Moving Window Algorithm Research Articles

Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds

Accurate and efficient path planning in indoor environments relies on high-quality navigation networks that faithfully represent the spatial and semantic structure of the environment. Three-dimensional semantic point clouds provide valuable spatial and semantic information for navigation tasks. However, extracting detailed navigation networks from 3D semantic point clouds remains a challenge, especially in complex indoor spaces like staircases and multi-floor environments. This study presents a comprehensive framework for developing and extracting robust navigation network models, specifically designed for indoor path planning applications. The main contributions include (1) a preprocessing pipeline that ensures high accuracy and consistency of the input semantic point cloud data; (2) a moving window algorithm for refined node extraction in staircases to enable seamless navigation across vertical spaces; and (3) a lightweight, JSON-based storage structure for efficient network representation and integration. Additionally, we presented a more comprehensive sub-node extraction method for hallways to enhance network continuity. We validated the method using two datasets—the public S3DIS dataset and the self-collected HoloLens 2 dataset—and demonstrated its effectiveness through Dijkstra-based path planning. The generated navigation networks supported practical scenarios such as wheelchair-accessible path planning and seamless multi-floor navigation. These findings highlight the practical value of our approach for modern indoor navigation systems, with potential applications in smart building management, robotics, and emergency response.

English grammar intelligent error correction technology based on the n-gram language model

Abstract With the development of the Internet, the number of electronic texts has increased rapidly. Automatic grammar error correction technology is an effective safeguard measure for the quality of electronic texts. To improve the quality of electronic text, this study introduces a moving window algorithm and linear interpolation smoothing algorithm to build a Cn-gram language model. On this basis, a syntactic analysis strategy is introduced to construct a syntactic error correction model integrating Cn-gram and syntactic analysis, and English grammar intelligent error correction is carried out through the model. The results show that compared with the Bi-gram and Tri-gram, the precision of the Cn-gram model is 0.85 and 0.91% higher, and the F1 value is 0.97 and 1.14% higher, respectively. Compared with the results of test set Long, the Cn-gram model has better performance on verb error correction of the Short test set, and the precision rate, recall rate, and F1 value are increased by 0.86, 3.94, and 1.87%, respectively. The comparison of the precision, recall rate, and F1 value of the proposed grammar error correction model on the complete test set shows that the precision of the study is 19.10 and 5.41% higher for subject–verb agreement errors. The recall rate is 9.55 and 10.77% higher, respectively; F1 values are higher by 12.65 and 10.59%, respectively. The above results show that the error-correcting technique of the research design has excellent error-correcting performance. It is hoped that this experiment can provide a reference for the relevant research of automatic error correction technology of electronic text.

Influence of the peel on online detecting soluble solids content of pomelo using Vis-NIR spectroscopy coupled with chemometric analysis

The application of Visible-near infrared spectroscopy (Vis-NIRs) to internal quality detection of pomelo with large sizes and thick peels is still challenging. Considering that the peel has a large mass ratio in pomelo and a great added value in industrial processing, a postharvest processing mode of grading after peeling without affecting consumption of pomelos may be an approach to avoid adverse influence of peel on soluble solids content (SSC) evaluation. In this study, we investigated the performance variation of Vis-NIRs for SSC online determination of pomelos with and without peel for the first time. An online detection system for pomelo spectra acquisition was developed, and the spectral characteristic difference of the intact and peeled pomelos was analyzed. Subsequently, the performance of calibration models was gradually improved and comparatively analyzed by various spectral processing and wavelength selection methods. Furthermore, convolutional neural network (CNN) was utilized to explore its ability for feature extraction. The results showed that combined with standard normal variate (SNV) and second order detrending and changeable-size moving window algorithms, the partial least squares regression (PLSR) model using spectra of peeled pomelos achieved the best prediction results. Specifically, the model attained a determination coefficient of prediction (Rp2) of 0.88, a root mean square error of prediction (RMSEP) of 0.294%, and a residual predictive deviation (RPD) value of 2.57. This study demonstrates that the peel has a significantly negative effect on the prediction performance and the CNN could be an alternative to conventional PLSR method. Our work may open new avenues for the internal quality assessment of agro-products with complex tissue structure.

