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

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Jinsong Yu , Jie Yang , Diyin Tang , Jing Dai ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.17531/ein.2019.1.10

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.

Related Topics

Particle Swarm Optimization

Electric Battery

Benchmark (Surveying)

Artificial Intelligence

Electrical Engineering

Concepts

Particle swarm optimization Battery (electricity) Benchmark (surveying) Voltage Computer science Process (computing) Lithium-ion battery Degradation (telecommunications) Control theory (sociology) Algorithm Engineering Artificial intelligence Electrical engineering Power (physics) Physics Telecommunications Control (management) Geography Quantum mechanics Operating system Geodesy

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Type: article
Language: en
Landing Page: https://doi.org/10.17531/ein.2019.1.10
PDF: https://doi.org/10.17531/ein.2019.1.10
OA Status: gold
Cited By: 5
References: 32
Related Works: 10
OpenAlex ID: https://openalex.org/W2903851685

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DOI: https://doi.org/10.17531/ein.2019.1.10

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

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Type: article

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Language: en

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Publication year: 2018

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Publication date: 2018-12-19

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Authors: Jinsong Yu, Jie Yang, Diyin Tang, Jing Dai

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Landing page: https://doi.org/10.17531/ein.2019.1.10

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PDF URL: https://doi.org/10.17531/ein.2019.1.10

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Concepts: Particle swarm optimization, Battery (electricity), Benchmark (surveying), Voltage, Computer science, Process (computing), Lithium-ion battery, Degradation (telecommunications), Control theory (sociology), Algorithm, Engineering, Artificial intelligence, Electrical engineering, Power (physics), Physics, Telecommunications, Control (management), Geography, Quantum mechanics, Operating system, Geodesy

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Cited by: 5

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Citations by year (recent): 2025: 1, 2022: 1, 2021: 1, 2019: 2

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abstract_inverted_index.discharge	13, 22, 50, 72
abstract_inverted_index.remaining	12
abstract_inverted_index.accurately	129
abstract_inverted_index.algorithms	106
abstract_inverted_index.available.	117
abstract_inverted_index.discharge.	96
abstract_inverted_index.parameters	64, 79, 91, 111
abstract_inverted_index.prediction	134
abstract_inverted_index.regardless	136
abstract_inverted_index.Correlation	61
abstract_inverted_index.decomposing	48
abstract_inverted_index.degradation	29, 139
abstract_inverted_index.information	18, 42
abstract_inverted_index.literature,	38
abstract_inverted_index.predictions	10
abstract_inverted_index.demonstrates	121
abstract_inverted_index.optimization	100
cited_by_percentile_year.max	96
cited_by_percentile_year.min	89
countries_distinct_count	0
institutions_distinct_count	4
citation_normalized_percentile.value	0.6836608
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False