ever larger-scale, ever more complex dynamical systems. Simulations in such large-scale settings lead in turn to unmanageably large demands on computational resources, which is the main motivation for Model Order Reduction.
Model Order Reduction is focused on reducing the complexity of large-scale dynamical systems, while preserving their input-output behavior. Reduced models mimic the complex behavior of large-scale dynamical systems, and can be efficiently used for design, optimization and sensitivity analysis. The resulting reduced order model can be used to replace the original system as a component in a larger simulation or it might be used to develop a simpler and faster controller suitable for real time applications.
Three model order reduction techniques are compared. The first method, Balanced Truncation, assumes access to the high-fidelity model structure is available whilst the other two, Nyquist and neural network approximation, assume only input/output behavioral knowledge is available. The reduced order models are compared, in simulation, to a high-fidelity model of a battery pack in terms of simulation speed-up, model accuracy and property preservation. The applicability of each technique for real-time applications, particularly in the context of xEV (grid connected electric vehicle) thermal management, together with pros and cons of each method is discussed.
|Technical Paper||Experiment Studies of Charging Strategy for Lithium-Ion Batteries|
|Technical Paper||Optimizing Battery Cooling System for a Range Extended Electric Truck|
|Technical Paper||Development and Build-up of a Hybrid Commercial Vehicle|
CitationMcGahan, P., Rouaud, C., and Booker, M., "A Comparison of Model Order Reduction Techniques for Real-Time Battery Thermal Modelling," SAE Technical Paper 2019-01-0503, 2019, https://doi.org/10.4271/2019-01-0503.
Data Sets - Support Documents
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