Etransportation最新文献

This paper compares the impact of two scaling methods of electric machines on the energy consumption of electric vehicles. The first one is the linear losses-to-power scaling method of efficiency maps, which is widely used in powertrain design studies. While the second is the geometric scaling method. Linear scaling assumes that the losses of a reference machine are linearly scaled according to the new desired power rating. This assumption is questionable and yet its impact on the energy consumption of electric vehicles remains unknown. Geometric scaling enables rapid and accurate recalculation of the parameters of the scaled machines based on scaling laws validated by finite element analysis. For this comparison, a reference machine design of 80 kW is downscaled with a power scaling factor of 0.58 and upscaled considering a power scaling of 1.96. For comparative purposes, optimal combinations of geometric scaling factors are determined. The scaled machines are derived to fit the driving requirements of two electric vehicles, namely a light-duty vehicle and a medium-duty truck. The comparison is performed for 9 standardized driving cycles. The results show that the maximal relative difference between linear and geometric scaling in terms of energy consumption is 3.5% for the case of the light-duty vehicle, compared with 1.2% for the case of the truck. The findings of this work provide evidence that linear scaling can continue to be used in system-level design studies with a relatively low impact on energy consumption. This is of high interest considering the simplicity of linear scaling and its potential for time-saving in the early development phases of electric vehicles.

As a nonlinear and dynamic system, the polymer electrolyte membrane fuel cell (PEMFC) system requires a comprehensive failure prediction and health management system to ensure its safety and reliability. In this study, a data-driven PEMFC health diagnosis framework is proposed, coupling the fault embedding model, sensor pre-selection method and deep learning diagnosis model. Firstly, a physical-based mechanism fault embedding model of PEMFC is developed to collect the data on various health states. This model can be utilized to determine the effects of different faults on cell performance and assist in the pre-selection of sensors. Then, considering the effect of fault pattern on decline, a sensor pre-selection method based on the analytical model is proposed to filter the insensitive variable from the sensor set. The diagnosis accuracy and computational time could be improved 3.7% and 40% with the help of pre-selection approach, respectively. Finally, the data collected by the optimal sensor set is utilized to develop the fault diagnosis model based on 1D-convolutional neural network (CNN). The results show that the proposed health diagnosis framework has better diagnosis performance compared with other popular diagnosis models and is conducive to online diagnosis, with 99.2% accuracy, higher computational efficiency, faster convergence speed and smaller training error. It is demonstrated that faster convergence speed and smaller training error are reflected in the proposed health diagnosis framework, which can significantly reduce computational costs.

In the face of the electrification trend in transportation, the internal combustion engine (ICE) is expected to continue playing a vital role in generating electricity for power systems or directly propelling vehicles in certain sectors. However, ICEs are also under significant pressure to achieve carbon neutrality, with the key lying in carbon-free fuels. Ammonia, compared to hydrogen, offers advantages in terms of hydrogen-carrying capacity, storage and transportation convenience, and safety, making it a promising carbon-free fuel for large-scale use in ICEs. Nonetheless, ammonia's combustion inertness poses challenges for its application, requiring efforts to enhance its combustion. Hydrogen, as a carbon-free and highly reactive fuel, serves as a powerful combustion promoter, maximizing the carbon-free effect of ammonia. Furthermore, on-board ammonia decomposition can produce hydrogen, ensuring a stable hydrogen supply and enabling ammonia-hydrogen synergy combustion while carrying only ammonia. This ammonia-hydrogen synergy combustion, based on on-board hydrogen production, presents a highly promising development direction for ammonia engines. When combined with hybridization, it further enhances the overall energy efficiency of ammonia. The objective of this paper is to review recent advancements in ammonia-hydrogen engines, covering topics such as ignition methods and combustion strategies, fuel supply, pollutants, and after-treatment. Based on this review, a conceptual ammonia-hydrogen engine for hybrid power systems is proposed. This engine ignites the ammonia-hydrogen mixture in the main chamber using hydrogen active jet ignition, achieving spark-assisted compression ignition. Technical measures for efficient engine combustion, synergistic utilization of exhaust heat for hydrogen production, and effective after-treatment of NO_x, unburned NH₃, and N₂O are discussed. At last, some perspectives on the development of ammonia-hydrogen engines are also presented.

