Industry 4.0 must respond to some challenges such as the flexibility and robustness of unexpected conditions, as well as the degree of system autonomy, something that is still lacking. The evolution of Industry 4.0 aims at converting purely mechanical machines into machines with self-learning capacity in order to improve overall performance  and contribute to the optimization of maintenance. An important contribution of Industry 4.0 in the industrial sector is predictive maintenance and prescriptive maintenance. This article should be analysed as a methodology proposal to implement an automatic forecasting model in a test bench for the recognition of a machine’s failure and contribute to the development of algorithms for preventive and descriptive maintenance. Keywords: Industry 4.0, Artificial intelligence, Machine learning, Predictive maintenance, Prescriptive maintenance

Plug-In hybrid vehicles have a complex propulsion system management, trying to manage the conventional and electric motorization in the most energy efficient way according to the driving dynamics, topography and battery charge state. In this sense, the aim of this work is to analyze the energy performance of plug-in hybrid vehicles, based on road tests, under real conditions of use, focusing on the management system of the two energy sources present, varying the level of battery charge at the start of the test to visualize the impact of this change. To complement the analysis and in order to better understand the operation of the management system, a methodology for applying the VSP parameter is used, which allows the load state to be approximated according to the vehicle’s operating mode, alternating between the three modes according to the conditions at the time in question, prioritizing the electric motor when the state of charge of the battery is maximum. These results confirm the fact that plug-in hybrid vehicles allow better electricity management due to the diversity of external or internal charging sources, which makes this type of vehicle more efficient and versatile than conventional hybrids, allowing a reduction in fossil fuel consumption and consequently a reduction in the emission of pollutant gases, making this type of vehicle a very competitive alternative in the transport sector in view of the current challenges due to the goals present in the current European regulations. Keywords: Plug-in hybrid vehicles, Energy assessment, Climatization systems, Load support, State of charge

In response to environmental impacts and all the limitations caused by fossil fuels, we have been witnessing in recent decades to the sharp development of hybrid electric and electric vehicles, particularly in heavy-duty passenger vehicles. Its proliferation is now widespread in virtually every major vehicle brand, reflecting operator confidence. In order to further mitigate the use of fossil fuels, the trend is to increase supply in 100% electric versions. However, the evolution of recent years, both in manufacturers commercial strategy of major brands and bodybuilders and in sales volume, seems to indicate a new demand stage for this kind of vehicles, which are still making the first steps in Portugal. However, high acquisition costs and limited autonomy are still major obstacles to a faster proliferation of electrification in heavy vehicles. Its strengths such as lower air and noise pollution, in addition to lower operating and maintenance costs, led to a growing acquisition in the Portuguese vehicle market, where 16 new heavy-duty passenger BEV have been sold this year. Real-world operational impacts of these vehicles indicate a energy use between 0.91 and 1.65 kWh/km depending on driving context. It has been also observed that operators are still adapting and not always using the full battery capacity. Keywords: Electrified heavy vehicles, Energy assessment, Driving mode, Load support, State of charge