This work aimed to develop an automatic new methodology based on establishing if a mechanical component, designed for a conventional propulsion system, is also suitable for hybrid electric propulsion. Change in propulsion system leads to different power delivery and vehicle dynamics, which will be reflected in different load conditions acting on the mechanical components. It has been shown that a workflow based on numerical simulations and experimental tests represents a valid approach for the evaluation of the cumulative fatigue damage of a mechanical component. In this work, the front half-shaft of a road car was analyzed. Starting from the acquisition of a speed profile and the definition of a reference vehicle, in terms of geometry and transmission, a numerical model, based on longitudinal vehicle dynamics, was developed for both conventional and hybrid electric transmission. After the validation of the model, the cumulative fatigue damage of the front half-shaft was evaluated. The new design methodology is agile and light; it has been dubbed “Smart Design”. The results show that changing propulsion led to greater fatigue damage, reducing the fatigue life component by 90%. Hence, it is necessary to redesign the mechanical component to make it also suitable for hybrid electric propulsion.

Nowadays, the automotive market has showed great interest in the diffusion of Hybrid Electric Vehicles (HEVs). Despite their low emissions and energy consumptions, if compared with traditional fossil fuel vehicles, their architecture is much more complex and presents critical issues in relation to the combined use of the internal combustion engine (ICE), the electric machine and the battery pack. The aim of this paper is to investigate lithium-ion battery usage when coupled with an optimization-based strategy in terms of the overall energy management for a specific hybrid vehicle. A mathematical model for the power train of a Peugeot 508 RXH was implemented. A rule-based energy management system (EMS) was developed and optimized using real data from the driving cycles of two different paths located in Messina. A mathematical model of the battery was implemented to evaluate the variation of its voltage and state of charge (SOC) during the execution of driving cycles. Similarly, a mathematical model was implemented to analyze the state of health (SOH) of the battery after the application of electrical loads. It was thus possible to consider the impact of the energy management system not only on fuel consumption but also on the battery pack aging. Three different scenarios, in terms of battery usage at the starting SOC values (low, medium, and maximum level) were simulated. The results of these simulations highlight the degradation and aging of the studied battery in terms of the chosen parameters of the rule-based optimized EMS.

The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s energy and fuel consumption are estimated using WLTC short trips remodulated with distances and traffic levels. Moreover, a vehicle numerical model was implemented and used to test the accuracy of fuel consumption and energy prediction for the proposed methodology. The results are promising since the average of the percentage errors’ absolute value between the experimental driving cycles and forecast ones is 3.89% for fuel consumption, increasing to 6.80% for energy.