Prospects for Chinese electric vehicle technologies in 2016–2020: Ambition and rationality

Du, Jiuyu; Ouyang, Minggao; Chen, Jingfu

doi:10.1016/j.energy.2016.11.114

Cited by 158 publications

(57 citation statements)

References 20 publications

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“…The most important obstacle in China at present is the lack of skilled scientists, while legal and institutional barriers have also hindered further development of the new energy industry [28,29]. China's NEV manufacturing enterprises have not broken through the key technologies of complete vehicles and some core components, and the cost of products is high [30]. It is necessary to focus on solving the autonomy of key components and gradually build an intelligent networked automobile industry chain to realize the strategic goal of being an automobile manufacturing powerhouse [31].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the Development Capability of the New Energy Vehicle Industry: An Empirical Study from China

Meng

Jin

2019

Sustainability

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In order to alleviate the pressures of environmental pollution and the energy crisis, and to lay out and capture huge emerging markets as soon as possible, all countries in the world are vigorously developing new energy vehicles (NEVs). This paper analyzes the factors influencing the development capability of the NEV industry from the aspects of autonomy, controllability, and stability, and constructs an evaluation index system. Based on the improved entropy method and the catastrophe progression method, we establish an evaluation model for the development capability of China’s NEV industry and comprehensively evaluate the development capability of 15 new energy auto companies. An empirical analysis finds that the outlook for the overall development capability of China’s NEV industry is not optimistic, and there is a big gap in the industry, lacking core technology and independent innovation capability. We propose countermeasures such as increasing research and development (R&D) investment and innovation, and improving policy support.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the Development Capability of the New Energy Vehicle Industry: An Empirical Study from China

Meng

Jin

2019

Sustainability

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“…As a cleaner means of transportation that is low-carbon and environmentally friendly, electric vehicles (EVs) can not only reduce greenhouse gas emissions [1,2], but they also improve country strategies to secure energy [3]. Moreover, EVs will introduce new connections and synergies between transportation networks and the power grid [4].…”

Section: Introductionmentioning

confidence: 99%

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

et al. 2020

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Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the 'Electric Vehicles-Power Grid-Traffic Network' fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners' charging behavior.Energies 2020, 13, 1412 2 of 32 331,000 public charging piles and 477,000 private charging piles in 2018, an increase of 74.2% over the same period in 2017 [6].However, the driving and charging behaviors of numerous EVs in urban internal networks are bound to interact with the energy and information generated by the traffic network and power grid [7,8]. The residents' trip rule, urban road network structure and charging facility distribution affect the vehicle driving distribution and charging selection, whereas the vehicle battery parameters, driving path plan and human decision-making behavior also affect the degree of traffic network obstruction and the power grid operation state [9][10][11][12]. Therefore, accurate prediction of EV charging demand and reasonable fast-charging station (FCS) recommendations are the premise of realizing compatibility between EVs and the power grid along with the transportation network.At present, various studies have developed EV charging demand models from the aspect of cooperation between EVs, the transportation system and power system all together. Hence, in [13,14], an origin destination (OD) matrix analysis method was utilized to track the all-weather driving trajectory of EVs and to predict the charging load distribution of the regional electricity grid as well as the flow status of road networks through vehicle traffic trip demands. Several studies [15][16][17] introduced Traffic Trip Chain and Markov Decision Chain to simulate the dynamic driving behavior and random charging behavior o...

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“…With the increasing urgency of environmental pollution and fossil oil shortage, the development of electric vehicles (EVs) has gained increased attention from governments and automotive industries around the world [1]. Under the support of the Chinese government and continuous efforts of automobile manufacturers [2], China has become the leading EV market, which has accounted for more than 60% of the global sales of EVs in 2016 [3]. Lithium-ion batteries are widely used as power sources for EVs because of their high energy and power density, wide operating temperature window and long cycle life [4].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

et al. 2019

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Accurate state-of-health (SOH) estimation for battery packs in electric vehicles (EVs) plays a pivotal role in preventing battery fault occurrence and extending their service life. In this paper, a novel internal ohmic resistance estimation method is proposed by combining electric circuit models and data-driven algorithms. Firstly, an improved recursive least squares (RLS) is used to estimate the internal ohmic resistance. Then, an automatic outlier identification method is presented to filter out the abnormal ohmic resistance estimated under different temperatures. Finally, the ohmic resistance estimation model is established based on the Extreme Gradient Boosting (XGBoost) regression algorithm and inputs of temperature and driving distance. The proposed model is examined based on test datasets. The root mean square errors (RMSEs) are less than 4 mΩ while the mean absolute percentage errors (MAPEs) are less than 6%. The results show that the proposed method is feasible and accurate, and can be implemented in real-world EVs.

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Prospects for Chinese electric vehicle technologies in 2016–2020: Ambition and rationality

Cited by 158 publications

References 20 publications

Evaluation of the Development Capability of the New Energy Vehicle Industry: An Empirical Study from China

Evaluation of the Development Capability of the New Energy Vehicle Industry: An Empirical Study from China

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles

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