基于PSO-ELM的储能锂电池荷电状态估算

对锂离子电池荷电状态(SOC)进行准确估算是保证电池管理系统安全稳定运行的关键。常用的安时积分法存在累积误差,卡尔曼滤波算法需要建立精确的电池模型,神经网络法不依赖具体的锂电池模型,能够反映锂电池的非线性关系,因而受到广泛关注,然而传统神经网络估算SOC训练时间长、精度低。针对以往电池SOC估算精度低等问题,文中提出粒子群(PSO)优化极限学习机(ELM)神经网络的方法。以电池电压、电流和温度作为PSO-ELM模型的输入向量,以SOC作为输出向量。将实验获得的数据导入模型进行训练和测试,采用PSO对ELM随机给定的输入权值和隐含层阈值进行寻优。仿真结果表明,与BP神经网络的预测结果相比,文中估算SOC的方法具有更高的精度。

Estimation of state of charge of energy storage lithium battery based on PSO-ELM

Accurate estimation of lithium ion battery state of charge(SOC)is the key to ensure safe and stable operation of battery management system. The commonly used ampere-hour integral method has cumulative errors. And the Kalman filter algorithm needs to establish an accurate battery model. The neural network method does not rely on a specific lithium battery model and can reflect the nonlinear relationship of lithium batteries, and thus has received extensive attention. However, traditional neural network has long training time and low precision when estimating SOC. For the low accuracy of SOC estimation in the past, the particle swarm optimization(PSO) of extreme learning machine(ELM) neural network method is proposed. In the PSO-ELM model, voltage, current and temperature are used as input vector and the value of SOC is used as output vector. The experimental data is imported into the model for training and testing, and the input weight matrix and hidden layer threshold of ELM are optimized by PSO. In addition, the simulation results show that compared with the prediction results of BP neural network, the method of estimating SOC in this paper has higher precision.