A solvent-free and scalable method was developed for the preparation of soybean-oil-based polyols by a thiol–ene photo-click reaction with a homemade photochemical reactor. The effect of reaction parameters, including photoinitiators, reaction time, molar ratios of thiols to carbon–carbon double bonds, and power of the mercury lamp, on the structures of the resulting polyols was investigated. The mechanism of the thiol–ene photo-click reaction was also discussed. On the basis of these novel polyols, several polyurethanes were prepared using different diisocyanates (aliphatic, cycloaliphatic, and aromatic isocyanate) and characterized. The resulting polyurethane films possess good performance, including the highest glass transition temperature of 41.3 °C, tensile strength of 15.7 MPa, and elongation at break of 471.0%.

Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of-charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are conducted to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.

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