SAE China 2018:NEVs and Big Data

Big data and understanding how it can be used in NEV utilization and the charging infrastructure



 The development of the New Energy Vehicle (NEV)industry in China has been a key element of China’s five-year development plans since 2001, as well as for the national strategies of major countries worldwide. Big data technology is a key technology associated with the government’s Made in China 2025 policy, which is commonly understood globally as being closely linked to the future success of the NEV industry. At the annual SAE China 2018 convention, participants from technical societies and related research organizations stated their opinions regarding NEVs and big data. This report focuses on the application of integrated NEV and big data technologies.

 The Society of Automotive Engineers of China (SAE-China), a national academic organization, was voluntarily established in 1963 by members of automotive technology-related industries. As a non-profit social organization, the SAE-China is a member of the China Association for Science and Technology. It is also an executive member of the International Federation of Automotive Engineering Societies (FISITA)and one of the sponsors of the International Pacific Conference on Automotive Engineering (IPC) (currently known as the Asia Pacific Automotive Engineering Conference (APAC)). Experts attending the conference discussed the state of NEV development and usage nationwide in China, in Shanghai, and around the world, and the ways in which big data technology can be used in conjunction with NEVs.

Related report:
Electrification, Intelligence, and Drivetrains of China's NEV Market(Nov.2018)
CES Asia 2018: Electric, intelligent, and connected vehicles(Aug.2018)


National big data on NEVs and applicable technologies in the automotive industry

Organization Brief introduction of the organization Panelist Position
Beijing Institute of Technology, and China’s National Big Data Alliance for NEVs

China’s National Big Data Alliance for NEVs was formed by the Beijing Institute of Technology, a major Chinese university. It is a nationwide, unionized, non-profit social coalition organized voluntarily with China's National Monitoring and Management Center for NEVs in collaboration with NEV automakers, parts suppliers, internet application providers servers, scientific research institutions, and related social organizations. It receives operational guidance and supervision management from the Ministry of Industry and Information Technology.

Peng Liu assistant professor

Assistant professor and master’s instructor, deputy secretary


The value of big data for NEVs

Presentation by Professor Peng Liu

 Currently, the structure of NEV industry data has already progressed from the stage where singular data rapidly emerges into the digitization stage in which industry data is linked and merged. Using peripheral data, it is now possible to generate behavioral habit data (such as consumption history to date)at various levels such as the consumer, the environment, roads, and geographical locations, as well as weather data, road altitude data, and vehicle position tracking data. Using data collected internally, the industry can now generate useful data for vehicles, production, sales, after-sales services, and multiple environmental stages related to vehicle usage and disposal. Digitization enhances the system value of the automotive industry, optimizes automaker production efficiency, increases dealership sales volumes, and improves consumer satisfaction. Big data and AI technologies have begun to merge with the automotive industry's four CASE (connected, autonomous, shared, electric)trends, which has the potential to cause major changes to the automotive industry's business model, rapidly expanding the ecosystem of the automotive industry and allowing for the entry of many new systems and fields.


NEV big data applications

1. Current state of the industry: 1.33 million NEVs, of which 73.3% are passenger vehicles, with the highest NEV population in Guangdong Province

 As of 2018, the number of vehicles entered or scheduled to be entered into the National NEV Monitoring Platform are as follows:

2018 y 2 million vehicles
2020y 7 million vehicles
2025y 80 million vehicles
2030y 150 million vehicles

 Using big data technology, it is possible to manage the traceability of NEV bus batteries from production to disposal. In addition, by using data from the National NEV Monitoring Platform, it is also possible to ascertain the current level of NEV proliferation. From January 2017 to October 2018, a total of 31 provinces, municipalities, and autonomous regions have connected to the National NEV Monitoring Platform. Information on a total of 1.33 million NEVs has been entered, with passenger cars comprising 73.3%, buses at 13.8%, and special equipment vehicles at 12.9%. By vehicle type and province classification, Guangdong Provide had the most data on passenger cars, buses, and special equipment vehicles with the percentages at 13.15%, 10.39%, and 22.72% respectively. Looking at the NEV data that has been fed into the National NEV Monitoring Platform in China by businesses, the data focuses on passenger cars, buses, and special equipment vehicles, and shows that blue-chip companies have a major share of the market. The market share held by the top 10 companies in the markets for passenger vehicles, buses, and special equipment vehicles reached 78.13%, 74.61%, and 62.11% respectively. By using big data, the National NEV Monitoring Platform allows for the monitoring of the reliability and effectiveness of NEV data.


