基于因果思维树的电动汽车电池SOC预测模型

彭自然; 杨肖阳; 李雪勇; 周玉

doi:10.12263/DZXB.20250450

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基于因果思维树的电动汽车电池SOC预测模型

学术论文 | 更新时间：2025-12-27

- 基于因果思维树的电动汽车电池SOC预测模型
- A Causal Tree-of-Thought-Based Model for Battery State-of-Charge Prediction in Electric Vehicles
- 电子学报 2025年53卷第8期页码：2970-2984
- 作者机构：
  
  湖南工业大学交通与电气工程学院，湖南株洲 412007
- 作者简介：
  
  [ "彭自然男，1969年10月出生于湖南省益阳市.现为湖南工业大学交通与电气工程学院副教授、硕士生导师.研究方向为人工智能、信号处理、智能检测仪表、智能移动终端等方面. E-mail: pengziran@hut.edu.cn" ]
  [ "杨肖阳男，2000年12月出生于河南省周口市.现为湖南工业大学交通与电气工程学院硕士研究生.研究方向为人工智能、为电动汽车动力电池荷电状态和健康状态估算.E-mail: 2873633605@qq.com" ]
- 基金信息：
  
  湖南省教育厅重点科研项目(22A0423);湖南省自科基金(2023JJ60267;2022JJ50073)
- DOI：10.12263/DZXB.20250450
  中图分类号： TN919.5;TP183
- 收稿：2025-05-30，
  
  录用：2025-07-28，
  
  纸质出版：2025-08-25
- 稿件说明：
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彭自然, 杨肖阳, 李雪勇, 等. 基于因果思维树的电动汽车电池SOC预测模型[J]. 电子学报, 2025, 53(08): 2970-2984.

PENG Zi-ran, YANG Xiao-yang, LI Xue-yong, et al. A Causal Tree-of-Thought-Based Model for Battery State-of-Charge Prediction in Electric Vehicles[J]. Acta Electronica Sinica, 2025, 53(08): 2970-2984.
彭自然, 杨肖阳, 李雪勇, 等. 基于因果思维树的电动汽车电池SOC预测模型[J]. 电子学报, 2025, 53(08): 2970-2984. DOI：10.12263/DZXB.20250450

PENG Zi-ran, YANG Xiao-yang, LI Xue-yong, et al. A Causal Tree-of-Thought-Based Model for Battery State-of-Charge Prediction in Electric Vehicles[J]. Acta Electronica Sinica, 2025, 53(08): 2970-2984. DOI：10.12263/DZXB.20250450

摘要

针对现有电动汽车实时荷电状态（State-Of-Charge，SOC）预测模型在运行状态感知、动态校准与长时序预测精度方面存在的瓶颈问题，本文提出了一种融合因果思维树推理机制与深度强化学习策略的时序预测框架.该框架通过引入动态进化与多分支因果推理机制，使其在保持单模型高效计算结构的同时，实现对复杂工况下电池状态变化的感知建模与自适应优化.首先，设计了基于因果分级结构的多级近端策略优化（Proximal Policy Optimization，PPO）模型，并提出了以时间序列网络为核心的Actor网络架构.该架构分层建模温度、电阻等关键变量对电池SOC的直接与间接因果影响，并通过值函数迭代与长期回报优化策略，实现了模型参数的持续进化与泛化能力提升，增强了预测的可解释性与因果认知能力.其次，引入了思维树结构构建多路径策略评估网络，结合策略搜索、路径追踪与回溯修正机制，在动态工况中实现策略的逐层优化与异常分支校正，提升了模型的预测鲁棒性与泛化能力.实验结果表明：在不同运行工况下，本文算法在多个评价指标上均显著优于Transformer、FED former、Mamba和长短期记忆（Long Short-Term Memory，LSTM）网络模型.本文算法的平均绝对误差（Mean Absolute Error，MAE）低于0.26%，均方根误差（Root Mean Squared Error，RMSE）低于0.35%，决定系数（Coefficient of Determination，

²）高于99.5%，在不同车型条件下均表现出卓越的鲁棒性与稳定性.

Abstract

To address the limitations of existing real-time state-of-charge (SOC) prediction models for electric vehicles in terms of operational state awareness

dynamic calibration

and long-sequence forecasting accuracy

this paper proposes a temporal prediction framework that integrates a causal tree-of-thought mechanism with a deep reinforcement learning strategy. By introducing dynamic evolution and multi-branch causal inference

the proposed framework maintains the computational efficiency of a single model while enabling adaptive modeling of battery state transitions under complex operating conditions. First

a multi-level proximal policy optimization (PPO) model based on a hierarchical causal structure is designed. A time-series network is constructed as the core of the Actor network to hierarchically model the direct and indirect causal influences of key variables such as temperature and internal resistance on SOC. Through value function iteration and long-term return optimization strategies

the model continuously evolves its parameters

enhancing its generalization capability

interpretability

and causal reasoning ability. Second

a tree-of-thought structure is introduced to build a multi-path policy evaluation n

etwork

which combines policy search

path tracking

and backtracking correction mechanisms to achieve layer-wise policy optimization and anomaly branch correction under dynamic conditions. This design significantly improves the robustness and generalization performance of the model. Experimental results show that under various operating conditions

the proposed algorithm significantly outperforms Transformer

FED former

Mamba

and long short-term memory (LSTM) models across multiple evaluation metrics

achieving a mean absolute error (MAE) below 0.26%

root mean squared error (RMSE) below 0.35%

and coefficient of determination (

²) above 99.5%

demonstrating outstanding robustness and stability across different vehicle types.

关键词

Keywords

references

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