No abstract

Lithium-ion battery Remaining Useful Life (RUL) estimation and prediction are very important in the fields of reliability, automatic test, power sources, and electric vehicles, etc. The performance of battery RUL estimation relies on the predictability. Multiscale entropy (MSE) can be used to analyze the predictability of time series across multiple time scales. MSE is used to analyze the predictability of lithium-ion battery RUL. Results show that the predictability of battery discharge voltage decreases along with time scale and discharge cycle, and when the cycle time exceeds certain value, the predictability of discharge voltage decreases sharply. As an input of RUL prediction, the predictability of discharge voltage influences the prediction performance of RUL. For better prediction of RUL, if the MSE of discharge voltage of certain cycle changes greatly, other models or different model parameters should be considered.

No abstract

The cellular automaton (CA) traffic models are widely used to simulate the real traffic. Considering the influence of driving states on randomization, a cellular automaton model based on the NS model is proposed. In our new model, the randomization probability is different for different driving states, which is determined by the velocity changes after the step of deterministic deceleration. Simulation results show that the maximum flux of the new model is greater than that of the NS model, and the traffic evolution of the new model is more complex than that of the NS model.

No abstract