Etransportation最新文献

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.

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.

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.

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.

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.

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.

Accurate and reliable prediction of the future capacity degradation of lithium-ion batteries is crucial for their application in electric vehicles. Recent publications have highlighted the effectiveness of deep learning, in particular, in generating precise forecasts regarding the aging patterns. However, large quantities of training data covering various aging behaviors are required to train such models effectively. Collecting such a large database centrally is not feasible due to privacy and data communication restrictions of data owners, such as testing facilities or fleet operators. Federated learning provides a solution to this open issue. A framework, which incorporates federated learning into the training of a data-based battery aging model, is presented in this paper. The benefit of federated learning is that even data owners with sensible information can participate in a collaborative model training, since the model training is only conducted locally and all the data remains local and does not have to be disclosed. Thus, more data owners are likely to participate in this collaborative training. This will improve the prediction performance due to the enlarged dataset that can be utilized for the model training. This work shows that the prediction accuracy of the model trained with federated learning is only slightly worse than the prediction results obtained by the ideal case in which all aging data is stored in a central database. A sensitivity analysis is presented to prove the robustness of federated learning even if the datasets between participating data owners are highly imbalanced or exhibit different aging behaviors. Within exemplary scenarios, it is shown that individual data holders can reduce their prediction errors from $MAP{E}_{mean}=7.07\text{\%}$ to $MAP{E}_{mean}=0.91\text{\%}$ by participating in the proposed federated learning-based framework.

To meet the increased requirements for efficiency and compactness of the Polymer Electrolyte Membrane Fuel Cell (PEMFC) system for heavy-duty vehicles (HDVs) application, PEMFC has to operate under high temperature and low humidity (HTLH) conditions to reduce the parasitic power consumption of radiators and the size of humidifier. However, HTLH would negatively affect the performance and durability of PEMFC. Through conducting in-situ current mapping, this paper found highly inhomogeneous current in-plane distribution during current cycling under HTLH conditions, which is attributed to the self-reinforced feedback of local current density and membrane water content. Instead, under the situations where current increase is prior to the temperature increase, PEMFC would have much more uniform in-plane current distribution and membrane water distribution, resulting in more efficient utilization of the produced water in a dry environment.

Automotive battery packs for electromobility applications consist of a large number of interconnected battery cells. Different cell-to-busbar joining techniques are utilized, with cylindrical cells frequently being contacted using wire bonding. Failure of individual connections can occur due to strong vibrations during operation and improper stress, making detection by the battery management system a necessity. This study investigates the identification of an electrical wire bond failure in a state-of-the-art electric vehicle module of a Lucid Air with 10 series-connected and 30 parallel-connected cells (10s30p). Four individual cells were characterized extensively in order to generate a simulation model taking into account parameter scatter. The failure case under investigation was simulatively incorporated in one parallel circuit and subsequently replicated in experimental validation measurements at the module level. The results show that this defect can be detected using pulses of C/3 or higher currents at various states of charge. An even more robust detection is achieved using differential voltage analysis of constant current C/20 discharge voltage trajectories. This defect identification method does not require any additional measurement sensors beyond the voltage taps and sensors provided by the manufacturer – with one voltage sensor per parallel circuit – and can therefore be implemented during electric vehicle usage, e.g. at dedicated service checks. A discussion on the applicability and scalability as well as the limitations of the method is provided. All measurement data of the state-of-the-art Lucid battery system is available as open source alongside the article.

The construction and development of the new power system with new energy sources as the main component will face significant challenges in terms of scarcity of flexible resources. User-side adjustable loads and energy storage, particularly electric vehicles (EVs), will serve as substantial reservoirs of flexibility, providing stability to the new power system. The rapid deployment of renewable energy and the surpassing of expectations in the penetration rate of EVs in China present opportunities for the significant growth of virtual power plants (VPPs) and vehicle-to-grid (V2G) interactions. The enormous potential and advantages of V2G as a primary user-side resource are further revealed. Under China's current electricity market policies, the pilot projects of user-side interactions are being analyzed. Furthermore, the prospects for the future development of VPPs and V2G, along with relevant recommendations and perspectives regarding business models, technologies, and policies, are highlighted. The development of VPPs and V2G interactions in China holds immense potential for leveraging adjustable resources, especially EVs and energy storages with lithium-ion batteries, to enhance grid flexibility and stability.