# Battery Sizing for Electric Vehicles Based on Real Driving Patterns in Thailand

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## Abstract

**:**

## 1. Introduction

_{10}, PM

_{2.5}, O

_{3}, and N

_{2}O in Bangkok, Thailand still exceed the standard level of national ambient air quality in 2019. This information demonstrates that the Bangkok area is still facing an air pollution problem, with a major contributing factor being transport activity, especially by hazardous particulate matter (PM 2.5) from diesel vehicles’ exhaust emissions. The Pollution Control Department has created a master plan for the Air Quality Management for a 20-year period (2018–2037) including an impact prevention and proactive prevention, which aim to reduce pollution by elevating the standards of exhaust for new vehicles, together with an improvement in fuel quality. This has led to a launch of “zero emission” regulations for new vehicles to promote the usage of electric vehicles and public transportation [3]. The electrification of public transport vehicles could be carried out by utilizing different technological solutions [4,5,6]. Many challenges facing electric vehicles such as limited range and speed, sparse of electric charging stations, long recharge time, etc. are related to an energy storage system design (energy and power), i.e., battery packs, for any specific application [6,7]. Several design approaches on battery sizing have been based on a real-world driving pattern [5,8]. Driving cycle is the series of data representing the speed of the vehicle versus time. It is important for a fleet to match routes to battery technology to achieve maximum benefit. Hence, knowledge of driving cycles is important for improving the electric vehicle performance and design purposes [7,8,9].

## 2. Materials and Methods

#### 2.1. Data Collection

#### 2.2. Driving Cycle Development

#### 2.2.1. Collected Driving Data Characteristics

_{sd}), average speed (V

_{avg}), maximum velocity (V

_{max}), and average time per one cycle. The operating characteristics from closed-area, inter-city, and local feeder of different service routes are shown in Table 2, Table 3 and Table 4. T

_{limit}is the averaged time per cycle (s) that is an important factor for driving cycle generation process.

#### 2.2.2. Microtrip Data Segmentation

#### 2.2.3. Driving Cycle Construction

#### 2.2.4. Generated Driving Cycle

_{1}, E

_{2}, …, E

_{8}, was the function of discrepancy between ${T}_{gen}$ and ${T}_{range}$ including weight factor by time spent of microtrip for each speed range presented in the database. The equation of the errors calculation in each speed range is given by:

#### 2.3. Energy Consumption Calculation

#### 2.3.1. Traction Energy Consumption Calculation

^{2}), ${C}_{d}$ is coefficient of drag, $\rho $ is air density (kg/m

^{3}), $A$ is frontal area of the vehicle (m

^{3}), ${f}_{r}$ is rolling resistance constant, $g$ is gravity acceleration (g = 9.81 m/s

^{2}), $m$ is a mass of vehicle (kg), $\theta $ is the road grade (degree), and $F$ is the Tractive force (N). Finally, the tractive force ($F$) is found in Equation (8) by combining Equation (5), Equation (6), and Equation (7). To calculate energy consumption, the power for vehicle traveling at velocity (v) was required. Required power could be determined from the relationship between F and v in Equation (9):

^{2}). In this study, the traction energy consumption was calculated by using geometric parameters of 9-meter EV bus prototype and other constants as shown in Table 5.

#### 2.3.2. EV Main Components and Auxiliary System Energy Consumption Calculation

#### 2.3.3. Total Energy Consumption

#### 2.4. Battery Sizing

## 3. Results and Discussion

#### 3.1. Driving Cycle

#### 3.2. Energy Consumption

#### 3.3. Battery Sizing

## 4. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Inter-City Routes: (

**a**) Salaya-Wit; (

**b**) Wit-Salaya; (

**c**) Salaya-Siriraj; (

**d**) Siriraj-Salaya.

**Figure 8.**Closed-area representative driving cycle (tram): (

**a**) Route 1; (

**b**) Route 2; (

**c**) Route 3; (

**d**) Route 4.

**Figure 9.**Inter-city representative driving cycle (shuttle bus): (

**a**) Salaya-Wit; (

**b**) Wit-Salaya; (

**c**) Salaya-Siriraj; (

**d**) Siriraj-Salaya.

Type of Route | Closed-Area | Inter-City | Local Feeder |
---|---|---|---|

Vehicles | Tram | Shuttle Bus | Salaya Link |

Configuration | |||

Number of seats | 29 | 35 | 35 |

D_{avg} (km) | 5.557 | 24.614, 21.100 | 15.41 |

N_{cycle} (cycle) | 17 | 6, 2 | 4 |

D_{total} (km) | 94.474 | 189.884 | 61.64 |

Measurement Equipment | VBOX (VB20SL3, Racelogic Ltd.) | ||

Acquired Data | Speed(km/h), Latitude, Longitude, Time(s), Brake Trigger |

Route | Velocity (km/h) | T_{limit} (h:mm:ss) | Passengers (Person) | |||
---|---|---|---|---|---|---|

