1185 results about "Equation of state" patented technology

The present invention provides a network state machine which supports the three network states of the Home Phone Line Networking Alliance specification version 2.0 (HPNA 2.0) using two network states has been disclosed. When a station is in the V1M2 mode, instead of transmitting this frame in the 10M8 format frame with the gap frame, the frame is transmitted in the 1M8 format frame without any gaps in the frame. With this, the three network state equations of HPNA 2.0 collapses into two equations. With only two network states, the complexity of the network state machine is reduced, and a Physical Layer (PHY) which supports only the two network states may be used.

The invention discloses an autocollimation method of a carrier aircraft rotating type strapdown inertial navigation system under a shaking base, and belongs to the field of navigation. The autocollimation method comprises the following steps of: firstly obtaining the longitude and latitude of the position of a carrier, secondly collecting output signals of a fiber-optic gyro and a quartz flexible accelerometer in an inertia measurement unit, and then carrying out automatic compensation on a constant error of an inertia device by adopting a double-shaft rotating table rotatory IMU (inertial measurement unit) to form a rotating type strapdown inertial navigation system; and then calculating a rough initial posture array by utilizing gravity acceleration in an inertial coordinate system as a reference vector, establishing a state equation and a measuring equation again of the system, designing a vanishing adaptation Kalman filter to accurately estimate a misalignment angle of the carrier, and correcting a strapdown posture matrix by utilizing the misalignment angle to finish initial alignment and enter a navigation state. The method isolates the influence of shake of a vessel to initial alignment of a carrier aircraft, and dynamic random disturbance in measured noise is restrained through estimating the initial posture array of the system via the vanishing adaptation Kalman filter method, so that rapid autocollimation of the carrier aircraft rotating type strapdown inertial navigation system is realized.

The present invention discloses a high-precision satellite attitude determination method based on star sensor and gyroscope, which comprises the following steps: step 1. establishing a status equation of a satellite attitude determination system; step 2. establishing a measurement equation of the satellite attitude determination system; step 3. performing an online real-time model error estimation through predictive filtering; and step 4. performing a status estimation on a compensated model through 2-order interpolation filtering to obtain the attitude of a satellite. By applying predictive filtering to performing an online real-time model error estimation and correcting the system model, the invention overcomes the shortcoming existing in the conventional estimation process that error is processed into zero-mean white noise; and in addition, the invention can process any nonlinear system and noise conditions to obtain an estimation result of higher precision and is applicable to thefield of high-precision attitude determination.

A method for modeling changes in the state of charge vs. open circuit voltage (SOC-OCV) curve for a lithium-ion battery cell as it ages. During battery pack charging, voltage and current data are gathered for a battery cell. A set of state equations are used to determine the stoichiometry and state of charge of the cathode half-cell based on the charging current profile over time. The voltage and current data, along with the stoichiometry and state of charge of the cathode half-cell, are then used to estimate maximum and minimum solid concentration values at the anode, using an error function parameter regression/optimization. With stoichiometric conditions at both the cathode and anode calculated, the cell's capacity and a new SOC-OCV curve can be determined.

The invention discloses an indoor positioning system and method based on Bluetooth and MEMS (Micro-Electro-Mechanical Systems) fusion. A particle filter for Bluetooth/MEMS fusion positioning is designed, usable information of two subsystems is sufficiently utilized, wherein a Bluetooth system matches and acquires information of a user on line after off-line database building, and an MEMS combined system acquires position prediction information of the user through course angle calculation and dynamic step length estimation; an extended Kalman approximate particle filter is taken as a fusion navigation filter, user position information data of the two subsystems are fused, a state equation and an observation equation are constructed by reasonably selecting the state variable and the observational variable, finally, the optimal user position information is obtained, and indoor accurate positioning is realized. Through Bluetooth/MEMS indoor fusion positioning, the defects that a conventional indoor positioning system is low in positioning accuracy, the equipment cost is high, the coverage range is narrow, the positioning continuity is poor and the like can be overcome.

