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 CCHHEEMMIICCAALL  EENNGGIINNEEEERRIINNGG  TTRRAANNSSAACCTTIIOONNSS  
 

VOL. 45, 2015 

A publication of 

 
The Italian Association 

of Chemical Engineering 

www.aidic.it/cet 
Guest Editors: Petar Sabev Varbanov, Jiří Jaromír Klemeš, Sharifah Rafidah Wan Alwi, Jun Yow Yong, Xia Liu  

Copyright © 2015, AIDIC Servizi S.r.l., 

ISBN 978-88-95608-36-5; ISSN 2283-9216 DOI: 10.3303/CET1545315 

 

Please cite this article as: Li. W, Li Z., Liang J., Liu P., Ma L., 2015, The optimal oil-saving pathway until 2030 for china road 
passenger transportation based on a cost-optimisation model, Chemical Engineering Transactions, 45, 1885-1890  
DOI:10.3303/CET1545315 

1885 

The Optimal Oil-Saving Pathway Until 2030 for China 

Road Passenger Transportation Based on a Cost 

Optimisation Model 

Weiqi Li, Zheng Li, Jingjing Liang, Pei Liu, Linwei Ma* 

State Key Laboratory of Power Systems, Department of Thermal Engineering, Tsinghua-BP Clean Energy Center, 

Tsinghua University, Beijing 100084, China 

malinwei@tsinghua.edu.cn 

Optimal planning of oil-saving pathway for road passenger transportation sector remains a challenging 

task, as it involves many powertrains and fuel alternatives in the course of traffic volume expansion. This 

manuscript proposed a cost-optimisation superstructure model (COSM) to derive the optimal oil-saving 

pathway for road passenger transportation up to 2030. In each year of the planning horizon, the model 

considered eight options of alternative fuels and powertrains for seven categories of newly registered 

passenger vehicles which was derived from the projected vehicle population and survival rates. The 

optimisation objective of the model was to minimize the accumulated costs of fuels and vehicles over the 

planning horizon, and the optimal oil saving pathway was then decided by choosing the most cost-effective 

options of alternative fuels and powertrains for annual newly registered vehicles from 2010 to 2030. Based 

on the COSM, the empirical study of China indicated that the cost-optimal oil saving potential was 61 and 

126 Mt (OE) in 2020 and 2030. The sensitivity analysis indicated that supply amount of vehicular natural 

gas was more sensitivity than that of vehicular gasoline, gasoline price was more sensitivity than natural 

gas price, and acquisition cost of PEV (pure electricity vehicle) was more sensitive than that of HFCV 

(hydrogen fuel cell vehicle). 

1. Introduction 

In recent years, vehicle population of road passenger transportation grew rapidly in China. During the 

period from 2001 to 2011, the passenger vehicle population had grown for 7.5 times, from 9.94 M in 2001 

to 74.78 M in 2011 (China, 2013). In future, vehicle population would continue to increase rapidly because 

the vehicle ownership per thousand people was still low compared to developed countries even the world 

average level (Ou et al., 2010). According to the statistics database (Wang, 2011), road passenger 

transportation of China consumed nearly all the gasoline (69 Mt) and a portion of diesel (89 Mt). 

Considering road passenger transportation was the main consumer of gasoline and diesel among all the 

end users, the rapid increase of oil consumption from road passenger transportation was the main driver 

for the increase of OID (oil imported dependency), which raised concerns for energy supply security. 

To mitigate the rapid increase of OID and oil consumption, it has become a common understanding of 

developing alternative fuels and powertrains (Börjesson and Ahlgren, 2012), such as natural gas vehicle 

(Ou et al., 2010a), hybrid vehicle (Baran and Legey, 2013), pure electric vehicle (PEV) (Kyle and Kim, 

2011), and hydrogen fuel cell vehicle (HFCV) (Ouyang, 2006). Several studies had evaluated the oil 

saving potential of these advanced vehicles based on designed share of specific alternative fuels and 

powertrains over a specific planning horizon. For instance, in the study of Hao et al. (2011a), it designed a 

scenario of promoting EV penetration that the proportion of electricity vehicle (EV) in all newly registered 

vehicle was assumed to be 20 %, among which HEVs account for 70 %, PHEVs accounted for 24 %, and 

BEV accounted for 6 % in 2030. In the study of Yan and Crookes (2009), it assumed that the share for 

private car using CNG will increase to 5 % by 2030, and the share for heavy duty bus and taxi using CNG 

will increase by 50 % by 2030. All of these studies evaluated oil saving potential of several possible 



 
1886 

 
reduction measures by deploying a specific scale of alternative vehicles or fuels in a specific time, and 

designed the process of changing share. However, the deployment time and scale of alternative fuels and 

powertrains differed significantly for different studies.  