Spatial downscaling of SMAP soil moisture estimation using multiscale geographically weighted regression during SMAPVEX16

Passive microwave remote sensing missions such as Soil Moisture Active Passive (SMAP) are widely used for global soil moisture (SM) estimation. But the low spatial resolution of passive microwave SM products limits their applications at regional and local scales (normally 1–10 km). Using high-resolution auxiliary data to downscale passive microwave SM products is the primary way to obtain high-resolution SM data. However, the existing SM downscaling methods do not entirely account for the scale differences in the impact of various variables on the distribution of SM. To solve this problem, this paper introduced MGWR (Multiscale Geographically Weighted Regression) as a novel way to analyze the scale differences of normalized difference vegetation index (NDVI) and land surface temperature (LST) on the spatial pattern of SM. Accordingly, a new spatial downscaling algorithm for SMAP SM products is proposed and compared with the geographically weighted regression (GWR) algorithm and moving window algorithm (MW). The algorithm was applied to data taken during the SMAP Validation Experiment 2016 (SMAPVEX16), and the downscaled SM evaluated with in situ SM and aircraft observations. These results show that: 1) the SM downscaling conversion function, based on the MGWR, effectively reveals how different surface parameters relate to SM, with LST influencing SM globally and NDVI affecting it locally, 2) compared with GWR and MW, the 1 km SM obtained by the MGWR-based downscaling method showed better spatial agreement with airborne SM in the semiarid region with the ubRMSE decreasing by 0.023 m3/m3 and 0.026 m3/m3 for GWR and MW respectively, 3) when evaluating the 1 km SM with in situ SM in the semiarid region, the SM obtained through MGWR exhibited lower ubRMSE values as compared to both GWR and MW. Specifically, the median ubRMSE values for MGWR, GWR, and MW were 0.043 m3/m3, 0.053 m3/m3, and 0.094 m3/m3, respectively. In conclusion, this study demonstrates that MGWR can effectively improve the accuracy of downscaled SM in semiarid regions.

A multi-scale blocking moving window algorithm for geostatistical seismic inversion

Markov chain Monte Carlo (McMC) methods are suitable for solving the high-dimensional geostatistical seismic inverse problem. However, traditional McMC is impractical to generate desired lithofacies distribution since the repeated geostatistics and seismic forward simulators are very time-consuming. Recently, various strategies (e.g., the sequential geostatistical resampling (SGR) idea, multi-scale strategy, and annealing process) have been individually designed to alleviate the computational burden. However, the coordination between multiple corresponding key parameters (e.g., blocking window sizes, grid level, and temperatures) adds difficulties to combining multiple strategies with McMC. In this context, we propose a Multi-scale Blocking Moving Window algorithm (MsBMW) to effectively combines blocking McMC updating, a multi-scale strategy, and a new simulated annealing (SA) algorithm. In the iterative process, we monitor the mean acceptance rate (AR) to update window sizes, grid level, and temperatures, keeping the AR within the desired range (e.g., between 25% and 50%) for more efficient sampling. To assess the performance of the MsBMW, we tested it on the Standford VI synthetic reservoir. The standard Metropolis-Hastings (MH) sampling is performed to present the posterior probability density function (pdf) as the reference in this study. Compared with the Blocking Moving Window algorithm (BMW) proposed by Alcolea and Renard (2010), the MsBMW allows better quality results in less time. The results show that the multi-scale approach has the effect of accelerating the convergence of inversion. The adaptive cooling schedule circumvents the problem that the cooling rate is difficult to control in SA. Finally, we applied the proposed methodology to two-dimensional (2-D) and three-dimensional (3-D) real study areas and quickly generated multiple realizations that fit geological patterns, well data, and seismic data for uncertainty evaluation.

