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估计器​

电荷状态、端电阻和健康状态估计器

利用库仑计数和卡尔曼滤波器算法估计电池的荷电状态、端电阻和健康状态。

模块

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Cell Delta SOC Estimator (Kalman Filter)State of charge estimator with cell delta Kalman filter (自 R2025a 起)
Cell Delta SOC Estimator (Kalman Filter, Variable Resistance)State of charge estimator with cell delta Kalman filter and variable resistance (自 R2025a 起)
Pack Bar SOC Estimator (Adaptive Kalman Filter)Average state-of-charge and terminal resistance estimator for battery pack with Kalman filter (自 R2025a 起)
Pack Bar SOC Estimator (Kalman Filter)Average state-of-charge estimator for battery pack with Kalman filter (自 R2025a 起)
SOC Estimator (Adaptive Kalman Filter)State of charge and terminal resistance estimator with adaptive Kalman filter (自 R2022b 起)
SOC Estimator (Adaptive Kalman Filter, Variable Capacity)State of charge and terminal resistance estimator with adaptive Kalman filter and variable capacity (自 R2023b 起)
SOC Estimator (Coulomb Counting)State of charge estimator with Coulomb counting (自 R2022b 起)
SOC Estimator (Coulomb Counting, Variable Capacity)State of charge estimator with Coulomb counting and variable capacity (自 R2023a 起)
SOC Estimator (Kalman Filter)State of charge estimator with Kalman filter (自 R2022b 起)
SOC Estimator (Kalman Filter, Variable Capacity)State of charge estimator with Kalman filter and variable capacity (自 R2023b 起)
SOE Estimator (Adaptive Kalman Filter)State of energy and terminal resistance estimator with adaptive Kalman filter (自 R2023b 起)
SOE Estimator (Adaptive Kalman Filter, Variable Energy Capacity)State of energy and terminal resistance estimator with Kalman filter and variable energy capacity (自 R2023b 起)
SOE Estimator (Energy Counting)State of energy estimator with energy counting (自 R2023b 起)
SOE Estimator (Energy Counting, Variable Energy Capacity)State of energy estimator with energy counting and variable energy capacity (自 R2023b 起)
SOE Estimator (Kalman Filter)State of energy estimator with Kalman filter (自 R2023b 起)
SOE Estimator (Kalman Filter, Variable Energy Capacity)State of energy estimator with Kalman filter and variable energy capacity (自 R2023b 起)
Battery Capacity Estimator (Kalman Filter)Battery capacity estimator with Kalman filter (自 R2023b 起)
Battery Capacity Estimator (Least Squares)Battery capacity estimator using least-squares algorithms (自 R2024a 起)
Battery Capacity Estimator (Least Squares, Variable Weights)Battery capacity estimator using least-squares algorithms and variable weights (自 R2024a 起)
Battery Power EstimatorCalculate maximum charging and discharging power of battery pack over time horizon (自 R2024b 起)
SOH EstimatorState of health estimator (自 R2022b 起)
SOH Estimator (Capacity-Based)State of health estimator based on capacity fade (自 R2023b 起)

精选示例

Estimate Maximum Charging and Discharging Power of Battery Module

Estimate Maximum Charging and Discharging Power of Battery Module

Estimate the maximum charging and discharging power of a battery module by using the Battery Power Estimator block.

Estimate Battery State of Charge Using Bar-Delta Filtering

Estimate Battery State of Charge Using Bar-Delta Filtering

Estimate the state of charge (SOC) of a battery cell by using bar-delta filtering. The battery pack comprises five series-connected cells. Each battery cell has an initial SOC that varies between 0.45 and 0.65. The estimation technique uses a pack bar SOC estimator to obtain the pack-average SOC. A cell delta SOC estimator uses the pack-average SOC to estimate the cell SOC. The battery keeps charging and discharging for six hours. The estimator converges to the real value of the SOC in less than 10 minutes and then follows the real SOC value. The bar-delta filtering is computationally efficient as it uses one full Kalman filter and Ns one-state Kalman filters, where Ns is the number of series-connected cells.

估计磷酸铁锂电池的荷电状态

估计磷酸铁锂电池的荷电状态

此示例说明了如何使用带误差校正的库仑计数法估计磷酸铁锂 (LFP) 电池的荷电状态 (SOC)。库仑计数方法以 1 秒的采样时间实现。为了校正库仑计数方法的估计值，该示例实现了采样时间为 10 秒的扩展卡尔曼滤波器。电池的初始 SOC 等于 0.4。估计器使用初始条件，其中 SOC 等于 0.6。LFP 电池持续充电和放电六小时。估计器在大约半个小时内收敛到 SOC 的实际值，然后跟随实际 SOC 值。

电池荷电状态估计

电池荷电状态估计

此示例说明了如何使用卡尔曼滤波器估计电池的荷电状态 (SOC)。电池的初始 SOC 等于 0.5。估计器使用的 SOC 初始条件等于 0.8。电池持续充电和放电 6 小时。扩展卡尔曼滤波器估计器在不到 10 分钟的时间内收敛到 SOC 的实际值，然后跟随 SOC 的实际值。要使用不同的卡尔曼滤波器实现，在 SOC Estimator (Kalman Filter) 模块中，将滤波器类型参数设置为所需值。

电池健康状态估计

电池健康状态估计

此示例说明了如何使用自适应卡尔曼滤波器估计电池的内阻和健康状态 (SOH)。电池的初始荷电状态 (SOC) 为 0.6。估计器使用初始条件，其中 SOC 等于 0.65。电池持续充电和放电 10 小时。无香卡尔曼滤波器估计器在估计内阻的同时，还收敛到 SOC 的实际值。要使用不同的卡尔曼滤波器实现，在 SOC Estimator (Kalman Filter) 模块中，将滤波器类型参数设置为所需值。

带荷电状态估计器的充电和放电电池模组

带荷电状态估计器的充电和放电电池模组

此示例说明了如何循环充电和放电电池模组，同时估计模组中三个并联组件随时间变化的荷电状态 (SOC)。此示例使用 SOC 估计值在充电和放电配置文件之间进行切换。为了进行估计，卡尔曼滤波器使用初始 SOC 估计值以及并联组件的电压、电流和温度。该模型根据每个并联组件的电芯平均温度来估计并联组件的温度。

基于容量衰减估计电池健康状态

基于容量衰减估计电池健康状态

此示例说明了如何使用卡尔曼滤波器估计电池容量和健康状态 (SOH)。电池的初始荷电状态 (SOC) 为 0.5。估计器使用的 SOC 初始条件等于 0.8。电池持续充电和放电长达 50 小时。该示例使用扩展卡尔曼滤波器估计电池容量（以安培小时为单位）和 SOC。电池容量的估计误差小于 4%。SOC 使用扩展卡尔曼滤波器进行估计。使用固定容量时，估计的 SOC 值与真实值会出现偏差。为了演示估计器的功能并限制仿真的持续时间，此示例对容量衰减率的增加进行了建模。