利用优化的长短期记忆对电动汽车电池充电进行时序预测

Alfredo Tumi Figueroa Figueroa, Hayder M. A. Ghanimi, Senthil Raja M, Shamia D, Samrat Ray, Jorge Ramos Surco
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引用次数: 0

摘要

过去十年间,在环境问题、技术进步和政府支持的推动下,电动汽车(EV)的应用和发展显著增加。电池作为电动汽车的核心部件,在能量密度、充电速度和使用寿命方面都有了突破性的创新。充电基础设施的不断扩大以及汽车行业对电动汽车研究的投入,使电动汽车成为主流。有效的电池管理(BM)包括监测基本参数和热管理,对于电动汽车的使用寿命和可靠性至关重要。准确的充电预测尤其有助于制定行程计划,减少续航焦虑,并促进具有成本效益的充电与动态电价的协调。线性回归和自回归整合移动平均(ARIMA)等传统模型一直是电动汽车电池电量预测的标准模型。然而,这些模型往往难以应对电动汽车充电数据的动态特性。即使是善于识别长期模式的香草长短期记忆(LSTM)等模型,也需要进行细致的超参数调整。这项研究引入了 DWT-DE-LSTM 模型,该模型利用离散小波变换 (DWT) 以不同分辨率剖析电池充电数据,并利用差分进化 (DE) 策略进行模型优化。使用松下 18650PF 锂离子电池数据集进行的测试表明,DWT-DE-LSTM 模型具有卓越的功效,强调了它在实际电池充电预测中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging
The last decade has seen a significant rise in the adoption and development of Electric Vehicles (EVs), driven by environmental concerns, technological advancements, and governmental support. Batteries, central to EVs, have witnessed groundbreaking innovations in terms of energy density, charging speeds, and longevity. Expanding charging infrastructure and the automotive industry's investment in EV research have made them more mainstream. Effective Battery Management (BM), which includes monitoring essential parameters and thermal management, is critical for the longevity and reliability of EVs. Accurate charge prediction, in particular, aids in trip planning, reduces range anxiety and facilitates cost-effective charging coordinated with dynamic electricity pricing. Traditional models like linear regression and Autor-Rgressive Integrated Moving Average (ARIMA) have been standard for EV battery charge prediction. However, these often struggle with the dynamic nature of EV charging data. Even models like the vanilla Long Short-Term Memory (LSTM), which are adept at recognizing long-term patterns, require meticulous hyperparameter tuning. This work introduces the DWT-DE-LSTM model, which utilizes the Discrete Wavelet Transform (DWT) to dissect battery charging data at different resolutions and a Differential Evolution (DE) strategy for model optimization. Tests using the Panasonic 18650PF Li-ion Battery Dataset revealed the superior efficacy of the DWT-DE-LSTM model, emphasizing its suitability for real-world battery charge prediction.
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