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The purpose of this paper is to combine the entropy weight method with the cloud model and establish a fire risk assessment method for airborne lithium battery.

In this paper, the fire risk assessment index system is established by fully considering the influence of the operation process of airborne lithium battery. Then, the cloud model based on entropy weight improvement is used to analyze the indexes in the system, and the cloud image is output to discuss the risk status of airborne lithium batteries. Finally, the weight, expectation, entropy and hyperentropy are analyzed to provide risk prevention measures.

In the risk system, bad contact of charging port, mechanical extrusion and mechanical shock have the greatest impact on the fire risk of airborne lithium battery. The fire risk of natural factors is at a low level, but its instability is 25% higher than that of human risk cases and 150% higher than that of battery risk cases.

The method of this paper can evaluate any type of airborne lithium battery and provide theoretical support for airborne lithium battery safety management.

After the fire risk assessment is completed, the risk cases are ranked by entropy weight. By summarizing the rule, the proposed measures for each prevention level are given.

This paper aims to forecast technological change for laptop batteries. The most promising technology to replace laptop batteries emerging today is micro fuel cells.

The authors use several sources of technical data like the Department of Energy Sandia National Laboratory Technical Library for exploring this topic further. Patents were searched for fuel cell and lithium battery development and to perform a technology cycle time analysis, identify countries filing patents, and discover what areas they are working on development.

Based on the analysis, fuel cells promise to be the technology that will replace laptop lithium batteries.

This paper attempts to draw a framework bringing different scientific data sources together for technology forecasting.

Lithium-polymer batteries have common usage in aviation industry especially unmanned aerial vehicles (UAV). Overheating is a serious problem in lithium-polymer batteries. Various cooling methods are performed to keep lithium-polymer batteries in the desired temperature range. The purpose of this paper is to examine pouch type lithium-polymer battery with plate fins by using particle image velocimetry (PIV) and computational fluid dynamics (CFD) for UAV.

Battery models were produced with a 3D printer. The upper surfaces of fabricated battery models were covered with plate fins with different fin heights and fin thicknesses. Velocities were obtained with PIV and CFD. Temperature dissipations were acquired with numerical simulations.

At the end of the study, the second battery model gave the lowest temperature values among the battery models. Temperature values of the seventh battery model were the highest temperatures. Fin cooling reduced the maximum cell temperatures noticeably. Numerical simulations agreed with PIV measurements well.

This paper takes into account two essential tools such as PIV and CFD, for fluid mechanics, which are significant in the aviation industry and engineering life.

The originality of this paper depends on investigation of both PIV and CFD for UAV and developing a cooling method that can be feasible for landing and take-off phases for UAV.

The materials and energy density of current electric vehicles (EV) battery technology means that the vehicles are heavier and have a shorter range in comparison to internal combustion engine vehicles (ICEV). Battery cost also means EVs are relatively expensive for the consumer, even with government incentives, and dependent on sometimes-rare resources being available. These factors also limit the applicability of battery-electric technologies to heavy-duty vehicles. However, a number of next generation technologies are under laboratory development which could radically change this situation. Using a follow-the-money methodology, the strategic innovations of companies and public institutions are examined. The chapter will review the potential for changes in resource inputs, higher-density batteries and cost reductions, considering options such as lithium-air, metal-air and solid-state technologies. The innovations outlined in these technologies are considered from an economic perspective, identifying their advantages and disadvantages in commercialisation. At the same time, innovations, and investments in infrastructure electrification (Electric Road Service) and battery exchange point with swapping technology will be also considered due their implications and contribution to solving battery-related challenges and shortcomings. It is concluded that only a joint investment in effort on technologies would allow the use of EVs to be extended to a broad public in terms both of users and geography.

With the promotion of lithium-ion battery, it is more and more important to ensure the safety usage of the battery. The purpose of this paper is to analyze the battery operation data and estimate the remaining life of the battery, and provide effective information to the user to avoid the risk of battery accidents.

The particle filter (PF) algorithm is taken as the core, and the double-exponential model is used as the state equation and the artificial neural network is used as the observation equation. After the importance resampling process, the battery degradation curve is obtained after getting the posterior parameter, and then the system could estimate remaining useful life (RUL).

Experiments were carried out by using the public data set. The results show that the Bayesian-based posterior estimation model has a good predictive effect and fits the degradation curve of the battery well, and the prediction accuracy will increase gradually as the cycle increases.

This paper combines the advantages of the data-driven method and PF algorithm. The proposed method has good prediction accuracy and has an uncertain expression on the RUL of the battery. Besides, the method proposed is relatively easy to implement in the battery management system, which has high practical value and can effectively avoid battery using risk for driver safety.