Challenges of micro/mild hybridisation for construction machinery and applicability in UK. (August 2018) - Truong, D.Q.; Marco, J.; Greenwood, D.; Harper, L.; Corrochano, D.G.; Yoon, J.I.
Abstract: In recent years, micro/mild hybridisation (MMH) is known as a feasible solution for powertrain development with high fuel efficiency, less energy use and emission and, especially, low cost and simple installation. This paper focuses on the challenges of MMH for construction machines and then, pays attention to its applicability to UK construction machinery. First, hybrid electric configurations are briefly reviewed; and technological challenges towards MMH in construction sector are clearly stated. Second, the current development of construction machinery in UK is analysed to point out the potential for MMH implementation. Thousands of machines manufactured in UK have been sampled for the further study. Third, a methodology for big data capturing, compression and mining is provided for a capable of managing and analysing effectively performances of various construction machine types. By using this method, 96% of data memory can be reduced to store the huge machine data without lacking the necessary information. Forth, an advanced decision tool is built using a fuzzy cognitive map based on the big data mining and knowledge from experts to enables users to define a target machine for MMH utilization. The numerical study with this tool on the sampled machines has been done and finally realized that one class of heavy excavators is the most suitable to apply MMH technology. Highlights: There are technological gaps between hybrid electric vehicles and hybrid electric machines, including system architecture and components, working cycles, energy management system and reliability. Based on these gaps, the design challenges of a micro/mild hybrid machine are pointed out. These comprise machine architectures, energy storage devices, energy management strategies, additional features, and applicability. Focusing on the UK construction market, it is recognized that there are suitable conditions for the utilization of MMH technology. Over 12, 000 sample machines manufactured by JCB have been selected for the further analysis and machine development. The big data management approach using the ASBBS method and all-in-one concept has been developed. Using this approach, the large data amount from various JCB machine variants, 193 GB, has been compressed remarkably into the small data package, only 7.43 GB (96% of the data package size could be reduced). This allows users to store and access easily any necessary data for machine analyses as well as other maintenance services. The FCM-based decision tool is constructed in order to find out which machine variant has the highest potential for MMH technology implementation. The evaluation with 20 typical machine variants has been then performed using the designed tool and the experts' knowledge. The evaluation result has indicated that the heavy excavator – type 2 with the highest MMH potential is the best choice for the MMH implementation. In addition, this tool enables the markers go quickly in developing their new product lines which satisfy all design requirements. Based on the outcomes of this study, the next research stage is to design the MMH technologies, starting from micro hybridisation for the target machine, a heavy excavator – type 2 from JCB, including machine powertrain modelling, energy management strategies, simulations and real-time implementation.