基于改进型Thevenin模型的锂电池SOC估算研究

为实现锂离子电池SOC的准确估算,对传统的Thevenin等效电路模型进行了改进:构建考虑容量变化的二阶Thevenin模型;探究电池实际容量的变化情况,并利用其在模型中进行容量修正;改进HPPC实验对电路模型进行参数辨识,获取相关参数的变化情况;将获取的参数应用于仿真中,将等效电路模型与扩展型卡尔曼滤波结合起来,将仿真与实际测试结果进行比较。研究结果表明:温度和放电倍率对电池实际可用容量影响较大;等效电路模型的参数值也和环境温度密切相关;细化参数的辨识条件后的模型与卡尔曼滤波结合,可以将SOC的估计精度控制在2%以内。

SOC estimation based on data driven exteaded Kalman filter algorithm for power battery of electric vehicle and plug-in electric vehicle

State of charge(SOC)estimation has always been a hot topic in the field of both power battery and new energy vehicle(electric vehicle(EV),plug-in electric vehicle(PHEV)and so on).In this work,aiming at the contradiction problem between the exact requirements of EKF(extended Kalman filter)algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period,a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed.The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery.Through the combination of data-based and model-based SOC estimation method,the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods,and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment(such as in an EV or PHEV).A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.

Tracking with nonlinear measurement model by coordinate rotation transformation

A new filtering method is proposed to accurately estimate target state via decreasing the nonlinearity between radar polar measurements(or spherical measurements in three-dimensional(3D) radar) and target position in Cartesian coordinate. The degree of linearity is quantified here by utilizing correlation coefficient and Taylor series expansion. With the proposed method, the original measurements are converted from polar or spherical coordinate to a carefully chosen Cartesian coordinate system that is obtained by coordinate rotation transformation to maximize the linearity degree of the conversion function from polar/spherical to Cartesian coordinate. Then the target state is filtered along each axis of the chosen Cartesian coordinate. This method is compared with extended Kalman filter(EKF), Converted Measurement Kalman filter(CMKF), unscented Kalman filter(UKF) as well as Decoupled Converted Measurement Kalman filter(DECMKF). This new method provides highly accurate position and velocity with consistent estimation.

Identification of the nonlinear properties of rubber-bearings in base-isolated buildings with limited seismic response data

Seismic behaviors of base-isolated structures are highly affected by the nonlinear characteristics of the isolated systems. Most of the currently available methods for the identification of nonlinear properties of isolator require either the measurements of all structural responses or the assumptions of the proper mathematic models for the rubber-bearings. In this paper, two algorithms are proposed to identify the nonlinear properties of rubber-bearings in base-isolated buildings using only partial measurements of structural dynamic responses. The first algorithm is applicable to the case that proper mathematical models are available for the base isolators. It is based on the extended Kalman filter for the parametric identification of nonlinear models of rubber-bearing isolators and buildings. For the general case where it is difficult to establish a proper mathematical model to describe the nonlinear behavior of a rubber-bearing isolator, another algorithm is proposed to identify the model-tYee nonlinear property of rubber-bearing isolated system. Nonlinear effect of rubber-bearing is treated as ＇fictitious loading＇ on the linear building under severe earthquake. The algorithm is based on the sequential Kalman estimator for the structural responses and the least-squares estimation of the ＇fictitious loading＇ to identify the nonlinear force of rubber-bearing isolator. Simulation results demonstrate that the proposed two algorithms are capable of identifying the nonlinear properties of rubber-bearing isolated systems with good accuracy.