# Standardized Comparison of 40 Local Driving Cycles: Energy and Kinematics

^{*}

## Abstract

**:**

## 1. Introduction

#### 1.1. Comparison of Kinematic and Energy Parameters of Driving Cycles

^{2}, ∆IT = 15%), as well as when compared with FTP-75 cycle (∆V = 14.2 km/h, ∆a = 0.08 m/s

^{2}, ∆IT = 12%). Tong et al. [13] proposed an LDC for Hanoi (Vietnam), concluding that there was a difference between their cycle and the standard cycle used at the time, NEDC (∆V = 13.7 km/h, ∆a = 0.13 m/s

^{2}, ∆IT = 15%). Knez et al. [14] compared previous cycles to their proposed driving cycle for Celje (Slovenia), observing that small towns have higher average speed (∆V = 6.1 km/h, ∆a = 0.17 m/s

^{2}, ∆IT = 5%). Mayakuntla and Verma [11] developed a driving cycle for Bangalore (India), finding that different regions in India need different cycles (∆V = 2.3 km/h, ∆IT = 13% in their comparison). They also emphasized the need to abandon the SDC used for legislative purposes in favor of LDCs (they found ∆V = 10.7 km/h, ∆a = 0.93 m/s

^{2}, and ∆IT = 6% when comparing their LDC with an SDC).

#### 1.2. Contribution of the Study

## 2. Methodology

#### 2.1. Data Selection, Extraction, and Filtering

#### 2.2. Evaluating Characteristic Parameters

^{2}; Amirjamshidi and Roorda [1] and Arun et al. [12] required two combined conditions, when V > 5 km/h and a > 0.1 m/s

^{2}. Koossalapeerom et al. [25] and Ma et al. [2] considered the fractions of time to reach a ≥ 0.27 m/s

^{2}and a > 0.28 m/s

^{2}, respectively. In this study, to compare the LDCs, we use the same set of parameters to characterize all of the driving cycles, as defined in Table 2. It is important to emphasize that the parameters listed in Table 2 should be used only subsequent to the filtering process described in Table 1.

#### 2.3. Evaluating the Energy Parameters

_{eng}, Equation (1)) to overcome the resistance and perform a desired movement.

_{trans}is the percentage of the engine power that reaches the wheel. The first bracket in the right side of Equation (1) is the inertial power, which is directly influenced by the mass (m), speed (V), and acceleration (dV/dt). The second bracket is the sum of all the resistance powers (aerodynamic drag power, rolling resistance power, and gravitational resistance power), and the third bracket is the braking power. In the aerodynamic drag power, the aerodynamic factor, k

_{A}(i.e., 1/2ρ

_{air}AC

_{D}), considers the fluid specific mass (ρ

_{air}), vehicle frontal area (A), vehicle shape coefficient (C

_{D}), and wind speed (W). The rolling resistance power considers the power to deform the tires and dampers and to overcome the vehicle internal friction. It considers the rolling resistance coefficient, C

_{R}, and the inclination of the road, θ. The gravitational resistance power considers the road inclination and the vehicle mass, m. In the braking power (third term in the brackets), β is the braking factor used by the pilot, μ is the friction coefficient between a tire and the road, m is the vehicle mass, g is the gravitational force, and θ is the road inclination. The braking power is null (β = 0) when dV/dt ≥ 0 (the vehicle is accelerating), or when it is decelerating, i.e., dV/dt < 0. However, the resistance power is sufficient to allow the desired retarding movement. When the resistances are unable to provide the desired deceleration, the breaking pedal is activated (β > 0). In this study, all of the simulations considered horizontal roads (θ = 0) and still air (W = 0).

_{eng}, Equation (1)) is typically lower than the maximum power it can generate at a desired engine speed, Ω (P

_{max,eng}). In this study, we calculate P

_{max,eng}using an empirical correlation described by Ni and Henclewood [34], as expressed in Equation (2). According to it, the curve of P

_{max,eng}is a cubic function of the engine speed, and C

_{1}and C

_{2}are constants described in the original paper, Equations (3) and (4). These constants are responsible for adjusting the power curve considering the peak power (P

_{max}), the engine speed at peak power (Ω

_{peak,pow}) and engine speed at peak torque (Ω

_{peak,tor}). The engine power is considered constant below the minimum engine speed, Ω

_{min}, simulating the clutch usage, and is null above the maximum engine speed, Ω

_{max}, simulating the rev limiter.

_{eng}) and the maximum power available by the engine (P

_{max,eng}) is obtained from the throttle usage, α (0–1), at a particular engine speed.

_{eng}≤ P

_{max,eng}, and the condition, α = [0,1] in Equation (5)). Actually, in that case, the vehicle cannot perform the acceleration required by the cycle, even with α = 1. This condition only occurs for low-powered vehicles, such as mopeds, or heavy vehicles when they were not tested in the cycles developed particularly for them.

_{eng}, Equation (6)) we use the empirical correlation provided by Ben-Chaim et al. [35], considering that η

_{eng}is a function of the maximum engine efficiency, η

_{0}, corrected by factors µ

_{rev}(Equation (7), considers the engine speed) and µ

_{pow}(Equation (8), considers the throttle usage, α, 0–1) under any given speed.

_{eng}in Equation (2)) and the vehicle speed, V, is given by the gear geometry and the wheel diameter. With the determination of the delivered power Equations (1)–(5) and the engine efficiency (Equation (6)) variation during the entire driving cycle, it is possible to calculate the vehicle energy consumption per distance traveled:

#### 2.4. Characterization of the Reference Vehicle

## 3. Results and Discussion

#### 3.1. Original Characteristic Parameters for Local Driving Cycles

_{ORIG}) for the 36 LDCs developed for passenger cars, and Table 5 lists CP

_{ORIG}for the 41 LDCs for the other vehicle classes (buses, trucks, and motorcycles). The occasional appearance of dashes in both the tables corresponds to the cases in which the authors did not inform the CP value. We emphasize that each author uses a specific formula to calculate the CPs. The last columns of Table 4 and Table 5 list the number of local driving cycles (NCs) considered in each paper.

