ArticleScientific reports2024
Enhanced SOC estimation of lithium ion batteries with RealTime data using machine learning algorithms.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The trial behind it
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Who cites it
12 citing papers in PubMed.
- Multi-scale temporal convolution attention network for state-of-charge estimation in Li-ion batteries.Scientific reports · 2026Article
- Adaptive reinforcement learning framework for sustainable microgrid optimization in arid urban environments.Scientific reports · 2026Article
- Uncertainty aware hybrid learning framework for fast and safe charging of lithium-ion batteries using multi-fidelity observers.Scientific reports · 2026Article
- NBE-VLT-PFO: hybrid deep learning transformer architecture for joint estimation of lithium-ion batteries.Frontiers in artificial intelligence · 2026Article
- Temporal-aware transformer networks for state of charge multi-output prediction in unmanned aerial vehicle lithium-ion batteries.Scientific reports · 2025Article
- Multistage prediction approach of EVs charging performance in smart transportation systems by deep learning technique.Scientific reports · 2025Article
- CNN-LSTM optimized with SWATS for accurate state-of-charge estimation in lithium-ion batteries considering internal resistance.Scientific reports · 2025Article
- State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms.Sensors (Basel, Switzerland) · 2025Article
- Energy consumption prediction of PEVs incorporating traffic flow information.Scientific reports · 2025Article
- SOC estimation for a lithium-ion pouch cell using machine learning under different load profiles.Scientific reports · 2025Article
- Generative Adversarial Network for Synthesizing Multivariate Time-Series Data in Electric Vehicle Driving Scenarios.Sensors (Basel, Switzerland) · 2025Article
- Advanced battery management system enhancement using IoT and ML for predicting remaining useful life in Li-ion batteries.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Accurately estimating Battery State of Charge (SOC) is essential for safe and optimal electric vehicle operation. This paper presents a comparative assessment of multiple machine learning regression algorithms including Support Vector Machine, Neural Network, Ensemble Method, and Gaussian Process Regression for modelling the complex relationship between real-time driving data and battery SOC. The models are trained and tested on extensive field data collected from diverse drivers across varying conditions. Statistical performance metrics evaluate the SOC prediction accuracy on the test set. Gaussian process regression demonstrates superior precision surpassing the other techniques with the lowest errors. Case studies analyse model competence in mimicking actual battery charge/discharge characteristics responding to changing drivers, temperatures, and drive cycles. The research provides a reliable data-driven framework leveraging advanced analytics for precise real-time SOC monitoring to enhance battery management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.