ArticleHeliyon2024
Efficient state of charge estimation in electric vehicles batteries based on the extra tree regressor: A data-driven approach.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- SIFA: A two-stage adaptive ensemble framework for solar irradiance forecasting using a wrapper-based feature selection and chaotic manta ray optimization.Scientific reports · 2026Article
- 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
- An enhanced CNN with ResNet50 and LSTM deep learning forecasting model for climate change decision making.Scientific reports · 2025Article
- Enhanced state of charge estimation in electric vehicle batteries using chicken swarm optimization with open ended learning.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Global warming, a significant outcome of climate change, exerts detrimental effects on the daily lives of individuals and industries. As a result, there is an increased demand for Electric Vehicles (EVs) to reduce carbon emissions contributing to climate change. This shift underscores the critical need for accurate estimation of the State of Charge (SoC) in battery systems, which is essential for optimizing EVs' performance and ensuring effective energy utilization. This paper introduces a methodically constructed and tested SoC prediction model utilizing a comprehensive dataset derived from various driving cycles and battery records. The battery performance of EVs was assessed in our study. The essence of our innovation resides in the meticulous choice of representative driving cycles, effectively replicating real-world conditions. This methodology improves the model's capacity to apply to various driving patterns and conditions. During these cycles, a comprehensive set of battery data, encompassing voltage, current, temperature, and SoC, was systematically documented to facilitate thorough analysis. To achieve superior accuracy and robustness, our predictive model considers the strengths of the Extra Tree Regressor (ETR) and Light Gradient Boosting algorithms. Our experimental results demonstrate the remarkable performance of the ETR model in predicting SoC, surpassing the LightGBM model. The ETR model exhibited higher
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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.