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

On optimal mission planning for conventional and electric heavy duty vehicles

Ever-growing energy consumption and CO2 emissions due to the increase in road transport are major challenges that attract international attention, especially policy makers, logistic service providers and customers considering environmental, ecological and economic issues. Other negative side-effects caused by the growth of the road transport are the extensive economic and social costs because of traffic congestion. Thus, there is a strong motivation to investigate possible ways of improving transport efficiency aiming at achieving a sustainable transport, e.g. by finding the best compromise between resource consumption and logistics performance. The transport efficiency can be improved by optimal planning of the transport mission, which can be interpreted as optimising mission start and/or finish time, and velocity profile of the driving vehicle. This thesis proposes a bi-layer mission planner for long look-ahead horizons stretched up to hundreds of kilometers. The mission planner consists of logistics planner as its top level and eco-driving supervisor as its bottom level. The logistics planner aims at optimising the mission start and/or finish time by optimising energy consumption and travel time, subject to road and traffic information, e.g. legal and dynamic speed limits. The eco-driving supervisor computes the velocity profile of the driving vehicle by optimising the energy consumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in MPC framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. The mission planner has been applied to conventional and fully-electric powertrains. It is observed that total travel timeis reduced up to 5.5 % by optimising the mission start time, when keeping anaverage cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %

On Optimal Mission Planning for Vehicles over Long-distance Trips

This thesis proposes a mission planner for vehicles over long-distance trips, for finding the optimal trade-off between trip time, energy efficiency, anddriver comfort, subject to road information, traffic situations, and weather conditions. The mission planner consists of three components, i.e. logisticsplanner, eco-driving supervisor, and thermal and charging supervisor. The logistics planner aims at optimising the mission start and/or finish time byminimising energy consumption and trip time. The eco-driving supervisor computes the velocity profile of the driving vehicle, by optimising the energyconsumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in a model predictive control framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. Eco-driving has also been achieved for a vehicleconfronted with wind, by applying stochastic dynamic programming method. The thermal and charging supervisor regulates battery temperature and state of charge by coordinating the energy use of different thermal components. Within the thermal and charging supervisor design, a heat pump has been included for waste heat recovery purposes. Also, the charging stops have been optimally planned, in favour of energy efficiency and trip time. The performance of the proposed algorithms over a road with a hilly terrain is assessed using simulations. According to the simulation results, it is observed that total travel time is reduced up to 5.5 % by optimising the mission start time, when keeping an average cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %. Furthermore, total charging time and energy consumption are reduced by up to 19.4 % and 30.6 %, respectively by developing the thermal and charging supervisor, compared to a case without the heat pump activated and without charge point optimisation

A real-time robust ecological-adaptive cruise control strategy for battery electric vehicles

This work addresses the ecological-adaptive cruise control problem for connected electric vehicles by a computationally efficient robust control strategy. The problem is formulated in the space-domain with a realistic description of the nonlinear electric powertrain model and motion dynamics to yield a convex optimal control problem (OCP). The OCP is solved by a novel robust model predictive control (RMPC) method handling various disturbances due to modelling mismatch and inaccurate leading vehicle information. The RMPC problem is solved by semi-definite programming relaxation and single linear matrix inequality (sLMI) techniques for further enhanced computational efficiency. The performance of the proposed real-time robust ecological-adaptive cruise control (REACC) method is evaluated using an experimentally collected driving cycle. Its robustness is verified by comparison with a nominal MPC which is shown to result in speed-limit constraint violations. The energy economy of the proposed method outperforms a state-of-the-art time-domain RMPC scheme, as a more precisely fitted convex powertrain model can be integrated into the space-domain scheme. The additional comparison with a traditional constant distance following strategy (CDFS) further verifies the effectiveness of the proposed REACC. Finally, it is verified that the REACC can be potentially implemented in real-time owing to the sLMI and resulting convex algorithm

Robust Model Predictive Control Framework for Energy-Optimal Adaptive Cruise Control of Battery Electric Vehicles

The autonomous vehicle following problem has been extensively studied for at least two decades with the rapid development of intelligent transport systems. In this context, this paper proposes a robust model predictive control (RMPC) method that aims to find the energy-efficient following velocity of an ego battery electric vehicle and to guarantee a safe rearend distance in the presence of disturbances and modelling errors. The optimisation problem is formulated in the space domain so that the overall problem can be convexified in the form of a semi-definite program, which ensures a rapid solving speed and a unique solution. Simulations are carried out to provide numerical comparisons with a nominal model predictive control (MPC) scheme. It is shown that the RMPC guarantees robust constraint satisfaction for the closed-loop system whereas constraints may be violated when the nominal MPC is in use. Moreover, the impact of the prediction horizon length on optimality is investigated, showing that a finely tuned horizon could produce significant energy savings