4 results about "Kalman estimation" patented technology

The invention relates to the technical field of lithium ion battery safety monitoring, and discloses a method and a device for early warning and positioning thermal runaway of a lithium ion battery pack in advance. The method comprises the following steps: firstly, collecting real-time voltage, temperature, current and serial number data of each battery cell, and constructing a battery cell state observation tensor with a time-space label; running a battery cell thermoelectric state extended Kalman estimation algorithm based on the tensor to generate a battery cell health state partial sequence lattice; and then constructing a thermal runaway risk propagation phase space grid, finally executing manifold analysis-based local attractor region identification and early warning decision under the constraint of the grid, and outputting a high-risk source cell number and an early warning level. According to the invention, the problems of early warning lag, lack of cell group state consistency dynamic evaluation and incapability of accurately positioning fault cells caused by dependence on later physical symptoms in the prior art are solved, and the thermal runaway early warning timeliness and positioning accuracy are improved.

An estimating device for determining a new estimated movement value, which indicates the speed of a rail vehicle. A Kalman filter is supplied on the input side with at least torque values, vertical force values, wheel size values, and wheel speed values. Based on these values, and based on a Kalman filtering, the filter determines a Kalman estimated vehicle speed value. A slip module, which is supplied on the input side with the torque values, the wheel size values, the wheel speed values, and the respective previous estimated movement value of the estimating device, determines a wheel circumferential speed value and a sliding indication, which indicates any sliding of the respective axle. An evaluation module determines the new estimated movement value of the estimating device on the basis of the Kalman estimated vehicle speed value, the wheel circumferential speed value, and the sliding indications.

The present application relates to the technical field of lithium ion battery safety monitoring, and discloses a method and device for early warning and positioning thermal runaway of lithium ion battery pack; first, real-time voltage, temperature, current and number data of each cell are collected to construct a cell state observation tensor with a space-time label; then, a cell thermal-electric state extended Kalman estimation algorithm is run based on the tensor to generate a cell health state partial order lattice; then, a thermal runaway risk propagation phase space grid is constructed, and finally, local attractor region identification and early warning decision based on manifold analysis are executed under grid constraints to output high-risk source cell number and early warning level. The present application solves the problems of existing technology, such as lagging early warning caused by dependence on late physical signs, lack of dynamic evaluation of cell group state consistency, and inability to accurately locate faulty cells, and improves the timeliness and positioning accuracy of thermal runaway early warning.

The invention relates, among other things, to an estimation device (1) for determining a new estimated motion value (v_virt), which indicates the speed of a railway vehicle (200) having at least two axles, comprising: a Kalman filter (30), which is fed on the input side with at least torque values ​​(m_p_i, m_e_i), vertical force values ​​(Q_i), wheel size values ​​(r_i) and wheel speed values ​​(ω_i) and on the basis of these, as well as on the basis of a Kalman filter, determines a Kalman estimated vehicle speed value (v_k);a slip module (40), which receives on the input side the values ​​of the torque (m_p_i, m_e_i), the values ​​of the wheel size (r_i), the values ​​of the wheel speed (ω_i) and the respective previous estimated motion value (v_virt) from the estimating device (1), and determines a circumferential speed value of the wheel (v_umlauf) and a slip indication, which indicates any slippage of the respective axle; and an evaluation module (50), which determines the new estimated motion value (v_virt) of the estimating device (1), based on the Kalman estimated vehicle speed value (v_k), the circumferential speed value of the wheel (v_umlauf) and the slip indications.