Data‐driven lithium‐ion battery states estimation using neural networks and particle filtering

Abstract

The state of charge and state of health estimations are two of the most crucial functions of a battery management system, which are the quantified evaluation of driving mileage and remaining useful life of electric vehicles. This paper investigates a novel data-driven–enabled battery states estimation method by combining recurrent neural network modeling and particle-filtering–based errors redress. First, a recurrent neural network with long-short time memory is employed to learn the long-term nonlinear relation between batteries states and measurable signals of lithium-ion batteries, such as current, voltage, and temperature. Second, to denoise the estimation errors of the neural network model, particle filtering is employed to smooth the state of charge estimation results. Third, the terminal voltage difference of battery is highly related to the internal resistance of the battery, which is thus taken as a new input to track the internal resistance of the battery. The performance of the proposed method is verified by multiple comparisons with conventional techniques under randomized loading profiles and different temperatures.