^{2}) reconfirm numerically that the LDCs worldwide are distinct, thus justifying their differences. In relation to the comparison of the LDCs with SDCs, FTP-75 cycle, and WLTC (class 3) present an average speed of 34.1 km/h and 46.5 km/h, respectively, both being higher than the mean average speed of the LDCs (27.2 km/h).

^{2}; Table 5: a = 0.56 ± 0.28 m/s

^{2}). To understand these results, we divided Table 5 based on the vehicle category, obtaining a greater speed for trucks (V = 24.3 ± 12.4 km/h), then motorcycles (V = 23.9 ± 8.5 km/h), and then buses (V = 20.6 ± 10.3 km/h). Motorcycles cycles have lower average speeds, probably because trucks cycles have more highway stretches. In terms of acceleration, the higher values were for the motorcycles (a = 0.75 ± 0.27 m/s

^{2}), as expected from their lower masses, followed by those for the trucks (a = 0.38 ± 0.15 m/s

^{2}). There is no acceleration information for buses. Another interesting result is found by analyzing the modes of movement. For trucks, we calculated a very short cruising time (CT = 3% ± 3%), whereas the motorcycles interestingly presented a longer time for the cruising speed (CT = 21% ± 8%); because they are nimble, one would expect motorcycles to spend more time accelerating.

#### 3.2. Validation

_{ORIG}refers to the CPs obtained directly from the papers, as informed by their authors. In group II, the original numerical vector (ONV) is obtained from agencies (in the case of SDCs) or authors (in the case of LDCs). CP

_{ONV}refers to our standardized characterization (calculation) of the CPs using the ONV. In Group III, the ENV is the numerical vector we extracted from the figures presented in the papers studied and CP

_{ENV}and EP

_{ENV}refer to our calculation of the CPs and EPs obtained from the ENV.

_{ORIG}(as presented by the authors), CP

_{ONV}(calculated by us from the ONVs), and CP

_{ENV}(calculated by us from the ENVs).

_{ORIG}[50] and CP

_{ONV}for FTP-75 cycle and WLTC. From Table 6, we can verify that the characterization is effective, because there is no considerable difference between the values of CP

_{ORIG}and CP

_{ONV}, for both the SDCs.

_{ONV}(calculated from the ONV) is compared to the CP

_{ENV}(calculated from the ENV). For FTP-75 cycle, the results for the average speed, distance, time, acceleration, and deceleration were virtually equal. There was a little difference in the time proportion for each driving mode (CT, IT, AT, and DT), because the CPs were more sensitive to the acceleration and speed fluctuations. The validation using the WLTC data presented similar results.

_{ENV}(characterized following the extraction of the numerical vector from the figure) to CP

_{ONV}(from the vectors shared by the authors). For all the cycles, the speed difference was under 0.1 km/h, and the distance difference was shorter than 0.2 km. For the acceleration and deceleration, the difference was less than 0.2 m/s

^{2}. There are some differences between the CP

_{ENV}and CP

_{ONV}in Table 6, but with little effect on the energy consumption. For example, the relative error for the acceleration in Athens could be considered high (20%). However, the acceleration difference was 0.14 m/s

^{2}, equivalent to a speed variation of 0.5 km/h per second. This is low, because, for example, in the Brazilian law for driving cycle tests, there is a tolerance of 3.2 km/h in relation to the instantaneous velocity, in the type approval test [51]. Therefore, in general, we can deduce that our extraction process is also validated.

#### 3.3. Characteristic Kinematic Parameters from Local Driving Cycles

_{ENV}for 28 LDCs for passenger cars, and Table 8 shows that for the 12 LDCs of other vehicles classes. These CP

_{ENV}values were obtained using the methodology described in Figure 4, comprising extraction and characterization.

^{2}, ${a}_{\mathrm{ORIG}}$ = 0.54 ± 0.24 m/s

^{2}(only for the cycles in both Table 4 and Table 7), ${a}_{\mathrm{FTP-75}}$ = 0.51 m/s

^{2}and ${a}_{\mathrm{WLTC-3}}$ = 0.41 m/s

^{2}. The average speed of CP

_{ENV}is lower than that of both the SDCs. The average acceleration for CP

_{ENV}is similar only to that of WLTC but not to that of FTP-75 cycle. Table 8 summarizes the 12 LDCs for the motorcycles and the trucks. These cycles are also provided in Table 5 (original values).

#### 3.4. Results for Energy

_{2}emitted using stoichiometric calculation. The energy consumption (MJ/km) can be converted to CO

_{2}emitted when multiplying it by a factor of 0.072 kg CO

_{2}/MJ of E22.

^{2}), than that for the cycles for trucks (38%; a = 0.33 m/s

^{2}). For comparison, by changing the characteristics of the vehicles (e.g., drag, mass, and power peak), if we simulated FTP-75 cycle with a motorcycle, then we would obtain FE = 32.1 km/L (E22) and EC = 0.9 MJ/kg; and for a truck, FE = 3.7 km/L (diesel) and EC = 9.6 MJ/kg.

^{3}. We did not find this correlation because the average speed for the studied LDCs (for cars) was low, 27.1 (±7.6) km/h. This is listed in Table 9, in which only 15% of the energy is owing to the aerodynamic drag.

