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Opinions | NEV Big Data Application in Transport, Energy Saving and Emission Reduction

With the development of the Internet and information technology, more work in transportation field seek data support. The development of big data is driving policy development, industry observation, and academic research to progress in a more scientific and precise direction. However, due to the wide coverage of the transportation industry and the number of departments involved, there are still high barriers to data and information among various regions and departments. This workshop discussed in-depth on the potential and problems of new energy vehicle data use.

On March 26th, more than 30 experts and scholars from 18 organizations participated in the second workshop of the China Clean Transportation Partnership (CCTP). The Workshop was held by CCTP partner, Shanghai Electric Vehicle Public Data Collecting, Monitoring and Research Center. It compiled the existing data resources in the China Clean Transportation Partnership, learned the data needs of partners, and explored possible cooperation. At the same time, it explored how new energy vehicle data will serve policy formulation and innovation in areas such as transportation, energy conservation, and emission reduction. The secretariat has summarized some of the brilliant views of the participating experts here, hoping to help the industry partners who are also concerned about NEV data.

List of experts:

An Feng Innovation Center for Energy and Transportation
Dai Jianjun Shenzhen Urban Transport Planning Center
Ding Xiaohua Shanghai Electric Vehicle Public Data Collecting, Monitoring and Research Center
Ge Peng CATARC
Gong Huiming Energy Foundation
Liu Haode China Academy of Transportation Sciences of MOT
Shi Qingge Hangzhou Traffic GPS Co., Ltd
Wang Hewu Tsinghua University
Wu Xiaoyuan Tongji University
Yang Jie Southeast University
Ye Jianhong Tongji University
Zhang Chengbin China EV100

Dr. An Feng, executive director of CCTP, pointed out that we now have two categories of data - static vehicle data and dynamic travel data. Strictly speaking, only the latter can be counted new energy vehicle big data. The dynamic travel data is mainly in the hands of private operators. So how can we use new energy vehicle big data?

Data application scenarios:

[Yang Jie] We started with the trajectory data of taxi GPS and studied the impact of the replacement of fuel taxis by new energy taxis. Three main reasons for choosing taxis as target samples:

1. Ridesharing is an important way to travel in the future, while the demand of taxis is similar to that of ridesharing;

2. Taxi drivers are more aware of traffic conditions, such as parking spots and roads, and centralized parking spots can be used as alternative locations for charging stations;

3. From the perspective of subsidy, taxi subsidy is mainly offered at purchase stage, while subsidy for charging is worth studying.

[Liu Haode] The application of big data can give more support to the policymaking of the traffic sector. We are currently conducting research on new energy bus policies. We have already completed the target of promoting 300,000 new energy vehicles during the 13th Five-Year Plan in advance. The Ministry of Transport hopes to predict how much sales it will increase by 2020 if the indicator increases. We China Academy of Transportation Sciences of MOT utilized some forecast data of new energy sales data, growth rate, environmental protection, and related industries, and gave a forecast of 600,000.

New energy vehicle big data should focus on the use of vehicles: commercial operations and private passenger vehicles. The current operating companies are mainly passive choices, how to use big data to improve their operational efficiency, and provide support in scheduling and travel options. For example, some vehicle carries 100 kWh of electricity while real run only requires 80 kWh, wasting the energy. Improving operational efficiency is the key to the present. For the private passenger car, the focus is on using big data to improve user convenience, such as reserving for charging.

[Ge Peng] How to set up the development goal of commercial vehicles in 2020 is one of the research directions. In addition, vehicle model selection and vehicle technology selection should be discussed. Current monitoring data of new energy vehicles can give corresponding support on these issues.

[Ye Jianhong] In the study of transportation, taxis and buses are able to obtain full-scale samples. However, private vehicles account for the largest proportion of traffic flow, but it is precisely what they do not currently have. The most valuable asset of new energy vehicle big data should be research on the field of private passenger vehicles. Can the application of new energy vehicle big data be realized from the recognition of traffic conditions to the monitoring of urban operation quality? For example, the annual running time, which is the most current core indicator, can reflect the quality of life of urban residents. This indicator was previously calculated mainly through a model. With the accurate prediction of big data, we can publish this data on a quarterly basis, reflecting changes in the operation of different regions and seasons, reflecting changes in the quality of the city's operations. Of course, it also includes the study of vehicle ownership, its relationship with surrounding transportation and land development, research on driving behavior, changes in living conditions, and social events.

