Smart batteries will penetrate all corners of the new energy field represented by Li-ion batteries. Smart batteries exhibit higher power performance, safety and durability.

During the usage of electric vehicles, the battery decays, and the cell variations expand in the battery pack. In the discharge process, both the low-capacity cell and the micro-short-circuit (MSC) cell have the abnormal feature that the SOC (state of charge) differences increase continuously. Hence, a low-capacity cell is likely to be misdiagnosed as a micro-short-circuit cell, and vice versa. In this paper, a fault identification approach based on the mutual information is proposed to detect the MSC cell and low-capacity cell. A decision tree for fault identification is established by analyzing the battery fault characteristics of the short circuit, low capacity and the abnormity of initial SOC difference. It is pointed out that the SOC deviation of the low-capacity cell is related to the mean SOC, while the MSC cell is related to time. A low-pass filter is used to get internal resistance differences in order to achieve the SOC deviations based on the cell different model. Finally, the MSC cell and the low-capacity cell can be identified using the mutual information which can quantitatively calculate the correlation between the SOC deviation and the mean SOC. Experimental results prove that the proposed method is reliable to identify the micro-short-circuit cell and the low-capacity cell.

Lithium-ion batteries on electric vehicles have been increasingly deployed for the enhancement of grid reliability and integration of renewable energy, while users are concerned about extra battery degradation caused by vehicle-to-grid (V2G) operations. This paper details a multi-year cycling study of commercial 24 Ah pouch batteries with Li(NiMnCo)O₂ (NCM) cathode, varying the average state of charge (SOC), depth of discharge (DOD), and charging rate by 33 groups of experiment matrix. Based on the reduced freedom voltage parameter reconstruction (RF-VPR), a more efficient non-intrusive diagnosis is combined with incremental capacity (IC) analysis to evaluate the aging mechanisms including loss of lithium-ion inventory and loss of active material on the cathode and anode. By analyzing the evolution of indicator parameters and the cumulative degradation function (CDF) of the battery capacity, a non-linear degradation model with calendar and cyclic aging is established to evaluate the battery aging cost under different unmanaged charging (V0G) and V2G scenarios. The result shows that, although the extra energy throughput would cause cyclic degradation, discharging from SOC 90 to 65% by V2G will surprisingly alleviate the battery decaying by 0.95% compared to the EV charged within 90–100% SOC, due to the improvement of calendar life. By optimal charging strategies, the connection to the smart grid can potentially extend the EV battery life beyond the scenarios without V2G.

Lithium dendrites, with their high reactivity, pose a critical challenge to the safety and longevity of lithium-based batteries. Effective regulation strategies are crucial for mitigating battery degradation and enhancing reliability. Conventional approaches, such as relaxation following lithium plating or regulated discharging, often fail to simultaneously address the formation of solid electrolyte interface and isolated lithium. Here, we demonstrate that rational utilization of electric field relaxation following dendrite growth can reduce defective solid electrolyte interface and isolated lithium by balancing solid electrolyte interface growth and dendrite morphology smoothing near the relaxation time constant. Building upon the mechanism, we propose a short-term relaxation method to manipulate lithium plating, which achieves an enhancement of capacity retention from 80% up to 95% at 3 C-rate (20 min) fast-charging on commercial batteries. These findings highlight the importance of relaxation after dendrites formation for safe, long-life and fast-charging batteries, particularly where dendrite growth is the limiting factor.

This review presents a thorough investigation of factors affecting fast charging, battery modeling, key state estimation and fast charging control strategies and provides a forward-looking perspective on AI enabled fast charging technology of LIBs.

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.