Energy big data: A survey <s> C. ELECTRIC VEHICLES <s> This paper deals with a project related to Plug-in Hybrid Electric Vehicles fleet studies. The project is part of a broader research consortium underway at Ohio State University's Center for Automotive Research called SMART@CAR. Main goals are to create and maintain a database containing all charging and duty cycle data collected from a growing PHEV fleet. Real-world data provided by these vehicles will be collected, archived, organized and analyzed. The availability of real world data will also help estimate the effects of PHEV penetration on the utility energy sales, generation capacity, the transmission grid, market economics, and environmental emissions. <s> BIB001 </s> Energy big data: A survey <s> C. ELECTRIC VEHICLES <s> In order to facilitate the integration of electric vehicles (EVs) into the Danish power system, the driving data in Denmark were analyzed to extract the information of driving distances and driving time periods which were used to represent the driving requirements and the EV unavailability. The Danish National Transport Survey data (TU data) were used to implement the driving data analysis. The average, minimum and maximum driving distances were obtained for weekdays, weekends and holidays to illustrate the EV users? driving requirements in different days. The EV availability data were obtained from the driving time periods to show how many cars are available for charging and discharging in each time period. The obtained EV availability data are in one hour time periods and one quarter time periods for different study purposes. The EV availability data of one hour time period are to be used for optimal EV charging study in energy power market. The EV availability data of quarter time periods are to be used to investigate the possibility of utilizing EVs for providing regulation power. The statistical analysis software, SAS, was used to carry out the driving data analysis. <s> BIB002 </s> Energy big data: A survey <s> C. ELECTRIC VEHICLES <s> The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) into the market brings up many technical problems that need to be addressed. In the near future, a large number of PHEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. An emerging issue is that a large number of PHEVs simultaneously connected to the grid may pose a huge threat to the overall power system quality and stability. In this paper, the authors propose an algorithm for optimally managing a large number of PHEVs (e.g., 3000) charging at a municipal parking station. The authors used the estimation of distribution algorithm (EDA) to intelligently allocate electrical energy to the PHEVs connected to the grid. A mathematical framework for the objective function (i.e., maximizing the average state-of-charge at the next time step) is also given. The authors considered real-world constraints such as energy price, remaining battery capacity, and remaining charging time. The authors also simulated the real-world parking deck scenarios according to the statistical analysis based on the transportation data. The authors characterized the performance of EDA using a Matlab simulation, and compared it with other optimization techniques. <s> BIB003 </s> Energy big data: A survey <s> C. ELECTRIC VEHICLES <s> Incorporation of data from multiple resources and various structures is necessary for accurate estimation of the driving range for electric vehicles. In addition to the parameters of the vehicle model, states of the battery, weather information, and road grade, the driving behavior of the driver in different regions is a critical factor in predicting the speed/acceleration profile of the vehicle. Following our previously proposed big data analysis framework for range estimation, in this paper we implement and compare different techniques for speed profile generation. Moreover we add the big data analysis classification results to especially improve the performance of the Markov Chain approach. The quantitative results show the significant influence of considering the big data analysis results on range estimation. <s> BIB004 </s> Energy big data: A survey <s> C. ELECTRIC VEHICLES <s> As the electric vehicle (EV) is becoming a significant component of the loads, an accurate and valid model for the EV charging demand is the key to enable accurate load forecasting, demand respond, system planning, and several other important applications. We propose a data driven queuing model for residential EV charging demand by performing big data analytics on smart meter measurements. The data driven model captures the non-homogeneity and periodicity of the residential EV charging behavior through a self-service queue with a periodic and non-homogeneous Poisson arrival rate, an empirical distribution for charging duration and a finite calling population. Upon parameter estimation, we further validate the model by comparing the simulated data series with real measurements. The hypothesis test shows the proposed model accurately captures the charging behavior. We further