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A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are often not caused by easily-observed cell state inconsistency, but derived from a certain cell failure with precursory signals untended, or occasional abuse, thus eventually thermal runaway. In this paper, a signal-based fault diagnosis method is presented, including signal analysis to eliminate the impact of state inconsistency on time-series feature extraction, feature fusion, and dimensionality reduction by manifold learning, with clustering-based outlier detection to identify abnormal signal features. The challenges in threshold determination of fused features can be effectively resolved by supplementary correction to largely reduce the amount of false alarms. Compared with the judgments from actual battery management systems, and other signal-based methods with single features, earlier detections can be achieved with robustness, verified by real-world pre-fault operation data of electric vehicles that suffered thermal runaway.

An improved method for the remaining useful life (RUL) prognostic of Lithium-ion batteries with Li(NiMnCo)O 2 cathode using improved unscented particle filter (UPF) is proposed with respect to capacity diving in later capacity degradation curve. Key points of this paper are: (1) An appropriate empirical model for the situation as the most contributive work, is put forward as an alternative to the widely used UPF models, and the prediction performance is respectively verified by least square fitting and the improved UPF; (2) Systematic noise in Gamma distribution is attempted in state space equations of the proposed method, so as to avoid potential shape shifting of the prediction curve after sampling the particles with Gaussian noise, for model parameters could get zero-crossed; (3) With training data preprocessed considering the capacity recovery phenomenon concisely, the residual error and root mean square error of fitting could get further reduced, as a supplement to traditional treatments like smoothing, thus relieving the sensitivity of datadriven methods to data by enhancing quality. Validations are implemented by applying the proposed method to the battery data by conducting cycle aging tests under different working conditions, where improved approximation and prediction performance can be obtained. INDEX TERMS Lithium-ion battery, remaining useful life, state of health, unscented particle filter, capacity diving phenomenon.

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