A powerful tool for near-infrared spectroscopy: Synergy adaptive moving window algorithm based on the immune support vector machine

Traditional trial-and-error methods are time-consuming and inefficient, especially very unfriendly to inexperienced analysts, and are sometimes still used to select preprocessing methods or wavelength variables in near-infrared spectroscopy (NIR). To deal with this problem, a new optimization algorithm called synergy adaptive moving window algorithm based on the immune support vector machine (SA-MW-ISVM) is proposed in this paper. Following the principle of SA-MW-ISVM, the original problem of calibration model optimization is transformed into a mathematical optimization problem that can be processed by the proposed immune support vector machine regression algorithm. The main objective of this optimization problem is the calibration model performance; meanwhile, the constraint conditions include a reasonable spectral data value, spectral data preprocessing method, and calibration model parameters. A unique antibody structure and specific coding and decoding method are used to achieve collaborative optimization in NIR spectroscopy. The tests on four actual near-infrared datasets, including a group of gasoline and three groups of diesel fuels, have shown that the proposed SA-MW-ISVM algorithm can significantly improve the calibration performance and thus achieve accurate prediction results. In the case of gasoline, the SA-MW-ISVM algorithm can decrease the prediction error by 44.09% compared with the common benchmark partial least square (PLS). Meanwhile, in the case of diesel fuels, the SA-MW-ISVM algorithm can decrease the prediction error of cetane number, freezing temperature, and viscosity by 9.99%, 28.69%, and 43.85%, respectively, compared with the PLS. The powerful prediction performance of the SA-MW-ISVM algorithm makes it an ideal tool for modeling near-infrared spectral data or other related application fields.

A Method of Winding Fault Classification in Transformer Based on Moving Window Calculation and Support Vector Machine

Winding fault is one of the most common types of transformer faults. The frequency response method is a common diagnosis method for winding fault detection. In order to improve the feature extraction ability of the frequency response curve before and after the winding fault, this paper proposes a winding fault feature extraction method based on the moving window algorithm to improve the Euclidean distance and correlation coefficient and uses a support vector machine to diagnose winding fault. “Moving window meter algorithm” refers to the fixed moving window width and window moving interval, scanning the entire frequency response curve from the initial point to the end point of the frequency response curve, using the correlation coefficient (CC) and Euclidean distance (ED) to calculate the mathematical index of each window. The mathematical index of each window is used as the characteristic quantity of fault type classification. Finally, the grid search algorithm is used to optimize the support vector machine to classify and identify the type of winding fault. At the same time, the standard support vector machine s(SVM) and back propagation neural network algorithm (BPNN) are compared with the support vector machine optimized by the grid search method to diagnose the fault type. The research shows that the improved correlation coefficient and Euclidean distance using the moving window algorithm are more sensitive to winding faults than the traditional calculation methods. The combination of the two calculation methods makes up for the shortcomings of their respective methods. The fault features obtained meet the requirements of the support vector machine for fault diagnosis, and the grid search method-optimized support vector machine classification algorithm has a good classification and recognition effect on the identification of fault types. The effectiveness and superiority of this method are further illustrated.

How long do population level field experiments need to be? Utilising data from the 40-year-old LTER network.

We utilise the wealth of data accessible through the 40-year-old Long-Term Ecological Research (LTER) network to ask if aspects of the study environment or taxa alter the duration of research necessary to detect consistent results. To do this, we use a moving-window algorithm. We limit our analysis to long-term (>10year) press experiments recording organismal abundance. We find that studies conducted in dynamic abiotic environments need longer periods of study to reach consistent results, as compared to those conducted in more moderated environments. Studies of plants were more often characterised by spurious results than those on animals. Nearly half of the studies we investigated required 10years or longer to become consistent, where all significant trends agreed in direction, and four studies (of 100) required longer than 20years. Here, we champion the importance of long-term data and bolster the value of multi-decadal experiments in understanding, explaining and predicting long-term trends.

Designing Transactive Market for Combined Heat and Power Management in Energy Hubs

This paper introduces a transactive market design for a combined heat and power (CHP) based energy hub (hub). The proposed model allows a hub operator to supply the hub’s demands by participating in the day-ahead market and a transactive market with CHPs and also in the real-time market by using a recursive moving window algorithm. The proposed local energy market for a hub operator and CHPs is based on the double auction P2P trading mechanism. The model develops an optimal bidding and offering strategies for CHPs and hub operators, respectively, to achieve optimal transactions. The CHPs may be equipped with boiler unit and heat buffer tank (HBT) beside CHP units. The uncertain nature of the hub’s electrical load, real-time and day-ahead markets prices and wind speed is addressed by using robust optimization. The procedure aimed at minimizing the worst-case CHP-based hub’s demand procurement cost even though flexibly regulating the solution robustness. Further, case studies investigate the economic impact of robustness on the hub’s cost.