A parameter estimation device using a filter has a preprocessing computing means 6, a state estimating means 7 and a state-of-charge estimating means 9. A low-pass filter 61 of the preprocessing computing means 6 transfers voltage preprocessing value V_p and current preprocessing value I_p from inputted discharge-and-charge current value I and terminal voltage value V, respectively. The state estimating means 7 estimates parameters of a state equation of a battery equivalent model 7A based on the battery equivalent model 7A containing a resistance and a capacitor from the voltage preprocessing value and the current preprocessing value inputted from the means 6. The state-of-charge estimating means 9 estimates the state of charge from state quantity obtained by the means 7. The time constant varies in such a way that the higher the temperature and the state of charge SoC becomes the smaller the time constant becomes.

The invention discloses a BD/DNS/IMU autonomous integrated navigation system and a method thereof. The system includes an airborne navigation computer, an IMU, a Doppler radar, a BD, an air data system, a data recorder, a monitoring system and an AHRS, wherein the IMU, the Doppler radar, the BD, the air data system, the data recorder, the monitoring system and the AHRS are connected with the airborne navigation computer. The method comprises the steps that a self-adapted Kalman filtering model is established by the adoption of a 'position + speed' matching mode, after pose matrix transformation is performed on the Doppler radar, differential operation is performed on velocity measurement information under the navigation system and velocity information calculated through inertial navigation solutions, and differential operation is performed on the position information outputted by the BD and the position information calculated through inertial navigation solutions; difference values obtained serve as observed quantity to perform Kalman filtering information fusion, and loop iteration is preformed on error variances of all branch systems in a state equation of a filtering estimating system; real-time closed-loop estimation is carried out, and all parametric variables in an inertial navigation system, the Doppler radar and the BD are amended to obtain final integrated navigation results after filtering correction. According to the BD/DNS/IMU autonomous integrated navigation system and the method thereof, long-endurance navigation can be carried out in an airborne and seamless mode in real time, navigation accuracy is high, and cost is low.

The invention discloses a lithium battery SOC online estimation method comprising the following steps that 1) the open-circuit voltage of a battery is measured, and the state-of-charge initial value of the battery is obtained according to an OCV-SOC curve; 2) the second-order RC equivalent model of the battery is established and the parameter initial value of the battery equivalent model is estimated; 3) the estimation program is started, and the matching coefficient initial value of a state equation is set according to the battery state-of-charge initial value and the parameter initial value of the battery equivalent model; 4) the current battery state-of-charge value is obtained by using an adaptive unscented Kalman filtering algorithm, and the current open-circuit voltage is obtained according to the OCV-SOC curve; 5) the least square method with the forgetting factor is started to identify the parameters of the current battery equivalent model, the matching coefficient of the state equation is updated by the identified parameters and the battery state-of-charge value of the next moment is solved; and 6) the steps 4) and 5) are repeated so that the battery state-of-charge value of each moment is obtained. Compared with the conventional unscented Kalman filtering algorithm, the method has higher accuracy and higher error convergence.

A combined initial alignment system and alignment method for a strapdown inertial navigation system of an underground coal mining machine belong to initial alignment system and alignment method for positioning navigation of underground coal mining machines. The system is composed of a strapdown inertial navigation system installed on the coal mining machine, a wireless sensor network mobile node, an inclination sensor, a geomagnetic field sensor and anchor nodes installed on a hydraulic support. After coarse alignment of the strapdown inertial navigation system, the wireless sensor network is used to detect the location information of the underground coal mining machine, the inclination sensor measures the roll and pitch angle, and the geomagnetic field sensor measures the yaw angle. A pose measurement equation of the coal mining machine is constructed, and the pose measurement equation is combined with an error model after the coarse alignment of the strapdown inertial navigation system to establish a state equation; fusion smoothing is carried out to obtain the accurate location information of the coal mining machine; and the precise alignment of the strapdown inertial navigation system is conducted to complete the initial alignment. The invention realizes precise initial alignment of strapdown inertial navigation combination under the severe closed condition in coal mine, and greatly improves the precision of the integrated positioning under large misalignment angle of the strapdown inertial navigation system of the coal mining machine.

