用张量分解+LSTM提升电动车电池电量预测精度
A Hybrid Tucker-LSTM Tensor Network Model for SOC Prediction in Electric Vehicles
- 将张量分解与LSTM结合，压缩高维电池数据
- 误差降低70.5%，相关系数达0.976
- 适合电池管理、智能电动汽车研究者
精确的电池荷电状态（SOC）估计对电动汽车电池管理至关重要，但传统方法存在累积误差大、依赖简化模型的问题。本文提出一种融合Tucker张量分解与LSTM网络的混合模型，利用全生命周期电动汽车实测数据进行SOC预测。输入包括充电状态、里程、电压、电流、单体差异及时间特征。通过Tucker分解有效降维并保留时序结构，实现与标准LSTM的公平对比。结果表明：该模型在所有指标上均优于基线，均方误差下降70.5%（从21.07降至6.22），平均绝对误差改善48.7%（从3.37%降至1.73%），均方根误差由4.59%降至2.49%，$R^2$从0.918提升至0.976。实验验证了张量分解在不损失预测精度的前提下高效压缩高维电池数据的能力，为电动汽车电池管理中的张量分析开辟新路径。
原文摘要 · Abstract (English)
Accurate state of charge estimation is critical for the success of electric vehicle battery management strategies, but it is well known that conventional estimators suffer from two fundamental shortcomings: cumulative errors that grow over time and reliance on simplified battery models that do not reflect real world dynamics. Therefore, this paper presents a novel hybrid approach combining Tucker tensor decomposition with LSTM networks, using full - lifecycle EV field data for SOC prediction. The inputs are charge status, mileage, voltage, current, cell differentials, and temporal features. Tucker decomposition is skillfully used to reduce dimensionality while maintaining the temporal structure, hence allowing a direct, fair comparison with standard LSTM. The result is unequivocal: Tucker - LSTM outperforms the baseline on all metrics, with MSE dropping 70.5\% (from 21.07 to 6.22 ), MAE improving 48.7\% (from 3.37\% to 1.73\%), RMSE falling from 4.59\% to 2.49\%, and $R^2$ rising from 0.918 to 0.976. Since the experimental results demonstrably demonstrate that tensor decomposition compresses high-dimensional battery data very well without loss of predictive fidelity, this paper naturally opens up a new direction for tensor-based analytics in electric vehicle battery management.
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