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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]

Road vehicles can obtain traffic information via Intelligent Transportation System (ITS), which yields more potential in improving driving performance. However, ITS is not available for off-road vehicles and only limited information can be obtained by onboard sensors. Meanwhile, the off-road terrain suffers from terrible road conditions, which brings great difficulties to ensuring driving safety. Therefore, how to coordinate fuel economy, vehicle mobility and driving safety with limited traffic information is a challenging problem for off-road vehicles. To tackle the problem, an online predictive eco-driving strategy is proposed in this paper, which consists of safety supervisory module, time reference generator and rolling optimization. Firstly, considering the off-road terrain characteristic, the safety supervisory module analysis the vehicle stability performance under different road conditions, and thus the driving safety on off-road terrain with low adhesion coefficient, high curvature and heavy grade can be guaranteed. Secondly, the time reference generator is designed to ensure vehicle mobility. With only a prior knowledge of distance to destination, the time reference generator can generate the reference time in prediction horizon fast and effectively. Finally, model predictive control is employed to construct the multi-objective eco-driving problem, with an ameliorated particle swarm optimization to minimize the fuel consumption while tracking the reference time and ensuring driving safety. Simulations are conducted to validate the effectiveness of the proposed strategy. The results exhibit that the fuel economy and vehicle mobility can be improved by 10.13% and 5.77% over practical strategy in the premise of ensuring driving safety under the 5 km off-road terrain scenario. Moreover, a hardware-in-loop test is implemented to verify the real-time ability of the proposed strategy. • An online predictive eco-driving control strategy is proposed. • The necessary conditions for vehicle stability are deduced to ensure driving safety. • The time reference generator is designed to control travel time precisely. • Coordinative optimization for vehicle velocity and power distribution. • The proposed strategy is validated in both simulation and hardware-in-loop test. [ABSTRACT FROM AUTHOR]

This paper presents a safety-enhanced eco-driving strategy for connected and autonomous vehicles (CAVs), which is implemented by a hierarchical and distributed framework. The driving risk field, shockwave theory, and motion planning and control method are integrated into this framework to optimize the trajectories of CAVs on a signalized arterial under mixed traffic flow, with the aim of reducing the driving risk and fuel consumption of CAVs simultaneously, while ensuring traffic efficiency. The optimization procedure is mainly composed of two parts: long-term trajectory planning based on optimal control and short-term trajectory control based on model predictive control, which makes the strategy more adaptable to the various traffic conditions. The results show that the proposed framework can effectively reduce the safety risk that vehicles are exposed to and their fuel consumption by 18%–24% and 20%–27%, respectively. Furthermore, it reveals that conventional eco-driving strategies may result in negative safety issues when only considering the impact of preceding vehicles on the eco-CAV. However, these negative impacts can be eliminated when the impacts of following vehicles on the eco-CAV are taken into account. In addition, the sensitivity analysis on the Market Penetration Rate (MPR) of CAVs and traffic demand is performed. The results show that the framework is robust and can work under various traffic conditions (including under-saturated and over-saturated ones) and different MPRs. • Propose a safety-enhanced eco-driving strategy for CAVs under mixed traffic flow. • Develop two algorithms to alleviate intersection queuing effects for eco-driving. • While ensuring traffic efficiency, optimize CAVs' fuel consumption and driving risk. • Perform numerical experiments to assess the benefits of the proposed framework. • Reveal conventional eco-driving strategy may result in safety risks. [ABSTRACT FROM AUTHOR]

