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The lithium-ion battery is a popular power source and is commonly used in a range of applications such as portable electronic devices and electric vehicles. Compared with other widely used energy sources, the lithium-ion battery possesses high energy density, high power density, long service life, and is environmentally friendly [14]. Early prediction of remaining discharge time (RDT) is crucial to battery health management and system stability. If a battery runs out without timely charging, it is harmful to battery health and longevity and can sometimes lead to system failure, or even precipitate a disaster. In most previous studies of RDT prediction, methods have utilized state-of-charge (SOC) and state-of-energy (SOE) as the indicators that announce the end of discharge, e.g., [6,32,12,28,11]. When the SOC or the SOE of a battery reaches a certain level, the battery is considered to have run out of power. In these methods, accurate values for SOC and SOE are of vital importance in RDT prediction. However, for most real applications, accurate estimation of SOC or SOE is difficult, for they are indirectly measured and require the introduction of additional relevant variables for estimation [17]. Therefore, some research has resorted to using more easily measured variables in RDT prediction, such as battery output voltage. Saha et al. [20, 22] and Dalal et al. [4] used battery output voltage to predict RDT in a lumped parameter battery model with particle filter, and Orchard et al. [16] used it in an empirical state-space model with sequential Monte Carlo. After indicators are selected, the usual way of predicting RDT in existing literature is through a physical model and a filtering-based method that updates the model parameters when observations become available. Particle filtering (PF) is a commonly used filtering-based method. PF is an entirely nonlinear state estimator based on probability that can be used to update battery model states and parameters based on new voltage data [34,15]. However, although PF can provide Jinsong Yu Jie YAng Diyin TAng Jing DAi

Early prediction of remaining discharge time for lithium-ion batteries considering parameter correlation between discharge stages