Battery state-of-charge estimation using data-driven Gaussian process Kalman filters-Reference-Cited by-同舟云学术

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Battery state-of-charge estimation using data-driven Gaussian process Kalman filters

Published:2023-11 Issue: Volume:72 Page:108392
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

Lee Kwang-Jae^ORCID,Lee Won-Hyung,Kim Kwang-Ki K.^ORCID

Funder

National Research Foundation of Korea

Ministry of Education

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference35 articles.

1. Optimal ecodriving control: Energy-efficient driving of road vehicles as an optimal control problem;Sciarretta;IEEE Control Syst.,2015

2. Service-oriented real-time energy-optimal regenerative braking strategy for connected and autonomous electrified vehicles;Kim;IEEE Trans. Intell. Transp. Syst.,2022

3. Gaussian process approximate dynamic programming for energy-optimal supervisory control of parallel hybrid electric vehicles;Bae;IEEE Trans. Veh. Technol.,2022

4. Economic nonlinear predictive control for real-time optimal energy management of parallel hybrid electric vehicles;Kim;IEEE Access,2020

5. M. Faraj, O. Basir, Range anxiety reduction in battery-powered vehicles, in: IEEE Transportation Electrification Conference and Expo, ITEC, 2016, pp. 1–6.

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2. An improved particle swarm optimization-cubature Kalman particle filtering method for state-of-charge estimation of large-scale energy storage lithium-ion batteries;Journal of Energy Storage;2024-10

3. State of charge estimation for lithium-ion batteries based on gate recurrent unit and unscented Kalman filtering;Ionics;2024-09-10

4. Research on lithium battery state of charge estimation based on improved Adaptive Iterative eXogenous Kalman Filter and multidimensional estimation scheme;Journal of Energy Storage;2024-09

5. Multi-scenario surface temperature estimation in lithium-ion batteries with transfer learning and LGT augmentation;Energy;2024-09

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