基于电池的戴维宁(Thevenin)模型,设计了多模型自适应卡尔曼滤波器,并将多模型自适应卡尔曼滤波器应用于电动汽车电池荷电状态(state-of-charge,SOC)估计。由于老化电池是未知系统,利用传统的单一模型卡尔曼滤波器估计老化电池SOC时,因...基于电池的戴维宁(Thevenin)模型,设计了多模型自适应卡尔曼滤波器,并将多模型自适应卡尔曼滤波器应用于电动汽车电池荷电状态(state-of-charge,SOC)估计。由于老化电池是未知系统,利用传统的单一模型卡尔曼滤波器估计老化电池SOC时,因模型不准确而使估计误差增大。与单一模型滤波估计相比,多模型滤波估计融合了电池的各种老化信息,适合于未知系统的状态估计,从而提高了SOC的估计精度,并通过实验证明了上述结论的正确性。利用多模型自适应卡尔曼滤波器估计电池SOC,老化电池的模型与权值最大的单一模型较接近,根据单一模型权值可以近似估计出老化电池的健康状态(state of health,SOH),并通过电池容量测量,证明了SOH估计的正确性。展开更多
The state-of-charge(SOC)and state-of-health(SOH)of lithium-ion batteries affect their operating performance and safety.The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging.This ...The state-of-charge(SOC)and state-of-health(SOH)of lithium-ion batteries affect their operating performance and safety.The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging.This paper proposes a novel transformer-embedded lithium-ion battery model for joint estimation of state-ofcharge and state-of-health.The battery model is formulated across temperatures and aging,which provides accurate feedback for unscented Kalman filter-based SOC estimation and aging information.The open-circuit voltages(OCVs)are corrected globally by the temporal convolutional network with accurate OCVs in time-sliding windows.Arrhenius equation is combined with estimated SOH for temperature-aging migration.A novel transformer model is introduced,which integrates multiscale attention with the transformer's encoder to incorporate SOC-voltage differential derived from battery model.This model simultaneously extracts local aging information from various sequences and aging channels using a self-attention and depth-separate convolution.By leveraging multi-head attention,the model establishes information dependency relationships across different aging levels,enabling rapid and precise SOH estimation.Specifically,the root mean square error for SOC and SOH under conditions of 15℃dynamic stress test and 25℃constant current cycling was less than 0.9%and 0.8%,respectively.Notably,the proposed method exhibits excellent adaptability to varying temperature and aging conditions,accurately estimating SOC and SOH.展开更多
文摘基于电池的戴维宁(Thevenin)模型,设计了多模型自适应卡尔曼滤波器,并将多模型自适应卡尔曼滤波器应用于电动汽车电池荷电状态(state-of-charge,SOC)估计。由于老化电池是未知系统,利用传统的单一模型卡尔曼滤波器估计老化电池SOC时,因模型不准确而使估计误差增大。与单一模型滤波估计相比,多模型滤波估计融合了电池的各种老化信息,适合于未知系统的状态估计,从而提高了SOC的估计精度,并通过实验证明了上述结论的正确性。利用多模型自适应卡尔曼滤波器估计电池SOC,老化电池的模型与权值最大的单一模型较接近,根据单一模型权值可以近似估计出老化电池的健康状态(state of health,SOH),并通过电池容量测量,证明了SOH估计的正确性。
基金financially supported by the Science and Technology Major Project of Fujian Province of China(No.2022HZ028018)the National Natural Science Foundation of China(No.51907030)。
文摘The state-of-charge(SOC)and state-of-health(SOH)of lithium-ion batteries affect their operating performance and safety.The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging.This paper proposes a novel transformer-embedded lithium-ion battery model for joint estimation of state-ofcharge and state-of-health.The battery model is formulated across temperatures and aging,which provides accurate feedback for unscented Kalman filter-based SOC estimation and aging information.The open-circuit voltages(OCVs)are corrected globally by the temporal convolutional network with accurate OCVs in time-sliding windows.Arrhenius equation is combined with estimated SOH for temperature-aging migration.A novel transformer model is introduced,which integrates multiscale attention with the transformer's encoder to incorporate SOC-voltage differential derived from battery model.This model simultaneously extracts local aging information from various sequences and aging channels using a self-attention and depth-separate convolution.By leveraging multi-head attention,the model establishes information dependency relationships across different aging levels,enabling rapid and precise SOH estimation.Specifically,the root mean square error for SOC and SOH under conditions of 15℃dynamic stress test and 25℃constant current cycling was less than 0.9%and 0.8%,respectively.Notably,the proposed method exhibits excellent adaptability to varying temperature and aging conditions,accurately estimating SOC and SOH.