A novel variable selection method based on combined moving window and intelligent optimization algorithm for variable selection in chemical modeling.

We propose a new wavelength selection algorithm based on combined moving window (CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spectral interval width and number. VDPSO algorithms improve the PSO algorithm. They can search the data space in different dimensions, and reduce the risk of limited local extrema and over fitting. Four different high-performance variable selection algorithms-BOSS, VCPA, iVISSA and IRF-are compared in three NIR data sets (corn, beer and fuel). The results show that VDPSO-CMW has better performance. The Matlab codes for implementing PSO-CWM and VDPSO-CMW are freely available on the website: https://www.mathworks.com/matlabcentral/fileexchange/75828-a-variable-selection-method.

Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons

With the rapid advancement of the high-performance computing technology and the increasing availability of the mass-storage memory device, the application of the data-driven models (e.g., the artificial neural network (ANN) model) for solar radiation prediction is appearing in abundance in the past decade. Although the performances of these models have been discussed in a large number of studies, how to further enhance the forecasting accuracies of these data-driven approaches to better facilitate the advanced controls in the building system such as model predictive control (MPC) in smart buildings remains a challenge. Deep learning, which is considered as a powerful tool to move machine learning closer to one of its original goals, i.e., Artificial Intelligence (AI), is a viable solution to this problem.In this study, an ANN model and a recurrent neural network (RNN) model are developed to investigate the performances of the deep learning algorithms for the solar radiation prediction. The actual meteorological data (AMY) from a local weather station in Alabama is used for the training process. Various scenarios, including different sampling frequencies and moving window algorithms, are included for a comprehensive evaluation of the accuracies and efficiencies. The results suggest that compared with the ANN model, the solar radiation prediction using the RNN model has a higher prediction accuracy, with a 47% improvement in Normalized Mean Bias Error (NMBE) and a 26% improvement in Root-Mean-Squared Error (RMSE). Besides, this forecasting accuracy could even be taken to a higher level by increasing the granularity of the data or adding a moving-window algorithm to the prediction model. By increasing the sampling frequency of the training data from 1 h to 10 min, the Root-Coefficient of Variation Mean-Squared Error (CV(RMSE)) of the ANN model dropped from 30.9% to 9.41%, while the CV(RMSE) of the RNN model dropped from 9.83% to 7.64%. For the RNN model, the NMBE was improved from 0.9% to 0.2% after the implementation of the moving-window algorithm. Besides, it was found that the cloud cover could have a significant impact on the prediction accuracy.

Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings

The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.

Identifying Sheep Activity from Tri-Axial Acceleration Signals Using a Moving Window Classification Model

Behaviour is a useful indicator of an individual animal’s overall wellbeing. There is widespread agreement that measuring and monitoring individual behaviour autonomously can provide valuable opportunities to trigger and refine on-farm management decisions. Conventionally, this has required visual observation of animals across a set time period. Technological advancements, such as animal-borne accelerometers, are offering 24/7 monitoring capability. Accelerometers have been used in research to quantify animal behaviours for a number of years. Now, technology and software developers, and more recently decision support platform providers, are integrating to offer commercial solutions for the extensive livestock industries. For these systems to function commercially, data must be captured, processed and analysed in sync with data acquisition. Practically, this requires a continuous stream of data or a duty cycled data segment and, from an analytics perspective, the application of moving window algorithms to derive the required classification. The aim of this study was to evaluate the application of a ‘clean state’ moving window behaviour state classification algorithm applied to 3, 5 and 10 second duration segments of data (including behaviour transitions), to categorise data emanating from collar, leg and ear mounted accelerometers on five Merino ewes. The model was successful at categorising grazing, standing, walking and lying behaviour classes with varying sensitivity, and no significant difference in model accuracy was observed between the three moving window lengths. The accuracy in identifying behaviour classes was highest for the ear-mounted sensor (86%–95%), followed by the collar-mounted sensor (67%–88%) and leg-mounted sensor (48%–94%). Between-sheep variations in classification accuracy confirm the sensor orientation is an important source of variation in all deployment modes. This research suggests a moving window classifier is capable of segregating continuous accelerometer signals into exclusive behaviour classes and may provide an appropriate data processing framework for commercial deployments.