All-solid-state batteries (ASSBs) are regarded as the most promising next-generation batteries for electric vehicles in virtue of their potential advantages of enhanced safety, high energy density and power capability. Among the ASSBs based on various solid electrolytes (SEs), sulfide-based ASSBs have attracted increasing attention due to the high ionic conductivity of sulfide SEs which is comparable to that of liquid electrolytes. Extensive efforts from academia and industry have been made to develop sulfide-based ASSBs, and several significant progress has been achieved in recent years. However, successful fabrication of high-performance sulfide-based ASSBs has been rarely reported, and the practical application of sulfide-based ASSBs still faces a variety of challenges. Herein, following a bottom-up approach, we present a comprehensive review of the critical issues of practical sulfide-based ASSBs from the material, interface, composite electrode to cell levels. The existing challenges, recent advances, and future research directions of sulfide-based ASSBs at multiple levels are discussed. Finally, several fabrication processes for scaling up sulfide-based ASSBs and existing pilot/mass production schedules of sulfide-based ASSBs of the leading companies are also introduced. Facing the existing challenges and future opportunities, we highly encourage joint efforts and cooperation across the battery community to promote the practical application of sulfide-based ASSBs.

Nowadays, multi-energy supply utility grid system has witnessed the destruction of increasing natural disasters. Under the disasters, the energy supply capability from the utility grid system to the end-user microgrids is decreased, which is due to the destruction of the system infrastructure. Thus, how to improve the resilience of the microgrids under disasters is an essential problem. In this paper, a mobile hydrogen truck-assisted methodology is proposed to deliver hydrogen tanks to end-user microgrids via transportation network to resist to the natural disasters. First, a temporal–spatial destructive model of the natural disasters based on the grid division is presented, and the dynamical energy supply ability of an IEEE30+gas20+heat14 utility grid system is derived. Second, a hydrogen tank delivering model from hydrogen company to microgrids based on transportation network is presented. Third, a real-world transportation network based on SUMO simulator is linked with Matlab to simulate the real-time coupling between transportation network and power network. Last, microgrids optimal operation based on the temporal–spatial destructive model and hydrogen tank delivering model is presented. The simulation results show that with the assistance of the arrived hydrogen tanks through real-world transportation network in microgrid, one can indeed reduce load shedding. However, when considering the damaged transportation network, the saving loads are reduced due to the increase of the mobile hydrogen storage delivery time. It reveals that delivering mobile hydrogen tanks to end-user microgrids can effectively improve the system resilience.

Data-driven approaches have gained increasing attention in the field of battery life-related prediction, as building a comprehensive mechanistic degradation model remains a challenge. Deep learning has emerged as a powerful data-driven fitting method for battery-related applications. However, interpretability remains an issue in this field, hindering the practical utilization of deep learning methods. With the development of interpretable techniques, deep learning methods not only can be conducted as black box tools for fitting, but also for exploring the relationship between external battery data and internal electrochemical changes. In this paper, an interpretable deep learning procedure is proposed and exemplified by accelerated fading point (knee-point) recognition based on an open battery dataset. The Gradient-weighted Class Activation Mapping (Grad-CAM) is conducted to explain the link between the input and output of the trained convolutional neural networks (CNN) model. The trained CNN model possesses deep insight into battery degradation, giving the very first warning when accelerated fading occurs. Through interpretability analysis, it is confirmed that the well-trained model can spontaneously focus on features associated with internal battery degradation and identify some additional features beyond existing human experience. The proposed method can be used to discover the relationship between battery data and degradation mechanism by artificial intelligence in the electric vehicles (EVs) field.

The capacity degradation of lithium-ion batteries (LIBs) will accelerate after long-term cycling, showing nonlinear aging features, which not only shortens the long-term life of LIBs, but also seriously endangers their safety. In this paper, by introducing the concept of nonlinear aging degree, a knee-point identification method based on the maximum distance method is established, and the nonlinear aging behavior of LIBs is identified and marked, so as to know whether the nonlinear aging phenomenon has occurred. Furthermore, two knee-point prediction methods have been proposed and compared. The direct knee-point prediction method based on stacked long short-term memory (S-LSTM) neural network and sliding window method is proposed for the scenarios of battery development, early performance evaluation and online application. For scenarios such as echelon utilization and post-safety evaluation, an indirect knee-point prediction method combining capacity prediction and knee-point identification algorithm is proposed. Through multi-dimensional comparison of the two methods, the strengths and weaknesses of their applicable scenarios are analyzed. Our work has guiding significance for finding the ideal replacement opportunity of LIBs in different scenarios, so that the user can be reminded whether to maintain or replace the battery, which greatly reduces the risk of battery safety problems.