^{2}, FE = 11.2 km/L, EC = 2.6 MJ/km, CT = 5%, D = 6.5 km, t = 1160 s) and Nanjing (V = 36.3 km/h, a = 0.29 m/s

^{2}, FE = 16.1 km/L, EC = 1.8 MJ/km, CT = 23%, D = 11.8 km, t = 1174 s), we again confirm that acceleration (reflected in cruising time, CT, as discussed above) is the main parameter influencing the energy consumption.

^{2}and the mean acceleration is 0.44 m/s

^{2}), contributing more to the energy consumption in the integral of Equation (5) than the higher accelerations.

## 4. Conclusions

^{2}) when compared to one of the most prominent SDCs worldwide, FTP-75 cycle (V = 34.1 km/h, a = 0.51 m/s

^{2}). In this study, we found (as partially expected) acceleration as the most important factor for energy consumption. The studied LDCs have relatively low average speed (14.7 to 44.7 km/h, Table 7); therefore, we could not obtain a correlation between the average speed and the energy consumption. However, this does not suggest that there is no such correlation in general.

^{2}; FE = 20.5 km/L) presented a higher fuel economy than Sydney (a = 0.73 m/s

^{2}; FE = 10.8 km/L).

^{2}and in speed is 7.6 km/h, resulting in a standard deviation in fuel economy of 2.3 km/L. For other vehicles, the standard deviation in fuel economy is 2.8 km/L. When the average results of the LDCs for passenger cars are compared to the SDCs, they present a difference in the fuel economy of 12.5% (1.5 km/L) and of 28.6% (3.0 km/L) when compared to FTP-75 cycle and WLTC-3, respectively. Therefore, this paper confirms the reasoning of the worldwide praxis of developing local driving cycles. The numerical results we obtained show that the SDCs might not be representative of specific regions, so they should not be used as the solely legal testing procedure. We suggest that countries and major cities consider developing their own LDCs, in addition to SDCs, to better evaluate their reality.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 4.**Fuel economy in function of: (

**a**) average speed; (

**b**) average acceleration; (

**c**) cruising time.

**Figure 5.**For Athens LDC and Nanjing LDC: (

**a**) vehicle speed, (

**b**) energy consumption, and (

**c**) distance, in function of time.

Condition (If) | Result (Then) | Reason |
---|---|---|

V < 3.6 km/h | V = 0 km/h | Provide null speed (Idling mode) |

a < 0.05 m/s^{2} | a = 0 m/s^{2} | Avoid acceleration fluctuation (Cruising mode) |

d < −0.05 m/s^{2} | d = 0 m/s^{2} | Avoid deceleration fluctuation (Cruising mode) |

a > 3 m/s^{2} | a = 3 m/s^{2} | Limit maximum acceleration |

d < −7 m/s^{2} | d = −7 m/s^{2} | Limit maximum deceleration |

**Table 2.**Parameters for the characterization process—to be used following the filtering process described in.

Characteristic Parameters | Symbol | Definition |
---|---|---|

Average speed | V (km/h) | Average speed including zero speed |

Distance | D (km) | Distance traveled |

Average acceleration | a (m/s^{2}) | Average acceleration rate above 0.0 m/s^{2} |

Average deceleration | d (m/s^{2}) | Average deceleration rate below 0.0 m/s^{2} |

Time | t (s) | Total test time |

Idling mode | IT (%) | Time proportion in which V = 0.0 km/h and a = 0.0 m/s^{2} |

Acceleration mode | AT (%) | Time proportion in which a > 0.0 m/s^{2} |

Deceleration mode | DT (%) | Time proportion in which a < 0.0 m/s^{2} |

Cruising mode | CT (%) | 100%—IT—AT—DT |

Parameter | Car | |
---|---|---|

P_{max} | Peak power | 55.16 kW |

${P}_{idle}$ | Idling power | 8.0 kW |

${\Omega}_{peak,pow}$ | Engine speed at peak power | 6200 rpm |

${\Omega}_{peak,tor}$ | Engine speed at peak torque | 4500 rpm |

${\Omega}_{min}$ | Minimum engine speed | 900 rpm |

${\eta}_{0}$ | Maximum engine efficiency | 22% |

${\eta}_{trans}$ | Transmission efficiency | 90% |

$m$ | Total mass (vehicle + driver) | 1126 kg |

$\mu $ | Tire/road friction coefficient | 0.80 |

${k}_{A}$ | Aerodynamic drag factor | 0.428 |

${C}_{R}$ | Rolling resistance coefficient | 0.014 |

FHV | Fuel heating value | 28.99 MJ/liter |

**Table 4.**Original characteristic kinematic parameter (CP

_{ORIG}) for the LDCs for passenger cars (PCs).

Local Driving Cycle | Reference | V | D | a | d | t | CT | IT | AT | DT | NC |
---|---|---|---|---|---|---|---|---|---|---|---|

Athens | [20] | 21.2 | 6.5 | 0.67 | - | 1160 | - | - | 36% | - | 1 |

Bangalore | [11] | 16.2 | 9.4 | 1.51 | −1.76 | 2088 | 14% | 22% | 35% | 30% | 7 |

Baqubah | [37] | 21.6 | 6.3 | 0.24 | −0.24 | 1052 | 0% | 25% | - | - | 1 |

Beijing | [33] | 26.1 | - | 0.51 | −0.51 | - | 15% | 13% | 36% | 37% | 11 |

Beijing | [24] | − | 14.5 | 0.37 | −0.40 | 2536 | 11% | 24% | 34% | 31% | 1 |

Beijing—Off-peak | [2] | 28.8 | - | 0.51 | −0.56 | - | 39% | 24% | 19% | 18% | 2 |

