In this manuscript, we address the problem of online optimal control for torque splitting in hybrid electric vehicles that minimises fuel consumption and preserves battery life. We divide the problem into the prediction of the future velocity profile (i.e. driver intention estimation) and the online optimal control of the hybrid powertrain following a Model Predictive Control (MPC) scheme. The velocity prediction is based on a bio-inspired driver model, which is compared on various datasets with two alternative prediction algorithms adopted in the literature. The online optimal control problem addresses both the fuel consumption and the preservation of the battery life using an equivalent cost given the estimated speed profile (i.e. guaranteeing the desired performance). The battery degradation is evaluated by means of a state-of-the-art electrochemical model. Both the predictor and the Energy Management System (EMS) are evaluated in simulation using real driving data divided into 30 driving cycles from 10 drivers characterised by different driving styles. A comparison of the EMS performances is carried out on two different benchmarks based on an offline optimization, in one case on the entire dataset length and in the second on an ideal prediction using two different receding horizon lengths. The proposed online system, composed of the velocity prediction algorithm and the optimal control MPC scheme, shows comparable performances with the previous ideal benchmarks in terms of fuel consumption and battery life preservation. The simulations show that the online approach is able to significantly reduce the capacity loss of the battery, while preserving the fuel saving performances.

Cooperative intelligent transport systems (C-ITS) are expected to considerably influence road safety, traffic efficiency and comfort. Nevertheless, their market penetration is still limited, on the one hand due to the high costs of installation and maintenance of the infrastructures and, on the other hand, due to the price of support automated driving functions. A breakthrough C-ITS technological solution was studied, designed, built and tested that is based on the implementation of custom low-cost on-road platforms (named “strips”) that embed micro/nano sensors, communication technologies and energy harvesting to shift intelligence from the vehicle to the road infrastructure. The strips, through a V2X and LTE communication gateway, transmit real-time, reliable and accurate information at lane level about the environmental and road condition, the traffic and the other road users’ position and speed. The exchanged information supports a series of C-ITS functions and services extending equipped vehicles capabilities and providing similar functions to non-equipped ones (including powered two wheelers). The general framework and the technological solution proposed is presented and the results of the field trials, conducted in three pilot sites around Europe, quantify the promising system performance as well as the positive effects of the C-ITS applications developed and tested on driver/rider’s behavior.