In this study, the optimal deployment of alternative fuels and powertrains for newly registered vehicles in 

each year over the planning horizon was defined as the optimal oil saving pathway, which was determined 

in the most cost-effective way because cost is a key factor in assessing the likelihood of alternative fuels 

and technologies becoming widely adopted (Gass et al., 2014). Firstly, a cost-optimisation model of 

China’s road passenger transportation was proposed to describe the interlinked relationship among the 

physical factors, and the fuel and powertrain option for newly registered vehicle was selected as the 

variable to be optimised. Secondly, based on the model, we developed an empirical study of China to 

derive the optimal oil saving pathway. Thirdly, we conducted a sensitivity analysis of some important 

impacted factors to check their influence on the optimal results.  

The rest of the paper was organized as follows: an introduction of the methodology was provided in 

section 2; an empirical study of China was conducted following with sensitivity analysis in section 3; the 

conclusion and discussion were given in section 4. 

2. Methodology 

2.1 Passenger vehicle classification 

In this study, passenger vehicles were classified into large-sized, middle-sized, and small-sized according 

to vehicle length (VL) and approved number (AN). In each of vehicle category, vehicle utility would 

influence vehicle travelled kilometres, vehicle acquisition cost, fuel economy, and further influence oil 

consumption. For instance, in small-sized vehicles, vehicles owned and used by enterprises and 

governments often travelled more kilometres than vehicles owned and used by individuals, and the 

acquisition cost of taxis were much lower than that of vehicles owned by enterprises and governments, 

and individuals. In large-sized vehicle, city buses used for carry passenger following a fixed route often 

travelled less kilometres than the other big buses. Therefore, we classified passenger vehicles into 7 

categories by vehicle length and approved number, and vehicle utility: private passenger vehicles (PPVs), 

taxis (TAXs), business passenger vehicles (BPVs), city buses (CBs), big buses (BBs), middle buses 

(MBs), and small and mini buses (SBs), which was shown in Figure 1. For each category of vehicle, it also 
had several alternative fuels and powertrains to choose, which included internal combustion engine using 

gasoline (ICE-G), internal combustion engine using diesel (ICE-D), hybrid using gasoline (Hybrid-G), 

hybrid using diesel (Hybrid-D), internal combustion engine using natural gas (ICE-NG), hydrogen fuel cell 

vehicle (HFCV) and pure electric vehicle (PEV) [7]. In this study, it is assumed that there were 45 possible 

options of alternative fuels and powertrains for 7 categories of vehicles, which were shown in Figure 2. 

Passenger 

vehicle

Large-

sized

VL≥6 or 

AN≥20

Middle-

sized

VL<6 and 

9<AN<20

Small-

sized

AL<6 and 

AN≤9

City bus

Middle bus

Private passenger 

vehicle

Big bus

Taxis

 Government and 

business vehicle

Small bus

PPVs SBs BBsCBsBPVsTAXs MBs

ICE-G Hybrid-D PEVICE-NGHybrid-GICE-D HFCV
                 

Figure 1: Passenger vehicle classification Figure 2: The options of alternative fuels and 

powertrains for different passenger vehicles 

2.2 Problem description and Model structure 
In this section, the problem of optimising oil-saving pathway until 2030 for China’s road passenger 

transportation could be described as a linear programming problem, which widely used in a broad range of 

fields (Gabriela et al., 2013). The optimisation objective was to obtain the most cost-effective mix of 



 
1887 

alternative fuels and powertrain options. The optimisation objective of this model was the accumulated 

cost over the planning horizon including vehicle acquisition cost, operation and maintenance cost (O&M), 

and fuel cost. To calculate three parts of cost, newly registered vehicles should be calculated firstly 

through forecasted vehicle population and survival rates. Vehicle acquisition cost could be then calculated 

by unit acquisition cost, vehicle O&M cost could be calculated by unit O&M cost, and fuel cost could be 

calculated by vehicle travelled kilometres, fuel consumption rate and unit fuel cost. 

2.3 Mathematical formulation 
According to the module structure presented in section 2.2, the module could be divided into two types: 

vehicle module and cost module. The mathematical formulation of each module and some key constraints 

of the module were presented in the rest of this section. The physical meanings of all variables with 

lowercase letters and parameters with capital letters were collected in Table 1.  

