A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

Influence of the control strategy on the performance of hybrid polygeneration energy system using a prescient model predictive control

Hybrid Polygeneration Energy Systems (HPES) can be effective solutions to reach COP26 goals. In particular, Combined Heat and Power (CHP) systems can increase the total system efficiency when both electrical and thermal power are required. The integration of a Battery Energy Storage System (BESS) can further improve the plant efficiency and economy, assuring higher operational flexibility. Configuration, sizing and control strategy definition are of primary concern for these systems when the best possible performances are sought. This work aims to quantitatively assess the importance of the adopted control strategy in the operation and performance of a possible sub-system of a grid-connected Hybrid Polygeneration Energy Systems (HPES), consisting in this study of a CHP plant assisted by a BESS. A simulation code of the plant from an energetic point of view was used, and the main economic indicator was also calculated according to the legislative reference scenario. Then a Prescient Model Predictive Control (MPC) was coded to achieve near-optimal plant operation, using a novel system state description to reduce the computational burden linked to the Optimal Control Problem (OCP) solution. The BESS system was modelled, including cycle battery ageing. Subsequently, the performances of the adopted prescient MPC have been compared to the previous results given by a multi-objective plant sizing with a rule-based control. The results show that the control strategy can enhance the performance of the CHP system, achieving remarkable overall better performances, with up to 12% higher Primary Energy Savings. Moreover, the research findings highlight how the proposed control variable ensure a reduction of the computational time by more than 70%, also improving the quality of the found solutions to the OCP. The results also suggest that proper control strategies should be adopted even in a preliminary optimal sizing phase of the CHP plant, since there is a large room of improvement in predicting the achievable plant performance when more basic rule-based control strategies are overcome and replaced by MPC

Hydrotreated Vegetable Oils for Compression Ignition Engines—The Way Toward a Sustainable Transport

The COP26 goals rapidly accelerate the shift of road transport to electric vehicles (EVs). However, the global transition to EVs should be assessed carefully. A forced transition to electric mobility without tailored solutions for each case can increase greenhouse gas (GHG) emissions. In this context, low-carbon fuels can be considered a promising short-term solution to efficiently reach the carbon neutrality target. This manuscript aims to highlight the competitive advantages of hydrotreated vegetable oil (HVO) over commercial diesel fuel. Recent works on HVO are considered, ranging from exploring the production processes and spray evolution characteristics to the various engine strategies to highlighting the potential. Greater emphasis was placed on environmental impact assessment, considering the results available for Life Cycle Assessment (LCA) and Well-To-Wheel. The main characteristics and influences of HVO in CI engines are assessed on the combustion process, GHGs, and pollutants emissions. The results show the high potential of the HVO to reduce the impact of the road transport sector actively. It is highly compatible with existing engines and fueling systems while ensuring lower CO2, CO, THC, PM emissions, and combustion noise levels with similar efficiency and fuel consumption. Additionally, the residual feedstock can assure up to 75% GHG over the whole life cycle. Therefore, sustainable fuels, such as HVO, combined with advanced technologies could not only support the reduction of tailpipe emissions but also benefit the overall CO2 assessment

Assessment of Battery–Supercapacitor Topologies of an Electric Vehicle under Real Driving Conditions

Road transport is shifting towards electrified vehicle solutions to achieve the Conference of the Parties of the United Nations Framework Convention on Climate Change (COP27) carbon neutrality target. According to life cycle assessment analyses, battery production and disposal phases suffer a not-negligible environmental impact to be mitigated with new recycling processes, battery technology, and life-extending techniques. The foundation of this study consists of combining the assessment of vehicle efficiency and battery ageing by applying supercapacitor technology with different topologies to more conventional battery modules. The method employed here consists of analysing different hybrid energy storage system (HESS) topologies for light-duty vehicle applications over a wide range of operating conditions, including real driving cycles. A battery electric vehicle (BEV) has been modelled and validated for this aim, and the reference energy storage system was hybridised with a supercapacitor. Two HESSs with passive and semi-active topologies have been analysed and compared, and an empirical ageing model has been implemented. A rule-based control strategy has been used for the semi-active topology to manage the power split between the battery and supercapacitor. The results demonstrate that the HESS reduced the battery pack root mean square current by up to 45%, slightly improving the battery ageing. The semi-active topology performed sensibly better than the passive one, especially for small supercapacitor sizes, at the expense of more complex control strategies