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—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]

Highlights • There is a need for safety to be included in eco-driving algorithms. • Eco-driving algorithms' efficiency reduces with unexpected traffic change. • We proposed a novel model predictive controller with hard constraint on safety. • The proposed method adapts in real-time to traffic. • Our model improves both safety and fuel efficiency. Abstract Road transport plays an important role in the economy but generates huge costs in terms of road trauma and greenhouse gas emissions. The vast majority of previous research efforts have focused on driving behaviours that either minimise fuel consumption, or advise on safe driving behaviours. However, a fuel-optimal behaviour is often difficult to implement due to traffic conditions and may lead to unsafe behaviour without proper constraints. While a range of optimisation techniques are currently available, most lack the ability to both consider time and distance dependent constraints necessary for computationally efficient determination of optimal behaviour. In this paper, a methodology to find an ecological and safe (EcoSafe) driving behaviour by formulating and solving an optimal control problem for the minimum fuel driving behaviour while respecting key safety constraints was developed. We first propose a modified distance-based dynamic programming (MDDP) technique that can treat distance and time inequality constraints while optimising. Then we propose a model predictive control (MPC) structure based on MDDP and Pontryagin's maximum principle by considering inter vehicle time and time-to-collision as hard safety constraints. In contrast to existing approaches, the proposed MPC method does not require ad hoc tuning of constraint penalty weights in the cost function to ensure safe following distances. Simulation results of the proposed method show that the proposed algorithm produces lower vehicle fuel consumption compared to existing approaches when safety constraints are limiting. Thus, the results demonstrate the importance of considering safety constraints within an eco-driving system. [ABSTRACT FROM AUTHOR]