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This paper addresses the design of a decision making and motion planning system for lane change maneuvers considering energy efficiency. A novel decision making algorithm is proposed to check the desirability of performing the lane change. The algorithm is based on a utility function that consists of different performance criteria, including energy consumption. The execution of the decided maneuver involves a lower-level motion planning and control system for the longitudinal and lateral directions. For the longitudinal direction, an energy-efficient Model Predictive Controller (MPC) is designed, which considers the safety boundaries as well as other constraints, such as comfort, traffic laws, and physical limitations of the system. For the lateral direction, the desired trajectory is planned based on a parameterized sigmoid function. The lateral tracking is then realized by a PID controller. Finally, to evaluate the performance of the designed algorithms, a fuel consumption map of an internal combustion engine (ICE) is approximated by a second-order multivariate polynomial. Simulation results demonstrate the capability of the proposed algorithm to safely perform the lane change maneuver in different scenarios and for two vehicle models, including a simplified vehicle dynamic model and a high-fidelity IPG CarMaker model. [Display omitted] • A new lane change decision making is proposed considering energy consumption. • An integrated energy-efficient planning and control framework is designed using MPC. • The framework considers safety, comfort, energy consumption, and actuator limitation. • The fuel map of an internal combustion engine is fitted to second-order polynomial. • The obtained polynomial is used in the optimization problem of energy-efficient MPC. • Simulation study for a simple mathematical model and a high-fidelity CarMaker model. [ABSTRACT FROM AUTHOR]

—This paper proposes two real-time energy-oriented driving strategies to minimize the energy consumption for electric vehicles on highways with varying slopes. First, a novel strategy, called normalized-energy consumption minimization strategy (NCMS), adopts a designed kinetic energy conversion factor to convert the vehicle kinetic energy change into the equivalent battery energy consumption. By minimizing the total normalized energy consumption, the energy-orientated vehicle control sequence is calculated. In addition, a logic car-following algorithm is developed to enhance NCMS for avoiding collisions with the potential preceding vehicle on the journey. Second, an improved model predictive control (MPC) is developed with a hierarchical framework, which achieves a balance between optimization and computational efficiency. In the upper level, a global, coarse-grained, iterative dynamic programming is employed to penalize the MPC terminal state, while the lower level performs online rolling optimization of the vehicle within a moderate time step. Thirdly, the performance of the proposed driving strategies is verified through a traffic simulation to evaluate the energy efficiency improvement and processor computation time compared to dynamic programming and constant speed strategy. Finally, a vehicle-in-the-loop test is carried out to validate the feasibility of the proposed two novel driving strategies. • A normalized energy consumption model is presented, combining two energy usage. • A novel eco-driving strategy is developed based on the proposed energy consumption. • The algorithm suits both free traffic and car-following scenarios with safety. • The characteristics of the four eco-driving strategies are compared. • The proposed strategy is verified by both simulation and vehicle-in-the-loop tests. [ABSTRACT FROM AUTHOR]

Forecast uncertainties pose a considerable challenge to the success of model predictive control (MPC) in buildings. Numerous possibilities for considering forecast uncertainties in MPCs are available, but an in-depth comparison is lacking. This paper compares two main approaches to consider uncertainties: robust and stochastic MPC. They are benchmarked against a deterministic MPC and an MPC with perfect forecast. The MPCs utilize a holistic building model to reflect modern smart homes that include photovoltaic power generation and storage, thermally controlled loads, and smart appliances. Real-world data are used to identify the thermal building model. The performance of the various controllers is investigated under three levels of uncertainty for two building models with different envelope performance. For the highly insulated building, the deterministic MPC achieves satisfactory thermal comfort when the forecast error is medium or low, but the thermal comfort is compromised for high forecast errors. In the poorly insulated building, thermal comfort is compromised at medium and high forecast errors. Compared to the deterministic MPC, the robust MPC increases the electricity cost by up to 4.5% and provides complete temperature constraint satisfaction while the stochastic MPC increases the electricity cost by less than 1% and fulfills the thermal comfort requirements. [ABSTRACT FROM AUTHOR]