Beijing—Peak | [2] | 23.9 | - | 0.51 | −0.57 | - | 37% | 26% | 19% | 18% | 2 |

Beijing | [3] | 38.5 | - | - | - | 1200 | 28% | 16% | 29% | 27% | 1 |

Celje | [14] | 25.5 | 13.0 | 0.79 | −0.84 | 2453 | 25% | 25% | 26% | 25% | 4 |

Changchun | [33] | 27.8 | - | 0.56 | −0.62 | - | 12% | 19% | 36% | 33% | 11 |

Chengdu | [33] | 31.3 | - | 0.55 | −0.60 | - | 15% | 12% | 38% | 35% | 11 |

Chennai—Peak | [12] | 17.7 | 5.2 | 0.45 | −0.54 | 1065 | 14% | 31% | 30% | 25% | 16 |

Chennai—Off-peak | [12] | 22.1 | - | 0.61 | −0.71 | - | 20% | 16% | 34% | 29% | 16 |

Chongqing | [33] | 31.3 | - | 0.49 | −0.56 | - | 16% | 8% | 41% | 35% | 11 |

Edinburgh | [38] | 20 | 4.2 | - | - | 835 | 9% | 31% | 31% | 29% | 1 |

Fortaleza | [23] | 23.8 | 8.4 | - | - | 1216 | 0% | 43% | 30% | 27% | 1 |

Hanoi | [13] | 19.4 | 10.0 | 0.41 | −0.38 | 1862 | 20% | 10% | 33% | 37% | 7 |

Hefei | [39] | 20.2 | - | - | - | 1237 | 25% | 12% | 29% | 34% | 3 |

Hong Kong | [32] | 15.5 | 6.3 | 0.55 | −0.59 | 1471 | 9% | 31% | 31% | 29% | 5 |

Hong Kong—Urban | [40] | 25.0 | 10.3 | 0.59 | −0.60 | 1548 | 12% | 18% | 35% | 34% | 3 |

Hong Kong—Sub Urb | [40] | 44.4 | 18.3 | 0.56 | −0.56 | 1476 | 14% | 5% | 40% | 40% | 3 |

Hong Kong—HWY | [40] | 38.3 | 14.9 | 0.40 | −0.41 | 1401 | 17% | 8% | 38% | 36% | 3 |

Jilin City | [33] | 36.8 | - | 0.35 | −0.50 | - | 20% | 6% | 44% | 31% | 11 |

Jiutai | [33] | 25.5 | - | 0.37 | −0.41 | - | 20% | 5% | 39% | 36% | 11 |

Mashad | [41] | 20.3 | - | 0.53 | −0.54 | - | 3% | 22% | 37% | 38% | 1 |

Mianyang | [33] | 35.3 | - | 0.48 | −0.57 | - | 15% | 12% | 40% | 33% | 11 |

Nanjing | [42] | 30.7 | - | 0.49 | −0.55 | - | 30% | 20% | 27% | 23% | 5 |

Ningbo | [33] | 23.7 | - | 0.51 | −0.58 | - | 11% | 20% | 37% | 33% | 11 |

Pune | [43] | 19.6 | - | - | - | 1533 | 56% | 18% | 14% | 11% | 1 |

Santa Maria—5 p.m. | [21] | 30.8 | 11.7 | - | - | 2017 | 2% | 0% | 51% | 48% | 2 |

Santa Maria—12 a.m. | [21] | 42.9 | 11.7 | - | - | 1294 | 3% | 0% | 45% | 53% | 2 |

Shanghai | [33] | 27.6 | - | 0.55 | −0.60 | - | 9% | 26% | 34% | 31% | 11 |

Sidney | [44] | 33.6 | 5.94 | - | - | 637 | - | 18% | - | - | 1 |

Singapore | [19] | 32.8 | 21.5 | - | - | 2344 | 26% | 21% | 29% | 25% | 1 |

Tianjin | [33] | 22.5 | - | 0.36 | 0.43 | - | 21% | 12% | 36% | 30% | 11 |

Zitong | [33] | 32.3 | - | 0.29 | −0.39 | - | 24% | 6% | 40% | 30% | 11 |

Average | 27.2 | 10.5 | 0.53 | −0.54 | 1521 | 17% | 17% | 34% | 31% | 8 | |

Standard Deviation | 7.5 | 4.8 | 0.23 | 0.33 | 534 | 12% | 10% | 7% | 8% | 4 |

**Table 5.**CP

_{ORIG}for the LDCs for vehicles different from passenger cars: bus (B), motorcycle (M), truck (T).

Local Driving Cycle | Reference | VT | V | D | a | d | t | CT | IT | AT | DT | NC |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Beijing—BRT LS ^{1} | [27] | B | 8.6 | 2.8 | - | - | 1167 | - | - | - | - | 9 |

Beijing—BRT MS ^{2} | [27] | B | 21.7 | 7.4 | - | - | 1220 | - | - | - | - | 9 |

Beijing—BRT HS ^{3} | [27] | B | 31.3 | 10.3 | - | - | 1185 | - | - | - | - | 9 |

Beijing—Express LS ^{1} | [27] | B | 7 | 2.4 | - | - | 1226 | - | - | - | - | 9 |

Beijing—Express MS ^{2} | [27] | B | 19.5 | 6.8 | - | - | 1257 | - | - | - | - | 9 |

Beijing—Express HS ^{3} | [27] | B | 35.9 | 11.3 | - | - | 1130 | - | - | - | - | 9 |

Beijing—Regular LS ^{1} | [27] | B | 8.1 | 2.7 | - | - | 1188 | - | - | - | - | 9 |

Beijing—Regular MS ^{2} | [27] | B | 19.9 | 6.0 | - | - | 1084 | - | - | - | - | 9 |

