Mechanical characterization of lithium-ion batteries with different chemistries and formats

Clerici, Davide; Martelli, Salvatore; Mocera, Francesco; Somà, Aurelio

doi:10.1016/j.est.2024.110899

Cited by 3 publications

(1 citation statement)

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“…There is a double relationship between climate change and agriculture. On the one hand, the agricultural sector is one of the main actors in terms of greenhouse gas emissions: indeed, agriculture is responsible for 11% of the overall anthropogenic GHG emissions [5] (with peaks of 35% for developing countries) [6][7][8][9]. On the other hand, the effects of climate change are causing extreme weather and unpredictable weather events, which may affect future crop production [10,11].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Driving Strategy for a Specialized Four-Wheel Differential-Drive Agricultural Rover

Martelli,

Mocera,

Somà

2024

AgriEngineering

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Recently, the agriconstruction machinery sector has been involved in a great technological revolution. The reasons that may explain this are strictly connected to the mitigation of climate change. At the same time, there is a necessity to ensure an adequate production level in order to meet the increasing food demand due to the current population growth trend. In this context, the development of autonomously driven agricultural vehicles is one of the areas on which tractor manufacturers and academics are focusing. The fundamental prerequisite for an autonomous driving vehicle is the development of an appropriate motion strategy. Hence, the vehicle will be able to follow predetermined routes, accomplishing its missions. The aim of this study was the development of path-planning and path-following algorithms for an agricultural four- whee differential-drive vehicle operating in vineyard/orchard environments. The algorithms were completely developed within the MATLAB software environment. After a brief description of the geometrical characteristics of the vehicle, a parametric process to build a virtual orchard environment is proposed. Then, the functional principles of the autonomous driving algorithms are shown. Finally, the algorithms are tested, varying their main tuning parameters, and an indicator to quantify the algorithms' efficiency, named relative accuracy, is defined. The results obtained show the strong dependence between the relative accuracy and lookahead distance value assigned to the rover. Furthermore, an analysis of rover positioning errors was performed. The results in this case show a lower influence of the location error when the accuracy of the positioning device is within 2 cm.

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