Accurate prediction of the remaining useful life for lithium‐ion battery is beneficial to prolong the life of the battery and increase safety. With the capacity degradation curve obtained from the data of the battery charge and discharge experiment, the remaining useful life of the battery was predicted by using particle filter. In order to improve the prediction accuracy, the particle filter with residual resampling method is used to overcome the lack of particle diversity which has an important effect on the accuracy of state estimation. Compared with the prediction result of the extended Kalman filter, it was found that the precision and stability of particle filter are better than those of extended Kalman filter. The research results presented in this paper provide some suggestions for the health monitoring of power battery for electric vehicles.
Adaptive cruise control (ACC) is an important part of the advanced driver assistance system (ADAS), which aims to improve the user’s driving experience by reducing their manual operation and optimizing the vehicles’ performance such as economy, comfort, and safety. With the gradual maturity of research on ACC, it presents various development trends. Among them, ecological adaptive cruise control (EACC) has great development potential in reducing emissions and improving driving range in internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), and electric vehicles (EVs). This paper firstly reviews the components of classic ACC. Then the main design methods, research hotspots, and development trends of EACC for HEVs and EVs are introduced in detail. This paper not only provides a comprehensive analysis of EACC design and application in different type of vehicles but also forecasts the future research direction and trend, expecting to promote the further development of EACC efficiently.
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