Lithium-ion batteries SOE estimation via SSA-optimized transformer coupled with extended Kalman filter

IF 2.6 4区 化学 Q3 CHEMISTRY, PHYSICAL
Ionics Pub Date : 2025-06-05 DOI:10.1007/s11581-025-06415-w
Yikun Li, Jinghan Bai, Linqi Zhu, Lu Lv, Lujun Wang
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引用次数: 0

Abstract

State of Energy (SOE) represents one of the most critical state parameters in battery management systems. Due to its inherent nonlinear characteristics, accurate estimation of SOE remains a significant challenge in this domain. This research proposes a novel lithium-ion battery SOE estimation methodology that integrates a Transformer network optimized by sparrow search algorithm (SSA-Transformer) with extended Kalman filter (EKF). The SSA algorithm, characterized by its unique producer-scout mechanism and defensive awareness behavior simulation, exhibits superior performance in global search capability and convergence efficiency. In comparison with the conventional particle swarm optimization (PSO) algorithm, SSA demonstrates enhanced capability to escape local optima and accelerated convergence rates, while simultaneously reducing the complexity of manual parameter tuning. The proposed SSA-Transformer-EKF methodology has been validated under various temperature conditions through neural network (NN) and Urban Dynamometer Driving Schedule (UDDS) operational profiles, with mean absolute error (MAE) and root mean square error (RMSE) constrained within 0.6% and 0.8% respectively, thus achieving high-precision real-time estimation. Relative to traditional Transformer and comparable algorithms, the proposed SSA-Transformer-EKF algorithm exhibits superior SOE prediction performance, with average RMSE and MAE values of 0.518% and 0.422%, respectively. Furthermore, at elevated temperatures of 45 °C, the algorithm maintains robust performance with average RMSE and MAE values of 0.523% and 0.442%, further substantiating that the SSA-Transformer-EKF model possesses exceptional fitting capability and generalization performance across diverse operational conditions.

基于ssa优化变压器和扩展卡尔曼滤波的锂离子电池SOE估计
能量状态(SOE)是电池管理系统中最关键的状态参数之一。由于国有企业固有的非线性特性,对国有企业的准确估计一直是该领域的一个重大挑战。本文提出了一种新的锂离子电池SOE估计方法,该方法将麻雀搜索算法(SSA-Transformer)优化的变压器网络与扩展卡尔曼滤波(EKF)相结合。该算法以其独特的生产者侦察机制和防御意识行为模拟为特征,在全局搜索能力和收敛效率方面表现出优异的性能。与传统的粒子群优化算法(PSO)相比,SSA算法具有更强的逃避局部最优的能力和更快的收敛速度,同时降低了人工参数调整的复杂性。本文提出的SSA-Transformer-EKF方法通过神经网络(NN)和Urban Dynamometer Driving Schedule (UDDS)运行概况在不同温度条件下进行了验证,平均绝对误差(MAE)和均方根误差(RMSE)分别被限制在0.6%和0.8%以内,从而实现了高精度的实时估计。与传统的Transformer及同类算法相比,本文提出的SSA-Transformer-EKF算法的SOE预测性能更优,平均RMSE和MAE分别为0.518%和0.422%。此外,在45°C高温下,该算法保持了稳健的性能,平均RMSE和MAE值分别为0.523%和0.442%,进一步证实了SSA-Transformer-EKF模型在不同运行条件下具有出色的拟合能力和泛化性能。
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来源期刊
Ionics
Ionics 化学-电化学
CiteScore
5.30
自引率
7.10%
发文量
427
审稿时长
2.2 months
期刊介绍: Ionics is publishing original results in the fields of science and technology of ionic motion. This includes theoretical, experimental and practical work on electrolytes, electrode, ionic/electronic interfaces, ionic transport aspects of corrosion, galvanic cells, e.g. for thermodynamic and kinetic studies, batteries, fuel cells, sensors and electrochromics. Fast solid ionic conductors are presently providing new opportunities in view of several advantages, in addition to conventional liquid electrolytes.
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