对比多种AI模型预测锂电池健康状态，发现BiLSTM最准
AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation
- 用FFNN、LSTM、BiLSTM比对电池健康度预测效果
- BiLSTM平均误差比LSTM低15%，在多场景下表现最优
- 适合电池管理、新能源汽车等需要精准寿命预估的场景
本文全面综述了人工智能驱动的锂离子电池健康状态（SoH）预测方法。对比了前馈神经网络（FFNN）、长短期记忆网络（LSTM）和双向长短期记忆网络（BiLSTM）在多个数据集（CALCE、NASA、UDDS）及不同工况（如温度变化、驾驶条件差异）下的表现。分析了温度、充放电速率等因素对SoH波动的影响，并通过仿真验证结果。结果显示，BiLSTM在所有测试中准确率最高，平均均方根误差（RMSE）较LSTM降低15%，展现出更强的现实应用鲁棒性。
原文摘要 · Abstract (English)
This paper presents a comprehensive review of AI-driven prognostics for State of Health (SoH) prediction in lithium-ion batteries. We compare the effectiveness of various AI algorithms, including FFNN, LSTM, and BiLSTM, across multiple datasets (CALCE, NASA, UDDS) and scenarios (e.g., varying temperatures and driving conditions). Additionally, we analyze the factors influencing SoH fluctuations, such as temperature and charge-discharge rates, and validate our findings through simulations. The results demonstrate that BiLSTM achieves the highest accuracy, with an average RMSE reduction of 15% compared to LSTM, highlighting its robustness in real-world applications.
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