基于PNGV模型的锂离子电池荷电状态估计

目的 提升不同老化情况下的锂离子电池荷电状态(SOC)估计精度。方法 基于PNGV模型(Partnership for a New Generation of Vehicles)，对锂离子电池SOC进行估计。首先通过双线性变换对PNGV模型进行离散化，采用带有遗忘因子的递归最小二乘法(FFRLS)，对电池模型参数进行在线辨识，利用卡尔曼滤波(EKF)算法进行SOC估计，并通过动态工况验证SOC估计精度。结果 以多种误差指标考察不同循环下的试验数据，在不同电池老化状态下具有较好的预测精度。相比基于Thevenin模型的算法，基于PNGV模型的算法可以将SOC平均绝对误差减少约60%，同时也可以将SOC估计最大绝对误差波动范围降低53.8%。结论 本算法引入PNGV模型后，解决了基于Thevenin模型算法误差大、不稳定的问题，提升了动力电池系统在不同老化环境下的适应性。     

An improved grey wolf optimizer algorithm for identification and location of gas emission

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.