Design and Implementation of a Battery Big Data Platform Through Intelligent Connected Electric Vehicles

The development of a battery management algorithm is highly dependent on high-quality battery operation data,especially the data in extreme conditions such as low temperatures.The data in faults are also essential for failure and safety management research.This study developed a battery big data platform to realize vehicle operation,energy interaction and data management.First,we developed an electric vehicle with vehicle navigation and position detection and designed an environmental cabin that allows the vehicle to operate autonomously.Second,charging and heating systems based on wireless energy transfer were developed and equipped on the vehicle to investigate optimal charging and heating methods of the batteries in the vehicle.Third,the data transmission network was designed,a real-time monitoring interface was developed,and the self-developed battery management system was used to measure,collect,upload,and store battery operation data in real time.Finally,experimental validation was performed on the platform.Results demonstrate the efficiency and reliability of the platform.Battery state of charge estimation is used as an example to illustrate the availability of battery operation data.

A Ka-band Solid-state Transmitter Cloud Radar and Data Merging Algorithm for Its Measurements

This study concerns a Ka-band solid-state transmitter cloud radar, made in China, which can operate in three different work modes, with different pulse widths, and coherent and incoherent integration numbers, to meet the requirements for cloud remote sensing over the Tibetan Plateau. Specifically, the design of the three operational modes of the radar（i.e., boundary mode M1, cirrus mode M2, and precipitation mode M3） is introduced. Also, a cloud radar data merging algorithm for the three modes is proposed. Using one month＇s continuous measurements during summertime at Naqu on the Tibetan Plateau,we analyzed the consistency between the cloud radar measurements of the three modes. The number of occurrences of radar detections of hydrometeors and the percentage contributions of the different modes＇ data to the merged data were estimated.The performance of the merging algorithm was evaluated. The results indicated that the minimum detectable reflectivity for each mode was consistent with theoretical results. Merged data provided measurements with a minimum reflectivity of -35 dBZ at the height of 5 km, and obtained information above the height of 0.2 km. Measurements of radial velocity by the three operational modes agreed very well, and systematic errors in measurements of reflectivity were less than 2 dB. However,large discrepancies existed in the measurements of the linear depolarization ratio taken from the different operational modes.The percentage of radar detections of hydrometeors in mid- and high-level clouds increased by 60% through application of pulse compression techniques. In conclusion, the merged data are appropriate for cloud and precipitation studies over the Tibetan Plateau.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.

Cyber-physical Modeling Technique based Dynamic Aggregation of Wind Farm Considering LVRT Characteristic

With the increasing penetration of wind power,large-scale integrated wind turbine brings stability and security risks to the power grid.For the aggregated modeling of large wind farms,it is crucial to consider low voltage ride-through(LVRT)characteristics.However,in aggregation methods,the approximate neglect behavior is essential,which leads to inevitable errors in the aggregation process.Moreover,the lack of parameters in practice brings new challenges to the modeling of a wind farm.To address these issues,a novel cyber-physical modeling method is proposed.This method not only overcomes the aggregation problem under the black-box wind farm but also accurately realizes the aggregation error fitting according to the operation data.The simulation results reveal that the proposed method can accurately simulate the dynamic behaviors of the wind farm in various scenarios,whether in LVRT mode or normal mode.

Spatiotemporal Variations and Controls on Anthropogenic Heat Fluxes in 12 Selected Cities in the Eastern China