Table 1 Physical meanings of the variables and parameters in the model 

Symbol Physical meaning 

(VP/cap)t In year t, vehicle population per capita 

Et In year t, elasticity coefficient of GDP 

(GDP/cap)t In year t, GDP per capita 

pm For m category of vehicle, the slope of linear extrapolation  

qm For m category of vehicle, the intercept of linear extrapolation 

SRm,t,a In year t, survival rate of m category of vehicle registered in year a 

VPm,t,a In year t, vehicle population of m category of vehicle registered in year a 

VPm,t In year t, vehicle population of m category vehicle 

NRm,a For m vehicle, newly registered vehicle in year a 

km,a For m vehicle, survival parameter for vehicle registered in y a 

km,a0 For m vehicle, survival parameter for vehicle registered in base y 

Tm,a For m vehicle, survival parameter for vehicle registered in year a 

TR For m vehicle, annual changing rate of parameter Tm, a 

kr For m vehicle, annual changing rate of parameter km, a 

VKTm,f,t In year t, vehicle travelled km for m vehicle with f fuel  

VFEm,f,t,a In year t, fuel economy for m
th

 vehicle with f
th

 fuel registered in year a  

FCm,f,t,a In year t, annual fuel cost of m
th
 vehicle with f

th
 fuel registered in year a  

FPf,t In year t, the unit cost of f
th

 fuel 

VACm,f,t,a 
In year t, annual acquisition cost of m

th
 vehicle with f

th
 fuel registered in 

year a 

AFRm,f,t,a 
In year t, equal share coefficient of annual acquisition cost for m

th
 vehicle 

with f
th

 fuel registered in year a 

i Discount rate 

OMCm,f,t,a In year t, single O&M cost of m vehicle with f
th
 fuel registered in year a  

OMPRm,f,t,a In year t, the factor of O&M cost for singe m
th

 vehicle with f fuel in year a 

ACOm,f,t,a In year t, the cost of single m
th
 vehicle with f

th
 fuel registered in year a  

ATC The accumulated cost over the planning horizon 

INCNRF Constrained parameters of annual vehicle production capacity 

FSGS,t 

FSDS,t 

FSNG,t 

In year t, constrained supply of gasoline 

In year t, constrained supply of diesel  

In year t, constrained supply of natural gas 

 

Considering private passenger vehicle increased rapidly while the other categories of passenger vehicle 

increased moderately, the method of GDP elasticity coefficient was employed to forecast private 

passenger vehicle population while that of linear extrapolation was employed to forecast the other 

categories of passenger vehicles: 

   

 

   

 
1 1

1 1

t t t t

t

t t

VP cap VP cap GDP cap GDP cap
E

VP cap GDP cap

 

 

 
 

 

(1) 

,

m t m

m t

VP
p g q

cap

 
   

 
 

(2) 

app:ds:capital
app:ds:letter


 
1888 

 
,

, ,

, ,

, ,

-
exp

m ak

m t a

m t a

m a m a

VP t a
SR

NR T

  
          

(3) 

In the planning horizon, the changing law of survival parameters were shown as follows: 

 

 

,

, 1 , 0

, 1 , 0

, ,

,

1

1

exp

m a

m a m a

m a m a

k

m t a

m a

T TR T

k kr k

t a
SR

T

 

 

  
         

(4) 

The population of m category vehicles using f powertrain and fuel could be calculated as follows:  

, , 1 , , , 1

1 1

,
( )

m m

t t

m a m t a m f a m t a

a t TL f a t TL

m t
NR S nr SVP

   

     

    
 

(5) 

In year t, fuel cost of m category vehicle registered in year a using f type fuel and powertrain: 

, , , , , , , ,m f t a m f t m f a f t
FC VKT VFE FP 

 
(6) 

In year t, the acquisition cost of m category vehicle registered in year a using f type fuel and powertrain: 

 
 

, ,

, , , , ,

1

m t a

m f t a m f a t a
t a

SR
VAC VC

i










 

(7) 

In year t, the operation and maintenance cost of m category vehicle registered in year a using f
th

 fuel: 

, , , , ,m f t a m f a
OMC OMPR VC 

 
(8) 

In year t, the total cost of m category vehicle registered in year a using f type fuel and powertrain: 

, , , , , , , , , , , ,m f t a m f t a m f t a m f t a
ACO VAC OMC FC  

 
(9) 

Over the planning horizon, the accumulated cost of road passenger transport in China, considering the 

discount rate, could be calculated using the following formula: 

 
 

2030
, , , , , , ,

2011
2011 1996 1

t
m f t a m f a m t a

t
t a m f

ACO NRF SR
ATC

i


 

 



  

 

(10) 

Constrained infrastructure construction cycle of vehicle production and fuel filling station, an upper limit 

was set for the annual increase of each category of vehicle, as presented in Eq(11), in which INCNRF 

stand for the upper increase speed: 

INCNRFNRFNRF
1a,f,ma,f,m


  
(11) 

Constrained by limited supply of various fuel sources, there existed an upper limit for the annual supply 

capability of each type of fuel, as presented by Eq(12), in which FS stands for the upper limit: 

, , , , , , , , , , , , , , , , ,

1996 1996

, , , , , , , , , , , , , , , , ,

1996 1996

, , ,

t t

m GSL t a m GSL t a m t a m GSL t a m GSL t a m t a GSL t

a m a m

t t

m DSL t a m DSL t a m t a m DSL t a m DSL t a m t a DSL t

a m a m

m NG t a

FD NRF SR FD NRF SR FS

FD NRF SR FD NRF SR FS

FD NRF

 

