Comparison of Estimation Techniques for the Crankshaft Dynamics of an Opposed Piston Engine

2019 ◽  
Author(s):  
Joseph A. Drallmeier ◽  
Jason B. Siegel ◽  
Anna G. Stefanopoulou
Keyword(s):  
Kalman Filter ◽  
Internal Combustion Engines ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Weight Ratio ◽  
Expansion Ratio ◽  
Piston Engine ◽  
Lower Estimation ◽  
Crank Angle ◽  
Scavenging Efficiency

Abstract This study provides a comparison of a linear and unscented Kalman filter to estimate the dynamics of a two-stroke opposed piston engine. These engines maintain several advantages over conventional internal combustion engines including reduced heat transfer, higher power to weight ratio, and a larger expansion ratio. Additionally, a crank angle phasing is introduced between the two cranks to improve scavenging efficiency. However, the coupling of the two crankshafts through a large geartrain can cause high amounts of noise and vibration harshness (NVH) and mechanical efficiency losses. By removing the geartrain and controlling the decoupled crankshafts with motor-generators, the NVH can be minimized while maintaining the benefits of crank angle phasing. To control the input torque of the motors to the engine, accurate estimations of the crankshaft position and dynamics are necessary. While the unscented Kalman filter exhibits lower estimation error, the filter is sensitive to model uncertainty relating to cylinder pressure, demanding further investigation of the robustness of the nonlinear filter.

scholarly journals Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3123 ◽  
Author(s):  
Quan Sun ◽  
Hong Zhang ◽  
Jianrong Zhang ◽  
Wentao Ma
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Measurement Data ◽  
Estimation Method ◽  
State Of Charge ◽  
Computing Technique ◽  
Soc Estimation ◽  
Lower Estimation ◽  
Non Gaussian

As an effective computing technique, Kalman filter (KF) currently plays an important role in state of charge (SOC) estimation in battery management systems (BMS). However, the traditional KF with mean square error (MSE) loss faces some difficulties in handling the presence of non-Gaussian noise in the system. To ensure higher estimation accuracy under this condition, a robust SOC approach using correntropy unscented KF (CUKF) filter is proposed in this paper. The new approach was developed by replacing the MSE in traditional UKF with correntropy loss. As a robust estimation method, CUKF enables the estimate process to be achieved with stable and lower estimation error performance. To further improve the performance of CUKF, an adaptive update strategy of the process and measurement error covariance matrices was introduced into CUKF to design an adaptive CUKF (ACUKF). Experiment results showed that the proposed ACUKF-based SOC estimation method could achieve accurate estimate compared to CUKF, UKF, and adaptive UKF on real measurement data in the presence of non-Gaussian system noises.

scholarly journals End-Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mingrui Luo ◽  
En Li ◽  
Rui Guo ◽  
Jiaxin Liu ◽  
Zize Liang
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Unscented Kalman Filter ◽  
Rbf Neural Network ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Data ◽  
Measurement Unit ◽  
Complex Environments ◽  
End Effector

Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

scholarly journals Ground Reaction Force Estimation in Prosthetic Legs With Nonlinear Kalman Filtering Methods

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Seyed Fakoorian ◽  
Vahid Azimi ◽  
Mahmoud Moosavi ◽  
Hanz Richter ◽  
Dan Simon
Keyword(s):  
Kalman Filter ◽  
Continuous Time ◽  
Lyapunov Functions ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Reaction Force ◽  
Force Estimation ◽  
Joint Angles ◽  
Estimation Errors ◽  
Reaction Forces

A method to estimate ground reaction forces (GRFs) in a robot/prosthesis system is presented. The system includes a robot that emulates human hip and thigh motion, along with a powered (active) transfemoral prosthetic leg. We design a continuous-time extended Kalman filter (EKF) and a continuous-time unscented Kalman filter (UKF) to estimate not only the states of the robot/prosthesis system but also the GRFs that act on the foot. It is proven using stochastic Lyapunov functions that the estimation error of the EKF is exponentially bounded if the initial estimation errors and the disturbances are sufficiently small. The performance of the estimators in normal walk, fast walk, and slow walk is studied, when we use four sensors (hip displacement, thigh, knee, and ankle angles), three sensors (thigh, knee, and ankle angles), and two sensors (knee and ankle angles). Simulation results show that when using four sensors, the average root-mean-square (RMS) estimation error of the EKF is 0.0020 rad for the joint angles and 11.85 N for the GRFs. The respective numbers for the UKF are 0.0016 rad and 7.98 N, which are 20% and 33% lower than those of the EKF.

