锂离子动力电池荷电状态联合估计应用

为了进一步提高锂离子动力电池荷电状态(SOC)的估计精度问题,在分析了电池电压、温度、电流和放电电量对电池SOC值的影响后,提出了一种新颖的混沌萤火虫算法(chaos firefly algorithm,CAF)和小波神经网络(WNN)相结合的锂离子动力电池SOC联合估计方法,该方法首次利用于电池SOC值估计中,通过新颖的混沌萤火虫算法优化小波神经网络,加入动量项优化网络的权值和调整修正参数,提高了网络的学习效率和SOC估计精度。克服神经网络进化缓慢并且容易陷入局部最小的缺陷,通过仿真和电池实际工况下实验,结果表明与WNN算法相比,所提出的方法具有更高的预测精度,均方根误差小于2%,验证了这一算法的可行性和有效性。

Joint Estimation of State-of-charge for lithium-ion Power Battery

In order to further improve the prediction accuracy of the SOC value of the charge state of the lithium battery. Based on the analysis of the influence of battery voltage, temperature, current and discharge power of the battery SOC value, this paper proposes a novel chaotic firefly algorithm (Chaos Firefly Algorithm, CAF) and wavelet neural network (WNN) combined with lithium ion battery SOC estimation method, this method was first used in battery SOC in the estimation, through optimizing wavelet neural network chaotic firefly algorithm is novel, adding momentum to optimize the weight and adjust parameters, improve the learning efficiency and prediction accuracy, overcome the evolutionary neural network is slow and easy to fall into local minimum, the actual condition of the simulation and experimental results show that the battery, compared with WNN the proposed algorithm, the method has higher prediction accuracy, the RMSE error is less than 2%, which verifies the feasibility and effectiveness of the algorithm.