基于深度学习的电池健康预测,以提高电动汽车的性能
Tawfikur Rahman1, Nibedita Deb2, Md Moniruzzaman3
1Department of Electrical and Electronic Engineering, Faculty of Engineering, International University of Business Agriculture and Technology, Uttara, Dhaka, 1230, Bangladesh.
Scientific reports
|February 19, 2026
概括
本研究引入了用于电动汽车 (EV) 电池健康诊断的混合深度学习模型,实现了高精度和效率. 先进的框架支持可持续的移动和清洁能源目标.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 可靠的电池诊断对于电动汽车 (EV) 的进步和可持续发展目标7 (SDG7) 至关重要.
- 当前的诊断方法在复杂的电池退化模式的准确性和效率方面面临挑战.
研究的目的:
- 为智能电动汽车电池健康诊断开发混合深度学习框架.
- 提高电池管理系统 (BMS) 的健康状况 (SOH) 预测准确性和效率.
主要方法:
- 提出了一种混合深度学习模型,将1D-CNN,TCN和LSTM与注意力机制集成在一起.
- 分压 (dV/dQ),分流 (dI/dV) 和增量容量分析 (ICA,dQ/dV) 的特征被提取出来,并进行了denoised.
- 该模型在广泛的电池降解数据集 (NASA PCoE,牛津,CALCE) 上进行了训练和验证.
主要成果:
- 混合模型实现了高的SOH预测准确性 ([公式:见文本]) 和低的RMSE (0.021).
- 它的准确性高达25%,超过了基线模型 (CNN,LSTM,XGBoost).
- 该模型展示了低参数数量 (0.35万),快速推断 (6.1毫秒) 和降低能耗 (0.63mJ/样本),优于变压器架构的性能.
结论:
- 拟议的框架为嵌入式BMS提供了一个强大的,可扩展的,实时可行的解决方案.
- 它提高了诊断精度,延长了电池寿命,为可持续的移动性做出了贡献.
- 该方法通过减少计算需求来支持SDG 7下的能源效率和循环经济目标.
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