An Improved Method Based on Support Vector Regression With Application Independent Training for State of Charge Estimation
Author:
Affiliation:
1. Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, Parma, Italy
Funder
Project “Advanced Approaches for Batteries State of Charge (SoC) Evaluation,”
2021 by Programme “Fondo Incentivante Locale (FIL)-Quota Incentivante” of University of Parma
Fondazione Cariparma
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/19/10012124/10225394.pdf?arnumber=10225394
Reference51 articles.
1. Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
2. A strong tracking adaptive fading‐extended Kalman filter for the state of charge estimation of lithium‐ion batteries
3. State of Charge Estimation by Joint Approach With Model-Based and Data-Driven Algorithm for Lithium-Ion Battery
4. Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review
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1. An Optimized Long Short Term Memory and Gaussian Process Regression Based Framework for State of Charge Estimation;2024 IEEE International Workshop on Metrology for Automotive (MetroAutomotive);2024-06-26
2. FPGA Implementation of Support Vector Regression for Battery SoC Estimation;2024 IEEE International Workshop on Metrology for Automotive (MetroAutomotive);2024-06-26
3. Battery Management System for Electric Garbage Compactor Trucks;IEEE Access;2024
4. Robust SOC Estimation of Li-Ion Battery Combining Gated Recurrent Unit and Square Root Ckf with Gmcc for Electric Vehicle Applications;2024
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