Active Disassembly is technology that has been developed to allow assemblies to readily separate for recycling when they are exposed to certain triggering conditions. It is based around fasteners that use `Smart' Materials, typically Shape Memory Alloys (SMA) or Shape Memory Polymers (SMP). This has led to research in the field to be known as Active Disassembly Using Smart Materials (ADSM). Particularly within the context of the EU End of Life Vehicle (ELV) legislation, ADSM has the potential to enable the achievement of the recycling levels required. In this thesis, active disassembly solutions have been developed which have focused on the disassembly of the Instrument Panel, and the glazing within a vehicle. To achieve this, a number of novel Smart fastening devices have been developed, two of which are triggered by integral heating elements. This investigation also led to the creation of a new releasable hook and loop fastening system, known as `Shape Memory Hook and Loop Fasteners' (SM-HALF). SM-HALF is a repositionable fastening system that can be released remotely under a thermal stimulus. Research into the residual energy content of ELV batteries has been a significant part of the investigation. It has been found that it is possible to use the energy from `dead' car batteries to power at least 16 shape-memory alloy devices constructed from 25-micron diameter wire, at End of Life. No external energy input is required for disassembly. This research is timely as it provides a means of reclaiming 10% of a vehicle that would otherwise be lost to the shredder. The technology can: increase the number of parts available for recycling and reuse, separate waste streams, decrease shredder residue otherwise destined for landfill and increase economic returns for either the vehicle dismantling yards or shredder operator.

This work presents an investigation into the parameter estimation of suspension components and the vertical motions of wheeled vehicles from experimental data. The estimation problems considered were for suspension dampers, a single wheel station and a full vehicle. Using conventional methods (gradient-based (GB), Downhill Simplex (DS)) and stochastic methods (Genetic Algorithm (GA) and Differential Evolution (DE)), three major problems were encountered. These were concerned with the ability and consistency of finding the global optimum solution, time consumption in the estimation process, and the difficulties in setting the algorithm's control parameters. To overcome these problems, a new technique named the discrete variable Hybrid Differential Evolution (dvHDE) method is presented. The new dvHDE method employs an integer-encoding technique and treats all parameters involved in the same unified way as discrete variables, and embeds two mechanisms that can be used to deal with convergence difficulties and reduce the time consumed in the optimisation process. The dvHDE algorithm has been validated against the conventional GB, DS and DE techniques and was shown to be more efficient and effective in all but the simplest cases. Its robustness was demonstrated by its application to a number of vehicle related problems of increasing complexity. These include case studies involving parameter estimation using experimental data from tests on automotive dampers, a single wheel station and a full vehicle. The investigation has shown that the proposed dvHDE method, when compared to the other methods, was the best for finding the global optimum solutions in a short time. It is recommended for nonlinear vehicle suspension models and other similar systems.