Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

In this article, a regenerative braking control strategy with fault-tolerant capability for the electric vehicle driven by in-wheel switched reluctance motors is presented to achieve comprehensive improvement among vehicle endurance mileage, braking safety, and braking stability. First, after analyzing generation characteristic of SRM, a novel partition braking force distribution strategy is presented, combined with ideal braking force distribution and Economic Commission for Europe regulation curves. Then, a fault detection method is proposed to accurately detect the open-circuit fault of SRM drive system based on the detected phase current and position angle signals. Next, besides vehicle velocity, braking intensity, and battery state of charge, the effect of the fault on electromechanical coupling braking system (ECBS) is also considered to evaluate vehicle braking performance, further a coupling braking force control method is proposed, which can achieve open-short fault tolerance by employing mechanical braking force to replace electrical braking force. Furthermore, considering vehicle comprehensive performance, a multiobjective optimization strategy for the motor switch angles is proposed and an optimized controller is developed based on genetic algorithm. Subsequently, an improved ECBS is established and the performance of the system is analyzed and compared to the initial ECBS. The real-time capability and validity of the proposed control strategy for ECBS is validated by hardware in loop experiment.

The rapid development of automated vehicle technology requires reasonable test scenarios and comprehensive evaluation methods. This paper proposes an evaluation method for automated vehicles combining subjective and objective factors. First, we propose a method for automatically generating test scenarios and for batch testing autonomous vehicles. Then, the use of the target layer, total index layer, and index layer of automated vehicles is proposed to establish a more comprehensive evaluation system for automated vehicles. Specifically, the analytic hierarchy process (AHP, subjective) and improved criteria importance though intercriteria correlation (CRITIC, objective) methods are used to determine the weight of the indicators, and a two-level fuzzy comprehensive (subjective and objective) evaluation method is adopted to comprehensively evaluate the performance of the automated vehicles. Finally, the effectiveness of the proposed evaluation method combining subjective and objective factors is verified through virtual simulations and real-world experiments. Through a combination of subjective and objective methods, improved results can be obtained for safety, efficiency, economy, intelligence, and comfort tests.

In this paper, a control method for trajectory planning and tracking of an intelligent vehicle is proposed. In terms of trajectory planning, a trajectory planning method for curved lane changes is designed based on conventional lane change trajectory planning and considering the adaptive correction of road curvature. In addition, curve trajectory tracking control strategy based on model predictive control is designed. Model predictive control is suitable for multi-input and multi-output nonlinear models, and it has the advantage of considering model constraints. This type of control makes the model output more in line with vehicle dynamics characteristics and improves the trajectory tracking accuracy. Finally, the simulation shows that the method proposed in this paper can generate a reasonable curved lane-changing trajectory, and under the consideration of the vehicle dynamics constraints, the MPC algorithm is used to effectively follow the expected trajectory, so that the vehicle can change lanes smoothly.

To achieve the accurate energy estimation and improve the economy performance of Extended Range Electric Vehicle (EREV) under various information from vehicle-road-cloud systems, an energy consumption prediction and optimal control strategy for EREV is proposed based on fused average vehicle speed, road segment length, and traffic congestion information. In the dataset, the average speed of different road segments, segment length, and congestion information are selected as the three neurons in the input layer of Back Propagation (BP) neural network model. Through iterative gradient search training, the network parameters are determined, with the model output set as the energy consumption of future road segments. Real-time traffic information is obtained, and the predicted energy consumption output by the BP model is utilized to dynamically adjust the State of Charge (SOC) balance point. The operating point of the range extender engine is optimized in real time to achieve optimal energy efficiency. The Simulations results indicate that when the numbers of predicted road segments are 4, 5, and 6, the predictive strategy enables the pre-estimation of energy consumption levels for future road segments. By reasonably adjusting SOC balance point, energy replenishment is provided in high-efficiency zones of the range extender for congested segments.