Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.

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A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter

Learning observer based and event-triggered control to spacecraft against actuator faults

In this paper, a fault-tolerant control scheme is proposed for the rigid spacecraft attitude control system subject to external disturbances, multiple system uncertainties, and actuator faults. The angular velocity measurement is unavailable, which increases the complexity of the problem. An observer is first designed based on the super-twisting sliding mode method, which can provide accurate estimates of the angular velocity in finite time. Then, an adaptive fault-tolerant controller is proposed based on neural networks using the information from the observer. It is shown that the attitude orientations converge to the desired values exponentially. Finally, a simulation example is utilized to verify the effectiveness of the proposed scheme.

Fault-Tolerant Attitude Control for Rigid Spacecraft Without Angular Velocity Measurements

Aiming at the tough requirements for safety and maintainability in mobile hydraulic systems, in this article, an active fault-tolerant control (FTC) system is proposed against the valve faults of an independent metering valve Not only moderate faults of activated valves, but also significant faults and unactivated valve faults are considered Without additional hardware redundancy, analytical redundancy is derived by the coordinate control of other fault-free valves Accordingly, an FTC system in parallel with a normal controller is designed based on the pressure feedback It consists of a set of reconfigurable controllers and a decision mechanism The control signals, control loops, and operating modes can all be precisely reconfigured to adaptively match unmodeled fault dynamics A bumpless transfer controller based on a latent tracking loop is designed to smooth the switching between the normal controller and FTC Consequently, random valve faults can be tolerated with minor degradations in motion tracking and energy-saving performance The feasibility of the FTC system is evaluated by a 2-ton excavator

Active Fault-Tolerant Control for Electro-Hydraulic Systems With an Independent Metering Valve Against Valve Faults

Appointed-time fault-tolerant attitude tracking control of spacecraft with double-level guaranteed performance bounds

Fault-tolerant adaptive finite-time attitude synchronization and tracking control for multi-spacecraft formation

https://onlinelibrary.wiley.com/doi/pdf/10.1002/asjc.1775

Jihe Wang

Papers

Fault-tolerant adaptive finite-time attitude synchronization and tracking control for multi-spacecraft formation

Learning observer based and event-triggered control to spacecraft against actuator faults

Synchronization and Tracking of Multi-Spacecraft Formation Attitude Control Using Adaptive Sliding Mode

Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

Multi-spacecraft attitude cooperative control using model-based event-triggered methodology