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.

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.

The vehicle sideslip angle is a core state parameter in vehicle dynamics control. Its accurate estimation is critical for vehicle stability control and the development of active safety systems. In the vehicle sideslip angle estimation method using the traditional Unscented Kalman Filter (UKF), the process noise covariance matrix Q and observation noise covariance matrix R are difficult to adjust adaptively, leading to estimation accuracy degradation under complex driving conditions. This paper proposes a vehicle sideslip angle estimation method that integrates UKF and Deep Reinforcement Learning (DRL), leveraging the adaptive decision-making capability of DRL to dynamically optimize the noise parameters in UKF. A state space incorporating vehicle motion states and filtering performance metrics is constructed, along with an action space that outputs adjustment quantities for the noise covariance matrices. A reward function based on estimation errors and uncertainties is formulated, and the Proximal Policy Optimization (PPO) algorithm is employed to train the policy network. The results indicate that the proposed method effectively improves vehicle sideslip angle estimation accuracy under various driving conditions, including different vehicle speeds, road surface adhesion coefficients, and sensor noise disturbances. Compared with the traditional UKF method, the Root Mean Square Error (RMSE) is reduced by over 30%, and the method demonstrates strong stability and robustness under complex scenarios. This approach provides a new solution for the accurate estimation of key vehicle state parameters and can be extended to fields such as autonomous driving and vehicle active safety.