A computer system and method of simulating the behavior of an oil and gas reservoir including changes in the margins of frangible solids. A system of equations including state equations such as momentum, and conservation laws such as mass conservation and volume fraction continuity, are defined and discretized for at least two phases in a modeled volume, one of which corresponds to frangible material. A material point model technique for numerically solving the system of discretized equations, to derive fluid flow at each of a plurality of mesh nodes in the modeled volume, and the velocity of at each of a plurality of particles representing the frangible material in the modeled volume. A time-splitting technique improves the computational efficiency of the simulation while maintaining accuracy on the deformation scale. The method can be applied to derive accurate upscaled model equations for larger volume scale simulations.

The invention discloses a multiwave beam-based depth-sounding joint inversion method for a sound velocity profile and a submarine topography. The method comprises the following steps of: (1) transmitting multiwave beams to the seafloor of a sounded sea area through a transmitting transducer array of a multiwave-beam depth sounding system, and receiving echo signals through a receiving transducer array of the multiwave-beam depth sounding system; (2) obtaining the arrival angle and the arrival time of echoes according to the received echo signals by using the multiwave-beam depth sounding system; (3) establishing a state space model formed by using a state equation and a sounding equation; (4) obtaining inversion values of the sound velocity gradient and the seafloor depth of the sounded sea area by using a sequential filter method according to the established state space model and the arrival angle and the arrival time of received echoes, and obtaining an estimate of the sound velocity profile of the sounded sea area by using the inversion value of the sound velocity gradient; and further, calculating the water temperature profile of the sounded sea area by using the estimate of the sound velocity profile. According to the multiwave beam-based depth-sounding joint inversion method for the sound velocity profile and the submarine topography, disclosed by the invention, the estimates of the sound velocity profile and the depth of the seafloor can be obtained quickly and accurately.

The invention discloses a near-space missile-borne strap-down inertial navigation system transfer alignment method based on a star sensor. The method comprises the following steps: 1) establishing a missile-borne strap-down inertial navigation system transfer alignment state equation by taking an inertial coordinate system (launching point coordinate system for short) on a carrier launching point as a navigation coordinate system and a strap-down inertial navigation system (SINS) on a missile to be launched as sub-inertial navigation; 2) calculating navigation information and observed quantity of the missile-borne strap-down inertial navigation system; 3) establishing a measurement equation; 4) by depending on the state equation and the measurement equation established, estimating a mathematics platform misalignment angle, a speed error, a position error and an installation error of the missile as well as flexural deflection of the carrier through a sparse grid integral kalman filter, and correcting a sub-inertial navigation system, thus finishing a transfer alignment process.

The invention discloses an airplane angular rate signal reconstruction method based on unscented kalman filter. The method comprises the following steps of: (1), denoising an attitude angle measurement signal containing measurement noise through a nonlinear tracking differentiator to obtain a differential signal of an attitude angle, and constructing a virtual measurement equation; (2), constructing a state equation of a system on the basis of natural characteristics of an airplane and according to tri-axial angular rate signals and torque characteristics of the airplane; and (3), reconstructing an angular rate signal of the system by selecting the unscented kalman filter technology as the processing manner because both the state equation and the measurement equation are nonlinear and the estimation precision can be influenced by traditional operation for linearizing a nonlinear equation.

The invention discloses a battery SOC online estimation method based on double Kalman filtering algorithm, comprising: S1) obtaining the initial value of the battery SOC; S2) creating a battery equivalent circuit model; obtaining the state equation and the output equation of the battery; S3) using the initial value of the battery SOC as the input state amount and the voltage equation corresponding to the battery equivalent circuit model as the output equation; and utilizing the expanded Kalman filtering algorithm to perform battery SOC estimation; S4) using the battery SOC estimated by the expanded Kalman filtering algorithm as the input state amount and the amper-hour integral method as the output equation; and performing battery SOC estimation through the use of the Kalman filtering algorithm for the estimation value of the battery SOC. The battery SOC online estimation method based on double Kalman filtering algorithm can obtain the SOC estimation value more accurately. Without its excessive reliance on a battery model, the requirement of the method on the current accuracy is also reduced. The Battery SOC online estimation method based on double Kalman filtering algorithm of the invention can find wide applications in the battery identification and estimation field.

