# Integration of a Chassis Servo-Dynamometer and Simulation to Increase Energy Consumption Accuracy in Vehicles Emulating Road Routes

^{*}

## Abstract

**:**

## 1. Introduction

_{2}emissions [21,22]. For example, Hu et al. conducted an analysis two different energy management strategies and battery sizes, where smaller batteries represented less energy consumption by fewer passengers carried as system uptime decreased [23]. Kivekäs et al. numerically evaluated six different bus models analyzing their driving cycle and passenger load sensitivity, where aggressive driving was found as the main factor in increasing energy consumption [24]. Not surprisingly, Perger et al. conclude topography has the greatest impact on public transport routes, where the shortest route possible is often not the most efficient, either in passenger distribution (amount of passengers transported) or energy required per passenger [25]. Most of these studies comment on the impossibility of reaching a general design solution which is optimal for the different topographical, climatical and passenger requirements within different cities and even different routes within the same city.

## 2. Materials and Methods

- Energy used by the motor to carry out the tasks of transportation.
- Energy used to turn the wheels of the vehicle when the vehicle is going down a slope pushed by gravity.
- Energy used to turn the wheels of the vehicle when the driver’s command is to cut the motor’s energy. At this point, the mass of the vehicle and the rotating masses have stored kinetic energy and release it; however, in this method, only linear masses are considered.

#### 2.1. Vehicle Mathematical Model

#### 2.1.1. Speed Control

#### 2.1.2. Braking

#### 2.2. Dynamometer Mathematical Model

#### 2.2.1. Energy Counting

#### 2.2.2. Virtual Route Recreation

#### 2.3. Emulation on the Dynamometer

#### Model Evaluation with Dynamometer Tests

^{®}(Medina, MN, USA) Primary Drive Clutch for Sportsman 500. Driven Pulley—Team

^{®}(Audubon, MN, USA) Tied Clutch Arctic Cat Pro Chassis reference 421896. Belt—Gates

^{®}(Denver, CO, USA) 4430V560 Multispeed; (d) Torquemeter—Galoce

^{®}(Xi’an, China) GTS100 Dynamic Torque Sensor. Torque range 0–150 Nm. (e) Torquemeter—Forsentek

^{®}(Shenzhen, China) Rotary Torque Sensor fyd. Torque range 0–1000 Nm, not shown in this image. (f) Gearbox: 40-97001-3, Flowfit, relation: 3.8:1; (g) Wheels and contact surface; (h) Brakes and Compressor: Minimum brake pressure (each): 0.2 atm/Minimum brake torque: 0.9 Nm; Maximum brake pressure: 8 atm/Maximum brake torque: 1400 Nm.

## 3. Results

#### 3.1. General Route Information and Data Overview

#### 3.2. Analysis of Specific Torque Interactions

#### 3.2.1. Accelerating Downhill Stretch with Constant Slope

#### 3.2.2. Downhill Section with Changing Slope

#### 3.2.3. Accelerating Uphill Changing Section

## 4. Discussion

#### 4.1. Inertia Torque Ripple and Iteration Time

#### 4.2. Energy Calculation Comparison

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

${F}_{m}$ | motor driving force |

${F}_{g}$ | gravitational force |

${F}_{r}$ | rolling resistance force |

${F}_{d}$ | aerodynamic drag force |

${F}_{i}$ | inertia force, equivalent to $M\frac{d\dot{x}}{dt}$ in expression (1) and $m\frac{\mathsf{\Delta}{v}_{v}}{\mathsf{\Delta}t}$ in expression (16) |

${T}_{i}$ | inertia torque |

${T}_{m}$ | motor torque, equivalent to ‘torque motor’ on the results section |

${T}_{w}$ | torque sum at wheel, equivalent to ’torque sum at wheel’ in the results section |

${T}_{b}$ | braking torque |

${F}_{w}$ | resultant force summatory at the wheel |

${r}_{w}$ | wheel radius |

${\dot{\theta}}_{m}$ | angular speed of the motor |

${\dot{\theta}}_{w}$ | angular speed of the wheels |

${\eta}_{t}$ | powertrain transmission efficiency from the motor to the wheels |

${R}_{r}$ | torque relation between the motor and the wheel |

${I}_{m}$ | inertia moment of elements on the motor side of the transmission |

${I}_{w}$ | inertia moment of elements on the wheel side of the transmission |

%γ | throttle percentage |

$RP{M}_{m}$ | motor angular speed in terms of revolutions per minute |

${\eta}_{m}$ | motor efficiency |

${v}_{vr}$ | ratio between vehicle speed and the reference speed for current segment of the route |

${X}_{i}$ | current distance traveled |

${t}_{i}$ | time elapsed since the start of the route |

$\beta $ | parameters used for the input of route characteristics as stops or speed reductions |

${\beta}_{1}$ | indicates whether the vehicle is approaching a regular stop or a traffic light |

${\beta}_{2}$ | indicates the presence and distance of vehicles in front |

${\beta}_{3}$ | indicates a passenger’s request to stop |

${\beta}_{4}$ | indicates varying road conditions: humidity, sand on the road, visibility, etc. |

$\tau $ | driver’s personality: calm, fast, aggressive |

${T}_{brg}$ | regenerative braking torque |

$\%set$ | percentage of braking performed by the regenerative braking |

${a}_{v}$ | linear acceleration of the vehicle |

${v}_{v}$ | current vehicle speed on the dynamometer |

$\alpha $ | angle of inclination of the route |

$\mathsf{{\rm M}}$ | rolling coefficient of the road in relation to that of the dynamometer |

