In this paper, the leader-follower fixed-time consensus problem of networked nonlinear multi-agent systems (NNMASs) with unknown disturbance is investigated. A new event-triggered super-twisting fixed-time sliding mode consensus control (ESFSMCC) method is proposed, enabling all agents to achieve consensus in a fixed time. Firstly, a terminal sliding mode variable is designed to eliminate the convergence time dependence on the initial values of the system and avoid singularity. Secondly, a distributed event-triggered mechanism is developed to effectively reduce inter-agent communication frequency in a networked environment. In contrast to the existing fixed-time consensus control method, the improved super-twisting algorithm is adopted to construct a control protocol to achieve both global fixed-time consensus robustness and alleviate the issue of high-frequency chattering. Thirdly, the Lyapunov theory is utilized without the piecewise sliding mode technique to derive sufficient conditions for establishing the fixed-time stability of NNMASs, which still avoids the singularity problem. In this paper, the non-segmented terminal sliding mode is employed to prove the global fixed-time stability of the system states, thereby avoiding computational complexity. Finally, the effectiveness and advantages of the proposed method are verified through numerical simulations.

The parameters of a lithium-ion battery are important to construct an effective battery management system. Parameter estimation assisted by the pseudo-two-dimensional (P2D) model is much more cost-effective than direct measurement methods. However, this is a nontrivial task, because the simulation of the P2D model is time-consuming. Alternatively, surrogate models such as reduced-order/data-driven models are often used to accelerate the parameter estimation process. Each category of surrogate models has its own strengths and weaknesses. Traditionally, reduced-order models run faster than data-driven models, while data-driven models are more accurate than reduced-order models. To leverage the complementary advantages of these two kinds of surrogate models, we make an interesting attempt to combine them compactly, thus proposing a two-phase surrogate model-assisted parameter estimation algorithm (TPSMA-PEAL). In the first phase, a fast reduced-order model is designed for parameter prescreening. In the second phase, a high-fidelity data-driven model is developed for fine estimation. In TPSMA-PEAL, except the time-consuming simulation, the other two challenges (i.e., the overfitting problem and the low observability of some parameters) are also considered from the perspective of optimization. Note that TPSMA-PEAL takes advantage of differential evolution and parameter sensitivity analysis to address them. Simulations and experiments verify that TPSMA-PEAL is efficient and accurate.

Unknown distributed incipient thermal fault detection and localization are vital to the safe operation of batteries while they have not been given sufficient attention in existing works compared to the studies on estimation of State of Charge (SoC) as well as State of Health (SoH). In order to fill this gap, a backstepping-based fault localization filter (FLF) is presented. Generally, full-state temperature measurement is required to achieve fault localization, which is impossible in applications. However, with the help of interpolation-based approximation, the required number of sensors decreases from infinity to only a few, which guarantees the usability of FLF. A comprehensive methodology framework, including the FLF design, residual evaluation in a distributed manner, and threshold computation, is introduced to guarantee reliable and robust performance in a real-time pattern. Theoretic analysis as well as experiment validations are presented to for validation.