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An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s. [ABSTRACT FROM AUTHOR]

An ultracapacitor State-of-Charge (SOC) fusion estimation method for electric vehicles under variable temperature environment is proposed in this paper. Firstly, Thevenin model is selected as the ultracapacitor model. Then, genetic algorithm (GA) is adopted to identify the ultracapacitor model parameters at different temperatures (−10 °C, 10 °C, 25 °C and 40 °C). Secondly, a variable temperature model is established by using polynomial fitting the temperatures and parameters, which is applied to promote the ultracapacitor model applicability. Next, the off-line experimental data is iterated by adaptive extended Kalman filter (AEKF) to train the Nonlinear Auto-Regressive Model with Exogenous Inputs (NARX) neural network. Thirdly, the output of the NARX is employed to compensate the AEKF estimation and thereby realize the ultracapacitor SOC fusion estimation. Finally, the variable temperature model and robustness of the proposed SOC fusion estimation method are verified by experiments. The analysis results show that the root mean square error (RMSE) of the variable temperature model is reduced by 90.187% compared with the non-variable temperature model. In addition, the SOC estimation error of the proposed NARX-AEKF fusion estimation method based on the variable temperature model remains within 2.055%. Even when the SOC initial error is 0.150, the NARX-AEKF fusion estimation method can quickly converge to the reference value within 5.000 s.

In this work, constant and increasing temperature fatigue interaction effect on fatigue behavior of 2017-T4 aluminum alloy was investigated. Fatigue tests at constant load constant temperature and constant load increasing temperature were performed for five applied stresses which are (350,275,200,175 and 150 MPa) that based on the tensile test behavior .The constant temperatures were room temperature (RT) (25 ̊C) and 100 ̊C. While the increasing temperatures were RT, 50 ̊C, 100 ̊C and 150 ̊C for one test program. The constant fatigue property of the increasing temperatures was observed the worst case compared to the others constant fatigue properties. A new variable temperature fatigue damage model was proposed. It is based on the S-N curve and taking into account the effects of constant loads and variable temperature. A comparison between prediction of the proposed model and crack growth rate due to Miner rule was made. The results proved that this model is satisfactory and gave safe results than Miner rule compared to experimental data.

To improve the security of long and steep downgrade in mountain highway, we establish a temperature model of truck brake speed on long and steep downgrade with the theory of energy conservation. Some parameters of the model have been modified through field tests and mathematical statistics. The critical gradient I and II concept of long and steep downgrade is proposed, and the Security risk rating index of long and steep downgrade is proposed. The results show that the critical gradient based on the brake temperature is the control gradient of the smooth transition section design in the long 'and steep downgrade section of freeway in mountain areas. Brake temperature model for truck owners is as an effective means of rapid assessment of road safety. It can quantify the security risk rating of long and steep downgrade, which is helpful for p the selection of hedge lane location and the implementation of other security support measures. [ABSTRACT FROM AUTHOR]