Yupu Chen

This work proposes a centralized data-based orderly charging strategy that considers the user’s charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimization model not only considers the demand of the grid side and the user side, but also takes the driving data of electric vehicles as the driver. The grid-side optimization involves minimizing the equivalent load fluctuation, and the user-side is optimized to minimize the charging cost and maximize the charging electric quantity. The scheduling capabilities of the three charging strategies are analyzed based on a series of driving data of electric vehicles. The results show that the peak-valley difference and equivalent load fluctuation of the power grid in the data-based orderly charging strategy reduced by 22.2% and 22.7%, respectively, and the charging cost of users also reduced much more than the other two charging strategies. Additionally, the effect of users’ charging choices on the charging strategy is analyzed, and the results show that the orderly charging strategy that considers users’ charging choices can effectively decrease the scheduling deviation caused by users’ charging choices. It greatly improves the security and economy of the grid.

Lithium-ion battery plays a key role in electric vehicles, which is critical to the system availability. One of the most important aspects in battery managements systems(BMS) in electric vehicles is the stage of health(SOH) estimation. The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. The classical approach of current integration(coulomb counting) can't get the accurate values because of accumulative error. In order to provide timely maintenance and replacements of electric vehicles, several estimation approaches have been proposed to develop a reliable and accurate battery state of health estimation. A common drawback of previous algorithm is that the computation quantity is huge and not quite accurate, that is updated partially in this study. In this paper, a training strategy is diagrammed and models based on autoregressiveparticle filter (AR-PF) are developed with novel parameters to provide better prediction of electric vehicle state of health. The estimations are compared to measurements using autoregressive algorithm and particle filter respectively. The proposed training strategy achieves a better prediction precision than other strategies used in this paper. Battery experimental data and AR-PF predicted value are presented in X-Y axis figure as well as other two algorithms in this paper that we can observe them directly. Experiments based on NASA battery data set show that the proposed method yields a good performance in SOH estimation of Lithium-ion battery. The results showed that the health condition parameters accurately reflected the real time SOH of the battery and ensured that an accurate capacity estimation can be calculated.