在充电条件下对凝电解质电池进行预测的SoH双向长短期记忆模型
Ting-Jung Kuo1, Wei-Ting Chao2
1Department of Applied Artificial Intelligence, Ming Chuan University, Taoyuan 33348, Taiwan.
Gels (Basel, Switzerland)
|December 22, 2023
概括
本研究引入了一种双向长期短期记忆模型,以评估充电电流和温度如何影响凝电池的健康. 该模型准确地预测了电池老化,证实凝电池是可再生能源储存的理想选择.
科学领域:
- 电池技术是电池的技术.
- 可再生能源系统可再生能源系统
- 在能源领域的人工智能
背景情况:
- 电池寿命对于电动汽车和能源存储至关重要.
- 充电电流和环境温度显著影响电池的退化.
- 准确的健康状况 (SoH) 估计对于可靠的能源系统至关重要.
研究的目的:
- 开发一个模型来量化充电电流和温度对凝电池衰老的影响.
- 在各种条件下准确计算凝电池的健康状况 (SoH).
- 评估凝电池对于可再生能源储能应用的适用性.
主要方法:
- 提出了一个双向长期短期记忆 (BiLSTM) 模型.
- 在受控条件下的300个充放电周期中收集了训练数据.
- 测试数据从一个小型储能装置收集了220个周期的不同参数的测试数据.
主要成果:
- 该BiLSTM模型在预测电池衰老方面表现出高准确度.
- 平均绝对误差 (MAE) 为0.0133,根平均平方误差 (RMSE) 为0.0251.1.
- 凝电池表现出稳定且持久的性能.
结论:
- 拟议的BiLSTM模型有效量化了对凝电池健康的环境和运营影响.
- 凝电池是可靠和持久的选择,用于可再生能源系统的储能.
- 这项研究证实了人工智能驱动的模型在电池管理和预测方面的潜力.
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