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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]

Eco-driving is one of the many options to reduce fuel consumption and emissions from transit operations. However, it is not yet clear how effective eco-driving is for different transit service and fuel types. As policymakers consider implementing eco-driving, they also need comparisons of eco-driving against other fuel-conserving strategies, such as purchasing alternative fuel vehicles. Using a case study of transit operations in Atlanta, Georgia, United States, this paper evaluated eco-driving for two very different service types – local urban service and express service. The authors simulated the implementation of transit eco-driving strategies using an innovative, streamlined algorithm designed to minimize fuel consumption by limiting instantaneous vehicle specific power while maintaining average speed and conserving total distance. Fuel consumption and fuel-cycle emissions were compared across the monitored driving cycles and their modified eco-driving cycles. The savings from eco-driving were also compared to fuel and emissions reductions expected via the conversion of the transit fleets to compressed natural gas (CNG), another popular fuel conservation strategy. The results showed that eco-driving would be a potentially very cost-effective strategy for local and express bus transit operations. [ABSTRACT FROM AUTHOR]

The advanced cruise control system has expanded the energy-saving potential of the hybrid electric vehicle (HEV). Despite this, most energy-saving researches for HEV either only optimize the energy management strategy (EMS) or integrate eco-driving through a hierarchically optimized assumption that optimizes EMS and eco-driving separately. Such kinds of approaches may lead to sub-optimal results. To fill this gap, we design a multi-agent reinforcement learning (MARL) based optimal energy-saving strategy for HEV, achieving a cooperative control on the powertrain and car-following behaviors to minimize the energy consumption and keep a safe following distance simultaneously. Specifically, a plug-in HEV model is regarded as the research object in this paper. Firstly, the HEV energy management problem in the car-following scenario is decomposed into a multi-agent cooperative task into two subtasks, each of which can conduct interactive learning through cooperative optimization. Secondly, the energy-saving strategy is designed, called the independent soft actor–critic, which consists of a car-following agent and an energy management agent. Finally, the performance of velocity tracking and energy-saving are validated under different driving cycles. In comparison to the state-of-the-art hierarchical model predictive control (MPC) strategy, the proposed MARL method can reduce fuel consumption by 15.8% while ensuring safety and comfort. [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]

• Co-optimization of speed trajectory and power management is investigated. • Pontryagin's Minimum Principle is used to reduce the search space for control. • The performance of co- and sequential optimization is comprehensively analyzed. With the advent of vehicle connectivity via intelligent transportation systems, the simultaneous optimization of powertrain operation and vehicle dynamics has become an important research problem to improve energy efficiency. However, this problem is still very challenging due to computational loads in terms of time and memory usage, and hence very little effort has been spent to reveal the full potential of co-optimization. Studies in the literature on eco-driving or energy-efficient operation typically preferred using a sequential optimization or eliminating some constraints for fast computation. This paper extends the authors' previous work of determining energy-efficient speed profiles focused on vehicle dynamics by including additional state and control variables due to vehicle hybridization in application to a fuel-cell/battery electric vehicle. The results from Pontriagyn's Minimum Principle analysis reveal that the co-optimization can be formulated with one discrete variable describing vehicle operation and another continuous variable for power distribution to reduce computation in implementing Dynamic Programming. Then, the performance of co-optimization and sequential optimization for energy-efficient driving is comprehensively analyzed and compared in terms of energy consumption by each powertrain component, operating modes, and computational cost. The total energy savings by co-optimization range from 5.3% to 24.2% for aggressive driving on a hilly terrain. Meanwhile, on a relatively flat road, the benefit of co-optimization is less significant. Nonetheless, co-optimization achieved 0.5%–5.3% reduction in total energy consumption as compared to sequential optimization. [ABSTRACT FROM AUTHOR]