对比多种模型，发现决策树在电池电量预测上表现最佳。
A Comparison of Baseline Models and a Transformer Network for SOC Prediction in Lithium-Ion Batteries
- 用多种神经网络与回归模型对比电量预测效果
- 决策树在真实驾驶数据上误差最小，优于复杂变压器模型
- 适合关注电池管理实用方案的工程师参考
准确预测锂离子电池的荷电状态（SOC）对电动汽车电池管理系统性能至关重要。电动汽车普及缓慢的主要原因之一是续航焦虑。电池管理系统若能精确估算荷电状态，有助于缓解该问题。本文比较了多种数据驱动的SOC估计方法，包括不同变体的Transformer网络、神经网络、套索回归、线性回归和决策树。实验基于宝马i3电池的真实驾驶循环数据进行。结果显示，决策树在所有模型中表现最优，甚至优于具有自注意力机制和位置编码的复杂Transformer网络。
原文摘要 · Abstract (English)
Accurately predicting the state of charge of Lithium-ion batteries is essential to the performance of battery management systems of electric vehicles. One of the main reasons for the slow global adoption of electric cars is driving range anxiety. The ability of a battery management system to accurately estimate the state of charge can help alleviate this problem. In this paper, a comparison between data-driven state-of-charge estimation methods is conducted. The paper compares different neural network-based models and common regression models for SOC estimation. These models include several ablated transformer networks, a neural network, a lasso regression model, a linear regression model and a decision tree. Results of various experiments conducted on data obtained from natural driving cycles of the BMW i3 battery show that the decision tree outperformed all other models including the more complex transformer network with self-attention and positional encoding.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。