Radical changes are necessary to address challenges related to global warming and pollution. Ever-tightening emission standards for combustion engines have already led to a drastic reduction in the amount of harmful gas and matter emitted. Drivetrain hybridization and electrification, which are becoming increasingly popular in all sectors, are two additional ways to achieve that goal. However, within the forestry sector most of the equipments still rely on conventional mechanic or hydraulic drivetrains. An example of this is tower yarders, the workhorse of the alpine logging industry. This work simulates the duty cycle and energy flow of tower yarders in logging operations, both with conventional diesel–hydraulic configuration and a proposed hybrid configuration. The objective is to determine the potential of hybridized drivetrains for tower yarder applications. Detailed models are developed to describe the cable-based extraction of timber and tower yarder internal processes. Extensive simulations were performed to determine force, power and energy components during the harvesting operation for both the diesel–hydraulic and hybrid drivetrains. Results confirm the large potential of the hybrid configuration for efficiency improvement and emission reduction, with estimated fuel savings of 45% and 63% in the uphill and downhill configurations, respectively. Extensive sensitivity analysis further demonstrates that the hybrid concept remains effective across a wide range of cable setup and transport characteristics. This confirms the large potential of electrified drivetrains, especially in the presence of very dynamic duty cycles, as is the case in cable-based logging equipment.
Electric and hybrid vehicles are two of the most promising solutions to meet the new emission requirements of the transportation sector. The energy storage system is their most critical component in terms of performance. Therefore, battery modeling plays an important role for designing and controlling battery modules. Up to now, the design of battery modules is conditioned by the use of expensive tools that involve long simulation times; in response to this, the present work introduces an open-source tool developed in Python to study the performance of battery modules and contribute to their design. To show the scope and use of the tool, a theoretical case study is presented. In particular, the effect of slight differences in cells behavior, due to manufacturing process or aging, is shown at the module level. A SOC difference of 2.5%, due to current imbalances, was obtained among the cells after discharge for a small battery module with new cells. This result points out the importance of accurate and fast module models to correctly predict the remaining travel range and the need for an online parameters identification procedure. In addition, the temperature distribution in the module along with the heat generated by the cells, also estimated by the tool, can be used for proper design and control of the battery cooling system.
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