China Automotive Engineering Research Institute

Organic cathode materials (OCMs) are promising sustainable alternatives to inorganic counterparts for next-generation batteries, yet their widespread application is largely hindered by intrinsically low electrical conductivity (below 10-6 S cm-1) and material dissolution. The vast chemical space for exploration complicates the discovery of optimal OCMs. In this work, we utilized a machine learning (ML)-based discovery process with a pretrained transformer model in ZINC organic molecules database, yielding a couple of potential high-performance OCMs candidates, including isoindigo-type redox units. The output of such efficient screening inspires the design of poly-benzodifurandione (PBFO) as a free-standing cathode material for high-performance Li-ion and Na-ion storage. The flexible PBFO film exhibits a breakthrough conductivity of 5.9×102 S cm-1, setting a new benchmark for additive-free organic cathodes. The neat PBFO cathodes achieve a reversible capacity of 262 mAh g-1 averaging at 2.5V versus Li+/Li at 25mA g-1, delivering a high electrode-level energy density of 655Wh kg-1, among the highest reported for OCMs. This work provides the first flexible, high-conductivity organic cathodes without conductive additives and binders, opening a new direction toward viable organic batteries.

Compressed gaseous hydrogen (CGH 2 ) with a nominal working pressure (NWP) of 70 MPa is currently the fuel of choice for weight sensitive fuel cell electric vehicle applications (e.g. light-commercial vehicles and long-haul trucks), where higher cargo capacity compared to battery electric vehicles due to its high gravimetric energy density. The refuelling time of fuel cell vehicles should ideally be within a few minutes, comparable to conventional vehicles using liquid fossil fuels. Due to the rapid and high pressure increase during filling, usually starting from 2 MPa, high temperatures over 80 °C are generated inside the tank owing to enthalpy input and compression heat, while during the emptying low temperatures down to −40 °C can be reached because of the gas expansion enthalpy decrease. The fuelling protocol for light duty gaseous hydrogen vehicles according to the standard SAE J2601 (SAE-J2601, 2014) establishes that a safe filling or emptying up to the design capacity can only be guaranteed if the internal gas and temperature are evenly distributed during the process and does not exceed the design temperature range between − 40 ° C ≤ T g a s ≤ 85 ° C . This paper presents a simulation methodology, which allows to predict the gas temperature rise and fall inside a high-compression hydrogen tank and its heat losses through the multi-layer shell during and after a refuelling or emptying process. It is a zero dimension (0D)-thermodynamic-transient-model developed in MATLAB/Simulink and established to investigate the detailed temperature history of heat transfer from gas through tank wall due to thermal conduction and convection. The thermodynamic model includes two different methodologies obtained from the literature to calculate the convective heat transfer coefficient that occurs between the fluid and the solid domain during the refuelling and emptying process. To assess the accuracy of the thermodynamic model, the results are compared and validated with experimental data obtained from measurements performed by the European -HyTransfer- project (European Commission, 2013) in a 37-litre type IV cylindrical high-pressure hydrogen tank on-board. The temperature profiles of the gas inside tank, at the surface between the plastic liner and carbon fibre composite as well as on the outer surface itself predicted by the simulation model show good accordance against the available experimental data. Result of this work is a fast-calculating simple model allowing to obtain rapid estimations of the averaged temperature of the inner fluid. This can be used to optimize fast filling of compressed hydrogen tanks and to design/develop new filling protocols, e.g. reduce the need for pre-cooling, while guaranteeing safety. • Loading and unloading of compressed gaseous hydrogen tanks. • 0D-thermodynamic-transient model to simulate thermal behaviour. • Modelling convective heat transfer between hydrogen, tank wall and the atmosphere.

With the development of wireless communication technologies, cooperation of automated vehicles (CAVs) becomes a key roadmap to promote the intelligent level and traffic efficiency. In this study, a distributed optimization framework is firstly designed to utilize more computation resources by introducing auxiliary variables and equality constraints to separate the coupling parts in the original centralized optimization problem. Bench test results show that more resources can be used by this framework compared with the centralized one, which is beneficial to the real time performance and scale of CAVs. But extra exchanges of the consensus variables between nodes lead to much more communication load, which easily causes packet loss. To ensure the cooperative performance, a robust interactive algorithm is further designed to ensure the convergence of the numerical optimization process in the presence of packet loss. Its global convergence is analyzed theoretically by the operator method under the assumption that the feasible domain is convex. The performances of CAVs controlled by the robust distributed optimization algorithm are validated and verified by several comparative tests under the intersection scenario. The test results show that compared with the centralized structure, the balance of computation load among different nodes is improved by 5 times at least, and the maximum computation period is smaller than 50 ms.

Timely and accurate multi-fault diagnosis of lithium-ion battery systems is crucial for ensuring safety in large-scale applications. This paper presents a new multi-fault diagnosis method based on cloud low-density segment charging voltage curves. Firstly, the sampling drift fault of the voltage sensor is detected and repaired based on the voltage sampling principle in the series-connected battery module. The segment charging voltage curves are extracted, and then the fused feature is constructed using mean normalization and local outlier factor feature of the segment charging voltage curves. The multi-fault of low-capacity, low state of charge, and micro short-circuit faults in the series-connected battery pack are diagnosed with the adaptive dynamic threshold and 3σ criterion, the detection accuracy can be up to 98.6%, 98.7%, and 98.2%. This method based on partial charging voltage curve does not rely on charging conditions and completed charging curves, ensuring that the batteries can be frequently monitored during daily use to improve system safety.

<div class="section abstract"><div class="htmlview paragraph">In low-light driving scenarios, in-vehicle camera images encounter technical challenges, including severe brightness degradation and short exposure times. Conventional driving image enhancement algorithms are susceptible to issues such as the loss of image features and significant color distortion. The proposed solution to this problem is a multi-scale attention fusion network (MAF-NET) for the enhancement of images captured during low-light driving conditions. The network’s structural design is uncomplicated. The model incorporates a meticulously designed multi-scale attention fusion module (MAFB), along with all essential components for network connectivity. The MAF is predicated on a heavy parameter residual feature block design and incorporates a multi-scale channel attention mechanism to capture richer global/local features. A substantial body of experimental evidence has demonstrated that, in comparison with prevailing algorithms, MAF-NET exhibits superior performance in low-light enhancement, detail retention, and color reproduction. Moreover, it attains commendable results in both subjective visibility assessments of nighttime driving scenarios and objective image quality metric tests, such as PSNR and SSIM.</div></div>