 

     

     



   

   

, , , , , ,

1996

t

m NG t a m t a NG t

a m

SR FS


  
 

(12) 

Based on the modules presented above, the problem of optimising oil-saving pathway for road passenger 

transportation could be formulated as the linear programming: 

 
 

2030
, , , , , , ,

2011
2011 1996 1

. . (1) (12

( )

)

t
m f t a m f a m t a

t
t a m f

min
ACO NRF SR

ATC
i

t

in

s Eq

m


 

 







   

 

(13) 



 
1889 

3. An empirical study of China 

In this section, an empirical study of China was developed with the data input and assumptions, and along 

with the optimal results. 

3.1 Data input and assumptions 

Based on the proposed model, we developed an empirical study of China. In this study, 2010 was selected 

as the base year, and from 2011 to 2030 was selected as the planning horizon. For the optimisation, there 

were three types of essential data including vehicle data, cost data and constrained fuel supply amount. 

The data of historical sales and vehicle population of different categories of vehicles was collected from 

several yearbooks and studies. Survival rate was mainly derived from the study of Hao (2011b), to make 

the data of historical population and sales keep consistent, some modifications had been made. Vehicle 

acquisition cost of different categories of vehicles was also collected from several studies (CAERC, 2012). 

The data of annual travelled distance was mainly derived from studies of Hong H. Over the planning 

horizon, the annual change rate for the fuel consumption rate was assumed as 0.5 %, 0.75 % and 1 % for 

light duty vehicle, middle duty vehicle and heavy duty vehicle. The operation and maintenance cost of 

each vehicle was assumed 4 % of the vehicle acquisition cost. Discount rate was set as 0.1. Considering 

the infrastructure construction cycle of vehicle production and fuel filling station, the annual newly 

increased vehicle number was assumed to be less than 300M. It also assumed that the supply amount of 

gasoline would be 94 and 144 Mt in 2020 and 2030, the supply amount of diesel would be 124 Mt and 199 

Mt in 2020 and 2030. The supply amount of vehicular natural gas would be 16.1 Gm
3
 and 27.3 Gm

3
 in 

2020 and 2030 (CAERC, 2012). 

 

Figure 3: The optimal pathway in terms of vehicle 

category, powertrain and fuel for road passenger 

transportation in China  

Figure 4: The decomposition of accumulated 

passenger vehicle fuel demand 

3.2 Results 
On the basis of the methodology presented in section 2, and data input and assumptions presented in 

section 3.1, the optimal development pathway of road passenger transportation was obtained, which was 

shown in Figure.3. It could be seen that: (1) the demand of natural gas and PEV increased slowly with a 

small amount; (2) the demand of HFCV began to increase moderately in the year of 2023; (3) gasoline and 

diesel used in the powertrain of ICE increased slightly until 2014 and 2015, then decreased gradually 

because newly registered vehicles began to choose the powertrain of hybrid along with its acquisition cost 

decrease. The optimal results also indicated that oil would still remain the dominant vehicle fuel for road 

passenger transportation. Accumulated oil demand accounted for 88 % of the total fuel demand, amongst 

which gasoline accounted for 39 % and diesel accounted for 49 %. The amount of diesel demand 

surpassed that of gasoline, and would be the largest among all the other fuels because the dieselization of 

private passenger vehicles. The accumulated demand of natural gas, hydrogen and electricity was 1.3, 

0.61, and 0.91 Gt (OE) and accounted for 6 %, 3.2 % and 2.8 %, which was shown in Figure.4. 

4. Conclusion and discussion 

This paper proposed a cost-optimisation superstructure model to plan the most cost-effective oil-saving 

pathway for road passenger transportation. Based on the model, an empirical study of China’s road 



 
1890 

 
passenger transportation was developed over the planning horizon between 2010 and 2030. The optimal 

oil-saving pathway indicated that hybrid vehicle using gasoline and diesel would offer a promising path to 

achieve cost-effective reduction in oil use. At the same time, other advanced vehicles, including hydrogen 

fuel cell or pure electric vehicles, would continue to suffer from high cost and other limitations. Their limited 

market penetration means that their impact on fuel use is unlikely to be significant over the next two 

decades. Although vehicle powertrain would transfer to hybrid, gasoline and diesel would still remain the 

dominant vehicle fuel, and the accumulated demand of gasoline and diesel accounted for 88 % of total fuel 

demand, while electricity, hydrogen and natural gas accounted for approximately 3.2 %, 2.8 % and 7 %. 

From the optimal results, it also could be concluded that the cost-optimal oil saving potential was 61 and 

126 Mt (OE) in 2020 and 2030 through comparing with another study. 

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