RESEARCH PAPER
Early prediction of remaining discharge time for lithium-ion batteries considering parameter correlation between discharge stages
| 1 | School of Automation Science and Electrical Engineering Beihang University No.37 Xueyuan Road, Haidian District, Beijing 100191, China Collaborative Innovation Center of Advanced Aero-Engine, Beijing 100191, China |
| 2 | School of Automation Science and Electrical Engineering Beihang University No.37 Xueyuan Road, Haidian District, Beijing 100191, China |
| 3 | China Academy of Launch Vehicle Technology R&D center NO.1 Nan Da Hong Men Road, Feng Tai District, Beijing 100191, China |
Publication date: 2019-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(1):81–89
KEYWORDS
Lithium-ion batteryparameter correlationparticle swarm optimizationparticle filterremaining discharge time prognostics
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.
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