An adaptive method for quantum state fidelity estimation in bipartite higher dimensional systems is established. This method employs state verifier operators which are constructed by local POVM operators and adapted to the measurement statistics in t...
In this paper an adaptive tolerant estimator using singular value decomposition is proposed for a distribution network under model uncertainty in power systems. The adaptive tolerant estimator was designed with adjusted parameters and adjusted weight...
The premise of vehicle intelligent decision making is to obtain vehicle motion state parameters accurately and in real-time. Several state parameters cannot be measured directly by vehicle sensors, so estimation algorithms based on filtering are effe...
To combat the impacts of uncertain noise on the estimation of vehicle state parameters and the high cost of sensors, a state-observer design with an adaptive unscented Kalman filter (AUKF) is developed. The design equation of the state observer is de...
This paper proposes a multi-sensory Joint Adaptive Kalman Filter (JAKF) through extending innovation-based adaptive estimation (IAE) to estimate the motion state of the moving vehicles ahead. JAKF views Lidar and Radar data as the source of the local...
This paper puts forward a new adaptive observer scheme for joint estimation of state and multi-parameters for nonlinear dynamic systems. The adaptive observer uses chaos differential evolution algorithm to improve global optimality of estimation in t...
State estimation in tightly coupled nonlinear GNSS/INS navigation is complicated by persistent variation in measurement quality, deteriorating satellite geometry, and occasional abnormal updates that produce large position corrections. This paper dev...
Monitoring the current operation status of the power system plays an essential role in the enhancement of the power grid for future requirements. Therefore, the real-time state estimation (SE) of the power system has been of widely-held concern. The...
Accurate State of Energy (SOE) estimation is a critical yet challenging requirement for reliable Battery Management Systems (BMSs), as traditional model-based algorithms like the Extended Kalman Filter (EKF) are severely limited by their dependence o...
Precise modeling and state of charge (SoC) estimation of a lithium-ion battery (LIB) are crucial for the safety and longevity of battery systems in electric vehicles. Traditional methods often fail to adapt to the dynamic, nonlinear, and time-varying...
Unmanned aerial vehicle (UAV) state estimation is fundamental across applications like robot navigation, autonomous driving, virtual reality (VR), and augmented reality (AR). This research highlights the critical role of robust state estimation in en...
A state of charge (SOC) estimation method is proposed. An Adaptive Extended Kalman Particle filter (AEKPF) based on Particle Filter (PF) and Adaptive Kalman Filter (AKF) is used in order to decrease the error and reduce calculations. The second-order...
In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium–ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circui...
Data-driven methods have been extensively used for state of charge (SOC) estimation because of their nonlinear regression capability. However, when the driving cycle differs from the training conditions, their estimation accuracy may deteriorate beca...
Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, w...
We propose an adaptive multi-timescale particle filter (AMTS-PF) for nonlinear state estimation in wastewater treatment plants (WWTPs) to address multi-scale temporal dynamics. The AMTS-PF decouples the problem into minute-level state updates and hou...
The sideslip angle and the yaw rate are the key state parameters for vehicle handling and stability control. To improve the accuracy of the input parameters and the time-varying characteristics of noise covariance in state estimation, a combined meth...
For electric vehicles widely used recently, lithium-ion batteries serve as the primary energy storage units, affecting the vehicles’ performance, safety, and lifespan. Accurate state of charge (SOC) estimation is pivotal for the battery managem...
With the increasing demand for intelligence and automation, and the continuous strengthening of safety and efficiency requirements, the disadvantages of traditional “blind use” of nickel–cadmium batteries have become increasingly pr...
Accurate state of charge (SOC) estimation is essential for reliable battery management systems operating over a wide temperature range. This study proposes a joint estimation framework that combines a temperature-matched dual open-circuit-voltage (OC...
The state-of-charge (SOC) is a fundamental indicator representing the remaining capacity of lithium-ion batteries, which plays an important role in the battery’s optimized operation. In this paper, the model-based SOC estimation strategy is stu...
We propose a two-stage joint estimation method combining a multi-physics-constrained physics-informed neural network (PINN) with an adaptive extended Kalman filter (AEKF) for lithium-ion battery state-of-charge (SOC) estimation. Three constraints&mda...
Aiming at tracking sharply maneuvering targets, this paper develops novel variational adaptive state estimators for joint target state and process noise parameter estimation for a class of linear state-space models with abruptly changing parameters....
Accurate estimation of vehicle sideslip angle and lateral velocity is essential for the stability control of Advanced Driver Assistance Systems (ADASs). Traditional physics-based observers often exhibit dynamic response distortions under stability-li...