2. Battery applications: Modeling with big data, and the establishment models to evaluate battery states

 After analysis model processing, the collected data can be used for various analysis purposes such as battery life degradation state, energy consumption, and driving behavior. The results can help to improve driving efficiency, optimize driving behavior, and reduce operational costs. Modeling with big data also makes it possible to establish an analysis model to evaluate battery degradation. A more comprehensive relationship for analytic models can be based on using actual driving data, taking into consideration a range of indices such as the number of times a battery is used, cumulative discharge times, battery charge balance, and evaluations of battery pack exteriors. Later, battery degradation status can be accurately evaluated using static and kinetic data from various sources and artificial neural networks (abbreviated as NN: Neural Network). Through the use of big data technology, parameter screening measurements can also be established. Information on the initial charge state of the battery is provided, ensuring subsequent analysis of the background data and conditional status of the battery. Additionally, background data can be extracted to increase energy usage rates of energy storage systems. Ultimately, it will be possible to grasp indicators for the industry as a whole. For example, by compiling statistical data on an NEV’s battery’s state of charge (SOC), it will be possible to accurately ascertain the reliability of range information. With the SOC for most vehicles currently hovering around 50%, users are understandably concerned about the range of NEVs.


3. Operation analysis: NEV driving distances and charge status per day by model

 Currently, NEV passenger vehicles are driven a total daily distance of around 2 to 20 km, primarily for short-distance outings. NEV buses are driven a total distance of around 160 to 180 km per day and used for roundtrip excursions. The total mileage of NEV special equipment vehicles is around 40 and 60 km, and in many cases are used for near- and mid-range transport. SOC value distributions can be discerned from the charge status of NEV taxis by combining map data, which can then be reflected into the establishment of infrastructure such as charging stations.


4. Construction of safety indices: Battery fault warnings via big data platforms

 By using big data technology, it is possible to monitor the overall vehicle status on a macro-scale as well as on a micro-scale, and conduct singular vehicle checks. This makes it possible to immediately process data when a battery fails, as well as manage safety systems. According to statistics, there have been 19 accidents related to fires caused by NEVs in China since 2017, involving with more than 100 other vehicles. 42% of the fire accidents occurred during charging, while 21% of the accidents occurred while driving. By using big data platforms, advanced warnings can be established that utilize the battery system's time scale. Mid- and long-term battery status and risk evaluations can be conducted based on data history. Using real-time information, along with mid- and long-term warning information (values), the battery's degradation state and risk information can be combined to provide early warning of short-term safety risks online. In addition, big data can incorporate the safety limit values provided by automakers to compare data vertically and horizontally to establish a remote fault diagnostic and warning system, creating a highly safe battery system. With these two systems, an analysis of the type of battery system failure, the failure location, and the number of failures can be conducted to help improve EV failure diagnostics, warning efficiency and accuracy, thereby ensuring the safe usage of EVs.

Analysis of NEV vehicle driving data in Shanghai

Organization Brief introduction of the organization Panelist Department and Position
Shanghai EV Public Data Collecting, Monitoring and Research Center Guidance by the Shanghai Economic Informatization Commission, a non-profit organization (NPO)established by Shanghai Municipal Administration of Social Organizations Siwen Deng Analysis Dept. Manager


Overview of the global and Shanghai NEV markets

Presentation by Siwen Deng

 Globally, NEV ownership increased rapidly from 2013 to 2017, exceeding 3 million in 2017, with the growth of EVs slightly exceeding that of PHVs. The majority of these vehicles are owned in the U.S., Europe, and China, with China having the greatest ownership volume at approximately 1.3 million vehicles. Likewise, the cumulative total of NEVs owned in Shanghai from 2013 to 2017 increased to 188,863 vehicles. Consumers in Shanghai tend to prefer PHVs over EVs, with PHVs comprising 65% (61,354 vehicles) of all NEVs in 2017, a year with the lowest share of PHVs in the market nationwide.


Introduction of Shanghai's public NEV data platform

 Data collected through October 2018 by the Shanghai EV Public Data Collecting, Monitoring, and Research Center recorded 60 OEMs, 360 models, and 202,113 passenger vehicles, and 53 OEMs, 264 models, and 19,176 commercial vehicles, totaling 113 OEMs, 624 models, and 221,289 vehicles. The center established a big data open platform for the collection and analysis of data integrating specialized and safety-related data to provide data consulting services to governmental bodies, industry, and society. The center’s researchers on site are focused on using the center’s methods to help ensure that the data that they release guarantees vehicle safety. While researchers may observe, use, and analyze data, they are not permitted to take any data off the premises. For example, public NEV data collected at coordinates on maps of Shanghai can be provided as sample data after being processed for privacy protection, before releasing such data for the national standards (GB).

 The center established a comprehensive 5-point EV system that is comprised of a data indicator system, data management system, data labeling system, analytical index system, and a system for application scenarios. The database is daily acquiring more abundant and diverse data, and is currently capable of providing NEV driving data in real time, monthly NEV sales data, data relating to the population of Shanghai and the flow of traffic, Shanghai map data related to NEVs as well as vehicles, charging stations, and network points, Shanghai road network and OD (origin-destination)survey data, and daily Shanghai weather data. The precision of real-time data collection is increasing daily and, based on the national standard GB/T 2960.3-2016, covers vehicle data, engine data, drive battery data, alarm data, position data, and battery extreme data. Cross-analysis can also be conducted by integrating factors such as different vehicle types, user attributes, battery types, and prices.