Beijing—Regular HS ^{3} | [27] | B | 30.9 | 10.9 | - | - | 1275 | - | - | - | - | 9 |

Chennai | [12] | M | 22.8 | 9.1 | 0.65 | −0.73 | 1448 | 24% | 19% | 30% | 27% | 16 |

Edinburgh—Urban | [45] | M | 33.5 | 7.3 | 1.28 | −2.59 | 770 | 7% | 2% | 44% | 47% | 9 |

Edinburgh—Rural | [45] | M | 49.7 | 9.0 | 0.89 | −0.95 | 656 | 8% | 1% | 45% | 46% | 9 |

Hanoi | [13] | M | 20.1 | 11.5 | 0.42 | −0.46 | 2061 | 21% | 8% | 37% | 34% | 7 |

Kaohsiung | [18] | M | 19.2 | 4.3 | 0.66 | −0.64 | 803 | 24% | 24% | 25% | 26% | 5 |

Kaohsiung | [17] | M | 21 | 6.6 | 0.58 | −0.61 | 1126 | 9% | 28% | 33% | 31% | 3 |

Khon Kaen | [46] | M | 25.0 | 8.1 | 0.64 | −0.69 | 1164 | 18% | 21% | 32% | 29% | 5 |

Khon Kaen—Electric | [25] | M | 22.6 | 5.0 | 1.42 | −1.05 | 781 | 38% | 24% | 15% | 21% | 2 |

Khon Kaen—Gasoline | [25] | M | 22.5 | 4.9 | 0.64 | −0.65 | 775 | 19% | 27% | 28% | 26% | 2 |

Pingtung | [18] | M | 30.2 | 6.8 | 0.69 | −0.79 | 810 | 22% | 10% | 36% | 32% | 5 |

Shanghai | [31] | B | 23 | - | 0.71 | −0.83 | - | 5% | 34% | 33% | 28% | 1 |

Shanghai—Electric | [26] | M | 19.9 | 9.43 | 0.50 | −0.45 | 1704 | 33% | 11% | 27% | 30% | 1 |

Shenyang | [47] | - | 28.1 | - | 0.31 | −0.36 | - | 44% | 0% | 31% | 25% | 1 |

Taichung | [18] | M | 18.9 | 3.8 | 0.63 | −0.61 | 714 | 25% | 23% | 26% | 26% | 5 |

Taipei | [22] | M | 19.4 | 5.1 | 0.80 | −0.83 | 950 | 19% | 20% | 32% | 30% | 1 |

Taipei | [18] | M | 16.6 | 3.5 | 0.68 | −0.68 | 763 | 22% | 30% | 24% | 24% | 5 |

Toronto—HDT ^{4} Freeway | [1] | T | 40.9 | - | 0.15 | −0.28 | - | 3% | 2% | 63% | 33% | 21 |

Toronto—MDT ^{5} Freeway | [1] | T | 39.7 | - | 0.14 | −0.34 | - | 1% | 2% | 69% | 28% | 21 |

Toronto—LDT ^{6} Freeway | [1] | T | 52.7 | - | 0.29 | −0.54 | - | 4% | 2% | 61% | 33% | 21 |

Toronto—LDT ^{6} M. Artl. | [1] | T | 18.4 | - | 0.58 | −0.71 | - | 3% | 17% | 44% | 36% | 21 |

Toronto—MDT ^{5} M. Artl. | [1] | T | 15.2 | - | 0.36 | −0.65 | - | 1% | 18% | 52% | 29% | 21 |

Toronto—HDT ^{4} M. Artl. | [1] | T | 16.6 | - | 0.40 | −0.59 | - | 2% | 16% | 49% | 34% | 21 |

Toronto—HDT ^{4} LS. Blvd. | [1] | T | 28.4 | - | 0.28 | −0.61 | - | 1% | 12% | 59% | 28% | 21 |

Toronto—MDT ^{5} LS. Blvd. | [1] | T | 25.7 | - | 0.27 | −0.65 | - | 1% | 10% | 63% | 26% | 21 |

Toronto—LDT ^{6} LS. Blvd. | [1] | T | 34.8 | - | 0.57 | −0.75 | - | 8% | 12% | 46% | 35% | 21 |

Toronto—HDT ^{4} U. Ave. | [1] | T | 12.4 | - | 0.40 | −0.60 | - | 1% | 22% | 46% | 31% | 21 |

Toronto—MDT ^{5} U. Ave. | [1] | T | 13.1 | - | 0.38 | −0.63 | - | 0% | 19% | 50% | 30% | 21 |

Toronto—LDT ^{6} U. Ave. | [1] | T | 13.9 | - | 0.60 | −0.75 | - | 1% | 20% | 44% | 36% | 21 |

Toronto—HDT ^{4} Artl Transit | [1] | T | 16.9 | - | 0.41 | −0.56 | - | 6% | 19% | 43% | 32% | 21 |

Toronto—MDT ^{5} Artl Transit | [1] | T | 15.8 | - | 0.36 | −0.59 | - | 6% | 18% | 48% | 29% | 21 |

Toronto—LDT ^{6} Artl Transit | [1] | T | 19.4 | - | 0.57 | −0.57 | - | 12% | 14% | 37% | 37% | 21 |

URB | [18] | M | 17.4 | 4.2 | 0.66 | −0.60 | 877 | 20% | 28% | 25% | 27% | 5 |

Average | 23.3 | 6.6 | 0.56 | −0.67 | 1089 | 13% | 16% | 41% | 31% | 13 | ||

Standard Deviation | 10.3 | 2.8 | 0.28 | 0.42 | 332 | 12% | 9% | 13% | 6% | 7 |

^{1}Low Speed;

^{2}Medium Speed;

^{3}High Speed;

^{4}Heavy Duty Trucks;

^{5}Medium Duty Trucks;

^{6}Light Duty Trucks.

