Accelerated aging is a significant issue for various lithium-ion battery applications, such as electric vehicles, energy storage, and electronic devices. Effective early diagnosis is prominent to restrict battery failure. Typical battery classification data-driven methods are structured to capture features from data without considering the underlying aging mechanism. On the other hand, analysis of the detailed aging mechanism that can generate electrochemistry-based models can be highly complicated and may not be suitable for real-time battery management. In this article, the accelerated aging diagnosis method is systematically investigated. The accelerated aging mechanisms of the Li[NiCoMn]O2 (NCM) battery are analyzed by the nondestructive quantitative diagnostic method. We prove the feasibility of accelerated aging diagnosis based on the accelerated aging mechanism analysis. An integrated framework of aging mechanisms and data-driven methods (IFAMDM) is introduced for lithium-ion battery-accelerated aging diagnosis. Highly adaptable features reflecting the accelerated aging mechanism are proposed for lithium-ion battery-accelerated aging. Then, we propose a combination method to diagnose battery-accelerated aging. The IFAMDM was verified on two types of battery datasets. The IFAMDM is proved to be highly generic and accurate for lithium-ion battery-accelerated aging diagnosis at the 100th cycle.

Early prediction of aging trajectories of lithium-ion (Li-ion) batteries is critical for cycle life testing, quality control, and battery health management. Although data-driven machine learning (ML) approaches are well suited for this task, unfortunately, relying solely on data is exceedingly time-consuming and resource-intensive, even in accelerated aging with complex aging mechanisms. This challenge is rooted in the highly complex and time-varying degradation mechanisms of Li-ion battery cells. We propose a novel method based on physics-guided machine learning (PGML) to overcome this issue. First, electrode-level physical information is incorporated into the model training process to predict the aging trajectory’s knee point (KP). The relationship between the identified KP and the accelerated aging behavior is then explored, and an aging trajectory prediction algorithm is developed. The prior knowledge of aging mechanisms enables a transfer of valuable physical insights to yield accurate KP predictions with small data and weak correlation feature relationship. Based on a Li[NiCoMn]O ₂ cell dataset, we demonstrate that only 14 cells are needed to train a PGML model for achieving a lifetime prediction error of 2.02% using the data of the first 50 cycles. In contrast, at least 100 cells are needed to reach this level of accuracy without the physical insights.

Battery life is of critical importance for the reliable and economical operation of electric vehicles (EVs). Normal aging accounts for more than 80% of the battery available cycle range. Accurate and robust battery life models of normal aging are essential for battery health management systems and life evaluation before accelerated aging. Capacity recovery, test errors and accelerated aging all affect life model building during normal aging. Therefore, this paper proposes an improved life model based on wavelet transform (WT) signal processing to accurately predict the decline trend of the battery in the normal aging stage. In this paper, the capacity recovery, test noise and capacity diving in the aging trend are effectively removed by wavelet transform. We obtained an optimized life model through the analysis of the model structure and the analysis of the parameter sensitivity of the life model. The particle swarm algorithm (PSO) is employed to identify the parameters of the empirical models with the normal aging data extracted by the WT. Through verification, it is found that the modified cycle life model proposed in this paper can accurately predict the normal aging trajectory of batteries under different discharge rates and temperatures. The prediction error of the improved life model for normal aging is 1.09%.

The identification of knee points in lithium-ion (Li-ion) batteries is crucial for predicting the battery life, designing battery products, and managing battery health. Knee points (KPs) refer to the transition points in the aging speed and aging trajectory of Li-ion batteries. KPs can be identified using a wealth of aging data and various regression-based methods. However, KP identification relies on a large amount of aging data, which is exceedingly time-consuming and resource-intensive. To overcome this issue, we propose a novel method based on KP characteristics to identify the KPs and critical aging speed. Firstly, we extract the main aging trajectory using curve-fitting techniques. Secondly, we calculate the aging speed at each cycle to identify the KPs. We then explore the relationship between the KPs and cycle life and develop a knee point identification algorithm. The correlation coefficient between the KPs and cycle life provides a valuable indicator of the critical aging speed, enabling accurate identification of KPs. To validate our approach, we apply it to the Li(NiCoMn)O₂, LiFePO₄, and LiCoO₂ cell datasets. Our results demonstrate a strong correlation between the KPs and cycle life for these battery types. By employing our proposed method, KPs can be identified for battery life prediction, product design, and health management. Moreover, we summarize a critical degradation speed of −0.03%/cycle can serve as an empirical threshold for warning against capacity diving and KPs. The statistical transition speed threshold can eliminate the dependence on extensive aging data throughout the entire battery’s lifecycle for identifying capacity knee points.