Use of a Gaussian Sum filter (GSF) to efficiently solve the initialisation problem in bearing-only simultaneous localisation and mapping (SLAM) is the main contribution of this paper. When information about the range is not available, the initial probability density function (pdf) of a landmark in the environment can not be represented using a Gaussian. The GSF is an attractive candidate for estimation in this scenario as it can deal with arbitrary pdfs represented as sets of Gaussians. However, the implementation of the GSF requires maintaining a bank of extended Kalman filters. The resulting computational complexity needs to be reduced by employing a minimum number of filters. In this work, the performance of each extended Kalman filter (EKF) in the GSF is evaluated using the sequential probability ratio test (SPRT). As such the number of members in the Gaussian sum can be reduced rapidly and the efficiency of the GSF can be significantly increased, providing a solution to the important problem of bearing-only SLAM. The effectiveness of the proposed approach is demonstrated by simulation and experiment conducted using a Pioneer mobile robot.

Bearing-only SLAM Using a SPRT Based Gaussian Sum Filter

In Nordic countries, distribution networks are unearthed or compensated. Earth faults, particularly in compensated networks, provide fault currents that are low compared to the load currents. Identification of the faulty feeder is therefore difficult. A preliminary description of discrete wavelet transform (DWT)-Bayesian selectivity technique was introduced in reference 1 to identify the faulty feeder. It was dependent on a conditional probabilistic method applied to transient features extracted by using the DWT. However, a practical setting for this technique has not yet been presented. Furthermore, its sensitivity is limited to 1.5-k fault resistances, and is further reduced to 170 when considering current transformer and network noise. In this paper, the ratio between the absolute sums of the DWT detail level from each feeder is used as an input to the conditional probability approach, providing an enhanced selectivity decision. This input contributes to discriminating the faulty feeder during high resistance faults. The relay setting is introduced as a function of the number of feeders and their characteristic impedances, as the proposed algorithm is dependent on the discharge's initial transients. The performance is evaluated taking into account current-transformer and network noises. A digital implementation is experimentally verified by using two digital signal processing boards.

Bayesian Selectivity Technique for Earth Fault Protection in Medium-Voltage Networks

Parallel battery pack (PBP) is an important unit for its application in electric vehicles and energy storage, and precise state of charge (SOC) is the basic parameter for battery efficient operation. However, the SOC is an internal hidden immeasurable variable, and the measurable battery parameters of the PBP are limited, which makes it difficult to precisely estimate SOC for the PBP. The main efforts are as follows: An improved equivalent circuit model of the PBP is first established on the basis of the fuzzy-proportional integral model regulator, which can accurately describe the influence of battery cell inconsistency on the PBP discharging characteristics. Under constant current and UDDS operating conditions, the battery model voltage can accurately capture the measured voltage during the discharging process, especially at the final stage of discharge with the maximum voltage absolute error below 0.12 V (about 3.2%). A model-based SOC prediction algorithm using an adaptive unscented Kalman filter (AUKF) with a sliding window noise estimator is developed for the PBP. It can adaptively achieve accurate process and measurement noise statistics of the PBP for the AUKF. The SOC of the PBP can be precisely estimated using the developed method with the absolute errors below 2% even if the noise statistics are randomly given respectively. Moreover, compared to the unimproved AUKF and the Sage-Husa method, the presented algorithm illustrates the highest SOC prediction precision with the lowest root mean square error of 1.12% and the minimum mean absolute error of 1.08%. 

State of charge estimation for a parallel battery pack jointly by fuzzy-PI model regulator and adaptive unscented Kalman filter

State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis

This work presents an empirical modeling approach combining a bilinear modeling technique, partial least squares, with the universal function approximation abilities of single hidden layer nonlinear artificial neural networks. This approach, referred to as neural network partial least squares (NNPLS), is compared to the common autoassociative artificial neural network. The NNPLS model is embedded into a graphical user interface and implemented at the Electrical Power Research Institute's Instrumentation and Control Center located at Tennessee Valley Authority's Kingston fossil power plant. Results are presented for 51 process signals with an average absolute estimation error of {approx}1.7% of the mean value, and sample drift detection performances are shown.