Driving Cycle | V | D | a | d | t | CT | IT | AT | DT | ||
---|---|---|---|---|---|---|---|---|---|---|---|

SDC | FTP-75 cycle | CP_{ORIG} | 34.1 | 17.8 | 0.51 | −0.58 | 1874 | 7.7% | 17.9% | 39.4% | 35.0% |

CP_{ONV} | 34.1 | 17.8 | 0.51 | −0.58 | 1874 | 7.7% | 17.9% | 39.4% | 35.0% | ||

CP_{ENV} | 34.0 | 17.7 | 0.51 | −0.59 | 1874 | 11.4% | 18.6% | 37.5% | 33.3% | ||

SDC | WLTC (class 3) | CP_{ORIG} | 46.5 | 23.3 | 0.41 | −0.45 | 1800 | 3.7% | 12.6% | 43.8% | 39.8% |

CP_{ONV} | 46.5 | 23.3 | 0.41 | −0.45 | 1800 | 3.6% | 12.6% | 43.9% | 40.0% | ||

CP_{ENV} | 46.5 | 23.3 | 0.44 | −0.46 | 1800 | 9.8% | 13.9% | 39.2% | 37.0% | ||

LDC | Athens | CP_{ONV} | 20.2 | 6.5 | 0.71 | −0.72 | 1160 | 4.1% | 27.3% | 34.5% | 34.1% |

CP_{ENV} | 20.0 | 6.5 | 0.57 | −0.59 | 1160 | 5.2% | 25.4% | 35.4% | 34.0% | ||

LDC | Chennai Car Peak | CP_{ONV} | 17.7 | 5.2 | 0.42 | −0.50 | 1064 | 7.2% | 31.8% | 33.4% | 27.9% |

CP_{ENV} | 17.6 | 5.2 | 0.32 | −0.50 | 1065 | 12.7% | 30.0% | 34.6% | 22.7% | ||

LDC | Chennai Car Off-Peak | CP_{ONV} | 22.1 | 7.9 | 0.57 | −0.67 | 1292 | 14.1% | 17.0% | 37.2% | 31.8% |

CP_{ENV} | 22.4 | 8.0 | 0.37 | −0.42 | 1294 | 17.4% | 16.2% | 35.5% | 31.0% | ||

LDC | Toronto HDT ^{1} Freeway | CP_{ONV} | 41.0 | 16.3 | 0.15 | −0.28 | 1428 | 2.4% | 1.9% | 62.8% | 32.8% |

CP_{ENV} | 40.8 | 16.2 | 0.18 | −0.25 | 1430 | 33.2% | 3.0% | 37.6% | 26.2% |

^{1}Heavy Duty Trucks.

Local Driving Cycle | Reference | V | D | a | d | t | CT | IT | AT | DT |
---|---|---|---|---|---|---|---|---|---|---|

Athens | [20] | 20.0 | 6.5 | 0.57 | −0.59 | 1160 | 5% | 25% | 35% | 34% |

Bangalore | [11] | 16.1 | 9.3 | 0.37 | −0.49 | 2088 | 8% | 32% | 34% | 26% |

Beijing | [33] | 25.8 | 7.8 | 0.44 | −0.46 | 1081 | 12% | 12% | 39% | 37% |

Beijing | [24] | 20.4 | 14.4 | 0.38 | −0.43 | 2536 | 14% | 27% | 31% | 28% |

Beijing | [3] | 34.1 | 11.3 | 0.57 | −0.71 | 1192 | 15% | 14% | 39% | 32% |

Changchun | [33] | 27.4 | 8.7 | 0.51 | −0.62 | 1137 | 12% | 22% | 36% | 30% |

Chengdu | [33] | 31.0 | 10.3 | 0.48 | −0.53 | 1191 | 13% | 13% | 39% | 36% |

Chennai—Car Peak | [12] | 17.6 | 5.2 | 0.32 | −0.50 | 1065 | 13% | 30% | 35% | 23% |

Chennai—Car Off-Peak | [12] | 22.4 | 8.0 | 0.37 | −0.42 | 1294 | 17% | 16% | 36% | 31% |

Chongqing | [33] | 31.0 | 10.0 | 0.46 | −0.49 | 1157 | 14% | 11% | 38% | 36% |

Edinburgh | [38] | 19.7 | 4.6 | 0.58 | −0.63 | 835 | 8% | 32% | 32% | 29% |

Fortaleza | [23] | 23.7 | 8.0 | 0.51 | −0.57 | 1217 | 8% | 30% | 33% | 29% |

Hanoi | [13] | 22.1 | 9.8 | 0.33 | −0.31 | 1862 | 18% | 14% | 33% | 34% |

Hong Kong | [32] | 14.7 | 6.0 | 0.46 | −0.50 | 1471 | 3% | 34% | 33% | 30% |

Hong Kong—Urban | [40] | 24.0 | 10.3 | 0.50 | −0.52 | 1548 | 14% | 22% | 33% | 32% |

Hong Kong—Sub Urb | [40] | 44.7 | 18.3 | 0.40 | −0.40 | 1475 | 12% | 6% | 42% | 41% |

Hong Kong—HWY | [40] | 36.7 | 14.9 | 0.28 | −0.29 | 1460 | 16% | 14% | 36% | 35% |

