The rapid development of battery technology has promoted the deployment of electric vehicles (EVs). To ensure the healthy and sustainable development of EVs, it is urgent to solve the problems of battery safety monitoring, residual value assessment, and predictive maintenance, which heavily depends on the accurate state-of-health (SOH) estimation of batteries. However, many published methods are unsuitable for actual vehicle conditions. To this end, a data-driven method based on the random partial charging process and sparse Gaussian process regression (GPR) is proposed in this article. First, the random capacity increment sequences (△Q) at different voltage segments are extracted from the partial charging process. The average value and standard deviation of △Q are used as features to indicate battery health. Second, correlation analysis is conducted for three types of batteries, and high correlations between the features and battery SOH are verified at different temperatures and discharging current rates. Third, by using the proposed features as inputs, sparse GPR models are constructed to estimate the SOH. Compared with other data-driven methods, the sparse GPR has the highest estimation accuracy, and its average maximum absolute errors are only 2.88%, 2.52%, and 1.51% for three different types of batteries, respectively.

State of health (SOH) is critical to evaluate the life expectancy of lithium-ion battery (LIB), thus should be estimated accurately in practical applications. This paper proposes a computationally efficient model-based method for SOH estimation of LIB. A revised Lorentzian function-based VC (RL-VC) model is exploited to accurately capture the voltage plateaus of LIB which reflect the material-level phase transition phenomenon. A full set of new features of interest (FOIs) are extracted by simply fitting the RL-VC model leveraging data collected from the constant-current charging process. Correlation analysis is then performed for the captured FOIs, based on which linear models are calibrated to estimate the battery SOH. The proposed method is validated with experimental data from different battery chemistries. Results show that the extracted FOIs have high linearities with the battery capacity, suggesting a good potential for SOH estimation and better feasibility over traditionally-used methods. The proposed method shows a high accuracy for battery SOH estimation and an expected robust performance against the initial aging status and practical cycling condition.

The accurate estimation of the state-of-health (SOH) is vital to the life management of lithium-ion batteries (LIBs). In this article, we propose a fusion-type SOH estimation method by combining the model-based feature extraction and data-based state estimate. Particularly, a novel model-based voltage construction method is proposed to eliminate the unfavorable numerical condition and reshape the disturbance-free incremental capacity (IC) curves. Leveraging the modified IC curves, a set of informative features-of-interest is extracted and evaluated, while eventually several cautiously selected ones are used to estimate the SOH of LIBs accurately. Furthermore, the impact of model order on the estimation performance is scrutinized to give insights into the parameterization in practical applications. Long-term cycling tests on different types of LIB cells are used for evaluation. The proposed method is validated with a good robustness to the cell inconsistency, temperature uncertainty, noise corruption, and a satisfied generality to different battery chemistries.

The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries. A novel fusion-based SOH estimator is proposed in this study, which combines an open circuit voltage (OCV) model and the incremental capacity analysis. Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOIs) from the measured terminal voltage during constant-current charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness.

The state of charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This paper proposes a novel method for online SOC estimation which manifest itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure a high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early-stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.