Based on the Unscented Kalman Filter to Estimate the State of Four-Wheel-Independent Electric Vehicle with X-by-Wire

2015 ◽  
Vol 8 (2) ◽  
pp. 316-322 ◽  
Author(s):  
Xingjian Gu ◽  
Guoying Chen ◽  
Changfu Zong
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Unscented Kalman Filter ◽  
The State

State of Energy Estimation Based on AUKF for Lithium Battery Used on Pure Electric Vehicle

2012 ◽  
Vol 608-609 ◽  
pp. 1627-1630
Author(s):  
Hong Wei Liu ◽  
Hai Feng Wang ◽  
Chong Guo
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Unscented Kalman Filter ◽  
The State ◽  
Estimation Accuracy ◽  
Battery Management System ◽  
Battery Management ◽  
Process Noise ◽  
Energy Estimation ◽  
Kalman Filter Algorithm

State of Energy can be used to predict the driving mileage of electric vehicles, design the control strategy of vehicle energy distribution, and improve the safety of electric vehicle. Accurate estimaion of state of energy is one of the key technologies in the study on battery management system of electric vehicle. In this paper, the State of Energy is estimated by using Unscented Kalman Filter, while the process noise and measurement noise is adjusted by using the Sage-Husa adaptive algorithm, as a result the estimation accuracy is improved. The result shows that the State of Energy estimation by using Adaptive Unscented Kalman Filter algorithm is satisfactory to electric vehicle.

scholarly journals Estimation of the State of Charge of Lithium Batteries Based on Adaptive Unscented Kalman Filter Algorithm

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1425
Author(s):  
Jiechao Lv ◽  
Baochen Jiang ◽  
Xiaoli Wang ◽  
Yirong Liu ◽  
Yucheng Fu
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Unscented Kalman Filter ◽  
The State ◽  
State Of Charge ◽  
Soc Estimation ◽  
System Noise ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Covariance ◽  
Observation Noise

The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.

Spherical Simplex Unscented Kalman Filter-Based Jumping and Static Interacting Multiple Model

2017 ◽  
Vol 32 (04) ◽  
pp. 1850010
Author(s):  
Yi Pan ◽  
Hui Ye ◽  
Keke He
Keyword(s):  
Kalman Filter ◽  
Nonlinear Filtering ◽  
System Parameter ◽  
Unscented Kalman Filter ◽  
Continuous System ◽  
The State ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Real Time System ◽  
Spherical Simplex

A modified interacting multiple model (IMM) method called spherical simplex unscented Kalman filter-based jumping and static IMM (SSUKF-JSIMM) is proposed to solve the problem of nonlinear filtering with unknown continuous system parameter. SSUKF-JSIMM regards the continuous system parameter space as a union of disjoint regions, and each region is assigned to a model. For each model, under the assumption that the parameter belongs to the corresponding region, one sub-filter is used to estimate the parameter and the state when the parameter is presumed to be jumping, and another sub-filter is used to estimate the parameter and the state when the parameter is presumed to be static. Considering that spherical simplex unscented Kalman filter (SSUKF) is more suitable for a real-time system than the unscented Kalman filter (UKF), SSUKFs are adopted as the sub-filters of SSUKF-JSIMM. Results of the two SSUKFs are fused as the estimation output of the model. Experimental results show that SSUKF-JSIMM achieves higher performance than IMM, SIR, and UKF in bearings-only tracking problem.

Information Fusion for State Estimation of Power Battery in Electric Vehicle Based on Unscented Kalman Filter

2013 ◽  
Vol 303-306 ◽  
pp. 975-978
Author(s):  
Hong Yu Zheng ◽  
Chang Fu Zong
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Information Fusion ◽  
Unscented Kalman Filter ◽  
Accurate Estimate ◽  
Fusion Algorithm ◽  
Power Battery ◽  
Battery State Of Charge ◽  
New Generation

The power battery state of charge (SOC) in electric vehicles is not easy to measure accurately or apply a sensor but the expense is increased. However the variable of SOC is great importance to control of electric vehicles. A power battery model is built by the Partnership for a New Generation of Vehicles (PNGV) model to estimate the state of SOC. In order to make a high accurate estimate for SOC value, an information fusion algorithm based on unscented kalman filter (UKF) is introduced to design an observer. The test results show that the observer based information fusion and UKF are effective and accuracy, so it is may apply it the electric vehicle control and observation.

scholarly journals Research on the State of Charge of Lithium-Ion Battery Based on the Fractional Order Model

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6307
Author(s):  
Lin Su ◽  
Guangxu Zhou ◽  
Dairong Hu ◽  
Yuan Liu ◽  
Yunhai Zhu
Keyword(s):  
Kalman Filter ◽  
Fractional Order ◽  
Colored Noise ◽  
Unscented Kalman Filter ◽  
The State ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Order Model ◽  
Discrete State ◽  
Fractional Order Model

Accurate estimation of the state of charge (SOC) of lithium batteries is paramount to ensuring consistent battery pack operation. To improve SOC estimation accuracy and suppress colored noise in the system, a fractional order model based on an unscented Kalman filter and an H-infinity filter (FOUHIF) estimation algorithm was proposed. Firstly, the discrete state equation of a lithium battery was derived, as per the theory of fractional calculus. Then, the HPPC experiment and the PSO algorithm were used to identify the internal parameters of the second order RC and fractional order models, respectively. As discovered during working tests, the parameters identified via the fractional order model proved to be more accurate. Furthermore, the feasibility of using the FOUHIF algorithm was evaluated under the conditions of NEDC and UDDS, with obvious colored noise. Compared with the fractional order unscented Kalman filter (FOUKF) and integer order unscented Kalman filter (UKF) algorithms, the FOUHIF algorithm showed significant improvement in both the accuracy and robustness of the estimation, with maximum errors of 1.86% and 1.61% under the two working conditions, and a terminal voltage prediction error of no more than 5.29 mV.

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