A data-driven architecture fusing Kolmogorov-Arnold feature extraction and contextual-attention long short-term memory network for accurate state-of-charge estimation in lithium-ion batteries under dynamic operating conditions

IF 8.9 2区 工程技术 Q1 ENERGY & FUELS
Syed Abbas Ali Shah , Syed Maooz Ali Shah , Shunli Wang , Mahrukh , Shungang Ning , Ziqiang Xu , Mengqiang Wu
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Abstract

Accurate estimation of lithium-ion battery state of charge (SOC) under dynamic conditions remains challenging. This study introduces a hybrid architecture that combines a Kolmogorov-Arnold Network (KAN) for theoretical nonlinear decomposition of battery signals, a feature-wise contextual attention (FCA) mechanism for adaptive channel weighting, and stacked long short-term memory (LSTM) layers for temporal modeling, collectively termed KALSTM framework. The KAN front-end leverages the Kolmogorov–Arnold representation theorem to factorize current, voltage, and temperature into independent univariate mappings, recombining them through a single affine mixer to achieve universal approximation with far fewer parameters than a dense input layer. FCA replaces quadratic self-attention with a context-conditioned probability distribution over latent channels at each timestep, ensuring linear complexity in sequence length when feature dimension is fixed while selectively amplifying salient sensor information. The LSTM stack captures multiscale temporal dependencies within these refined embeddings to produce a single SOC estimate. Training was conducted on a subset of a publicly available battery dataset comprising urban-to-highway drive cycles collected at 0 °C, 10 °C, and 25 °C. Validation utilized the remaining records, including both fixed-temperature cycles and continuously varying thermal conditions, along with data from a different battery chemistry to evaluate generalization. The proposed KALSTM model achieved optimal accuracy, attaining an RMSE of 0.77 % and MAE of 0.63 % under fixed temperatures, and RMSE of 1.10 % and MAE of 0.89 % during dynamic thermal variations. It consistently outperformed a parameter-matched LSTM baseline and recent literature benchmarks. These results highlight its potential as a reliable and transferable tool for advanced battery state estimation.
一种融合Kolmogorov-Arnold特征提取和上下文关注长短期记忆网络的数据驱动架构,用于锂离子电池动态运行条件下的准确充电状态估计
动态条件下锂离子电池荷电状态(SOC)的准确估计一直是一个挑战。本研究介绍了一种混合架构,该架构结合了用于电池信号理论非线性分解的Kolmogorov-Arnold网络(KAN),用于自适应信道加权的特征上下文注意(FCA)机制,以及用于时间建模的堆叠长短期记忆(LSTM)层,统称为KALSTM框架。KAN前端利用Kolmogorov-Arnold表示定理将电流、电压和温度分解为独立的单变量映射,并通过单个仿射混合器将它们重新组合,以实现比密集输入层参数少得多的通用近似。FCA在每个时间步用潜在通道上的上下文条件概率分布取代了二次型自注意,保证了特征维固定时序列长度的线性复杂性,同时选择性地放大了显著传感器信息。LSTM堆栈捕获这些精细嵌入中的多尺度时间依赖性,以产生单个SOC估计。训练是在一个公开可用的电池数据集的子集上进行的,该数据集包括在0°C、10°C和25°C下收集的城市到公路的驾驶循环。验证使用了剩余的记录,包括固定温度循环和连续变化的热条件,以及来自不同电池化学的数据来评估通用性。所提出的KALSTM模型在固定温度下的RMSE为0.77%,MAE为0.63%,在动态温度变化时RMSE为1.10%,MAE为0.89%。它始终优于参数匹配的LSTM基准和最近的文献基准。这些结果突出了它作为先进电池状态估计的可靠和可转移工具的潜力。
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来源期刊
Journal of energy storage
Journal of energy storage Energy-Renewable Energy, Sustainability and the Environment
CiteScore
11.80
自引率
24.50%
发文量
2262
审稿时长
69 days
期刊介绍: Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.
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