The hybrid-electric powertrain presents an immediate solution to energy and environmental challenges encountered within the realm of transportation. Targeting the optimization of hybrid-electric powertrains, deep reinforcement learning (DRL) has been intensively and increasingly investigated to develop intelligent energy management systems in the context of augmented vehicular and traffic information. After a brief introduction to the Markov Decision Process and DRL, this paper presents a comprehensive survey of the recent advancements in DRL-based energy management. The survey categorizes the progress based on the various roles that DRL plays in energy management systems, highlighting the flexibility and advantages of integrating DRL for achieving energy efficiency, safety, and reliable performance. Furthermore, the study concludes with an analysis of open issues and future prospects, including the learning and application of DRL-based energy management strategies, development of novel DRL algorithms, and integration of DRL-based energy management in intelligent and sustainable transportation contexts.

The fuel cell engine mechanism model is used to research fault diagnosis based on a data-driven method to identify the failure of proton exchange membrane fuel cells in the process of operation, which leads to the degradation of system performance and other problems. In this paper, an extreme learning machine and a support vector machine are applied to classify the usual faults of fuel cells, including air compressor faults, air supply pipe and return pipe leaks, stack flooding faults and temperature controller faults. The accuracy of fault classification was 78.67% and 83.33% respectively. In order to improve the efficiency of fault classification, a genetic algorithm is used to optimize the parameters of the support vector machine. The simulation results show that the accuracy of fault classification was improved to 94% after optimization.

An electrochemical Lithium ion battery model was built taking into account the electrochemical reactions. The polarization was divided into parts which were related to the solid phase and the electrolyte mass transport of species, and the electrochemical reactions. The influence factors on battery polarization were studied, including the active material particle radius and the electrolyte salt concentration. The results showed that diffusion polarization exist in the positive and negative electrodes, and diffusion polarization increase with the conducting of the discharge process. The physicochemical parameters of the Lithium ion battery had the huge effect on cell voltage via polarization. The simulation data show that the polarization voltage has close relationship with active material particle size, discharging rate and ambient temperature.