Sara Luciani

Thermal management strategies implemented on-board fuel cell electrified vehicles (FCEVs) are currently based on heuristic reactive approaches. In this framework, developing predictive thermal management approaches that anticipate the travel needs of FCEV users could lead to improved hydrogen savings. However, the theoretical hydrogen saving achievable by predictive thermal management needs assessment first to quantify the technical and economical viability of the technology proposal. This paper lays the foundations in this domain by analyzing the a priori optimal thermal management of a fuel cell system in an FCEV. Initially, an electrochemical and thermal modeling technique for the fuel cell system is described. A reactive rule-based approach is then selected as the baseline controller for the coolant rate and the instantaneous radiator fan state of the FCEV. Then, the optimal control problem formulation for the thermal management of fuel cell systems in FCEVs is discussed and solved using dynamic programming (DP) based optimization approach that makes use of a priori information about the entire driving mission. The fuel cell system is evaluated while the FCEV performs one to ten repetitions of the Worldwide Harmonized Light Vehicle Test Procedure (WLTC) at different ambient temperatures ranging from -20 °C to 40 °C. When compared to the baseline reactive control technique, the offline optimal benchmark can save up to 10.2% hydrogen. Results presented in this paper demonstrate the potential of hydrogen saving achievable by improving the thermal management of automotive fuel cell systems. Moreover, they may be used to develop and benchmark real-time capable predictive thermal management strategies for fuel cell systems in FCEVs.

Concerns about climate change, air pollution, and the depletion of oil resources have prompted authorities to enforce increasingly strict rules in the automotive sector. There are several benefits to implementing fuel cell hybrid vehicles (FCHV) in the transportation sector, including the ability to assist in reducing greenhouse gas emissions by replacing fossil fuels with hydrogen as energy carriers. This paper examines different control strategies for optimizing the power split between the battery and PEM fuel cell in order to maximize the PEM fuel cell system efficiency and reduce fuel consumption. First, the vehicle and fuel cell system models are described. A forward approach is considered to model the vehicle dynamics, while a semi-empirical and quasi-static model is used for the PEM fuel cell. Then, different rule-based control strategies are analyzed with the aim of maximizing fuel cell system efficiency while ensuring a constant battery state of charge (SOC). The different methods are evaluated while the FCHV is performing both low-load and high-load drive cycles. The hydrogen consumption and the overall fuel cell system efficiency are considered for all testing conditions. The results highlight that in both low-load cycles and high-load cycles, the best control strategies achieve a fuel cell system efficiency equal or greater to 33%, while achieving a fuel consumption 30% less with respect to the baseline control strategy in low-load drive cycles.

This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (SOC) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery SOC in real-time, with 2% accuracy during real-time hardware testing.

Advanced brake assist systems can avoid road accidents since the vehicles impact speed can be significantly reduced. To this end, different autonomous emergency braking systems are designed for recent vehicles on the market. This paper presents a pneumo-hydraulic Emergency Braking System (EBS) for autonomous racing vehicles. The purpose of the system is safely stopping the vehicle in case of any failures during autonomous driving. Failures can be detected both by the autonomous system itself and by human supervisors. The actuation system involves passive energy storage of compressed air to directly activate the hydraulic braking lines through pneumo-hydraulic pressure intensifiers. The coupling component between failures detection and actuation is a normally-open solenoid valve. The system is designed to respect deceleration and actuation time requirements, together with packaging constraints due to integration in an existing racing prototype. Specifically, the system requirements are specified by the racing competition rules: the overall reaction time of the retained EBS must be lower than 0.2 s, and the actuated mean deceleration must be greater than 8 m/s² on a dry track surface while keeping stable driving conditions. The validation and tuning of the system is performed in a simulated environment. Therefore, an extensive experimental validation of the system is required in the real applications.

Self-driving vehicles have experienced an increase in research interest in the last decades. Nevertheless, fully autonomous vehicles are still far from being a common means of transport. This paper presents the design and experimental validation of a processor-in-the-loop (PIL) architecture for an autonomous sports car. The considered vehicle is an all-wheel drive full-electric single-seater prototype. The retained PIL architecture includes all the modules required for autonomous driving at system level: environment perception, trajectory planning, and control. Specifically, the perception pipeline exploits obstacle detection algorithms based on Artificial Intelligence (AI), and the trajectory planning is based on a modified Rapidly-exploring Random Tree (RRT) algorithm based on Dubins curves, while the vehicle is controlled via a Model Predictive Control (MPC) strategy. The considered PIL layout is implemented firstly using a low-cost card-sized computer for fast code verification purposes. Furthermore, the proposed PIL architecture is compared in terms of performance to an alternative PIL using high-performance real-time target computing machine. Both PIL architectures exploit User Datagram Protocol (UDP) protocol to properly communicate with a personal computer. The latter PIL architecture is validated in real-time using experimental data. Moreover, they are also validated with respect to the general autonomous pipeline that runs in parallel on the personal computer during numerical simulation.

In the context of automated driving, Electric Power Steering (EPS) systems represent an enabling technology. They introduce the ergonomic function of reducing the physical effort required by the driver during the steering maneuver. Furthermore, EPS gives the possibility of high precision control of the steering system, thus paving the way to autonomous driving capability. In this context, the present work presents a performance assessment of an EPS system designed for a full-electric all-wheel-drive electric prototype racing in Formula Student Driverless (FSD) competitions. Specifically, the system is based on the linear actuation of the steering rack by using a ball screw. The screw nut is rotated through a belt transmission driven by a brushless DC motor. Modeling and motion control techniques for this system are presented. Moreover, the numerical model is tuned through a grey-box identification approach. Finally, the performance of the proposed EPS system is tested experimentally on the vehicle through both sine-sweep profiles and co-simulated driverless sessions. The system performance is assessed in terms of reference tracking capability, thus showing favorable results for the proposed actuation solution.

When assisted and autonomous vehicles are released, the driver will no longer influence the driving style. The vehicle itself will accomplish this task, and, thus, safety and comfort should be guaranteed by the overall system. This paper presents a methodology for the tuning of a Model Predictive Control (MPC) for assisted and autonomous car to maximize the comfort of the