The current engine developmental activities are driven by stringent emission norms and global warming concern s, and hence are directed towards finding viable alternative fuel resources that reduce the life cycle carbon emissions. The biodiesel fuel has been under active consideration for automotive as well as stationary engine applications for quite some time primarily due to its renewable nature, reduced carbon emissions and proximity of its properties with fossil diesel. Biodiesel can be produced from a wide variety of feedstocks and thus, exhibits composition variability depending upon the feedstock used for its production. There is a lack of rapid chemical kinetics based models with few reactions for predicting biodiesel–autoxidation, ignition and combustion-emission processes from an engine modelling standpoint. This research gap is addressed in the present research work wherein the chemical kinetics aspects of autoxidation, ignition and combustion-emission for any generic biodiesel are investigated using skeletal kinetics and phenomenological models with a major consideration given to the composition variability of biodiesel. A skeletal chemical kinetics autoxidation model for biodiesel fuels is proposed considering their major fatty acid methyl ester constituents, namely methyl stearate (as a representative of all saturated methyl esters), methyl oleate, methyl linoleate and methyl linolenate. The pre-exponential factors for important reactions are optimized using Genetic Algorithm (GA) based on the experimental data available in the literature. The proposed chemical kinetics scheme is useful to predict the autoxidation of biodiesel constituents within an error of 30%. The model also enables the prediction of the Rancimat induction period to facilitate a-priori evaluation of autoxidation characteristics of biodiesel fuels. Shell ignition model is a widely accepted multi-step skeletal reaction scheme for predicting the ignition delay of conventional fuels. In the present study, the Shell model is optimized for predicting the ignition delay of major biodiesel constituent esters, namely methyl palmitate, methyl stearate, methyl oleate, methyl linoleate, and methyl linolenate. The Shell model parameters for biodiesel constituent esters are optimized using GA and are applied to predict the ignition delay of any biodiesel fuel whose composition is known. The optimized Shell model is validated with available experimental data over a wide range of pressure (10-20 atm), temperature (800-1200 K) and equivalence ratio (0.5-1) with an average error of 32%. Biodiesel combustion and exhaust NO emission characteristics are predicted using an already existing phenomenological spray model with modifications made to include the biodiesel composition variability in the sub-models, namely, physical property, spray and ignition. The model predictions are validated with measurements done in a turbocharged multi-cylinder heavy-duty engine fuelled with two biodiesels having significantly different compositions, namely karanja having a higher proportion of unsaturated methyl esters and palm a having higher proportion of saturated methyl esters. The pressure histories and peak pressures are predicted within 5% and 3% error, respectively. However, the heat release rate predictions are only qualitative. The NO emissions are predicted using the extended Zeldovich mechanism within 23% error. A better correlation between biodiesel composition based parameter namely SCSF with the predicted maximum spray temperature and NO emission is established. The predictions from the proposed models for autoxidation, ignition and combustion-emission of biodiesel are fairly accurate with a significantly lower run time of the order of a few minutes.