Dr Davide Domenico Sciortino

A novel surrogate model is presented, which predicts the engine-out Particle Number (PN) emissions of a light-duty, spray-guided, turbo-charged, GDI engine. The model is developed through extensive CFD analysis, carried out using the Siemens Simcenter STAR-CD, and considers a range of part-load operating conditions and single-variable sweeps where control parameters such as start of injection and injection pressure are varied in isolation. The work is attached to the Ford-led APC6 DYNAMO project, which aims to improve efficiency and reduce harmful emissions from the next generation of gasoline engines.
The CFD work focused on the air exchange, fuel spray and mixture preparation stages of the engine cycle. A combined Rosin-Rammler and Reitz-Diwakar model, calibrated over a wide range of injection pressure, is used to model fuel atomization and secondary droplets break-up. A validated approach, based on the Bai-Onera model of droplet-wall interaction, is used to capture the details of liquid film formation. A multi-component surrogate fuel blend model reproduces the relevant characteristics of the E5 95RON gasoline used in parallel experiments. A fixed, but region-specific, wall temperature scheme is used for the in-cylinder simulations, based on available experimental data.
An Elastic Net (EN) regression technique was used to construct a novel PN surrogate model, through the identification of relevant relationships between experimental engine-out PN emission levels and modelled air-fuel mixture quality indicators. To maximize model usefulness and applicability, these indicators are then correlated through sub-models to engine control parameters and easily-accessible measurements. The sub-models are obtained via Radial Basis Function (RFB) or a combination of RBF and EN regression. Within limits, engine sooting tendencies can be reliably predicted without reliance on combustion characteristics, which are complex to measure in real time.

Lithium-ion Cell thermal characterisation is foundational for battery modelling and the optimisation of electric vehicle battery pack, thermal management systems. This is especially the case for electric vehicles subject to very high peak power requirements, such as motorsport or large construction vehicle applications. Conventional techniques for assessing cell thermal parameters, such as the anisotropic thermal conductivity and bulk cell heat capacity, can be expensive or complicated. For example, some methods require expensive equipment, in addition to conventional cell testing equipment, such as Li-ion cell calorimeters. Other methods require the dismantling of the cell into its constituent components, also requiring additional laboratory equipment and subsequent thermal analysis. Recent studies propose an alternative method that relies on, standard cell testing equipment and a quasi 1D/3D lumped thermal network to solve the thermal balance of a cell generating heat during charge/discharge cycles. Despite these advancements, the existing literature frequently overlooks essential details regarding the battery fixture used and the impact of key operational variables, thus compromising reproducibility. In particular these recent studies typically utilise a constant OCV value when estimating cell heat generation [1].

This study introduces a reproducible and cost-effective method for rapid thermal characterisation, leveraging an in-house Modular Battery Thermal Fixture (MBTF). A Li-ion cell is used to evaluate different cycling protocols and test conditions, aiming to identify the most effective configuration for accurately predicting the heat capacity validated against Accelerating Rate Calorimeter tests. The thermal network is solved for two convective states and yields the heat capacity value necessary to evaluate the anisotropic thermal conductivity. The results underscore the sensitivity of thermal parameters with C-rate, SOC and ambient temperature. In particular, the study attempts to increase thermal parameter estimation accuracy by utilising a cell model to more accurately account for cell OCV variation and cell heat generation. This analysis offers valuable implications for advancing the thermal characterisation of Li-ion cells and emphasises the importance of standardising the determination of thermal parameters to enhance the reliability and comparability of research outcomes. Future research will explore the proposed methodology across large prismatic cells and diverse chemistries, to enhance its applicability and foster broader adoption in both industrial and scientific communities.

[1] Bryden, T.S., Dimitrov, B., Hilton, G., de León, C.P., Bugryniec, P., Brown, S., Cumming, D. and Cruden, A., 2018. Methodology to determine the heat capacity of lithium-ion cells. Journal of Power Sources, 395, pp.369-378.

Cell thermal characterisation is foundational for battery modelling and the optimisation of thermal management systems. Conventional techniques for assessing thermal parameters, such as the anisotropic thermal conductivity and the cell heat capacity, typically require expensive equipment like calorimeters or involve the dismantling of the cell. Recent studies propose an alternative method that relies on a 1D/3D lumped thermal network to solve the thermal balance of a cell generating heat during charge/discharge cycles. Despite these advancements, the existing literature frequently overlooks essential details regarding the battery fixture used and the impact of key operational variables, thus compromising reproducibility. This study introduces a reproducible and cost-effective method for rapid thermal characterisation, leveraging an in-house Modular Battery Thermal Fixture (MBTF). A pouch lithium-polymer cell is used to evaluate different cycling protocols and test conditions, aiming to identify the most effective configuration for accurately predicting the heat capacity validated against Accelerating Rate Calorimeter tests. The thermal network is solved for two convective states and yields the heat capacity value that is necessary to evaluate the anisotropic thermal conductivity. The results underscore the sensitivity of thermal parameters with C-rate, SOC and ambient temperature. This analysis offers valuable implications for advancing the thermal characterisation of Li-ion cells and emphasises the importance of standardising the determination of thermal parameters to enhance the reliability and comparability of research outcomes. Future research will explore the proposed methodology across large prismatic cells and diverse chemistries, to enhance its applicability and foster broader adoption in both industrial and scientific communities.