The maximum correntropy criterion (MCC), as an effective method for dealing with anomalous measurement noise, is widely applied in the design of filters. However, its performance largely depends on the proper setting of the kernel bandwidth, and curr...
In this study, we introduce an advanced framework for state estimation in electro-hydraulic systems, utilizing a structurally adapted Kalman filter. The proposed model was designed to enhance estimation accuracy and robustness under dynamic load vari...
The integration of distributed renewable energy sources into distribution networks is a key approach to achieving sustainable and low-carbon power systems. However, high renewable penetration significantly increases the volatility and uncertainty of...
Many dynamic systems experience unwanted actuation caused by an unknown exogenous input. Typically, when these exogenous inputs are stochastically bounded and a basis set cannot be identified, a Kalman-like estimator may suffice for state estimation,...
State of health (SOH) estimation plays a vital role in ensuring the safe and stable operation of lithium-ion battery management systems (BMSs). Data-driven methods are widely used to estimate SOH; however, existing methods often suffer from fixed or...
With the high penetration of power electronic devices, modern power systems exhibit complex fractional-order dynamic characteristics. Addressing this, along with the prevalent issues of multi-modal non-Gaussian noise, outliers, and sudden load change...
The acquisition of vehicle driving status information is a key function of vehicle dynamics systems, and research on high-precision and high-reliability estimation of key vehicle states has significant value. To improve the state observation effect,...
State-of-the art physics-model based dynamic state estimation generally relies on the assumption that the system’s transition matrix is always correct, the one that relates the states in two different time instants, which might not hold always on rea...
This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective...
Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charg...
In this paper, we present a novel approach to state estimation in dynamic systems by combining Physics-Informed Neural Networks (PINNs) with an adaptive Unscented Kalman Filter (UKF). Recognizing the limitations of traditional state estimation method...
Unbiased Finite Impulse Response (UFIR) filters are widely used in engineering applications, such as vehicle attitude estimation, due to their advantages, including independence from initial conditions and insensitivity to noise. However, the perform...
The accurate estimation of a lithium-ion battery’s state of charge (SOC) plays an important role in the operational safety and driving mileage improvement of electrical vehicles (EVs). The Adaptive Extended Kalman filter (AEKF) estimator is com...
This paper presents an adaptive nonlinear control and state observation framework for energy management in standalone photovoltaic (PV) systems integrated with battery energy storage. A unified nonlinear dynamic model is developed to describe the int...
In order to improve the speed regulation accuracy, dynamic response and operation robustness of an automatic guided vehicle (AGV) in a complex road disturbance environment, this paper studies an adaptive AGV speed regulation system based on EKF state...
State of charge (SOC) of the lithium-ion battery is an important parameter of the battery management system (BMS), which plays an important role in the safe operation of electric vehicles. When existing unknown or inaccurate noise statistics of the s...
To ensure the safety and reliability of an echelon-use lithium-ion battery (EULIB), the performance of a EULIB is accurately reflected. This paper presents a method of estimating the combined state of energy (SOE) and state of charge (SOC). First, ai...
Robust robot manipulation hinges on effective state estimation. The VRT 6 robot leverages an inertia measurement unit with triaxial gyroscopes, magnetometers, and accelerometers, as well as a position sensor, but these sensors are plagued by noise th...
As the main power source of new energy electric vehicles, the accurate estimation of State of Charge (SOC) of Li-ion batteries is of great significance for accurately estimating the vehicle’s driving range, prolonging the battery life, and ensu...
Conventional grid-forming (GFM) inverter control strategies often rely on fixed parameters and overlook the dynamic variation in energy stored in the DC link capacitor. This limitation can degrade transient performance and stability, particularly und...
Owing to the degradation of the performance of a retired battery and the unclear initial value of the state of charge (SOC), the estimation of the state of power (SOP) of an echelon-use battery is not accurate. An SOP estimation method based on an ad...
As a new type of transportation, the distributed drive electric vehicle is regarded as the main development direction of electric vehicles in the future. Due to the advantages of the independently controllable driving torque of each wheel, it provide...
State of charge (SOC) is an important evaluation index for lithium-ion batteries (LIBs) in electric vehicles (EVs). This paper proposes a nonlinear observer with a new adaptive gain structure for SOC estimation based on a second-order RC model. It is...
An echelon-use lithium-ion battery (EULB) refers to a powered lithium-ion battery used in electric vehicles when the battery capacity is attenuated to less than 80% and greater than 20%. Aiming at the degradation of the performance of the EULB and th...
For the safe and efficient use of lithium-ion batteries, the state of charge (SOC) is a particularly important state variable. In this paper, we propose a method for the online estimation of SOC and model parameters based on a fractional-order equiva...
Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is...
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