Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing point in the two-phase model and cannot update the change time in real time. Therefore, this paper proposes a method based on the combination of the two-phase Wiener model and an extreme learning machine (ELM). The two-phase Wiener model is used to derive the mathematical expression of the remaining useful life (RUL), and the ELM is implemented to adaptively detect the changing point. Based on the Poisson distribution, the distribution of the changing time is derived as a gamma distribution. To evaluate the theoretical results and practicality of the proposed method, we perform both numerical and practical simulations. The results of the simulations show that due to the precise and adaptive detection of changing points, the proposed method produces a more accurate RUL prediction than existing methods. The error of our method for detecting the changing point is about 4% and the mean prediction error of RUL in the second phase is improved from 4.39 cycles to 1.61 cycles.

Sequential fault diagnosis is a kind of important fault diagnosis method for large scale complex systems, and generating an excellent fault diagnosis strategy is critical to ensuring the performance of sequential diagnosis. However, with the system complexity increasing, the complexity of fault diagnosis tree increases sharply, which makes it extremely difficult to generate an optimal diagnosis strategy. Especially, because the existing methods need massive redundancy iteration and repeated calculation for the state parameters of nodes, the resulting diagnosis strategy is often inefficient. To address this issue, a novel fast sequential fault diagnosis method is proposed. In this method, we present a new bottom-up search idea based on Karnaugh map, SVM and simulated annealing algorithm. It combines failure sources to generate states and a Karnaugh map is used to judge the logic of every state. Eigenvalues of SVM are obtained quickly through the simulated annealing algorithm, then SVM is used to eliminate the less useful state. At the same time, the bottom-up method and cost heuristic algorithms are combined to generate the optimal decision tree. The experiments show that the calculation time of the method is shorter than the time of previous algorithms, and a smaller test cost can be obtained when the number of samples is sufficient.