The energy utilization efficiency of autonomous electric vehicles is seriously affected by the longitudinal motion control performance. However, the longitudinal motion control is constrained by the driving scene. This article proposes an energy-saving optimization and control (ESOC) method to improve the energy utilization efficiency of autonomous electric vehicles. In ESOC, the constraints from the driving scene are thoroughly considered, and the autonomous driving scene constraints are mapped to the vehicle dynamics and control domain. On this basis, the efficiency self-searching method and the multiconstraint energy-saving control strategy are designed. The main ideology of the proposed ESOC is that the energy utilization efficiency of an autonomous electric vehicle can be improved by optimizing and controlling the operation point distribution of the powertrain efficiency. The experimental results demonstrate that the operation point distribution of the autonomous electric vehicle’s powertrain efficiency can be well optimized by the proposed ESOC, and the energy consumption results indicate that the proposed ESOC outperforms the state-of-the-art methods.

The safety and energy efficiency of autonomous vehicles in the vehicle-following driving scenario is seriously affected by the decision-making framework. This article proposes a safe and energy-saving decision-making framework for autonomous vehicles to avoid the potential rear-end collision and improve the energy efficiency in vehicle-following driving scenario. To realize this framework, the vehicle longitudinal dynamics model is first presented, and the autonomous vehicle's requirements from the safety and energy efficiency perspectives are comprehensively analyzed in the vehicle-following driving scenario. Then, a two-stage decision-making framework is designed. The first stage determines the safety region in the powertrain control domain, and the second stage determines the optimal energy efficiency in the safety region. Based on the two-stage decision-making framework, the perspective of “safety first” principle can be enforced by simultaneously considering the driving safety and energy efficiency. Finally, the experiment is conducted to demonstrate the advantages of the proposed method by comparing it with two other state-of-art methods.

This paper proposes a dual level reinforcement estimator (DLRE) to estimate the electric mining haul trucks' (EMHTs') total mass and road slope. To design the estimator, the longitudinal dynamics model of EMHT is built and the model's parameters are identified by a recursive least square (RLS) with double forgetting factors. This DLRE consists of an initial level estimation and an extended level estimation. In the initial estimation, the total mass and the road slope are preliminarily estimated. In the extended estimation, to prevent the estimated mass error from leading to great errors in the road slope, the road slope is first estimated and then the total mass is estimated based on the estimated road slope. The DLRE overcomes the defects of the traditional recursive-strategy-based estimators whose cumulative estimation errors often lead to poor estimation performance or even lead to estimation divergence. The DLRE performance is validated with an EMHT of 930E, and the experimental results show that the total mass and the road slope are estimated with higher accuracy using the DLRE.