Projects
Projects: Projects for Investigator |
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| Reference Number | EP/P012620/1 | |
| Title | SURROGATE ASSISTED APPROACHES FOR FUEL CELL AND BATTERY MODELS | |
| Status | Completed | |
| Energy Categories | Other Power and Storage Technologies(Energy storage) 50%; Hydrogen and Fuel Cells(Fuel Cells, Stationary applications) 25%; Hydrogen and Fuel Cells(Fuel Cells, Mobile applications) 25%; |
|
| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Physics) 50%; PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 50%; |
|
| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Dr AA Shah No email address given School of Engineering University of Warwick |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 11 November 2016 | |
| End Date | 10 September 2017 | |
| Duration | 10 months | |
| Total Grant Value | £66,497 | |
| Industrial Sectors | Energy | |
| Region | West Midlands | |
| Programme | Energy : Energy | |
| Investigators | Principal Investigator | Dr AA Shah , School of Engineering, University of Warwick (100.000%) |
| Web Site | ||
| Objectives | ||
| Abstract | Physics-based simulation codes for fuel cells and batteries are highly complex, involving coupled nonlinear PDEs, numerous constitutive laws, complex geometries, multiphase transport and multiple layers with disparate spatial scales. Even for relatively simplified geometries and single scales, they can be highly expensive to run. In many important applications, however, accurate but rapid simulations are essential, rendering these full, 'high-fidelity' models impractical. To lower the computational burden, surrogate models can be employed as approximations.Given the complexity of fuel cell and battery models, the vast number of parameters they involve and the unavoidable uncertainties in parameter values and model assumptions, there is enormous scope for developing surrogates for application that include, but are not limited to, Design optimization (DO), Sensitivity analysis (SA), Uncertainty quantification (UQ), real-time control and inverse parameter estimation. These areas represent the next-generation challenges for those working in fuel cell and battery modelling and the activities within this proposal are aimed at establishing a systematic programme of research activity at the forefront of these areas through fundamental developments combined with large-scale applications, with a focus on high-dimensional (spatio-temporal) data sets.The focus in this project is on establishing an ambitious long term activity for predictive modelling for DO, SA and UQ by developing and implementing new surrogate assisted approaches, specifically for patio-temporal models (very high dimensional input and output spaces). General frameworks for DO, SA and UQ (using the surrogate models) will be explored and tested on high-fidelity models of H2 fuel cells and vanadium flow batteries. The methods developed will be of direct relevance to other areas such as real-time control and inverse parameter estimation, and will be directly applicable to other fuel cell/battery systems. We wish to explore a number of ambitious surrogate-assisted approaches building upon our very recent work. The overseas visits will allow us to identify promising methods, which will form the basis of collaborative work over the next few years on all aspects listed above. The lists will establishing/reinforcing international collaborations and will form the foundations for establishing internationally-leading activity in battery and fuel cell modelling with respect to the current and future challenges faced in modelling, developing and commercialising these technologies | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 14/02/19 | |