The invention relates to a three-dimensional gesture determining and local positioning method of a lunar surface rover, which comprises the following steps: (1) ascertaining the rolling and pitching angles by use of a triaxial accelerometer with sensitivity while the rover is still; (2) determining the drift angle gesture by means of a sun sensor; (3) using the axial gesture and the gyro deviation as the state quantity, the rolling and pitching angles established by the triaxial accelerometer, the drift angle determined by the sun sensor as well as three gyro outputs as the measuring information, building a state equation and a measuring equation, and estimating the triaxial and gyro deviations by means of extended Kalman filter; (4) after compensating the gyro outputs by virtue of the estimated gyro deviations while the rover is in motion, calculating the gesture changes of the rover, finishing the preestimation of the gyro gesture, and fulfilling gesture update; (5) acquiring the information about the rotation speed of the driving wheels of the rover, the rotating angle of the steering wheel, the rotating angle of the left and right rocker arms, and getting the position increment of the rover in the body coordinate system by use of the forward kinematics relationship. The invention has the advantages of high precision of gesture determining and positioning, simple calculation and easy implementation of the engineering.

An underwater moving target positioning algorithm based on single beacon is characterized in that one overwater moving beacon is used to position an underwater moving target; the moving beacon is provided with a satellite positioning receiver; and the underwater moving target is provided with a horizontal gesture sensor, a navigation speed sensor, and a vertical position sensor. Through the technology, the linear distance between the underwater target and the beacon can be obtained, at the same time, measured values of gesture, speed, and depth of underwater target can be obtained by the sensors carried by the underwater target; on the basis of the linear distance and measured values, a system model is established, the initial state is calculated, the state observability analysis is performed, the state equation is discretized, and finally Kalman filtering is carried out so as to obtain the estimate value of position of the underwater target.

The invention relates to a direct torque control system of a permanent-magnet synchronous motor. According to the control system, an extended kalman filter (EKF) observer is adopted for stator magnetic linkage and rotation speed estimation, according to a mathematical model of the permanent-magnet synchronous motor, the state variable x of the system equals to [Psi alpha, Psi beta, Omega gamma, Theta gamma] T, the input variable mu equals to [mu alpha, mu beta] T, the output variable y equals to [Iota alpha, Iota beta ] T, and state equations and output equations (11) of the system are obtained. The motor rotating speed is used as the input of an automatic data rate changer (ADRC) speed controller, the measurement value Te of the electromagnetic torque is calculated through the obtained stator magnetic linkage, in addition, the measurement value Te and electromagnetic torque given signals are compared, comparison results are used as the input of a space voltage vector pulse generator, and the output of the space voltage vector pulse generator is connected with a voltage source inverter of the permanent-magnet synchronous motor. The direct torque control system has the advantages of good dynamic performance, better stability performance, high robustness and high anti-interference capability.

The invention provides a SINS/GPS/polarized light combination navigation system modeling and a dynamic pedestal initial aligning method, which relates to the dynamic pedestal initial aligning method for vehicle, ship and aviation aircraft. The invention concretely comprises the following steps: (1) establishing a SINS error equation as an initial aligning state equation; (2) according to a polarization azimuth, a GPS outputted speed and a position, establishing an initial aligning measurement equation base on the polarization azimuth error, a speed error and a position error; (3) estimating an attitude error, a speed error and a position error using a Kalman filtering; (4) correcting the feedbacks of the attitude, the speed and the position of SINS. The SINS outputs the attitude, the speed and the position information of the carrier to users. The invention has the advantages of high precision, small computational complexity and good anti-interference capability, and is used for improving the aligning precisions of vehicle, ship and aviation aircraft, with reduced initial aligning time.