An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China

Accurate gridded precipitation products with both finer tempo-spatial resolutions are critical for various scientific communities (e.g., hydrology, meteorology, climatology, and agriculture). Downscaling on coarse satellite-based rainfall estimates is an optimal approach to obtain such datasets. The Integrated Multi-satellitE Retrivals for Global Precipitation Measurement (GPM) (IMERG) data provides the “best” satellite-based precipitation estimates at half-hourly/0.1° scales, while its spatial resolution is still coarse for certain hydrometeorology research. To acquire hourly downscaled precipitation estimates based on IMERG, there are two great challenges: (1) limited rainfall-related environmental variables (0.01°×0.01°, hourly) used to downscale the IMERG data; and (2) far few rainfall pixels used for regressing traditional relationships between precipitation and environmental variables. In this case, most traditional or commonly-used regression/empirical models and the state-of-art machine learning algorithms are not suitable to cater these requirements. Therefore, we proposed a new strategy to obtain hourly downscaled precipitation estimates based on IMERG called Geographically Moving Window Weight Disaggregation Analysis (GMWWDA). Additionally, we explored multiple cloud properties, including cloud effective radius (CER), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT), as covariates to downscale IMERG using GMWWDA method, and concluded as follows: (1) the downscaled results (0.01° × 0.01°, hourly) based on the above mentioned cloud properties outperformed the original IMERG data; (2) the downscaled results based on CER performed better than those based on CTH, CTT and COT, respectively; (3) the accuracy of satellite-based precipitation products pose significant effects on those of the downscaled results. This study provides a great potential solution for generating satellite-based precipitation dataset with finer spatio-temporal resolutions.

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

As an essential component and core power source of aircraft, the operational stability of aeroengine has important impact on system safety and reliability. Accurate degradation tendency prediction on an engine can not only improve its operational stability but also significantly reduce the maintenance costs. In this paper, a novel forecasting method that combines CEEMDAN permutation entropy and improved Grey–Markov model is proposed to perform multistep degradation tendency prediction of aircraft engines. In order to accurately quantify the degradation level of engines, a new integrated degradation index (IDI) is innovatively designed by multidimensional sensory data. And then, because of high speed and excellent performance, CEEMDAN algorithm is specifically employed to decompose the generated IDI series to eliminate the potential influence of stochastic fluctuations. Aiming at the complexity of intrinsic mode functions (IMFs) generated by CEEMDAN, an IMFs reconstruction strategy based on permutation entropy is developed to better characterize the degradation states. Finally, on the basis of above achievements and for higher forecasting efficiency and accuracy, an improved Grey–Markov model combined with the moving window algorithm, which is unique, is constructed to realize multistep degradation trend prediction of engines. The proposed method is applied to the degradation tendency prediction of aircraft engines. The experimental results validate the effectiveness and superiority of the proposed method, and it is more suitable for engineering applications in comparison with other methods.

A Density-Based Clustering Method for the Segmentation of Individual Buildings from Filtered Airborne LiDAR Point Clouds

Individual building segmentation is a prerequisite for building reconstruction. When building points or building regions are classified from raw LiDAR (Light Detection and Ranging) point clouds, the dataset usually contains numerous individual buildings as well as outliers. However, the applications to segment individual buildings from large datasets require the algorithms working with the minimal requirements of domain knowledge to determine the input parameters, working well on datasets with outliers and having good efficiency on big data. To meet these requirements, this paper presents a new segmentation method relying on a density-based clustering technique that is designed to separate individual buildings in dense built-up areas and is robust to outliers. As implemented in a spatial database, the algorithm benefits from the spatial index and the parallel computation capability offered by the system. The experimental results show that the proposed method is significantly more effective in segmenting individual buildings than the well-known moving window algorithm and the new boundary identification and tracing algorithm, and processes large volumes of data with good efficiency. Compared with the moving window algorithm, the proposed method (parallelized) consumed only 17.8% time and the quality improved from 88.8 to 94.8% on the Vaihingen dataset.