acquire the long-run average steady state probabilities and simultaneous rate of the EV charging demand through simulation output analysis. <s> BIB005 </s> Energy big data: A survey <s> C. ELECTRIC VEHICLES <s> This paper presents a framework and methodology to estimate the possible states of Electric Vehicles (EVs) regarding their location and periods of connection in the grid. A Monte Carlo Simulation (MCS) is implemented to estimate the probability of occurrence of these states. The framework assumes the availability of Information and Communication Technology (ICT) technology and previous data records to obtain the probabilistic states. A case study is presented using a fleet of 15 EVs considering a smart grid environment. A high accuracy was obtained with 1 million iterations in MCS. <s> BIB006 </s> Energy big data: A survey <s> C. ELECTRIC VEHICLES <s> In this paper, we address a big-data analysis method for estimating the driving range of an electric vehicle (EV), allowing drivers to overcome range anxiety. First, we present an estimating approach to project the life of battery pack for 1600 cycles (i.e., 8 years/160 000 km) based on the data collected from a cycle-life test. This approach has the merit of simplicity. In addition, it considers several critical issues that occur inside battery packs, such as the dependence of internal resistance and the state-of-health. Subsequently, we describe our work on driving pattern analysis of an EV, using a machine-learning approach, namely growing hierarchical self-organizing maps, to cluster the collected EV big data. This paper contains the analysis of energy consumption and driving range estimation for EVs, including powertrain simulation and driving behavior analysis. The experimental results, including both simulating battery degradation and analysis of driving behaviors, demonstrate a feasible solution for improving driving range estimation by the EV big data. <s> BIB007
Electric Vehicles becomes a promising alternative transportation method which is related to the smart grid domain. With the increasing number of EVs, many problems in various areas of EVs such as performance evaluation, driving range and battery capacity become great concerns by researchers. Besides, EV is one of the typical application examples of big data. Therefore, most of the issues above can be handled with big data technologies. For instance, Wu et al. BIB002 launched the EV grid integration study based on driving patterns analysis. Recently, most researches are focused on analyzing the energy efficiency, addressing routing problems by recording driving data, estimating the driving range. Su and Chow BIB003 proposed an Estimation of Distribution Algorithm (EDA) based on big data theories to allocate electric energy intelligently to EVs. The simultaneous connection of a large number of EVs to the power grid may have a influence on quality and stability of the overall power system. This proposed algorithm was devoted to optimally manage a large number of EVs charging at a municipal parking lot. Midlam-Mohler et al. BIB001 focused on the the analysis of energy efficiency via recording driving data. They developed a data collection system to support the study which allows 3856 VOLUME 4, 2016 data acquired not only from disparate data sources that are available on most modern vehicles but from supplemental instrumentation as well. As for the issues on EVs charging and driving range, Rahimi-Eichi et al. BIB004 proposed another range estimation framework based on big data technology which can collect energy data with various structures from various resources and incorporate them via the range estimation algorithm. They also used historical and real-time data for simulation and thus calculated the remaining driving range. Zhang and Grijalva BIB005 proposed a data-driven queuing model for EV charging demand through performing big data analysis on smart meter measurements. In addition, Zhang et al.  focused on the charging-transaction-record EV data for better charging service, by detecting, analyzing, and correcting the abnormal data with error types and distributed locations. Soares et al. BIB006 presented a novel methodology based on Monte Carlo Simulation (MCS) to estimate the possible EVs states referring to their locations and periods of connection in the power grid. Futhermore, Lee and Wu BIB007 provided a EV-battery big data modeling method which has been used to improve the driving range estimation of EVs, using data cloud analysis and processing technologies. The authors first presented a simple approach to project life cycles of battery packs based on collected test data. The operating voltage of a battery pack E (I ,SOC,T ) can be evaluated by: where I means the current, SOC is noted as the state of charge, T represents the temperature, OCV is short for the open circuit voltage, R is the resistance of battery cell and η represents the resistance caused by polarization. Then they used a machine learning approach to cluster the collected EV data by ever-increasing hierarchical self-organizing maps.