The invention provides an SOC (state of charge) estimation method for controlling equivalent charging and discharging of a lithium battery. The SOC estimation method includes: establishing a first-order RC (resistor and capacitor) equivalent circuit model of a single lithium battery, and determining a state equation and an observation equation of a lithium battery system; determining a Kalman filtering discrete state space model, a state parameter variable and an observation parameter variable; updating an predicted value of the state parameter variable and a covariance matrix of measuring errors, and acquiring an innovation sequence according to an observed voltage value at the end of the lithium battery; introducing a self-adaptive fading factor to track and correct a predicted covariance matrix of the lithium battery system; calculating a Kalman filtering gain matrix of the discrete state space model, and updating the optimal estimated value and an error covariance matrix value at the present moment; acquiring a statistical property of process noise; acquiring an SOC estimation value at the present moment, and putting the parameters of the present moment in recursive iteration calculation of strong-tracking self-adaptive Kalman filtering for the next moment. By the aid of the SOC estimation method, real-time and precise estimation of SOC of the lithium battery is realized.

The invention discloses an online calibrating method of a ship-based rotary strapdown inertial navigation system. The method comprises the following steps: establishing an inertial component output error model and an inertial navigation system error equation, and researching the calibration of inertial component parameter errors and determining the quantity of state and the quantity of measuration; determining the position and weight of a cubature point according to dimension of the quantity of state, deducing a state equation and a one-step state prediction and state prediction covariance matrix related to the cubature point, and introducing a multiple time-varying fading factor modified state prediction covariance matrix; and deducing a measuring equation related to the cubature point and the fading factors, a self-correlated covariance matrix, a cross-correlated covariance matrix, a gain matrix, a state estimated value and a state error covariance estimated value, and designing a strong tracking volume Kalman filtering method with strong tracking performance and strong robustness. The method disclosed by the invention estimates the inertial component parameter errors by a filtering algorithm and carries out online calibration and compensates the inertial component parameter errors, so that the navigation precision is effectively improved. The method has strong parameter-varying robustness.

The invention relates to a navigation method realized by means of loose combination of visual navigation/inertial navigation. The main research content is to periodically correct inertial navigation parameters by using the position of the visual navigation and velocity information. The navigation method solves the problem that inertial navigation error accumulates over time. According to the navigation method, an error equation of an inertial navigation system is used as a state equation of a filter, the difference between the position calculated by means of the visual navigation and the position solved by using the inertial navigation system is used as measurement of Kalman filtering, and the parameters of the inertial navigation system and the error of a sensor are estimated, so that the inertial navigation parameters are corrected. Compared with the prior art, by adopting the scheme, the navigation method can enable the inertial navigation system to be maintained to have high accuracy for a long time, and the accuracy of the inertial navigation system is not affected due to failure of visual navigation calculation.

The invention discloses a power lithium battery SOC estimation method based on the self-adaptive Kalman filtering method. The power lithium battery SOC estimation method comprises the following steps:at first, according to the dynamic characteristics of a lithium ion battery, establishing a dual-polarization equivalent circuit model of the battery; then, obtaining data through testing the performance of the composite pulse power, identifying the characteristic parameter of the model, and adopting the least squares fit to obtain a relation curve of the open-circuit voltage and SOC; based on the relation curve of the open-circuit voltage and SOC and the discrete equation of a DP model, establishing a state equation and an observation equation, and substituting the state equation and the observation equation into the EFK algorithm to obtain a system matrix; and finally, adopting the modified self-adaptive extended Kalman filtering algorithm to estimate the battery SOC. With adoption of the power lithium battery SOC estimation method, the problems that the filtering results diffuse and the operation is not stable when the traditional self-adaptive Kalman filtering method or the EFK algorithm is adopted for SOC estimation are effectively solved, and the speed that the SOC estimated value is convergent to the truth value is increased.

A method and apparatus for monitoring multiphase fluid flow passing within a pipe is provided. The method includes the steps of: a) providing a flow pressure value and a flow temperature value for the multiphase fluid flow within the pipe; b) providing a fluid flowmeter operable to be attached to an exterior of the pipe, the flowmeter including a spatial array of at least two sensors disposed at different axial positions along the pipe, which flowmeter is adapted to produce flow velocity signals indicative of a velocity of the fluid flow traveling within the pipe; c) providing a processor adapted to include an equation of state model for the pressure, volume, and temperature properties for the multiphase fluid flow, and further adapted to receive composition data values for the multiphase fluid flow, the flow pressure value, and the flow temperature value, and the flow velocity signals from the flowmeter; and d) determining a volumetric flow rate of one or both the gas phase and liquid phase of the fluid flow.