Dual-Spectroscopy Platform for the Surveillance of Mars Mineralogy Using a Decisions Fusion Architecture on Simultaneous LIBS-Raman Data.

A single platform, integrated by a laser-induced breakdown spectroscopy detector and a Raman spectroscopy sensor, has been designed to remotely (5 m) and simultaneously register the elemental and molecular signatures of rocks under Martian surface conditions. From this information, new data fusion architecture at decisions level is proposed for the correct categorization of the rocks. The approach is based on a decision-making process from the sequential checking of the spectral features representing the cationic and anionic counterparts of the specimen. The scrutiny of the LIBS response by using a moving-window algorithm informs on the diversity of the elemental constituents. The output rate of emission lines allows projecting in a loop the elements as the cationic counterpart of the rock. In parallel, the Raman response of the unknown is compared with all the molecular counterparts of the hypothesized cation that are stored in a spectral library. The largest similarity rate unveils the final identity of the unknown. The identification capabilities of the architecture have been underscored through blind tests of 10 natural rocks with different origins. The great majority of forecasts have matched with the real identities of the inspected targets. The strength of this platform to simultaneously acquire the multielemental and the molecular information from a specimen by using the same laser events greatly enhances the "on-surface" missions for the surveillance of mineralogy.

DendroSync: An R package to unravel synchrony patterns in tree-ring networks

Spatial synchrony refers to the presence of a common signal for a time-varying characteristic that, in dendrosciences, is shared among tree-ring chronologies from a particular area. Analysis and interpretation of synchrony patterns in tree-ring networks is currently limited by: (i) the requirement for flexible modelling of complex correlations and heteroscedastic errors and (ii) the availability of ready-to-use open software to fulfil this task. We present an R package (DendroSync) that facilitates estimating and plotting synchrony patterns for pre-defined groups. The package has been devised to work with traits derived from tree rings (e.g. ring-width), but other data types are also suitable. It combines variance-covariance mixed modelling with functions that quantify the degree to which tree-ring chronologies contain a common signal over a fixed time period. It also estimates temporal changes in synchrony using a moving window algorithm. The functionality and usage of DendroSync are illustrated using a simple example.

A hybrid degradation tendency measurement method for mechanical equipment based on moving window and Grey–Markov model

Accurate degradation tendency measurement is vital for the secure operation of mechanical equipment. However, the existing techniques and methodologies for degradation measurement still face challenges, such as lack of appropriate degradation indicator, insufficient accuracy, and poor capability to track the data fluctuation. To solve these problems, a hybrid degradation tendency measurement method for mechanical equipment based on a moving window and Grey–Markov model is proposed in this paper. In the proposed method, a 1D normalized degradation index based on multi-feature fusion is designed to assess the extent of degradation. Subsequently, the moving window algorithm is integrated with the Grey–Markov model for the dynamic update of the model. Two key parameters, namely the step size and the number of states, contribute to the adaptive modeling and multi-step prediction. Finally, three types of combination prediction models are established to measure the degradation trend of equipment. The effectiveness of the proposed method is validated with a case study on the health monitoring of turbine engines. Experimental results show that the proposed method has better performance, in terms of both measuring accuracy and data fluctuation tracing, in comparison with other conventional methods.

Electric Vehicle Regenerative Braking Control Strategy Based on Braking Time Identification

The angular velocity and displacement variation of the brake pedal with time during braking is studied. Using brake angular velocity as the judgment basis of the moving window algorithm, established the function model between the brake pedal angular velocity and displacement. Regenerative braking time was identified accurately, using brake angular velocity integral quantity. On this basis, analyzed the battery can accept the maximum charge current, with feedback cur-rent as the control object, considering the reasonable distribution of regenerative braking and hydraulic braking force, the regenerative braking control strategy based on braking time was proposed. The tests verify the feasibility and effective-ness of the braking time identification algorithms and the strategy for braking force distribution.