Analysis of NEV operations in Shanghai

1. Operational characteristics: 80% of PHVs drive an average annual distance of 5,000km to 22,000 km

Driving probability density function approximating Weibull distribution presented by Siwen Deng

 According to the research center, the yearly average PHV mileage in Shanghai is 12,253 km. Statistical analysis of 5,151 PHVs revealed the mileage distribution for 80% of PHVs to be from 5,000 to 22,000 km. The horizontal axis represents daytime driving, and the vertical axis represents driving probability density. Based on this analysis, the driving probability distribution for Shanghai’s PHV users approximates that of a Weibull distribution. (See photo in lower right.)

 The average daily driving needs of 90% of PHV drivers in Shanghai can be met if EV drive modes of PHVs can achieve a range of 100 km.


2. Charging characteristics: Charging time for a single PHV charge takes 1-4 hours

   With regard to charge times, according to statistical analysis of four PHV models, the single charge times for Shanghai PHVs is concentrated in the 1-4 hour range. Statistical analysis of four EV models, on the other hand, showed a distribution concentrated in the 1-3 hour range. As a result, the analysis results based on actual electricity usage data, suggests that daily shared charging could be utilized, with all charging stations in residential areas capable of charging 2-3 electric vehicles a night. In new residential areas, 40-50% of all parking spaces are equipped with charging stations that have a big data authorization mechanism (adjustment functionality), making it possible to meet the nighttime charging needs of all vehicles in residential areas. This will be beneficial for delimiting the time required for sequential charging, controlling the scale of distributed charging capacity in residential districts. In existing residences and workplaces, based on the actual charging data of consumers, it is possible to charge 2-3 electric vehicles using a single charging station at a parking space during nighttime and daytime hours, or to charge via time sharing.

 As for daily charging capacity, according to a statistical analysis of the four PHV models, the SOC distribution of PHVs in Shanghai is concentrated in the 30% to 60% range. According to a statistical analysis of the four EV models, the SOC distribution of EVs in Shanghai is concentrated in the 20% to 60% range. In conclusion, a single 3.5 kWh AC charging station can satisfy the daily charging needs of each private owner of a PHV or EV that drives 300 km or less. According to the data, a single 7 kWh AC charging station in a residential area can satisfy the charge needs of two EVs per night by sharing.


3. Commercial vehicle patterns: Electric delivery vehicles drive an average of 66.2 km/day and 3.5 hours/day

 The statistical analysis of electric delivery vehicles in Shanghai revealed that the average daily driving distance is 66.2 km, and that they are driven for an average of 3.5 hours a day. The vehicles are primarily driven in the urban areas of the central part of Shanghai. On weekdays, private vehicles and delivery vehicles clearly show distinct travel characteristics. Private vehicles are primarily used for commuting, with peak driving times in the early morning and afternoon. Conversely, delivery vehicles clearly avoided driving during the early morning and afternoon peak hours. On weekends the travel characteristics of all vehicles were fundamentally the same, with private vehicles showing slightly higher travel rates than delivery vehicles between 8 P.M. to 11 P.M.

 Thanks to big data analysis relating to driving distances, temperatures, driving behavior and charging behavior, the research center can predict battery deterioration status. The four models selected for research indicated a negative correlation between longer driving distances and a significant decrease in battery capacity. The battery capacity of all four models showed that driving distances of 8 km or less yields an energy consumption of 0.65% to 0.9% for every 10,000 km driven. Among these, lithium iron phosphate batteries showed slightly slower capacity deterioration in comparison to tertiary lithium-ion batteries.


The Research Center’s three guiding principles

1. Data Acquisition Principle

By using the Big Data Open Lab environment, science and technology companies and research institutes can collaborate to conduct deep dive analyses of data based on time series, statistical data, and neural networks, resulting in the creation of data valuable for the development of application scenarios and products.

2. Project Outsourcing Principle 

Consulting projects outsourced by OEMs, part suppliers and consulting firms are undertaken by the research center to analyze specific vehicle types, driving behaviors based on specific users, and charging behaviors. In turn, the research center provides qualitative data for the purposes of product planning and R&D.

3. Collaborative Development Principle 

Companies such as OEMs, battery manufacturers and insurance providers consign projects or form co-development agreements with the research center to create machine learning algorithms and long-term data-based statistical analyses. Additionally, close collaboration by the integration of data between two parties, that is based on SOH (States of Health)data has the potential to create new products , battery maintenance products, and insurance products tailored for vehicles as well as batteries.

Keywords:SAE, NEV, EV, PHV, Battery, Big Data,SOC

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