Spatiotemporal variations of anthropogenic heat flux(AHF)is reported to be associated with global warming.However,confined to the low spatial resolution of energy consumption statistical data,details of AHF was not well descripted.To obtain high spatial resolution data of AHF,Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS)nighttime light time-series product and Moderate Resolution Imaging Spectroradiometer(MODIS)satellite monthly normalized difference vegetation index(NDVI)product were applied to construct the human settlement index.Based on the spatial regression relationship between human settlement index and energy consumption data.A 1-km resolution dataset of AHF of 12 selected cities in the eastern China was obtained.Ordinary least-squares(OLS)model was applied to detect the mechanism of spatial patterns of AHF.Results showed that industrial emission in selected cities of the eastern China was accountable for 63%of the total emission.AHF emission in megacities,such as Tianjin,Jinan,Qingdao,and Hangzhou,was most significant.AHF increasing speed in most areas in the chosen cities was quite low.High growth or extremely high growth of AHF were located in central downtown areas.In Beijing,Shanghai,Guangzhou,Jinan,Hangzhou,Changzhou,Zhaoqing,and Jiangmen,a single kernel of AHF was observed.Potential influencing factors showed that precipitation,temperature,elevation,normalized different vegetation index,gross domestic product,and urbanization level were positive with AHF.Overall,this investigation implied that urbanization level and economic development level might dominate the increasing of AHF and the spatial heterogeneousness of AHF.Higher urbanization level or economic development level resulted in high increasing speeds of AHF.These findings provide a novel way to reconstruct of AHF and scientific supports for energy management strategy development.

Analysis on Operation Reliability of Generating Units in 2009

This paper presents the data on operation reliability indices and relevant analyses toward China's conventional power generating units in 2009.The units brought into the statistical analysis include 100-MW or above thermal generating units,40-MW or above hydro generating units,and all nuclear generating units.The reliability indices embodied include utilization hours,times and hours of scheduled outages,times and hours of unscheduled outages,equivalent forced outage rate and equivalent availability factor.

Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

Buildings have a significant impact on global sustainability.During the past decades,a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance.Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications.Recent advances in information technologies and data science have enabled convenient access,storage,and analysis of massive on-site measurements,bringing about a new big-data-driven research paradigm.This paper presents a critical review of data-driven methods,particularly those methods based on larger datasets,for building energy modeling and their practical applications for improving building performances.This paper is organized based on the four essential phases of big-data-driven modeling,i.e.,data preprocessing,model development,knowledge post-processing,and practical applications throughout the building lifecycle.Typical data analysis and application methods have been summarized and compared at each stage,based upon which in-depth discussions and future research directions have been presented.This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling.Furthermore,considering the ever-increasing development of smart buildings and IoT-driven smart cities,the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

Data Warehouse for Quality Management Systems

The objectives of quality management systems which are based on data warehouses are to acquire, store, and process quality control data within an enterprise, and to facilitate analysis, control and decision making based on this data. This paper discusses the DB/ODS/DW (traditional database/operational data store/data warehouse) architecture, data granularity and data partition in the data warehouse, describes the data model, and presents the client/server platform model.

In-service aircraft engines turbine blades life prediction based on multi-modal operation and maintenance data

The in-service life of turbine blades directly affects the on-wing lifetime and operating cost of aircraft engines.It would be essential to accurately evaluate the remaining useful life of turbine blades for safe engine operation and reasonable maintenance decision-making.In this paper,a machine learning-based mechanism with multiple information fusion is proposed to predict the remaining useful life of high-pressure turbine blades.The developed method takes account of the in-service operating factors such as the high-pressure rotor speed and exhaust gas temperature,as well as the engine operating environments and performance degradation.The effectiveness of this method is demonstrated on simulated test cases generated by an integrated blade creep-life assessment model,which comprises engine performance,blade stress,thermal,and creep life estimation models.The results show that the proposed method provides a prospective result for in-service life evaluation of turbine blades and is of significance to evaluating the engine on-wing lifetime and making a reasonable maintenance plan.

A novel data-driven method for mining battery open-circuit voltage characterization

Lithium-ion batteries(LiB)are widely used in electric vehicles(EVs)and battery energy storage systems,and accurate state estimation relying on the relationship between battery Open-Circuit-Voltage(OCV)and State-of-Charge(SOC)is the basis for their safe and efficient applications.To avoid the time-consuming lab test needed for obtaining OCV-SOC curves,this study proposes a data-driven universal method by using operation data collected onboard about the variation of OCV with ampere-hour(Ah).To guarantee high reliability,a series of constraints have been implemented.To verify the effectiveness of this method,the constructed OCV-SOC curves are used to estimate battery SOC and State-of-Health(SOH),which are compared with data from both lab tests and EV manufacturers.Results show that a higher accuracy can be achieved in the estimation of both SOC and SOH,for which the maximum deviations are less than 3.0%and 2.9%respectively.

