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

Abstract

Summary 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.

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

“…Advances in lithium‐ion battery technology make it ubiquitous in battery powered devices, ranging from electric terrestrial and aerial vehicles to consumer electronics. Therefore, battery health assessment becomes a crucial problem in providing safety and reliability of the system 1,2 …”

Challenges in data‐based degradation models for lithium‐ion batteries

“…Several DBM-AT models have been proposed for both calendar aging and cycling aging modes and different cell chemistries, mostly using the capacity fade as a health indicator [11][12][13][14][15][16][17] ; we will turn our attention first to calendar aging. Schmalstieg et al 18 performed calendar aging tests on 2.15-Ah lithium nickel manganese cobalt (NMC) oxide cells, and they fitted the data of capacity loss as a function of temperature, SOC, and time using a set of quadratic equations.…”

An online state of health estimation method based on battery management system monitoring data

“…Therefore, this method is difficult to apply in EVs. Besides, there are some machine learning methods represented by Support Vector Machines, Fuzzy Logic, Gaussian Process Regression, and Neural Networks . This method does not need a battery model that characterizes the complex internal principles of battery operation .…”