Jilin | [33] | 36.3 | 10.7 | 0.34 | −0.47 | 1062 | 17% | 7% | 44% | 32% |

Jiutai | [33] | 25.4 | 7.5 | 0.42 | −0.46 | 1070 | 15% | 5% | 42% | 38% |

Mashad | [41] | 19.8 | 5.6 | 0.52 | −0.50 | 1019 | 6% | 25% | 34% | 35% |

Mianyang | [33] | 35.1 | 9.9 | 0.42 | −0.60 | 1017 | 14% | 12% | 43% | 31% |

Nanjing | [42] | 36.3 | 11.8 | 0.29 | −0.35 | 1174 | 23% | 12% | 35% | 30% |

Ningbo | [33] | 23.4 | 7.4 | 0.46 | −0.51 | 1137 | 11% | 20% | 36% | 33% |

Santa Maria—12 a.m. | [21] | 37.6 | 13.5 | 0.20 | −0.17 | 1294 | 34% | 0% | 34% | 34% |

Shanghai | [33] | 27.3 | 8.6 | 0.50 | −0.50 | 1135 | 11% | 26% | 32% | 32% |

Sydney | [44] | 33.6 | 6.0 | 0.73 | −0.72 | 637 | 9% | 21% | 35% | 35% |

Tianjin | [33] | 22.2 | 6.6 | 0.34 | −0.42 | 1075 | 17% | 14% | 38% | 31% |

Zitong | [33] | 31.9 | 8.1 | 0.28 | −0.37 | 911 | 21% | 8% | 41% | 31% |

Average | 27.1 | 9.2 | 0.43 | −0.48 | 1261 | 14% | 18% | 36% | 32% | |

Standard Deviation | 7.6 | 3.2 | 0.11 | 0.12 | 383 | 6% | 9% | 4% | 4% | |

Correlation Coefficient in Relation to Fuel Economy | 0.35 | 0.32 | 0.81 | 0.84 | 0.04 | 0.90 | −0.72 | 0.13 | 0.17 |

Local Driving Cycle | Reference | VT | V | D | a | d | t | CT | IT | AT | DT |
---|---|---|---|---|---|---|---|---|---|---|---|

Hanoi | [13] | M | 19.9 | 11.4 | 0.33 | −0.36 | 2063 | 26% | 10% | 33% | 31% |

Khon Kaen | [46] | M | 25.0 | 8.1 | 0.51 | −0.62 | 1164 | 8% | 22% | 38% | 31% |

Khon Kaen—Electric | [25] | M | 22.9 | 5.0 | 0.47 | −0.61 | 781 | 7% | 26% | 38% | 29% |

Khon Kaen—Gasoline | [25] | M | 22.6 | 4.9 | 0.49 | −0.55 | 775 | 8% | 28% | 34% | 30% |

Shanghai—Electric | [26] | M | 19.7 | 9.3 | 0.33 | −0.32 | 1704 | 14% | 14% | 35% | 36% |

Taipei | [22] | M | 17.5 | 4.6 | 0.49 | −0.53 | 950 | 8% | 21% | 37% | 35% |

Toronto—HDT ^{1} M. Artl. | [1] | T | 12.2 | 6.0 | 0.40 | −0.47 | 1774 | 5% | 33% | 33% | 28% |

Toronto—HDT ^{1} Freeway | [1] | T | 40.8 | 16.2 | 0.18 | −0.25 | 1430 | 33% | 3% | 38% | 26% |

Toronto—LDT ^{2} M. Artl. | [1] | T | 18.1 | 9.0 | 0.57 | −0.57 | 1788 | 6% | 33% | 30% | 30% |

Toronto—LDT ^{2} Freeway | [1] | T | 53.1 | 26.5 | 0.29 | −0.44 | 1794 | 23% | 3% | 44% | 29% |

Toronto—MDT ^{3} M. Artl. | [1] | T | 15.0 | 7.5 | 0.34 | −0.53 | 1795 | 5% | 32% | 38% | 25% |

Toronto—MDT ^{3} Freeway | [1] | T | 39.5 | 18.9 | 0.19 | −0.24 | 1723 | 39% | 3% | 33% | 25% |

Average | 25.5 | 10.6 | 0.38 | −0.46 | 1478 | 15% | 19% | 36% | 30% | ||

Standard Deviation | 12.4 | 6.7 | 0.13 | 0.13 | 447 | 12% | 12% | 4% | 3% | ||

Correlation Coefficient in Relation to Fuel Economy | 0.27 | 0.33 | −0.65 | 0.85 | 0.27 | 0.76 | −0.75 | −0.19 | 0.15 |

^{1}Heavy Duty Trucks;

^{2}Light Duty Trucks;

^{3}Medium Duty Trucks.

Local Driving Cycle | Reference | FE | EC | drag | inertia | rolling | idling |
---|---|---|---|---|---|---|---|

