Accurate state of charge (SOC) is crucial to achieving safe, reliable, and efficient use of batteries. This article proposes an adaptive neural network (NN)-based event-triggered observer to estimate SOC. First, a stochastic battery equivalent circuit model (ECM) is established, where an adaptive NN is employed to approximate the unknown nonlinear part. The learning process of network weight is conducted online to observe the variations of model parameters and avoid time-consuming processes for parameter extraction. Besides, for the purpose of saving computational cost, an event-triggered mechanism (ETM) is employed in the weight updating law, which means the weights only update when it is necessary. Then, an adaptive radial basis function (RBF) NN-based SOC observer is designed, and its stability is proven by the Lyapunov theory. Moreover, the strictly positive lower bound of interevent time is derived, and undesirable Zeno behavior can be excluded. Finally, the accuracy and robustness of the proposed observer are evaluated by experiments and simulations. Results show that the proposed method can estimate SOC accurately in the presence of initial deviation and sensor noises.

Accurate state of charge (SOC) can effectively improve safety performance and prolong the cycle life of the batteries. The widely used model-based SOC estimation methods have underlying assumptions of complete measurements and accurate estimator gains, which are not always reasonable in practical applications. Thus, this article designs a dual Kalman filter-type resilient filter to estimate SOC and parameter jointly with the random missing measurement phenomenon which is modeled by a Bernoulli distributed sequence. Besides, the filter gain variations, in both online parameter identification and state estimation, are characterized by mutually independent multiplicative noise terms. Then, based on the minimum-variance principle, the filter gains are designed to minimize the effects of the missing measurement and gain variations on the estimation performance. Finally, extensive simulations and experiments are conducted to validate the effectiveness and resilience of the proposed method.