The emergence of the intelligent transportation system and cloud computing technology has brought the available traffic information and increasing computing power, which lead to a significant improvement in driving performance. In order to enhance energy economy and mobility simultaneously, a hierarchical eco-driving strategy is proposed in this paper, which is comprised of the cloud-level controller and the vehicle-level controller. The dynamic programming-based cloud-level controller optimizes the velocity and battery state-of-charge utilizing the global traffic information obtained from the intelligent transportation system. However, the global traffic information suffers from uncertainties, which deteriorates the effectiveness of the cloud-level controller. The vehicle-level controller is constructed on the model predictive control framework, aiming to cope with the uncertainties, improve fuel economy and reduce travel time. Besides, a transfer learning-based particle swarm optimization algorithm is presented for solving the optimization problem in model predictive control, which can achieve great control performance utilizing the knowledge from the cloud-level controller. To validate the effectiveness of the proposed strategy, simulation tests are conducted. The results demonstrate that the proposed strategy can achieve near-global-optimal performance in fuel economy and mobility. Moreover, the real-time performance of the proposed strategy is validated through the hardware-in-loop test. • A hierarchical eco-driving strategy is designed using vehicle-to-cloud connectivity. • The uncertainties in traffic information are considered and tackled. • A novel transfer learning-based partial swarm optimization algorithm is proposed. • Coordinative optimization for vehicle velocity and power distribution. • The proposed strategy is validated in both simulation and hardware-in-loop test. [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]

With inevitable random disturbance in traffic scenarios, electric vehicles (EVs) may face the driving safety issue, while, if operated over cautiously, the frequent speed variation deteriorates the energy economy of the EV. This conflict provokes a desire to understand the energy consumption behavior of EVs in a stochastic driving environment and reveal the corresponding energy optimal control policy. For addressing these issues, this paper develops a chance constraint stochastic model predictive control (CC-MPC) method for simultaneously optimizing the speed planning and the powertrain energy management strategy, which cooperates with a bi-level prediction model for estimating the future driving environment. Validated by massive car-following cases in the urban traffic flow, the proposed CC-MPC increases the success rate (no constraint violation) to 87%, while the deterministic MPC methods only achieve a success rate of 27%. Although the proposed CC-MPC method generates a sensitive driving style to variations of the driving environment, the conflict between energy economy and driving safety has been successfully removed. Validations suggest that when safety probability is 0.9, the success rate is 84% with only 0.8% deterioration in energy economy compared with the energy consumption resulting from the MPC with perfect knowledge of the leading vehicle speed. ● Individual speed prediction is corrected by the traffic state ● Driving safety in random environment is significantly improved by the proposed CC-MPC ● The CC-MPC removes the conflict between driving safety and the energy economy ● The novel solver improves optimality by 12% and computation efficiency by 25 times. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi -level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation. • A real-time PCC system was developed to minimize the energy consumption of EVs. • Two scenarios of car-following and signal anticipation were evaluated. • An ANN model was developed to predict the energy economy performance of the EV. • The proposed PCC can realize 8.5%–15.6% of the energy saving in the urban areas. [ABSTRACT FROM AUTHOR]

• A bi-level MPC-based eco-driving strategy for CAHEVs is proposed. • The control strategy uses real-time traffic information via V2V and V2I. • The driving scenario classifier is designed for driving in mixed driving scenarios. • The strategy is tested in a realistic traffic simulation environment. • The results are compared to the conventional rule-based strategy. This paper proposes a bi-level eco-driving control strategy for connected and automated hybrid electric vehicles (CAHEVs) under mixed driving scenarios. First, the hybrid electric vehicle powertrain is modelled, and the communications via Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) are introduced as the main data sources for the decision-making of the control system. Next, the problem is divided into three objectives, namely, (1) safe driving, (2) energy management, and (3) exhaust emission reduction. Based on the real-time road information, the driving scenario classifier (DSC) works towards determining the corresponding vehicle mode on which the cost function can be adjusted accordingly. The simulation is carried out in a realistic urban traffic simulation environment in SUMO. The results show that with the proposed model predictive control (MPC)-based strategy applied, safe driving in a trip involving a mixture of driving scenarios can be guaranteed throughout the entire driving. In addition, in comparison to the rule-based benchmark strategy, the proposed strategy can reduce the fuel consumption by 34.10% with battery kept in a healthy state of charge range, and the exhaust emissions (HC, CO, and NOx) are reduced by 25.36%, 72.30%, and 30.39%, respectively, which demonstrates the effectiveness and robustness of the proposed MPC-based strategy for CAHEVs. [ABSTRACT FROM AUTHOR]