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There is an ongoing proliferation of electric and electrified vehicles as manufacturers seek to reduce their carbon footprint and meet the carbon reduction targets mandated by governments around the world. An ongoing challenge in electric vehicle design is the efficient and safe design of battery packs. There are significant safety challenges for lithium battery packs relating to thermal runaway, which can be initiated through overheating and internal short from defects or external damage. This work proposes a robust method to couple the mechanical damage in a battery module calculated from a dynamic model with a thermal model of the battery that includes heating from electro-chemical sources as well as Arrhenius reactions from the battery cells. The authors identify the main sources of thermal runaway initiation and propagation in an impact scenario simulating a vehicle collision. The modeling approach was developed and validated using test data.

As advances in electrification continue within the vehicle industry, improving the front-end design process and managing the safety aspects of lithium-ion batteries is increasingly important. Structural damage to lithium-ion batteries can cause internal short circuit, leading to a large energy release that can lead to fire and thermal runaway that propagates throughout the battery pack. Southwest Research Institute has developed a mechanical model that can accurately predict mechanically-induced damage to lithium-ion battery cells and battery packs. This model also predicts whether the external damage will cause an internal short-circuit. This modeling process was illustrated using 21700 cylindrical cells with NCA cathode chemistry. High-speed impact tests were used to calibrate a single cell model, which was then scaled to a 12-cell battery module. This model was then used to accurately predict the outcome of an impact test on a 72-cell battery module.

There is growing interest in additive manufacturing technologies for prototype if not serial production of complex internal combustion engine components such as cylinder heads and pistons. In support of this general interest the authors undertook an experimental bench test to evaluate opportunities for cooling jacket improvement through geometries made achievable with additive manufacturing. A bench test rig was constructed using electrical heating elements and careful measurement to quantify the impact of various designs in terms of heat flux rate and convective heat transfer coefficients. Five designs were compared to a baseline - a castable rectangular passage. With each design the heat transfer coefficients and heat flux rates were measured at varying heat inputs, flow rates and pressure drops. Four of the five alternative geometries outperformed the baseline case by significant margins.

As the efforts to push capabilities of current engine hardware to their durability limits increases, more accurate and reliable analysis is necessary to ensure that designs are robust. This paper evaluates a method of Conjugate Heat Transfer (CHT) analysis for a gasoline and a diesel engine that combines combustion Computational Fluid Dynamics (CFD), engine Finite Element Analysis (FEA), and cooling jacket CFD with the goal of obtaining more accurate temperature distribution and heat loss predictions in an engine compared to standard de-coupled CFD and FEA analysis methods. This novel CHT technique was successfully applied to a 2.5 liter GM LHU gasoline engine at 3000 rpm and a 15.0 liter Cummins ISX heavy duty diesel engine operating at 1250 rpm. Combustion CFD simulations results for the gasoline and diesel engines are validated with the experimental data for cylinder pressure and heat release rate.

As the design of engines advances and continues to push the capabilities of current hardware closer to their durability limits, more accurate and reliable analysis is necessary to ensure that designs are robust. This research evaluates a method of conjugate heat transfer analysis for a diesel engine that combines the combustion CFD, Engine FEA, and cooling jacket CFD with the aim of getting more accurate heat loss predictions and a more accurate temperature distribution in the engine than with current analysis methods. A 15.0 L Cummins ISX heavy duty engine operating at 1250 RPM and 15 bar BMEP load is selected for this work. Spray combustion computational fluid dynamics (CFD) simulations are performed for the diesel engine and the results are validated with experimental data. Finite Element Analysis (FEA) simulations were performed in a separate software platform.