scholarly journals An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Hongjian Wang ◽  
Guixia Fu ◽  
Juan Li ◽  
Zheping Yan ◽  
Xinqian Bian
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Underwater Vehicle ◽  
Statistical Parameters ◽  
Sea Trial ◽  
Unmanned Underwater Vehicle ◽  
Noise Statistic ◽  
Localization And Mapping ◽  
Slam Algorithm

This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization and mapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAM algorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matching method. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system.

scholarly journals UKF-Based Vehicle Pose Estimation under Randomly Occurring Deception Attacks

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xinghua Liu ◽  
Dandan Bai ◽  
Yunling Lv ◽  
Rui Jiang ◽  
Shuzhi Sam Ge
Keyword(s):  
Kalman Filter ◽  
Pose Estimation ◽  
Upper Bound ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Accurate Estimation ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Attack Model ◽  
Error Covariance

Considering various cyberattacks aiming at the Internet of Vehicles (IoV), secure pose estimation has become an essential problem for ground vehicles. This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks. By modeling attacks as signals added to measurements with a certain probability, the attack model has been presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multisensor fusion. An unscented Kalman filter-based secure pose estimator is then proposed to generate a stable estimate of the vehicle pose states; i.e., an upper bound for the estimation error covariance is guaranteed. Finally, the simulation and experiments are conducted on a simple but effective single-input-single-output dynamic system and the ground vehicle model to show the effectiveness of UKF-based secure pose estimation. Particularly, the proposed scheme outperforms the conventional Kalman filter, not only by resulting in more accurate estimation but also by providing a theoretically proved upper bound of error covariance matrices that could be used as an indication of the estimator’s status.

Ultra-tight GPS/IMU Integration based Long-Range Rocket Projectile Navigation

2016 ◽  
Vol 66 (1) ◽  
pp. 64 ◽  
Author(s):  
Handong Zhao ◽  
Zhipeng Li
Keyword(s):  
Kalman Filter ◽  
Long Range ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Position Information ◽  
Cubature Kalman Filter ◽  
Carrier Phase Tracking ◽  
Output Information ◽  
Navigation Accuracy

<p>Accurate navigation is important for long-range rocket projectile’s precise striking. For getting a stable and high-performance navigation result, a ultra-tight global position system (GPS), inertial measuring unit integration (IMU)-based navigation approach is proposed. In this study, high-accuracy position information output from IMU in a short time to assist the carrier phase tracking in the GPS receiver, and then fused the output information of IMU and GPS based on federated filter. Meanwhile, introduced the cubature kalman filter as the local filter to replace the unscented kalman filter, and improved it with strong tracking principle, then, improved the federated filter with vector sharing theory. Lastly simulation was carried out based on the real ballistic data, from the estimation error statistic figure. The navigation accuracy of the proposed method is higher than traditional method.</p><p><strong>Defence Science Journal, Vol. 66, No. 1, January 2016, pp. 64-70, DOI: http://dx.doi.org/10.14429/dsj.66.8326</strong></p>

scholarly journals A Method to Identify Lithium Battery Parameters and Estimate SOC Based on Different Temperatures and Driving Conditions

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1391 ◽  
Author(s):  
Yongliang Zheng ◽  
Feng He ◽  
Wenliang Wang
Keyword(s):  
Kalman Filter ◽  
Electric Vehicles ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Battery Management System ◽  
Battery Management ◽  
Soc Estimation ◽  
Different Temperatures ◽  
Driving Conditions

