# A Comprehensive Model to Estimate Electric Vehicle Battery’s State of Charge for a Pre-Scheduled Trip Based on Energy Consumption Estimation

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## Abstract

**:**

## 1. Introduction

- (1)
- Propose a comprehensive model to calculate the amount of energy required to charge electric vehicles prior the departure time.
- (2)
- Applied the proposed method to evaluate the energy consumption of electric vehicles in different road characteristics and weather conditions.

## 2. Main Factors Impacting EV’s Energy Consumption

## 3. A Comprehensive Model for Estimating EV Battery’s SOC for Pre-Defined Trip

- Step 1: Collect trip information using Google map API.
- Step 2: Estimate the electric vehicle’s energy consumption based on the pre-identified trip information.
- Step 3: Estimate the essential EV battery’s Stage of Charge (SOC) which is required to make the trip.

#### 3.1. Step 1—Collect Trip Information Using Google Map

#### 3.2. Step 2—Estimate the Electric Vehicle’s Energy Consumption

_{EVconsumption}) is estimated using the estimation energy consumption model in article [6,7], as shown in the following equation:

_{EVconsumption}= E

_{driving}+ E

_{A/C}+ E

_{losses}

_{driving}is energy consumption from driving the car (Wh). E

_{A/C}is the energy consumption from air conditioner (Wh); E

_{losses}is other energy losses estimated as 5% (Wh).

_{EVconsumption}= α × E

_{driving}

_{driving}) model can be estimated using four terms of force in the following equation:

_{roll}is rolling resistance force (N). F

_{drag}is aerodynamic drag force (N). F

_{hill}is hill climbing force (N); F

_{acceleration}is acceleration force (N). $v$ is velocity ($\mathrm{m}/\mathrm{s}$); and $t$ is the travel duration (s).

#### 3.3. Step 3—Estimate the Electric Vehicle’s Battery S

_{contingency}is the additional SOC that is proposed to cover uncertainty situations. This parameter is determined based on user’s driving experience and vehicle specification. It can equal 20–40% of the current estimated SOC for the trip.

## 4. Applications

- The estimation model in paper [6].
- The proposed model in this paper.

- R1: Ambiance temperature (F)
- R2: A/C temperature (F)
- R3: Distance (m)
- R4: Duration (min)
- R5: Average Speed (m/s)
- R6: Number of Passengers

_{recorded_i}is the recorded values of the i-th test. E

_{estimated}is the estimated values of the i-th test.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

SOC | State of charge |

GHG | GreenHouse Gas |

EV | Electric vehicle |

V2G | vehicle-to-grid |

API | Application Programming Interface |

AC | Air conditioner |

MAPE | Mean absolute percentage Error |

E_{EVconsumption} | The energy consumption |

E_{driving} | energy consumption from driving the car |

E_{A/C} | the energy consumption from air conditioner |

E_{losses} | other energy losses |

E_{driving} | The driving energy consumption |

F_{roll} | rolling resistance force |

F_{drag} | aerodynamic drag force |

F_{hill} | hill climbing force |

F_{acceleration} | acceleration force |

$v$ | velocity |

$t$ | he travel duration |

$m$ | vehicle mass |

$g$ | gravitational force |

${\mu}_{rr}$ | rolling resistance coefficient |

$\phi $ | hill angle |

$\rho $ | density of the air |

${C}_{d}$ | drag coefficient |

$A$ | frontal area of electric vehicle |

${J}_{w}$ | inertia of the wheel |

$r$ | tyre radius |

${J}_{m}$ | inertia of motor |

$G$ | gear ratio |

$a$ | acceleration |

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**Figure 1.**Battery prices and number of EVs prediction until 2030 [2].

References | Estimation Algorithm’s Inputs | The Complication of the Estimation Algorithm | Including SOC Estimation for the Trip? | Estimation Algorithm’s Accuracy |
---|---|---|---|---|

EV’s energy consumption factors for different road types [13] | - -
- Road information
- -
- Weather information
- -
- EV’s average speed
| ✓ | No | N/A |

Estimation Model of Total Energy Consumptions [4] | - -
- Road information
- -
- Weather information
- -
- EV range
- -
- EV’s average speed
| ✓ ✓ | No | N/A |

Energy consumption estimation for a EV fleet management system [5] | - -
- Route information
- -
- Traffic
- -
- EV’s average speed
| ✓ ✓ | No | 80–98% |

Energy estimation based on the route information [6] | - -
- Driving behavior
- -
- Road information
- -
- Weather information
- -
- Traffic
| ✓ ✓ ✓ | No | 95% |

