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.

In this paper, a bias-correction least-squares (LS) algorithm is proposed for identifying block- oriented errors-in-variables nonlinear Hammerstein (EIV- Hammerstein) systems. Because both the input and output of the EIV-Hammerstein system are observed with additive white noises, the estimation bias of traditional LS algorithm is introduced. The estimation bias is derived from a consistency point of view, which is a function about noise variances and monomial of noiseless system input–output measurements. A bias-estimation scheme based only on the available noisy measurements is then proposed for consistent identification of the monomial of noiseless system input–output measurements in a recursive form. In particular, a specific algorithm based on minimizing the output prediction error is given to find out the unknown noise variances for practical applications, such that the noise effect can be eliminated and the consistent estimated parameters are obtained. The effectiveness of the proposed method is demonstrated by a simulation example and an experimental prototype of wireless power transfer system.

A gray-box parsimonious subspace identification method using sampled dynamical and steady-state data is proposed for block-oriented Hammerstein-type systems. Compared with the conventional subspace identification methods based on over-parameterized models, the proposed method assumes parsimonious models, where the number of parameters is as minimal as possible to ensure the model accuracy, especially for highly nonlinear models. Generally, the available dynamical data do not contain adequate information on the low-frequency characteristics of the system. To improve the accuracy of mode identification, the steady-state information of the system is taken into account, and a multi-regularization method is developed, where the whole model parameters are estimated in a hierarchical, iterative manner from two sets of data: the dynamical input-output data and the steady state input-output data. The use of parsimonious models and hierarchical estimation can significantly reduce the size the associated parameter estimation error covariance matrix, thus improving the model accuracy and decreasing the variance of the estimated parameters, compared with the over-parametrization methods only using dynamica data. The effectiveness and merits are demonstrated by a simulation example and a real-word example.

In this article, a consistent subspace identification method (SIM) is proposed for block-oriented errors-in-variables Hammerstein systems. Due to that the existing SIMs using parity subspace based on noisy measurements may result in biased parameter estimates, we propose a scheme for the consistent system parameter estimation, which estimates the noise-free Hankel matrix using available noisy measurements and noise variances. A 2-D search method is proposed to estimate the unknown noise variances from available noisy measurements. After that consistent estimations of the Hammerstein system parameters can be then retrieved from the estimated noise-free Hankel matrix following the same algorithm framework of the existing SIMs using parity subspace. Two simulation examples are included to support the effectiveness and merits of the proposed method.

In recent years, artificial neural networks (ANNs) have significantly advanced in both health estimation and lifetime prediction of lithium-ion batteries. The great success of ANNs stems primarily from their scalability in encoding large-scale data and manoeuvre billions of model parameters. However, there are still many challenges in balancing predictive accuracy and deployment feasibility. For instance, shallow ANNs are often more efficient but may sometimes sacrifice accuracy, whereas deep hybrid ANNs often achieve strong generalization capabilities, this comes with the trade-off of increased computational demands. To this end, this paper presents a comprehensive survey of ANN-based paradigms for estimating State-of-Health (SOH) and predicting the Remaining-Useful-Life (RUL) of lithium-ion batteries. It covers battery aging mechanisms, available datasets, network architecture, training schemes, advanced machine learning (AML) algorithms, and performance comparison. Furthermore, challenges in battery health diagnosis are reviewed in detail, and comments on future research prospects are discussed and forwarded.