The pulsed current has been proposed as a promising battery charging technique to improve the charging performance and maximize the lifetime for lithium-ion (Li-ion) batteries. However, the effect of the pulsed current charging is inconclusive due to the changeable current mode and conditions. This article systematically investigates the effect of various pulsed current charging modes, i.e., positive pulsed current mode, pulsed current-constant current mode, negative pulsed current mode, alternating pulsed current mode, sinusoidal-ripple current mode, and alternating sinusoidal-ripple current mode on battery performance. Moreover, a comprehensive analysis of the frequency impact on the quality of the current mode is performed. The current modes in this work are evaluated considering the maximum rising temperature, discharging capacity, and charging speed according to experimental results. Furthermore, this work provides guidance for developing pulsed current charging strategies to satisfy future charging requirements.

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.

Online parameter identification is vital for boosting the accuracy of the battery equivalent circuit model (ECM) under dynamic profiles. However, traditional recursive least squares (RLS) method easily decays with the noise corruption from sensors or insufficient exciting signal in reality, which further limits the performance of ECM in battery modeling and states estimation. This article thus proposes a reliable online parameter identification method for battery ECM, which utilizes a well-designed information appraisal procedure based on the Fisher-information-based Cramer–Rao lower bound (CRLB). Without increasing much computing complexity, a comprehensive appraisal indicator, derived recursively from CRLB, enables a new mechanism for online parameter updating. Simulation and experimental results prove the validity of the proposed method under different driving cycles, temperatures, and aging conditions. The results show that the identification accuracy of the proposed method has been significantly improved comparing with a typical RLS and a multiple adaptive forgetting factors RLS method.

Current pulses are convenient to be actively implemented by a battery management system. However, the short-term features (STF) from current pulses originate from various sensors with uneven qualities, which hinder one powerful and strong learner with STF for the battery state of health (SOH) estimation. This article, thus, proposes an optimized weak learner formulation procedure for lithium-ion battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto front-based selection strategy is designed to select the representative solutions from the nondominated solutions fed by a knee point driven evolutionary algorithm, which guarantees both the diversity and accuracy of the weak learners. Afterward, the weak learners, whose coefficients are obtained by self-adaptive differential evolution, are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of the SOH estimation. The validation of the proposed method is proved by LiFePO ${_4}$ /C batteries under accelerated cycling ageing test including one mission profile providing primary frequency regulation service to the grid and one constant current profile.

Some studies verified that the pulsed current charging technique could extend the battery lifetime and improve the charging performance of lithium-ion batteries. However, some researchers are skeptical of this opinion because their studies have not found any advantages of pulsed current charging over conventional constant current (CC) charging. Positive pulsed current (PPC), the most common pulsed current mode, was selected for the investigation in this work. The effect of the PPC with various parameters, including the duty cycle, amplitude, and frequency, on the performance and lifetime of lithium-ion batteries, are investigated by experiments. According to the experimental results, the charging speed, charging capacity, and maximum rising temperature are mainly determined by the duty cycle and amplitude of the PPC. The battery lifetime with PPC under the frequency range from 0.05 Hz to 2 kHz has been verified to be extendable. Moreover, the battery lifetime extension can even be over 100% under a specific frequency PPC compared with CC charging. This is the first work that summarizes the law of the pulse frequency on battery lifetime extension. In the future, the pulsed current is expected to be used in fast charging technologies without compromising battery lifetime.