State of charge (SOC) plays a significant role in the battery management system (BMS), since it can contribute to the establishment of energy management for electric vehicles. Unfortunately, SOC cannot be measured directly. Various single Kalman filters, however, are capable of estimating SOC. Under different working conditions, the SOC estimation error will increase because the battery parameters cannot be estimated in real time. In order to obtain a more accurate and applicable SOC estimation than that of a single Kalman filter under different driving conditions and temperatures, a second-order resistor capacitor (RC) equivalent circuit model (ECM) of a battery was established in this paper. Thereafter, a dual filter, i.e., an unscented Kalman filter–extended Kalman filter (UKF–EKF) was developed. With the EKF updating battery parameters and the UKF estimating the SOC, UKF–EKF has the ability to identify parameters and predict the SOC of the battery simultaneously. The dual filter was verified under two different driving conditions and three different temperatures, and the results showed that the dual filter has an improvement on SOC estimation.

scholarly journals Online State of Charge Estimation of Lithium-Ion Cells Using Particle Filter-Based Hybrid Filtering Approach

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Ming Zhang ◽  
Kai Wang ◽  
Yan-ting Zhou
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
State Of Charge ◽  
Soc Estimation ◽  
Hybrid Filtering ◽  
Li Ion

Filtering based state of charge (SOC) estimation with an equivalent circuit model is commonly extended to Lithium-ion (Li-ion) batteries for electric vehicle (EV) or similar energy storage applications. During the last several decades, different implementations of online parameter identification such as Kalman filters have been presented in literature. However, if the system is a moving EV during rapid acceleration or regenerative braking or when using heating or air conditioning, most of the existing works suffer from poor prediction of state and state estimation error covariance, leading to the problem of accuracy degeneracy of the algorithm. On this account, this paper presents a particle filter-based hybrid filtering method particularly for SOC estimation of Li-ion cells in EVs. A sampling importance resampling particle filter is used in combination with a standard Kalman filter and an unscented Kalman filter as a proposal distribution for the particle filter to be made much faster and more accurate. Test results show that the error on the state estimate is less than 0.8% despite additive current measurement noise with 0.05 A deviation.

scholarly journals State Estimation of RC Filters using Unscented Kalman Filter

2020 ◽  
Vol 9 (9) ◽  
pp. 91-96
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
State Space Model ◽  
Taylor Series Expansion ◽  
System Model ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass ◽  
High Pass

This paper implements unscented Kalman filter (UKF) for output voltage estimation of RC low pass filter (LPF) and high pass filter (HPF). At first, the state space model has been obtained using Kirchhoff’s current law (KCL). The performance of UKF has been compared with extended Kalman filter (EKF). The simulation results validate the superiority of UKF over EKF as the estimation error is smaller using UKF as compared to the EKF method. As the UKF uses unscented transform (UT) and EKF uses Taylor series expansion for linearization purpose, linearization error is smaller in UKF as compared to EKF method. Also, UKF implementation has the advantage that it does not require Jacobian computation of nonlinear system model.

Design an Unscented Kalman Filter-Based SoC Estimator for HEV Application

2012 ◽  
Vol 588-589 ◽  
pp. 424-428 ◽  
Author(s):  
Hong Wen He ◽  
Kai Zhao ◽  
Rui Xiong
Keyword(s):  
Kalman Filter ◽  
Lithium Ion Battery ◽  
Hybrid Electric Vehicles ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Lithium Ion ◽  
Soc Estimation ◽  
Non Linear ◽  
Non Linear System ◽  
Method Accuracy

An unscented Kalman filter (UKF) is adopted to estimate the state of charge (SoC) of a lithium ion battery for application in hybrid electric vehicles (HEVs). Generally, the extended Kalman filter (EKF) can be selected to estimate a non-linear system state. However, it may leads to large errors since the strong non-linear and stochastic performance. In this paper, the performance of the lithium-ion battery is tested by a design of experiment, such as hysteresis, polarization, coulomb efficiency, etc. And a combined battery model is selected for SoC estimation, while the model parameter was identified by using UKF algorithm. Finally, the federal urban driving schedule (FUDS) is used to evaluate the proposed method accuracy. And the results show that the maximum SoC estimation error is less than 3%.

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