FTP-75 cycle | [48] | 12.0 | 2.4 | 22% | 46% | 28% | 5% |

WLTC (class 3) | [49] | 10.5 | 2.8 | 38% | 36% | 25% | 2% |

Athens | [20] | 11.2 | 2.6 | 9% | 59% | 22% | 11% |

Bangalore | [11] | 11.6 | 2.5 | 17% | 47% | 34% | 3% |

Beijing | [33] | 13.5 | 2.1 | 15% | 53% | 30% | 2% |

Beijing | [24] | 13.0 | 2.2 | 9% | 51% | 26% | 15% |

Beijing | [3] | 11.1 | 2.6 | 22% | 51% | 27% | 0% |

Changchun | [33] | 12.1 | 2.4 | 25% | 32% | 39% | 3% |

Chengdu | [33] | 13.0 | 2.2 | 16% | 53% | 30% | 0% |

Chennai—Car Peak | [12] | 13.2 | 2.2 | 22% | 27% | 51% | 0% |

Chennai—Car Off-Peak | [12] | 16.2 | 1.8 | 10% | 48% | 40% | 2% |

Chongqing | [33] | 13.0 | 2.2 | 15% | 50% | 22% | 13% |

Edinburgh | [38] | 11.3 | 2.6 | 7% | 48% | 25% | 19% |

Fortaleza | [23] | 11.3 | 2.6 | 10% | 51% | 23% | 16% |

Hanoi | [13] | 17.0 | 1.7 | 7% | 45% | 37% | 11% |

Hong Kong | [32] | 11.1 | 2.6 | 4% | 52% | 21% | 22% |

Hong Kong—Urban | [40] | 11.9 | 2.4 | 17% | 45% | 30% | 8% |

Hong Kong—Sub Urb | [40] | 13.6 | 2.1 | 23% | 46% | 30% | 0% |

Hong Kong—HWY | [40] | 14.0 | 2.1 | 30% | 32% | 32% | 6% |

Jilin | [33] | 14.6 | 2.0 | 17% | 47% | 34% | 3% |

Jiutai | [33] | 15.4 | 1.9 | 8% | 58% | 33% | 2% |

Mashad | [41] | 12.1 | 2.4 | 9% | 51% | 26% | 15% |

Mianyang | [33] | 13.4 | 2.2 | 16% | 49% | 31% | 4% |

Nanjing | [42] | 16.1 | 1.8 | 25% | 32% | 39% | 3% |

Ningbo | [33] | 13.2 | 2.2 | 9% | 53% | 29% | 8% |

Santa Maria—12 am | [21] | 20.5 | 1.4 | 22% | 27% | 51% | 0% |

Shanghai | [33] | 12.5 | 2.3 | 18% | 47% | 29% | 7% |

Sydney | [44] | 10.8 | 2.8 | 15% | 50% | 22% | 13% |

Tianjin | [33] | 15.1 | 1.9 | 14% | 45% | 36% | 5% |

Zitong | [33] | 16.9 | 1.7 | 14% | 44% | 40% | 2% |

LDC Average | 13.5 | 2.1 | 15% | 46% | 32% | 7% | |

LDC Standard Deviation | 2.3 | 0.3 | 7% | 8% | 8% | 6% |

Local Driving Cycle | Reference | VT | FE | EC | drag | inertia | rolling | idle |
---|---|---|---|---|---|---|---|---|

Khon Kaen | [46] | M | 12.2 | 2.4 | 11% | 56% | 27% | 6% |

Khon Kaen—Electric | [25] | M | 12.1 | 2.4 | 9% | 49% | 28% | 14% |

Khon Kaen—Gasoline | [25] | M | 13.1 | 2.2 | 10% | 51% | 28% | 11% |

Shanghai—Electric | [26] | M | 17.8 | 1.6 | 7% | 42% | 39% | 10% |

Taipei | [22] | M | 13.7 | 2.1 | 4% | 62% | 29% | 5% |

Toronto—HDT ^{1} Arterial | [1] | T | 11.3 | 2.6 | 31% | 23% | 45% | 1% |

Toronto—HDT ^{1} Freeway | [1] | T | 16.7 | 1.7 | 31% | 21% | 47% | 1% |

Toronto—LDT ^{2} Arterial | [1] | T | 11.0 | 2.6 | 30% | 40% | 30% | 0% |

Toronto—LDT ^{2} Freeway | [1] | T | 12.5 | 2.3 | 3% | 48% | 22% | 27% |

Toronto—MDT ^{3} Arterial | [1] | T | 11.3 | 2.6 | 4% | 43% | 23% | 30% |

Toronto—MDT ^{3} Freeway | [1] | T | 17.6 | 1.7 | 8% | 56% | 21% | 15% |

Hanoi | [13] | M | 18.1 | 1.6 | 6% | 44% | 44% | 6% |

Average | 13.9 | 2.2 | 13% | 44% | 32% | 10% | ||

Standard Deviation | 2.8 | 0.4 | 11% | 12% | 9% | 10% |

^{1}Heavy Duty Trucks;

^{2}Light Duty Trucks;

^{3}Medium Duty Trucks.

Cases | EC (MJ/km) | Variation of EC | FE (km/L) |
---|---|---|---|

Standard | 2.39 | 12.1 | |

½ m | 1.98 | −17% | 14.7 |

½ ${C}_{R}$ | 2.06 | −14% | 14.1 |

½ ${k}_{A}$ | 2.11 | −12% | 13.8 |

½ P_{max_eng} | 2.08 | −13% | 13.9 |

2 P_{max_eng} | 2.40 | 0% | 12.1 |

Ethanol | 2.35 | −2% | 8.6 |

θ = 2° | 4.11 | 72% | 7.05 |

W = 5 km/h | 2.49 | 4% | 11.7 |

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## Share and Cite

**MDPI and ACS Style**

Andrade, G.M.S.d.; Araújo, F.W.C.d.; Santos, M.P.M.d.N.; Magnani, F.S.
Standardized Comparison of 40 Local Driving Cycles: Energy and Kinematics. *Energies* **2020**, *13*, 5434.
https://doi.org/10.3390/en13205434

**AMA Style**

Andrade GMSd, Araújo FWCd, Santos MPMdN, Magnani FS.
Standardized Comparison of 40 Local Driving Cycles: Energy and Kinematics. *Energies*. 2020; 13(20):5434.
https://doi.org/10.3390/en13205434

**Chicago/Turabian Style**

Andrade, Guilherme Medeiros Soares de, Fernando Wesley Cavalcanti de Araújo, Maurício Pereira Magalhães de Novaes Santos, and Fabio Santana Magnani.
2020. "Standardized Comparison of 40 Local Driving Cycles: Energy and Kinematics" *Energies* 13, no. 20: 5434.
https://doi.org/10.3390/en13205434