ROBOD, room-level occupancy and building operation dataset

The availability of the building’s operation data and occupancy information has been crucial to support the evaluation of existing models and development of new data-driven approaches.This paper describes a comprehensive dataset consisting of indoor environmental conditions,Wi-Fi connected devices,energy consumption of end uses(i.e.,HVAC,lighting,plug loads and fans),HVAC operations,and outdoor weather conditions collected through various heterogeneous sensors together with the ground truth occupant presence and count information for five rooms located in a university environment.The five rooms include two different-sized lecture rooms,an office space for administrative staff,an office space for researchers,and a library space accessible to all students.A total of 181 days of data was collected from all five rooms at a sampling resolution of 5 minutes.This dataset can be used for benchmarking and supporting data-driven approaches in the field of occupancy prediction and occupant behaviour modelling,building simulation and control,energy forecasting and various building analytics.

Trip energy consumption estimation for electric buses

This study aims to develop a trip energy consumption(TEC)estimation model for the electric bus(EB)fleet planning,operation,and life-cycle assessment.Leveraging the vast variations of temperature in Jilin Province,China,real-world data of 31 EBs operating in 14 months were collected with temperatures fluctuating from
27.0 to 35.0℃.TEC of an EB was divided into two parts,which are the energy required by the traction and battery thermal management system,and the energy required by the air conditioner(AC)system operation,respectively.The former was regressed by a logarithmic linear model with ambient temperature,curb weight,travel distance,and trip travel time as contributing factors.The optimum working temperature and regression parameters were obtained by combining Fibonacci and Weighted Least Square.The latter was estimated by the operation time of the AC system in cooling mode or heating mode.Model evaluation and sensitivity analysis were conducted.The results show that:(i)the mean absolute percentage error(MAPE)of the proposed model is 12.108%;(ii)the estimation accuracy of the model has a probability of 99.7814% meeting the requirements of EB fleet scheduling;(iii)the MAPE has a 1.746% reduction if considering passengers’boarding and alighting.

A physics-based framework for online surface roughness assessment for high-pressure turbines

Surface roughness is a critical health parameter of a turbine blade due to its implications on blade surface heat transfer and structural integrity.This paper proposes a physics-based online framework for Gas Turbine Engines(GTE),in order to assess the blade surface roughness in a highpressure turbine without engine shutdown.The framework consolidates Gas Path Analysis(GPA)based performance monitoring models and meanline turbomachinery analysis,using a novel GPAmeanline matching process.This extracts meaningful performance deviation trends from GPA,while addressing the uncertainties associated with the measurements and modelling.To relate efficiency loss to surface roughness severity,a meanline-based system-identification process has been developed to establish the meanline representation of the turbine stage,and to incorporate the empirical surface roughness loss correlations.The roughness loss correlations have been evaluated against recent transonic test data in the literature.A modification to the compressibility correction factor has been made according to the evaluation outcome,which improved loss predictions compared to the experimental measurements.The framework was tested on the three-year operational data of a cogeneration GTE,and the results verified the framework’s potential for online surface roughness monitoring.The predicted surface roughness showed agreement in both trend and the magnitude-level with the measurements reported in the literature.

Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations

Recently,water extraction based on the indices method has been documented in many studies using various remote sensing data sources.Among them,Landsat satellites data have certain advantages in spatial resolution and cost.After the successful launch of Landsat 8,the Operational Land Imager(OLI)data from the satellite are getting more and more attention because of its new improvements.In this study,we used the OLI imagery data source to study the water extraction performance based on the Normalized Difference Vegetation Index,Normalized Difference Water Index,Modified Normalized Water Index(MNDWI),and Automated Water Extraction Index(AWEI)and compared the results with the Thematic Mapper(TM)imagery data.Two test sites in Tianjin City of north China were selected as the study area to verify the applicability of OLI data and demonstrate its advantages over TM data.We found that the results of surface water extraction based on OLI data are slightly better than that based on TM in the two test sites,especially in the city site.The AWEI and MNDWI indices performs better than the other two indices,and the thresholds of water indices show more stability when using the OLI data.So,it is suitable to combine OLI imagery with other Landsat sensor data to study water changes for long periods of time.