Lithium-ion (Li-ion) batteries have been competitive in Electric Vehicles (EVs) due to their high energy density and long lifetime. However, there are still issues, which have to be solved, related to the fast-charging capability of EVs. The pulsed current charging technique is expected to improve the lifetime, charging speed, charging/discharging capacity, and the temperature rising of Li-ion batteries. However, the impact of the pulsed current parameters (i.e., frequency, duty cycle, and magnitude) on characteristics of Li-ion batteries has not been fully understood yet. This paper summarizes the existing pulsed current modes, which are positive Pulsed Current Mode (PPC) and its five extended modes, and Negative Pulsed Current (NPC) mode and its three extended modes. An overview of the impact of pulsed current techniques on the performance of Li-ion batteries is presented. Then the main impact factors of the PPC strategy and the NPC strategy are analyzed and discussed. The weight of these impact factors on lifetime, charging speed, charging/discharging capacity, and the temperature rising of batteries is presented, which provides guidance to design advanced charging/discharging strategies as well as to determine future research gaps.

Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized.

With the widespread use of Lithium-ion (Li-ion) batteries in Electric Vehicles (EVs), Hybrid EVs and Renewable Energy Systems (RESs), much attention has been given to Battery Management System (BMSs). By monitoring the terminal voltage, current and temperature, BMS can evaluate the status of the Li-ion batteries and manage the operation of cells in a battery pack, which is fundamental for the high efficiency operation of EVs and smart grids. Battery capacity estimation is one of the key functions in the BMS, and battery capacity indicates the maximum storage capability of a battery which is essential for the battery State-of-Charge (SOC) estimation and lifespan management. This paper mainly focusses on a review of capacity estimation methods for BMS in EVs and RES and provides practical and feasible advice for capacity estimation with onboard BMSs. In this work, the mechanisms of Li-ion batteries capacity degradation are analyzed first, and then the recent processes for capacity estimation in BMSs are reviewed, including the direct measurement method, analysis-based method, SOC-based method and data-driven method. After a comprehensive review and comparison, the future prospective of onboard capacity estimation is also discussed. This paper aims to help design and choose a suitable capacity estimation method for BMS application, which can benefit the lifespan management of Li-ion batteries in EVs and RESs.