Hybrid and Electric Vehicles Optimal Design and Real-time Control based on Artificial Intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

Electrified road transport through plug-in hybrid powertrains: Compliance by simulation of CO2 specific emission targets with real driving cycles

Worldwide targets on specific CO2 emissions (g/km) seem to make the use of internal combustion engines (ICE) prohibitive when adopting conventional driving cycles concerning road transport. This research comes therefore from the necessity of an accurate analysis of the real driving habits in order to evaluate whether its implementation on an alternative powertrain, suitable to differentiate urban (local zero emissions) and extra-urban travels (highest performances of ICEs, even better than electric motors when contemplating the entire energy chain), can guarantee the compliance with specific CO2 emissions reduction legislation; this last has been introduced with the aim of containing or even erasing global emissions from the transport sector in next years. After an overview of all the main available technological alternatives, as regards powertrains, the Plug-in Hybrid (PHEV) solution has been analysed. An experimental driving cycle is proposed by combining representative cycles obtained from a previous study, based on data provided by FCA, now Stellantis, where a clustering procedure has been applied to a sample of over two-thousand real journeys made in 2015 and 2016 in all Europe with conventional automobiles; appropriate ranges of distance, time, average speed in urban and extra urban conditions, idle times and stops have been identified thanks to a statistical analysis and the cycle has been created with all of these requirements to be as similar as possible to most of daily trips by road transport. PHEV market has been examined in order to identify the components and architectures that characterize the most registered automobiles; a realistic model has therefore been created and used for the experimental cycle simulation. Simulation results show that PHEV technology has the potential to consume 69% less fuel than a conventional vehicle counterpart with a consequent reduction of 71% in emitted tank-to-wheel (TTW) tons of CO2 and significant reductions in fuel expenditure, in one year, because of the different source of energy

Effect of Temperature Distribution on the Predicted Cell Lifetimes for a Plug-In Hybrid Electric Vehicle Battery Pack

Monitoring and preserving state-of-health of high-voltage battery packs in electrified road vehicles currently represents an open and growing research topic. When predicting high-voltage battery lifetime, most current literature assumes a uniform temperature distribution among the different cells of the pack. Nevertheless, temperature has been demonstrated having a key impact on cell lifetime, and different cells of the same battery pack typically exhibit different temperature profiles over time, e.g. due to their position within the pack. Following these considerations, this paper aims at assessing the effect of temperature distribution on the predicted lifetime of cells belonging to the same battery pack. To this end, a throughput-based numerical cell ageing model is firstly selected due to its reasonable compromise between accuracy and computational efficiency. Subsequently, experimentally measured temperature and C-rate profiles over time in a driving mission are considered for three cells of the high-voltage battery pack of a commercially available plug-in hybrid electric vehicle. Obtained results suggest that due to temperature distribution in the high-voltage battery pack, the predicted lifetime for the hottest cell might be as low as 61% compared with the coldest cell of the pack. Importance and advancement of monitoring and managing the state-of-health of the single cells of an electrified vehicle battery pack might be fostered in this way thanks to the proposed study

First characterization of the Hamamatsu R11265 multi-anode photomultiplier tube

The characterization of the new Hamamatsu R11265-103-M64 multi-anode photomultiplier tube is presented. The sample available in our laboratory was tested and in particular the response to single photon was extensively studied. The gain, the anode uniformity and the dark current were measured. The tube behaviour in a longitudinal magnetic field up to 100 G was studied and the gain loss due to the ageing was quantified. The characteristics and performance of the photomultiplier tube make the R11265-103-M64 particularly tailored for an application in high energy physics experiments, such as in the LHCb Ring Imaging Cherenkov (RICH) detector at LHC

Project and Development of a Reinforcement Learning Based Control Algorithm for Hybrid Electric Vehicles

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered

Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control

The real-time control optimization of electrified vehicles is one of the most demanding tasks to be faced in the innovation progress of low-emissions mobility. Intelligent energy management systems represent interesting solutions to solve complex control problems, such as the maximization of the fuel economy of hybrid electric vehicles. In the recent years, reinforcement-learning-based controllers have been shown to outperform well-established real-time strategies for specific applications. Nevertheless, the effects produced by variation in the reward function have not been thoroughly analyzed and the potential of the adoption of a given RL agent under different testing conditions is still to be assessed. In the present paper, the performance of different agents, i.e., Q-learning, deep Q-Network and double deep Q-Network, are investigated considering a full hybrid electric vehicle throughout multiple driving missions and introducing two distinct reward functions. The first function aims at guaranteeing a charge-sustaining policy whilst reducing the fuel consumption (FC) as much as possible; the second function in turn aims at minimizing the fuel consumption whilst ensuring an acceptable battery state of charge (SOC) by the end of the mission. The novelty brought by the results of this paper lies in the demonstration of a non-trivial incapability of DQN and DDQN to outperform traditional Q-learning when a SOC-oriented reward is considered. On the contrary, optimal fuel consumption reductions are attained by DQN and DDQN when more complex FC-oriented minimization is deployed. Such an important outcome is particularly evident when the RL agents are trained on regulatory driving cycles and tested on unknown real-world driving missions

Very low noise AC/DC power supply systems for large detector arrays

In this work, we present the first part of the power supply system for the CUORE and LUCIFER arrays of bolometric detectors. For CUORE, it consists of AC/DC commercial power supplies (0–60 V output) followed by custom DC/DC modules (48 V input, ±5 V to ±13.5 V outputs). Each module has 3 floating and independently configurable output voltages. In LUCIFER, the AC/DC + DC/DC stages are combined into a commercial medium-power AC/DC source. At the outputs of both setups, we introduced filters with the aim of lowering the noise and to protect the following stages from high voltage spikes that can be generated by the energy stored in the cables after the release of accidental short circuits. Output noise is very low, as required: in the 100 MHz bandwidth the RMS level is about 37 μVRMS (CUORE setup) and 90 μVRMS (LUCIFER setup) at a load of 7 A, with a negligible dependence on the load current. Even more importantly, high frequency switching disturbances are almost completely suppressed. The efficiency of both systems is above 85%. Both systems are completely programmable and monitored via CAN bus (optically coupled)