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Battery model is crucial for the accurate estimation of the state of charge (SOC) in a battery management system of electric vehicles. However, differences exist within optimal battery models corresponding to different types of batteries. Even for the same type of battery, the corresponding optimal battery model may vary with the change of the battery status. To solve the problem, this paper proposes a multi-model probability fusion estimation (MMPFE) method to realize an accurate description of battery characteristics and a precise SOC estimation. An improved adaptive unscented Kalman filter (AUKF) approach is developed for measurement noise variance online update based on the idea of orthogonality between residual and innovation during the SOC estimation. Finally, the proposed MMPFE method was verified by experiments using LiFeO 4 and LiMnO 2 batteries, respectively. Results indicate that when a voltage drift of +3 mV was applied on the LiFeO 4 battery under UDDS condition and an initial SOC error was applied on LiMnO 2 battery under FUDS condition at different temperatures, the proposed method still can estimated the precise SOC. Comparing with the results obtained by the other methods under the same conditions, the method presented in the paper shows a higher SOC estimation accuracy and better robustness. [ABSTRACT FROM AUTHOR]

An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior. [ABSTRACT FROM AUTHOR]

To overcome the issue of overheating and conserve cooling energy consumption, a superamphiphobic passive sub-ambient daytime radiative cooling (PSDRC) coating was extensively applied to distributed telecommunication base stations. The top surfaces of the battery and equipment cabinets coated with the PSDRC coating indicated prominent sub-ambient cooling effects regardless of the climate; this resulted in substantially lower surface temperatures compared with their counterparts. The applications of the PSDRC coating reduced the chamber interior temperature of an equipment cabinet by 10.4 °C during noon hours and by 6.0 °C before sunrise. The PSDRC-coated base stations could properly operate without air conditioners by replacing the closed door with a modified one containing an external exhaust system. The drastic decrease in the chamber interior temperature of the PSDRC base stations allowed them to conserve a considerable amount of cooling energy; this, resulted in cooling energy efficiencies between 31.7% and 64.9%. After more than one year, the surfaces of the PSDRC base station near busy city transit roads were still spotless and super non-wettable; this demonstrated the significance of the superamphiphobic self-cleaning topcoat and provided the top surfaces of the base stations in various regions with identical sub-ambient cooling effects on the initial state. • A passive subambient daytime radiative cooling (PSDRC) coating was prepared. • The PSDRC coating was superamphiphobic self-cleaning. • The PSDRC coating was used to distributed telecommunication base stations (TBSs). • The top surface of the PSDRC TBSs constantly showed subambient daytime cooling. • The cooling energy efficiency of the PSDRC TBSs was between 31.7% and 63.9%. [ABSTRACT FROM AUTHOR]

Abstract: The composition and manufacturing process of a three-layer anticorrosive cool coating for oil storage tanks are introduced. The optical, indoor thermal and physicochemical properties were systematically investigated. In addition, the outdoor cooling effect and the reduction of evaporation losses of crude oil were predicted. The coating developed has high solar reflectance and thermal emittance and thus good indoor and outdoor temperature reduction effects. The unique structural design and the careful selection of ingredients endow the coating with low thermal conductivity and good resistance to dirt, weather, salt spray and chemicals. The selected epoxy functional saline couple agent creates a chemical bridge between the coating's binder and the surface of carbon steel substrates and improves the adhesion of the coating. [Copyright &y& Elsevier]