A state-of-charge (SOC) versus open-circuit-voltage (OCV) model developed for batteries should preferably be simple, especially for real-time SOC estimation. It should also be capable of representing different types of lithium-ion batteries (LIBs), regardless of temperature change and battery degradation. It must therefore be generic, robust and adaptive, in addition to being accurate. These challenges have now been addressed by proposing a generalized SOC-OCV model for representing a few most widely used LIBs. The model is developed from analyzing electrochemical processes of the LIBs, before arriving at the sum of a logarithmic, a linear and an exponential function with six parameters. Values for these parameters are determined by a nonlinear estimation algorithm, which progressively shows that only four parameters need to be updated in real time. The remaining two parameters can be kept constant, regardless of temperature change and aging. Fitting errors demonstrated with different types of LIBs have been found to be within 0.5%. The proposed model is thus accurate, and can be flexibly applied to different LIBs, as verified by hardware-in-the-loop simulation designed for real-time SOC estimation.

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.

To enhance the operational reliability and safety of electric vehicles (EVs), big data platforms for EV supervision are rapidly developing, which makes a large quantity of battery data available for fault diagnosis. Since fault types related to lithium-ion batteries play a dominant role, a comprehensive fault diagnosis method is proposed in this paper, in pursuit of an accurate early fault diagnosis method based on voltage signals from battery cells. The proposed method for battery fault diagnosis mainly includes three parts: variational mode decomposition in the signal analysis part to separate the inconsistency of cell states, critical representative signal feature extraction by using a generalized dimensionless indicator construction formula and effective anomaly detection by sparsity-based clustering. The signal features of the majority of signal-based battery fault detection studies are found to be particular cases with a specific set of parameter values of the proposed indicator construction formula. With the sensitivity and stability balanced by appropriate moving-window size selection, the proposed signal-based method is validated to be capable of earlier anomaly detection, false-alarm reduction, and anomalous performance identification, compared with traditional approaches, based on actual pre-fault operating data from three different situations.

Lithium ion (Li-ion) batteries work as the basic energy storage components in modern railway systems, hence estimating and improving battery efficiency is a critical issue in optimizing the energy usage strategy. However, it is difficult to estimate the efficiency of lithium ion batteries accurately since it varies continuously under working conditions and is unmeasurable via experiments. This paper offers a learning-based simulation method that employs experimental data to estimate the continuous-time energy efficiency and coulombic efficiency of lithium ion batteries, taking lithium titanate batteries as an example. The state of charge (SOC) regions and discharge current rates are considered as the main variables that may affect the efficiencies. Over eight million empirical datasets are collected during a series of experiments performed to investigate the efficiency variation. A back propagation (BP) neural network efficiency estimation and simulation model is proposed to estimate the continuous-time energy efficiency and coulombic efficiency. The empirical data collected in the experiments are used to train the BP network model, which reveals a test error of 10⁻⁴. With the input of continuous SOC regions and discharge currents, continuous-time efficiency can be estimated by the trained BP network model. The estimated and simulated result is proven to be consistent with the experimental results.

Electrolyte leakage may cause lithium-ion battery performance degradation, and even lead to short-circuit, resulting in serious safety accidents. In order to improve the safety of lithium-ion battery, it is necessary to detect electrolyte leakage in time. This paper presents a fault diagnosis method for electrolyte leakage of lithium-ion based on support vector machine (SVM) by electrochemical impedance spectroscopy (EIS) test. And the distribution of relaxation time (DRT) method is also employed to analyze the effect of leakage on the dynamic reaction process with full and half cells. In the proposed method, the EIS and DRT features are all used for the diagnosis to improve accuracy. Furthermore, the sensitivity of different features, the accuracy of battery classification under different cycles and state of charge were also analyzed to reduce diagnostic time by decreasing the test frequency range and SOC points and then validate the feasibility of the method.