Uncertainty Interference Research Articles

Adaptive fault detection for lithium-ion battery combining physical model-based observer and BiLSTMNN learning approach

Lithium-ion batteries possess one of the best energy-weight ratios, while safety and reliability are critical issues for its continued operation. Due to extreme fast-charging, huge application sizes and variable usage environment, the risk of soft battery faults, including soft internal short circuit (SISC), is increased and may evolve into severe accidents. Therefore, accurate early detection of lithium-ion battery fault is imperative to guarantee the battery performance. Motivated by this fact, we proposed a real time fault detection framework for battery soft faults. Based on the Equivalent Circuit Model (ECM) and coupling thermal model, Extended Kalman Filter (EKF) observer is used for reliable monitoring of battery voltage and surface temperature. Inspired by uncertainty interference during the observation process, we explore an observed-based learning approach that combine physical model-based observer and Bidirectional Long Short-Term Memory neural networks (BiLSTMNN) to empower the observer with the ability to learn the uncertainties, which is efficient in distinguishing soft faults information and uncertainties from residual. Furthermore, the memory characteristics of BiLSTMNN are considered to improve the robustness of the detection system, including comprehensive training data and optimized input features. Finally, through the comparative analysis and the robustness evaluation experiments including various driving cycles and internal short circuits (ISC) faults test verity the good performance of the proposed method.

How to better match predicted loads of district heating system: A novel control approach focused on coupling source and network by data-driven methods

Deconstructing the load forecast into appropriate control parameters is essential for effective intelligent heating control. Drawing from the predicted load, a novel heating control approach is proposed, emphasizing the integration of source and network. The heating regulation of the energy station is achieved through controlling the supply water temperature, while the thermal entrance of the heating network is regulated by adjusting the flow rate using valve control. The heat transfer mechanism influencing the regulation of District Heating Systems (DHSs) operation and the alignment of supply and demand are analyzed to deconstruct the four key factors: load, flow, load-flow ratio, and supply water temperature. Multiple Linear Regression (MLR) method is employed to formulate the parameter equations relating to supply water temperature and valve opening. Given the uncertainty interference in DHS in practice, the model undergoes periodic dynamic revisions. The model’s validity is confirmed by the empirical data from three different heating seasons. The average absolute deviation of the calculated supply water temperature and valve opening from the actual DHS is 0.42℃ and 1.12%, respectively. When implemented in an actual system, the approach effectively decreased gas consumption by 29.3% while effectively fulfilling heating requirements.

Maneuverability indices estimation of nonlinear response model based on full-scale trial data and square root unscented Kalman filter

Research on ship maneuvering performance provides a theoretical reference for autonomous ship navigation control. The gain and time coefficients accurately describe the dynamic performance of the ship. Given the challenge that the unscented Kalman filter cannot ensure the nonnegativity of the state covariance matrix during the filtering process, this study proposed a parameter identification scheme for a second-order nonlinear response model based on the square root unscented Kalman filter. First, the maneuverability indices of the free-running model were identified based on simulation data. Thereafter, the feasibility and convergence performance of the proposed scheme were verified. Based on full-scale trial data with uncertainty interference, the parameters of the second-order nonlinear response model were estimated for the YUKUN ship. The feasibility, effectiveness, and generalizability of the proposed scheme were further verified. The proposed scheme was analyzed based on three perspectives: parameter convergence, estimation results of the maneuverability indices, and yaw angle prediction results. The results obtained using the proposed algorithm were compared with those obtained using the extended Kalman filter. Finally, the proposed scheme can provide a reference for online parameter identification and prior knowledge for course keeping controllers and autonomous navigation control.

Dynamic concentration measurement of micro/nano aluminum powder

An appropriate concentration of micro/nano aluminum powder (MNAP) cloud is necessary for fuel air explosive (FAE) detonation. However, because of the fast dynamics, uncertainty interference, and transient characteristics of MNAP diffusion, experimental research on the measurement of MNAP cloud concentration is still lacking. Here, a pair of ultrasonic transducers were developed based on pulse drive, to measure the dynamic MNAP cloud concentration during MNAP diffusion in a standard 20-L explosion vessel. The measurement method was based on Lloyd-Berry ultrasonic attenuation theory. The experimental results showed that the ultrasonic pulse had a strong anti-interference ability, high precision, and real-time response to enable the characterization of MNAP diffusion concentration. The diffusion concentration gradients of MNAP were established and agreed well with the trend predicted by computational fluid dynamics simulations. The results also indicated that aluminum nanoparticles have better distribution characteristics than those of aluminum microparticles. These analyses provide technical support to improve FAE detonation energy.