${T}_{dyna}$ | torque applied by the dynamometer brakes |

${T}_{d}$ | drag torque at the wheel |

${T}_{r}$ | rolling torque at the wheel |

$\rho $ | air density |

$m$ | vehicle mass, including that of the passengers |

${C}_{d}$ | drag coefficient of the vehicle front |

A | front area of the vehicle |

${P}_{w}$ | tire pressure for the wheels |

$R$ | bus rolling coefficient |

${T}_{rd}$ | dynamometer rolling torque or friction |

${I}_{dyna}$ | dynamometer inertia moment |

${\dot{\theta}}_{dyna}$ | dynamometer angular speed as measured after the main transmission element |

${T}_{mg}$ | proportion of the motor torque that corresponds to slope simulation |

${T}_{mi}$ | proportion of the motor torque that corresponds to inertia simulation |

$\mathsf{\Delta}t$ | time period between iterations |

${E}_{i}$ | iteration energy as obtained by iteration power and $\mathsf{\Delta}t$ |

$E$ | total energy for a route or segment |

$h$ | bus height on the route |

${F}_{v}$ | theoretical bus force obtained from expression |

${F}_{dyna}$ | total force equivalent as applied on dynamometer brakes |

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**Figure 2.**(

**a**) Vehicle accelerating in flat terrain; (

**b**) Vehicle decelerating in flat terrain; (

**c**) Vehicle in constant speed in flat terrain; (

**d**) Vehicle accelerating uphill; (

**e**) Vehicle decelerating uphill; (

**f**) Vehicle in constant speed uphill; (

**g**) Vehicle accelerating downhill; (

**h**) Vehicle decelerating downhill; (

**i**) Vehicle in constant speed downhill.

**Figure 13.**Accelerating downhill stretch with constant slope. (

**a**) Torques and RPMs measured by dynamometer sensors. (

**b**) Torques values per origin force. (

**c**) Dynamometer power and equivalent bus power. (

**d**) Accumulated energy for the whole route.

**Figure 14.**Downhill section with changing slope. (

**a**) Torques and RPMs measured by dynamometer sensors. (

**b**) Torques values per origin force. (

**c**) Dynamometer power and equivalent bus power. (

**d**) Accumulated energy for the whole route.

**Figure 15.**Accelerating uphill changing section. (

**a**) Torques and RPMs measured by dynamometer sensors. (

**b**) Torques values per origin force. (

**c**) Dynamometer power and equivalent bus power. (

**d**) Accumulated energy for the whole route.

Decelerating | Constant Speed | Accelerating | |
---|---|---|---|

Flat | Work (EV Sim) Braking (EV Sim) Regeneration (EV Sim) Inertia (EV Sim) | Work (Motor) | Work (Motor) Inertia (Motor) |

Uphill | Work (EV Sim) Braking (EV Sim) Regeneration (EV Sim) Slope (Motor) Inertia (Motor) | Work (EV Sim) Slope (Motor) | Work (Motor) Inertia (Motor) Slope (Motor) |

Downhill | Braking (EV Sim) Regeneration (EV Sim) Inertia (EV Sim) | Braking (EV Sim) Regeneration (EV Sim) | Work (EV Sim) Braking (EV Sim) Regeneration (EV Sim) Inertia (EV Sim) |

Dyna Energy (kWh) | Bus Energy (kWh) | Dyna Inertia Energy (kWh) | Dyna Slope Energy (kWh) | Inertia Energy (kWh) | Slope Energy (kWh) | |
---|---|---|---|---|---|---|

Mean | 0.1344 | 0.1153 | 0.0014 | 0.0122 | 0.0039 | 0.0459 |

Std | 0.1190 | 0.1132 | 0.0016 | 0.0054 | 0.0031 | 0.0380 |

Min | 0.0000 | −0.0023 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |

25% | 0.0127 | 0.0000 | 0.0000 | 0.0105 | 0.0006 | 0.0105 |

50% | 0.1104 | 0.0872 | 0.0000 | 0.0155 | 0.0038 | 0.0348 |

75% | 0.2636 | 0.2392 | 0.0030 | 0.0155 | 0.0071 | 0.0876 |

Max | 0.3021 | 0.2769 | 0.0041 | 0.0155 | 0.0083 | 0.0966 |

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**MDPI and ACS Style**

Arango, I.; Escobar, D.
Integration of a Chassis Servo-Dynamometer and Simulation to Increase Energy Consumption Accuracy in Vehicles Emulating Road Routes. *World Electr. Veh. J.* **2022**, *13*, 164.
https://doi.org/10.3390/wevj13090164

**AMA Style**

Arango I, Escobar D.
Integration of a Chassis Servo-Dynamometer and Simulation to Increase Energy Consumption Accuracy in Vehicles Emulating Road Routes. *World Electric Vehicle Journal*. 2022; 13(9):164.
https://doi.org/10.3390/wevj13090164

**Chicago/Turabian Style**

Arango, Ivan, and Daniel Escobar.
2022. "Integration of a Chassis Servo-Dynamometer and Simulation to Increase Energy Consumption Accuracy in Vehicles Emulating Road Routes" *World Electric Vehicle Journal* 13, no. 9: 164.
https://doi.org/10.3390/wevj13090164