Estimation of link-level energy consumption under real-world traffic conditions [7] | - -
- Road information
- -
- EV’s average speed
- -
- EV losses
- -
- Weather information
- -
- Traffic
| ✓ ✓ ✓ | No | 87–95% |

EV energy consumption prediction based on road information [14] | - -
- Road information
- -
- EV losses
- -
- EV’s Braking data
- -
- Road information
- -
- EV’s average speed
| ✓ ✓ ✓ ✓ | No | 95% |

Parameter | Value |
---|---|

Battery Capacity | 34.5 ($\mathrm{kWh}$) |

Nameplate Range | 150.17 miles |

Vehicle mass | 1715 kg |

Vehicle front area | 2.32 m^{2} |

Drag coefficient | 0.28 |

Rolling resistance coefficient | 0.012 |

Density of the air | 1.2 $\mathrm{kg}/{\mathrm{m}}^{3}$ |

Section | Distance (m) | Travel Duration (s) | Elavation (m) | SOC Estimation (%) |
---|---|---|---|---|

1 | 57 | 14 | 0.007477 | 0.08% |

2 | 83 | 58 | −0.00558 | 0.06% |

3 | 434 | 65 | 0.053772 | 0.00% |

4 | 86 | 28 | 0.003861 | 0.73% |

5 | 90 | 18 | −0.0003 | 0.00% |

6 | 294 | 40 | 0.041632 | 0.05% |

7 | 350 | 42 | 0.029166 | 0.39% |

8 | 673 | 64 | −0.05281 | 0.36% |

9 | 267 | 32 | 0.006341 | −0.52% |

10 | 293 | 37 | 0.043358 | 0.07% |

11 | 3913 | 413 | 0.063082 | 0.36% |

12 | 754 | 162 | 0.037003 | 6.76% |

Parameters | Estimation Using Model in [6] | Estimation of the Proposed Model | Recorded |
---|---|---|---|

Distance | 7294 m | 7294 m | 8047 m |

Traveling time | 16.22 min | 16.22 min | 15 min |

SOC used | 9.06% | 11.33% | 9% |

Trip | R1 | R2 | R3 | R4 | R5 | R6 | Recorded SOC Change (%) |
---|---|---|---|---|---|---|---|

1 | 78 | 60 | 6437.38 | 19 | 5.65 | 4 | 4 |

2 | 86 | 68 | 8046.72 | 17 | 7.89 | 4 | 9 |

3 | 78 | 68 | 4828.03 | 11 | 7.32 | 4 | −1 |

4 | 80 | 0 | 4828.03 | 11 | 7.32 | 4 | 6 |

5 | 78 | 0 | 4828.03 | 11 | 7.32 | 4 | −3 |

6 | 78 | 0 | 3218.69 | 9 | 5.96 | 4 | 1 |

7 | 84 | 68 | 38,624.26 | 34 | 18.93 | 4 | 15 |

8 | 86 | 68 | 38,624.26 | 40 | 16.09 | 4 | 17 |

9 | 78 | 68 | 14,484.10 | 17 | 14.2 | 4 | 12 |

10 | 73 | 68 | 11,265.41 | 30 | 6.26 | 4 | 0 |

11 | 77 | 68 | 16,093.44 | 41 | 6.54 | 4 | 9 |

12 | 82 | 68 | 22,530.82 | 29 | 12.95 | 4 | 8 |

Estimation Model in [6] | The Proposed Estimation Model | |
---|---|---|

Recorded data | 9/12 = 75% | 10/12 = 83.33% |

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## Share and Cite

**MDPI and ACS Style**

Tran, Q.T.; Roose, L.; Vichitpunt, C.; Thongmai, K.; Noisopa, K.
A Comprehensive Model to Estimate Electric Vehicle Battery’s State of Charge for a Pre-Scheduled Trip Based on Energy Consumption Estimation. *Clean Technol.* **2023**, *5*, 25-37.
https://doi.org/10.3390/cleantechnol5010002

**AMA Style**

Tran QT, Roose L, Vichitpunt C, Thongmai K, Noisopa K.
A Comprehensive Model to Estimate Electric Vehicle Battery’s State of Charge for a Pre-Scheduled Trip Based on Energy Consumption Estimation. *Clean Technologies*. 2023; 5(1):25-37.
https://doi.org/10.3390/cleantechnol5010002

**Chicago/Turabian Style**

Tran, Quynh T., Leon Roose, Chayaphol Vichitpunt, Kumpanat Thongmai, and Krittanat Noisopa.
2023. "A Comprehensive Model to Estimate Electric Vehicle Battery’s State of Charge for a Pre-Scheduled Trip Based on Energy Consumption Estimation" *Clean Technologies* 5, no. 1: 25-37.
https://doi.org/10.3390/cleantechnol5010002