.png){kind=icon}
返回
Advanced parameter estimation for lithium-ion battery model using the information sharing group teaching optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpowsour.2024.235615.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate battery modeling is crucial for optimizing the performance and safety of Lithium-ion batteries (LiBs), particularly in applications such as electric vehicles and smart grids. This paper introduces the Information Sharing Group Teaching Optimization Algorithm (ISGTOA), a novel human-based metaheuristic algorithm designed to estimate the 21 parameters of third-order Equivalent Circuit Model (ECM) for LiBs. Unlike existing methods, ISGTOA effectively manages the increased complexity of a model through advanced group teaching strategies and sophisticated information-sharing mechanisms inspired by classroom dynamics. We validated the effectiveness of ISGTOA using two distinct datasets-High Dynamic Profile (HDP) and Urban Dynamometer Driving Schedule (UDDS)-across various LiB chemistries (NCA and NMC) and temperature conditions (-5 degrees C, 25 degrees C and 45 degrees C). The results demonstrate that ISGTOA achieves superior parameter estimation accuracy and convergence speed compared to established and recent optimization methods such as TLBO, TLSBO, AISA, MTBO, and DTBO. Specifically, ISGTOA attained a minimum RMSE of 8.357 mV with rapid convergence and high stability in the HDP dataset and minimized RMSE to 10 mV within ten iterations at both 25 degrees C and 45 degrees C in the UDDS dataset. Additionally, ISGTOA exhibited high efficiency (up to 97.75 %) under varying thermal environments. This work not only introduces a novel algorithm for complex battery modeling but also sets the stage for future advancements in real-time battery management systems, emphasizing the importance of robust, versatile, and efficient parameter estimation techniques.
Keyword:
Group teaching optimization algorithm
Information sharing
Li-ion battery
Equivalent circuit model
Urban dynamometer driving schedule
Parameter identification
期刊
IF:
7.9
论文数:
3.7W
被引数:
15.0W
机构
引用论文
分享收藏Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries改进抗噪声自适应长短期记忆神经网络建模的锂离子电池鲁棒剩余寿命预测
OAAI
OAAI分享收藏Parameter identification for lithium batteries: Model variable-coupling analysis and a novel cooperatively coevolving identification algorithm锂电池的参数辨识: 模型变量耦合分析和新型的协同进化辨识算法.png)
ENERGY
ENERGYIF9.4
PREAI
分享收藏Parameter identification and state of charge estimation for lithium-ion batteries under wide temperature range宽温度范围下锂离子电池参数辨识与荷电状态估计IONICS
IONICSIF2.6
PREAI
分享收藏Battery technologies and functionality of battery management system for EVs: Current status, key challenges, and future prospectives电动汽车电池管理系统的电池技术和功能: 现状，关键挑战和未来前景
PREAI
分享收藏Adaptive model parameter identification for lithium-ion batteries based on improved coupling hybrid adaptive particle swarm optimization- simulated annealing method基于改进耦合混合自适应粒子群-模拟退火法的锂离子电池模型参数自适应辨识
PREAI
分享收藏Enhancing parameter identification and state of charge estimation of Li-ion batteries in electric vehicles using an improved marine predators algorithm使用改进的海洋捕食者算法增强电动汽车锂离子电池的参数识别和荷电状态估计
PREAI
分享收藏Optimization of capacitance in supercapacitors by constructing an experimentally validated hybrid artificial neural networks-genetic algorithm framework通过构建实验验证的混合人工神经网络-遗传算法框架来优化超级电容器中的电容
PREAI
分享收藏An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation考虑电流-电压-温度变化的锂离子电池全生命周期荷电状态预测的改进前馈-长短期记忆建模方法ENERGYOAAI
ENERGYIF9.4
OAAI分享收藏
查看更多内容