Abstract:This paper presents a model for the projection of Chinese vehicle stocks and road vehicle energy demand through 2050 based on low-, medium-, and high-growth scenarios. To derive a gross-domestic product (GDP)-dependent Gompertz function, Chinese GDP is estimated using a recursive dynamic Computable General Equilibrium (CGE) model. The Gompertz function is estimated using historical data on vehicle development trends in North America, Pacific Rim and Europe to overcome the problem of insufficient long-running d… Show more
“…Wang et al [11] made a scenario analysis of energy consumption and reductions in CO 2 emissions in China's transport sector in 2050 by using the Transportation Mode-Technology-Energy-CO 2 (TMOTEC) model, which is based on discrete choice method and general transport cost simulation. Wu et al [12] used the Gompertz model to forecast China's future vehicle ownership, and then measured the fuel demand of China's road transport sector in 2050.…”
Section: Related Literaturementioning
confidence: 99%
“…As China's vehicle ownership is still in a period of rapid growth and is not saturated, future vehicle ownership, which is regarded as the basis of predicting fuel demand by many researchers [12][13][14][15][16], is the most important factor affecting China's fuel demand. International experience shows that vehicle ownership rate of a country increases slowly at the lowest income levels, followed by an increasing rate of growth as income rises, and then slows down as saturation is approached.…”
Vehicle ownership is one of the most important factors affecting fuel demand. Based on the forecast of China’s vehicle ownership, this paper estimates China’s fuel demand in 2035 and explores the impact of new energy vehicles replacing fossil fuel vehicles. The paper contributes to the existing literature by taking into account the heterogeneity of provinces when using the Gompertz model to forecast future vehicle ownership. On that basis, the fuel demand of each province in 2035 is calculated. The results show that: (1) The vehicle ownership rate of each province conforms to the S-shape trend with the growth of real GDP per capita. At present, most provinces are at a stage of accelerating growth. However, the time for the vehicle ownership rate of each province to reach the inflection point is quite different. (2) Without considering the replacement of new energy vehicles, China’s auto fuel demand is expected to be 746.69 million tonnes (Mt) in 2035. Guangdong, Henan, and Shandong are the top three provinces with the highest fuel demand due to economic and demographic factors. The fuel demand is expected to be 76.76, 64.91, and 63.95 Mt, respectively. (3) Considering the replacement of new energy vehicles, China’s fuel demand in 2035 will be 709.35, 634.68, and 560.02 Mt, respectively, under the scenarios of slow, medium, and fast substitution—and the replacement levels are 37.34, 112.01, and 186.67 Mt, respectively. Under the scenario of rapid substitution, the reduction in fuel demand will reach 52.2% of China’s net oil imports in 2016. Therefore, the withdrawal of fuel vehicles will greatly reduce the oil demand and the dependence on foreign oil of China. Faced with the dual pressure of environmental crisis and energy crisis, the forecast results of this paper provide practical reference for policy makers to rationally design the future fuel vehicle exit plan and solve related environmental issues.
“…Wang et al [11] made a scenario analysis of energy consumption and reductions in CO 2 emissions in China's transport sector in 2050 by using the Transportation Mode-Technology-Energy-CO 2 (TMOTEC) model, which is based on discrete choice method and general transport cost simulation. Wu et al [12] used the Gompertz model to forecast China's future vehicle ownership, and then measured the fuel demand of China's road transport sector in 2050.…”
Section: Related Literaturementioning
confidence: 99%
“…As China's vehicle ownership is still in a period of rapid growth and is not saturated, future vehicle ownership, which is regarded as the basis of predicting fuel demand by many researchers [12][13][14][15][16], is the most important factor affecting China's fuel demand. International experience shows that vehicle ownership rate of a country increases slowly at the lowest income levels, followed by an increasing rate of growth as income rises, and then slows down as saturation is approached.…”
Vehicle ownership is one of the most important factors affecting fuel demand. Based on the forecast of China’s vehicle ownership, this paper estimates China’s fuel demand in 2035 and explores the impact of new energy vehicles replacing fossil fuel vehicles. The paper contributes to the existing literature by taking into account the heterogeneity of provinces when using the Gompertz model to forecast future vehicle ownership. On that basis, the fuel demand of each province in 2035 is calculated. The results show that: (1) The vehicle ownership rate of each province conforms to the S-shape trend with the growth of real GDP per capita. At present, most provinces are at a stage of accelerating growth. However, the time for the vehicle ownership rate of each province to reach the inflection point is quite different. (2) Without considering the replacement of new energy vehicles, China’s auto fuel demand is expected to be 746.69 million tonnes (Mt) in 2035. Guangdong, Henan, and Shandong are the top three provinces with the highest fuel demand due to economic and demographic factors. The fuel demand is expected to be 76.76, 64.91, and 63.95 Mt, respectively. (3) Considering the replacement of new energy vehicles, China’s fuel demand in 2035 will be 709.35, 634.68, and 560.02 Mt, respectively, under the scenarios of slow, medium, and fast substitution—and the replacement levels are 37.34, 112.01, and 186.67 Mt, respectively. Under the scenario of rapid substitution, the reduction in fuel demand will reach 52.2% of China’s net oil imports in 2016. Therefore, the withdrawal of fuel vehicles will greatly reduce the oil demand and the dependence on foreign oil of China. Faced with the dual pressure of environmental crisis and energy crisis, the forecast results of this paper provide practical reference for policy makers to rationally design the future fuel vehicle exit plan and solve related environmental issues.
“…The model is widely used to analyze policy impact, such as Carbone and Rivers' (2017) [21] studies on the impacts of unilateral climate policy on competitiveness and Pui and Othman (2017) [22] examining the impact on economic growth and sectoral performance with fuel subsidy savings being reallocated to the biofuel industry for research and development purposes. In research by Wu et al (2014) [23], Chinese GDP is estimated using a recursive dynamic CGE model. Kolsuz and Yeldan (2017) [24] studied the synergies between environmental abatement instruments and policies toward sustaining green jobs.…”
“…As automobile emission standards are getting more stringent [1], research on and the application of electric drive technology has become a hot topic. Electric drive systems are zero emission and their output torque can be modulated precisely [2].…”
Abstract:The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs) can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB ® and Carsim ® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption.
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