2-Phenyl­imidazolium nitrate monohydrate

In the title hydrated mol­ecular salt, C9H9N2 +·NO3 −·H2O, the dihedral angle between the aromatic rings in the cation is 11.09 (8)°. In the crystal, the components are linked into chains propagating in [101] by N—H⋯O and O—H⋯O hydrogen bonds

Bis(2-phenyl-1H-imidazole-κN 3)silver(I) nitrate

The asymmetric unit of the title compound, [Ag(C9H8N2)2]NO3, contains one complete [Ag(C9H8N2)2]+ cation and two half-cations (with the other halves generated through inversion) and two NO3 − anions. Each AgI ion shows a linear AgN2 coordination. The ions are linked by N—H⋯O hydrogen bonds

A novel hybrid propulsion system configuration and power distribution strategy for light electric aircraft

Similar to the electrification of the automotive industry, the growing concerns with the shortage of fossil fuels has also called for a paradigm shift in aviation industry. To promote the aviation electrification process, it is necessary to develop an efficient energy storage system and a stable power transmission system to improve the reliability and extend the endurance of electric aircraft. This paper designs a novel propulsion system topology and power distribution algorithm for light manned electric aircraft. Firstly, a novel aircraft hybrid propulsion system topology is designed, in which the battery energy storage system can work synergistically with the fuel cell to provide power to the aircraft electric engine. Then, an adaptive energy management framework is developed to distribute the aircraft power requirement between energy storage devices. Meanwhile, an aircraft power balance state recognizer is designed to enhance the dynamic performance of the aircraft and adjust the working state of the propulsion system. The proposed hybrid propulsion system configuration and power distribution algorithm are verified under a prototype two-seater electric aircraft: Alpha Electro. Numerical analysis results indicate that the developed methods can dynamically meet the power requirement of aircraft under fast-charging and peak power requirement scenarios. With the developed hybrid propulsion system, most of the fuel cell high-power working points are moved to the medium and low area, which indicates that the fuel cell is effectively protected. Furthermore, the quantified hydrogen consumption can be reduced by 7.63% comparing to fuel cell electric aircraft.</p

Adaptive energy management for hybrid power system considering fuel economy and battery longevity

The adoption of hybrid powertrain technology brings a bright prospective to improve the economy and environmental friendliness of traditional oil-fueled automotive and solve the range anxiety problem of battery electric vehicle. However, the concern of the battery aging cost is the main reason that keeps plug-in hybrid electric vehicles (PHEV) from being popular. To improve the total economy of PHEV, this paper proposes a win-win energy management strategy (EMS) for Engine-Battery-Supercapacitor hybrid powertrains to reduce energy consumption and battery degradation cost at the same time. First of all, a novel hierarchical optimization energy management framework is developed, where the power of internal combustion engine (ICE), battery and super capacitor (SC) can be gradationally scheduled. Then, an adaptive constraint updating rule is developed to improve vehicle efficiency and mitigate battery aging costs. Additionally, a control-oriented cost analyzing model is established to evaluate the total economy of PHEV. The quantified operation cost is further designed as a feedback signal to improve the performance of the power distribution algorithm. The performance of the proposed method is verified by Hardware-in-the-loop experiment. The results indicate that the developed EMS method coordinates the operation of ICE, driving motor (DM) and energy storage system effectively with the fuel cost and battery aging cost reduced by 6.1% and 28.6% respectively compared to traditional PHEV. Overall, the introduction of SC and the hierarchical energy management strategy improve the total economy of PHEV effectively. The results from this paper justify the effectiveness and economic performance of the proposed method as compared to conventional ones, which will further encourage the promotion of PHEVs.</p

Health-Conscious vehicle battery state estimation based on deep transfer learning

Establishing an accurate mathematical model is fundamental to managing, monitoring, and protecting the battery pack in electric vehicles (EVs). The application of the deep learning algorithm-based state estimation method can significantly improve the accuracy and stability of the battery model but is hindered by the great demand for training data. This paper addresses the challenge of health-conscious battery modeling by utilizing multi-source data based on a novel deep transfer learning method. Firstly, a cloud-based battery management framework is designed, which is able to collect and process battery operation data from various EVs and provide a foundation for deploying the transfer learning method. Battery healthy state information in the collected dataset is labeled by a generic perception model, which can be commonly used to quantify the aging state of different battery packs and facilitate the knowledge transfer process. Additionally, a deep transfer learning method is developed to boost the training process of the battery model, where the operation data from different types of EVs can be used for establishing state estimators. The method is verified by the battery operation data collected from two types of electric buses. With the developed healthy state perception model and transfer learning method, battery model error can be limited to 2.43% and 1.27% in the whole life cycle