针对锂离子电池荷电状态（state of charge， SOC）估算时存在估计效率不高，估计精度低以及受电池温度影响较大的问题，提出一种改进天鹰优化算法（improved aquila optimizer algorithm， IAO）和随机森林算法（random forest algorithm, RF）相联合的预测模型来估计锂离子电池SOC。采用改进的天鹰优化算法来优化随机森林的超参数，提升模型的泛化性能与抗干扰能力，避免陷入局部最佳缺陷。经实验表明，在考虑到温度对电池SOC影响下，单一的随机森林估计锂电池SOC的平均绝对误差（mean absolute error, MAE）为2.77%，均方根误差（root mean square error, RMSE）为3.73%，而基于IAO-RF联合预测模型估计锂电池SOC的MAE为0.407%，RMSE为0.871%，相较于单一的随机森林，平均绝对误差降低了0.85倍，根均方误差也降低了0.77倍，比其他SOC预测模型均有不同程度的提升。可见IAO-RF模型在考虑温度对电池的影响下，不仅显著提升了SOC的估计的精度，还有效降低了极大误差对整体SOC估计的干扰，表现出优越性能同时也兼顾了估计的时效性，对电池管理系统（battery management system, BMS）研究存在一定价值。

To address the challenges of low estimation efficiency, inadequate accuracy, and substantial impact of temperature on the state of charge (SOC) estimation for lithium-ion batteries, a novel approach incorporating an improved aquila optimizer (IAO) algorithm with a random forest (RF) algorithm was proposed. In this approach, the IAO algorithm was employed to optimize the hyperparameters of the RF algorithm, thereby enhancing the model's generalization capability and resistance to interference while preventing convergence to local optima. Experimental results reveal that when considering the temperature variations in SOC estimation, the RF model yields a mean absolute error (MAE) of 2.77% and a root mean square error (RMSE) of 3.73%. In comparison, the proposed IAO-RF model achieves a significantly lower MAE of 0.407% and an RMSE of 0.871%, reflecting reductions of 0.85 times and 0.77 times, respectively, compared to the single RF model. Furthermore, the IAO-RF model outperforms other SOC estimation models. These results highlight that even under temperature fluctuations, the IAO-RF model not only enhances estimation precision but also effectively reduces the impact of large errors on overall SOC prediction. The proposed model demonstrates superior accuracy and computational efficiency, providing valuable insights for battery management system (BMS) research.