[Wang Hewu] In terms of energy-saving and emission-reduction research, energy-saving and emission-reduction of pure electric vehicles, according to the index of travel substitution rate, the power consumption of the operating vehicles in different regions and different seasons, and various factors affecting power consumption, the final combination of regional different power supply configurations can lead to relatively complete picture of energy conservation and emission reductions. Regarding the situation of battery energy conservation and emission reduction, considering of battery life, the current new energy vehicle big data in the study of battery life, may face a problem, that is, the rapid progress of battery technology, the previous data cannot accurately explain the current battery condition.

Data Utilization: Problems and Lessons

[Ding Xiaohua] The common problem now is that data is on multiple platforms of the government. Multi-platform makes the data unable to benchmark and a consumer's multiple behaviors cannot be linked. In the current cooperation with telecommunication companies, there exists personal privacy issues. At present, group characteristics are easier to get while the precise individual behavior is more difficult to analyze. Hangzhou can share good lessons on this. Its greatest advantage is that the relevant data are all on one platform and information is open to each other.

[Shi Qingge] Hangzhou has achieved all traffic data available such as rail transit, taxis, and internet vehicles, and has achieved unified supervision. Under the platform where all data is available, one can better measure the time spent by a person on the road, how to save energy, and realize the calculation of real energy-saving and emission reduction. In terms of data comprehensiveness, data from taxis and online vehicles, and mobile phone data from mobile companies, can achieve 10% of the traffic scalars, and can basically meet application needs.

[Wang Hewu] When studying the energy-saving and emission-reduction of plug-in hybrid vehicles, the problems are more complicated. The timing of engine intervention differs greatly among different configurations, and it is very difficult to measure energy-saving emission reductions. In the future, it is necessary to distinguish the technical types of vehicles in order to analyze refined energy consumption and measure emission reductions. Measuring the energy-saving and emission-reduction of the battery is still the biggest challenge, especially at production and recycling stages. One of the measures we propose is to use to use standardized environmental impact assessment and production power data from upstream and downstream for measurement.

More General Advice

[Liu Haode] Integrating data on shared bicycles, time-sharing leases, and the introduction of commercial companies are helpful to data development. The government's data is limited but private companies actually have more data.

[Zhang Chengbin] For traffic flow analysis, new energy vehicle data may not be advantageous. In the future, travel may increase but the number of vehicles may decrease. What kind of vehicles do we need to meet future travel demand? The new energy car big data may give the answer.

[Dai Jianjun] Currently, many vehicles have installed monitoring equipment from car companies, transportation regulators, and even operating companies. In the future, monitoring equipment can be unified to improve the efficiency of monitoring.

[Wu Xiaoxiao] The analysis of cross-city new energy vehicle operation data and the operation data analysis and research work of logistics vehicles should be emphasized. Same as first-tier cities, the travel data of small and medium-sized cities should also receive attention.

At the end of the discussion, the host Ding Xiaohua from the Shanghai Electric Vehicle Public Data Collecting, Monitoring and Research Center, summed up several points on NEV data utilization:

1、NEV data is still not mature in identifying traffic status, but has advantages in urban operational status identification and traffic demand management.

2、NEV data must be combined with other data (especially data from private sectors) in order to achieve better value.

3、NEV data can serve policy formulation, including traffic policies, evaluation of bus applications, and dual-credit scheme of commercial vehicles. Data that may be useful are vehicle models, the design of vehicle indicators, and even vehicle OD data.

Dr. An Feng, Executive Director of the China Clean Transportation Partnership, concluded: "We have kept a lot of data interfaces which traditional cars don't have during the NEV promotion phase. As we continuously monitor the data, the value will become more apparent."

Finally, Mr. Gong Huiming, Executive Director of CCTP Executive Committee, proposed that the China Clean Transportation Partnership Platform may provide the basis for annual public data report. The public data can ultimately serve the organizations concerned about traffic from different angles, and help achieve the initial integration of multidimensional data, and link different data supply and use agencies. Through the integration of data, we form synergistic strength on certain issues, truly serving our clean transportation.