Abstract
In this paper, we propose a method for making early predictions of remaining discharge time (RDT) that considers information about future battery discharge process. Instead of analyzing the entire degradation process of a battery, as in the existing literature, we obtain the information about future battery condition by decomposing the discharge model into three stages, according to level of voltage loss. Correlation between model parameters at the first and last stages of discharge process allows the values of model parameters in the future to be used to predict the value of parameters at early stages of discharge. The particle swarm optimization (PSO) and particle filter (PF) algorithms are employed to update parameters when new voltage data is available. A case study demonstrates that the proposed approach predicts RDT more accurately than the benchmark PF-based prediction method, regardless of the degradation period of the battery.
Highlights
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
We take the case of RW3 as an example of remaining discharge time (RDT) prediction using our method, here
To express the uncertainty in our RDT predictions, we provide the probability density function (PDF) with 95% confidence bounds for the three discharge cycles
Summary
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. Prediction of remaining discharge time (RDT) is crucial to battery health management and system stability. 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
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