V_{sd} | V_{avg} | V_{max} | Mean | Max | ||

1 | 1.616 | 15.620 | 58.630 | 0:13:22 | 5 | 34 |

2 | 1.242 | 17.732 | 41.483 | 0:19:49 | 9 | 30 |

3 | 2.163 | 16.258 | 40.135 | 0:21:15 | 13 | 48 |

4 | 1.253 | 15.098 | 36.744 | 0:14:55 | 6 | 44 |

Route | Velocity (km/h) | T_{limit} (h:mm:ss) | Passengers (Person) | |||
---|---|---|---|---|---|---|

V_{sd} | V_{avg} | V_{max} | Mean | Max | ||

S-Wit | 5.8 | 26.27 | 85.44 | 1:01:15 | 27 | 63 |

Wit-S | 4.9 | 24.85 | 93.78 | 1:01:06 | ||

S-Si | 4.05 | 31.99 | 86.9 | 0:39:18 | ||

Si-S | 3.49 | 19.3 | 84.26 | 0:52:03 |

Route | Velocity (km/h) | T_{limit} (h:mm:ss) | Passengers (Person) | |||
---|---|---|---|---|---|---|

${\mathit{V}}_{\mathit{s}\mathit{d}}$ | ${\mathit{V}}_{\mathit{a}\mathit{v}\mathit{g}}$ | ${\mathit{V}}_{\mathit{m}\mathit{a}\mathit{x}}$ | Mean | Max | ||

Salaya Link | 6.76 | 32.06 | 101.46 | 1:21:55 | 22 | 44 |

General Characteristics of Vehicle (Medium-Sized Bus) | |
---|---|

Parameters | Value |

Curb weight (kg) | 9000 |

Vehicle frontal area (m^{2}) | 7.5 |

Rolling Resistance | 0.0015 |

Drag coefficient | 0.7 |

Air Density (kg/m^{3}) | 0.114 |

Gravity Acceleration (m/s^{2}) | 9.8 |

Components | Load (kW) |
---|---|

Pneumatic pump | 2.2 |

Air condition | 10 |

DC water cooling pump | 0.06 |

Steering pump and controller | 1.5 |

Accessory load | 0.5 |

Energy Consumption Rate (kWh/km) | Closed-Area | Inter-City | Local Feeder |
---|---|---|---|

${E}_{d}$ | 0.522 | 0.691 | 0.698 |

${E}_{total}$ | 1.438 | 1.988 | 1.894 |

${\mathit{E}}_{\mathit{r}\mathit{e}\mathit{q}\mathit{u}\mathit{i}\mathit{r}\mathit{e}\mathit{d}}\left(\mathbf{kWh}\right)$ | Closed-Area | Inter-City | Local Feeder |
---|---|---|---|

Traction | 49 | 131 | 43 |

Total | 136 | 377 | 117 |

P_{max} (kW) | Closed-Area | Inter-City | Local Feeder |
---|---|---|---|

Traction | 21.71 | 72.81 | 99.98 |

Total | 35.97 | 87.07 | 114.24 |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Duangsrikaew, B.; Mongkoltanatas, J.; Benyajati, C.-n.; Karin, P.; Hanamura, K.
Battery Sizing for Electric Vehicles Based on Real Driving Patterns in Thailand. *World Electr. Veh. J.* **2019**, *10*, 43.
https://doi.org/10.3390/wevj10020043

**AMA Style**

Duangsrikaew B, Mongkoltanatas J, Benyajati C-n, Karin P, Hanamura K.
Battery Sizing for Electric Vehicles Based on Real Driving Patterns in Thailand. *World Electric Vehicle Journal*. 2019; 10(2):43.
https://doi.org/10.3390/wevj10020043

**Chicago/Turabian Style**

Duangsrikaew, Bongkotchaporn, Jiravan Mongkoltanatas, Chi-na Benyajati, Preecha Karin, and Katsunori Hanamura.
2019. "Battery Sizing for Electric Vehicles Based on Real Driving Patterns in Thailand" *World Electric Vehicle Journal* 10, no. 2: 43.
https://doi.org/10.3390/wevj10020043