搜索资源列表
kalman-Algorithm-For-SOC
- 卡曼滤波算法用于锂电池的剩余电量估计（SOC）-Kalman filtering algorithms for lithium batteries estimated remaining charge (SOC)
EKF_nonGaussian
- 新型的EKF算法，用于估计汽车用锂电池的电池SOC-New EKF algorithm for estimating automotive lithium battery SOC
ekf
- 用扩展卡尔曼滤波法估计新能源车动力锂电池剩余电量-ekf for SOC
chargerall
- 电池充电模块，simulink模块，.mdl文件，锂电池SOC估计-soc estimation,.mdl,simulink charge
SOC estimation
- 基于改进Sage-H璐a的自适应无迹卡尔曼滤波 的锂离子电池SOC估计(Adaptive unscented Calman filtering based on improved Sage-H Lu a SOC estimation of lithium ion batteries)
soc_kalman
- 电动汽车锂离子动力电池SOC估计模型，基于电化学模型技术的卡尔曼滤波算法(SOC estimation model of Li ion power battery for electric vehicle and Calman filtering algorithm based on electrochemical model technique)
卡尔曼滤波估测电池SOC
- 利用卡尔曼滤波估计锂离子电池的SOC状态，可以达到良好的效果，误差很小。(Using Kalman filter to estimate SOC state of lithium-ion battery and it can achieve good results with little error.)
ssc_lithium_cell_1RC
- 基于一阶RC模型的锂离子电池的SOC估计(SOC estimation of lithium ion batteries based on first order RC model)
鍙屽崱灏旀浖SOC浼拌
- 锂电池荷电状态(SOC)的精确估计一直是电池管理系统的核心任务之一。电流传感器中存在非零均值的电流漂移噪声,这些噪声会造成不可避免的估计误差。为减少电流漂移噪声对估算造成的不利影响,提出了联合扩展卡尔曼滤波法,以Thevenin模型为锂电池等效电路模型,将电流漂移值作为状态变量与电池SOC进行同步预测。实验和仿真结果表明,该方法能有效抑制电流漂移噪声,提高估算精度。(The accurate estimation of the char
08-锂动力电池健康度评价与估算方法的研究_李然
- 可以用于锂电池建模与SOC估计，非常有用方便。(It can be used for lithium battery modeling and SOC estimation)
UKFF
- 利用ukf算法，通过辨识过的参数，进行状态方程的状态的估计，从而实现对soc的估计(Using UKF algorithm, the state of state equation is estimated by identifying parameters, thus the estimation of SOC is realized.)
data
- matlab 锂离子电池参数估计， soc仿真(matlab soc simulation)
EKF3
- 该Simulink仿真模型基于锂电池的二阶RC模型，采用扩展卡尔曼滤波算法实现锂电池的SOC估计。(The Simulink simulation model is based on the second-order RC model of lithium batteries, and the extended Kalman filter algorithm is used to estimate the SOC of lithium
SOC
- 该模型可用于锂离子电池SOC估计算法的研究(The model can be used to estimate SOC of lithium ion batteries.)
EKF
- 扩展卡尔曼滤波算法锂离子电池SOC估计中有较广泛的应用，其精度高，鲁棒性好，算法简单(The extended Kalman filtering algorithm has a wide range of applications in SOC estimation of lithium-ion batteries, with high accuracy, good robustness, and simple algorithm)