| 雷晓犇,胡新华,王浩.基于 WOA-RF 模型的航空镍镉电池SOC预测[J].电子测量与仪器学报,2026,40(1):61-69 |
| 基于 WOA-RF 模型的航空镍镉电池SOC预测 |
| Research on storage life prediction of aeronautical electromagneticrelay based on WOA-RF model |
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| DOI： |
| 中文关键词: 航空镍镉电池 SOC预测 鲸鱼优化算法 随机森林 |
| 英文关键词:aviation nickel-cadmium batteries SOC prediction whale optimization algorithm random forest |
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| 摘要点击次数: 509 |
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| 中文摘要: |
| 航空镍镉电池的荷电状态（SOC）预测是保障航空器安全运行的关键技术之一，针对传统预测模型精度不足、环境适应性差的问题，提出一种融合鲸鱼优化算法（WOA）与随机森林（RF）的WOA-RF混合预测模型。首先，基于随机森林回归算法构建初始预测模型，利用其多决策树集成优势处理非线性特征；其次，引入鲸鱼优化算法对随机森林的核心超参数（进行全局寻优，解决人工调参效率低下的问题）从而提升模型预测精度与泛化能力。为验证模型性能，在不同温度（20 ℃、0 ℃、-10 ℃、-20 ℃）环境下分别进行放电循环实验，对比分析WOA-RF与传统RF、反向传播神经网络（BPNN）、支持向量回归（SVR）以及粒子群优化RF（PSO-RF）、遗传算法优化RF（GA-RF）等模型的预测效果。实验结果表明，在标准温度下，WOA-RF模型的平均绝对误差（MAE）为1.22%、决定系数（R2）达到0.986、均方根误差（RMSE）为1.56%，优于对比模型；在低温环境下，WOA-RF的MAE仍保持在1.5%以内，RMSE为1.8%以内，R2高于0.975，表现出更强的环境鲁棒性。结果表明，WOA-RF模型有效提高了SOC预测的准确性和稳定性，尤其适用于航空极端工况下的镍镉电池状态监测，为电池管理系统提供了可靠的技术支持。 |
| 英文摘要: |
| The state of charge (SOC) prediction of aviation nickel-cadmium batteries is a critical technology for ensuring the safe operation of aircraft. To address the issues of insufficient accuracy and poor environmental adaptability in traditional prediction models, this study proposes a hybrid WOA-RF prediction model that combines the whale optimization algorithm (WOA) with random forest (RF). Firstly, an initial prediction model is constructed based on the random forest regression algorithm, leveraging its multi-decision tree ensemble advantage to handle nonlinear features. Secondly, the whale optimization algorithm is introduced to globally optimize the core hyperparameters of the random forest, resolving the inefficiency of manual parameter tuning and thereby enhancing the model’s prediction accuracy and generalization capability. To validate the model’s performance, discharge cycle experiments were conducted under different temperature conditions (20 ℃, 0 ℃, -10 ℃, -20 ℃), and the prediction results of the WOA-RF model were compared with those of traditional RF, backpropagation neural network (BPNN), support vector regression (SVR), as well as particle swarm optimization RF (PSO-RF) and genetic algorithm-optimized RF (GA-RF) models. The experimental results show that under standard temperature conditions, the WOA-RF model achieves a mean absolute error (MAE) of 1.22%, a coefficient of determination (R2) of 0.986, and a root mean square error (RMSE) of 1.56%, outperforming the comparison models. In low-temperature environments, the WOA-RF model maintains an MAE below 1.5%, an RMSE below 1.8%, and an R2 above 0.975, demonstrating stronger environmental robustness. The conclusion indicates that the WOA-RF model effectively improves the accuracy and stability of SOC prediction, making it particularly suitable for monitoring the state of nickel-cadmium batteries under extreme aviation operating conditions. This provides reliable technical support for battery management systems. |
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