A Novel Approach to Process Modeling for Instrument Surveillance and Calibration Verification

Adaptive robust unscented Kalman filter-based state-of-charge estimation for lithium-ion batteries with multi-parameter updating

This paper describes a method for detecting power system faults based on the sequential probability ratio test (SPRT) and Kalman filter. The SPRT is the optimal statistical test that makes the correct decision in the shortest time among all tests that are subject to the same level of decision errors. Simulation results are provided to show the superiority of the proposed method.

Fault detection using sequential probability ratio test

It is of great significance to estimate cell inconsistency for improving the service life and safety performance of the power battery pack. To accurately estimate the state-of-charge (SOC) inconsistency of cells with different performance parameters and working states, a method combining adaptive robust unscented Kalman filter and smooth variable structure filter with time-varying smoothing boundary layer (SVSF-VBL) is proposed. The cell mean-difference model is used to simulate the behavior characteristics of the battery module, including the cell mean model expressed by the dual polarization (DP) model and the cell difference model characterized by the hypothetical Rint model. Firstly, the improved forgetting factor recursive least square is applied to identify parameters of the DP model, and the unscented Kalman filter incorporating robust estimation and adaptive filter tuning is employed to estimate the SOC of the battery module. Then, SVSF-VBL is used to estimate the SOC difference between each cell and module based on the Rint model for improving the estimation accuracy and robustness. In addition, the comprehensive inconsistency of the cells can be captured by the secondary performance indicator inherent in SVSF-VBL, which contributes to the in-depth study of cell inconsistency. Finally, a series of tests are carried out to verify the performance of the proposed method, and the results show that the method can improve the estimation accuracy and convergence performance while effectively suppressing the system disturbance. 

Online estimation of state-of-charge inconsistency for lithium-ion battery based on SVSF-VBL

Regenerative braking can extend the driving range and reduce PM emissions from abrasion for battery electric heavy-duty trucks (BETs). The composite braking control strategy including torque distribution and dynamic coordinated control for the four-axle BET equipped with the electromechanical braking system is studied. A segmented torque distribution strategy is proposed to maximize energy recovery while ensuring braking stability. The simulation results reveal that the strategy shows better comprehensive braking performance than the two benchmark strategies, and the energy recovery rate in different load states under CHTC-D is above 40%. The proposed coordinated control strategy takes advantage of regenerative braking’s rapid response and precise control to compensate for torque deviations caused by the hysteresis of friction braking. For two common braking mode transition conditions, regenerative braking torque correction and advance of the mode switching timing are adopted to enable the motor to obtain the torque compensation ability. This method leads to a slight loss of braking energy, and the maximum torque deviation during the mode switching process is suppressed to less than 1.4 kN·m, and the jerk and braking distance is reduced accordingly, which is of great importance in improving driving comfort and braking safety.

/pdf/study-on-braking-energy-recovery-control-strategy-for-four-pxukmef5.pdf

Study on Braking Energy Recovery Control Strategy for Four-Axle Battery Electric Heavy-Duty Trucks

The braking intention is of great significance to the realization of driver assistant features, the improvement of braking safety, and the maximization of energy recovery efficiency for electric vehicles. With the aim of accurate identification of braking intention, an identification model based on Gated Recurrent Unit (GRU) Network with Attention mechanism is proposed in this paper. Based on numerous vehicle braking test data, braking process analysis, characteristic parameters selection, identification model training, and verification are carried out. Through the difference analysis based on the Kruskal-Wallis test and the importance evaluation based on random forest, combined with the real-time requirements of practical application, the appropriate characteristic parameters are selected as the model input. The attention mechanism is introduced into the proposed model, which can improve identification accuracy by capturing valuable feature information. The comparative verification results show that the Attention-GRU model performs better than the other three comparison models, and its identification accuracy is 96.7%, of which the accuracy of slight braking, normal braking, and emergency braking are 96.3%, 95.8%, and 100% respectively. The identified braking intention can provide an effective basis for the establishment of vehicle control strategies.

Research on characteristic parameter selection and attention-GRU-based model for braking intention identification

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