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battery-SOC-estimation-based-on-EKF
- 基于扩张卡尔曼滤波的磷酸铁锂蓄电池SOC检测，给出了电池模型和算法实现过程。-The extended Kalman filter (EKF) method for SOC estimation has some problems such as the lack of an accurate model, and model errors due to the variation in the parameters of the mo
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
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估算
- 基于卡尔曼滤波器的锂电池SOC预测，通过仿真并得到印证，证明了方法的可行性。(SOC prediction of lithium battery based on Calman filter)
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.)
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
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
kalmanfilters
- 算法用于锂离子电池，用卡尔曼滤波算法进行状态估计并进行预测(state estimation and estimation)
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)