Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles

Min, K.; Sim, Gyubin; Ahn, Seongju; Sunwoo, Myoungho; Jo, Kichun

doi:10.3390/s19194171

Cited by 11 publications

(4 citation statements)

References 32 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Vehicle localization (3): [ 33 , 34 , 35 ]. Path planning (1): [ 36 ] Smart regenerative braking systems for electric vehicles (2): [ 37 , 38 ]. Physical intelligence in sensors and sensing (3): [ 39 , 40 , 41 ] Driver assistance systems and automatic vehicle operation (3) Advanced driver assistance systems (1): [ 42 ].…”

Section: Special Issue On Intelligent Vehiclesmentioning

confidence: 99%

“…As a result, a deceleration prediction model which represents individual driving characteristics is required to ensure a more comfortable experience with automatic regenerative braking control. Thus, in [ 38 ], a deceleration prediction model based on the parametric mathematical equation and explicit model parameters is presented. The model is designed specifically for deceleration prediction by using the parametric equation that describes deceleration characteristics.…”

Section: Smart Regenerative Braking Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Sensors and Sensing for Intelligent Vehicles

Llorca

Daza

Hernández

et al. 2020

Sensors

View full text Add to dashboard Cite

Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.

show abstract

Section: Special Issue On Intelligent Vehiclesmentioning

confidence: 99%

Section: Smart Regenerative Braking Systemsmentioning

confidence: 99%

Sensors and Sensing for Intelligent Vehicles

Llorca

Daza

Hernández

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…However, powertrain, drivetrain, and comfort optimization are mostly related to the vehicle manufacturer's design, with little control given to the driver when considering the comfort settings. The vehicle motion profile is one factor that affects comfort (i.e., acceleration, deceleration) [27]. Therefore, acceleration and braking actions need to be constrained to provide a comfortable driving experience.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Regenerative Braking Strategy Design Based on Naturalistic Regeneration Performance for Intelligent Vehicles

Ziadia,

Kelouwani,

Amamou

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The effectiveness of regenerative braking strategies plays an important role in extending the driving range of electric vehicles. Since the driver is still an essential factor in levels 3 and 4 of intelligent electric vehicles, improving user acceptance and adoption of the braking control strategy is crucial. This paper puts forward a new regenerative braking strategy to find a compromise between optimal braking control performance and naturalistic regeneration performance while satisfying the maximum speed preference when driving between two-stop events. Unlike other similar works that only maximize regenerative braking energy while satisfying the physical limits of an electrified powertrain, this paper considers naturalistic regeneration performance. To achieve this, firstly, the power regenerated by three drivers is predicted with a long-horizon (30 seconds), using long-short-term memory networks (LSTM) and non-linear autoregressive exogenous model (NARX). Subsequently, an estimation of the energy recovery maximization rate is performed to give a perception of the naturalistic regeneration performance. As this performance varies, the deceleration planning employs three horizon scales of long, medium, and short, determined by the energy recovery maximization rate. Finally, dynamic programming (DP) is utilized to optimize a deceleration profile. The study utilizes real data of inverter efficiency, transmission efficiency, and motor-to-battery efficiency map. The outcome of this study shows that the proposed regeneration braking strategy is adaptive, improving regeneration efficiency by 39,6% for driver 1, 16% for driver 2, and 26% for driver 3, and forecasting the optimality of some deceleration behaviors.

show abstract

“…Valery Vodovozov et al [26] developed a neural network controller for electric vehicle braking, blending electrical and friction brakes to maximize energy recovery and outperforming traditional PID controllers in braking performance and energy efficiency. A study [27] on deceleration prediction models tailored to individual drivers improved regenerative braking by adapting to unique braking behaviors, enhancing comfort and efficiency. Panagiotis Lytrivis, George Thomaidis, and Angelos Amditis [28] emphasized the importance of sensor data fusion, integrating information from various sensors to enhance vehicle safety systems and proposing future integration with wireless communications.…”

mentioning

confidence: 99%

Smartphone Sensors in Motion: Advancing Traffic Safety with Mobile Technology

Ondruš,

Jančár,

Gogola

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This research investigates the feasibility of using smartphones as reliable instruments to measure vehicle deceleration under different conditions and compares their accuracy and reliability with traditional decelerometers. The research was conducted using a passenger vehicle (Audi A6 Avant) on different road surfaces—dry, wet, and gravel—at several speed intervals (30, 50, 70, and 90 km/h). The vehicle was equipped with an XL Meter decelerometer and three different smartphones in different price ranges. Each device recorded deceleration data, which was then analyzed to evaluate accuracy and reliability. The findings show that while the smartphones show promising results on dry and gravel surfaces, their accuracy decreases at lower speeds and on wet surfaces due to the limitations of the sensors in detecting subtle deceleration values. The research also highlights that mid-range smartphones can perform comparably to higher-end models, suggesting that excessive investment in more expensive technology may not be necessary for scientific purposes. However, some differences in measurements are attributed to variations in device mounting and orientation sensitivity. In conclusion, this research supports the potential of integrating smartphone technology in vehicle testing for road safety, although it highlights critical limitations that need to be addressed for standardized use.

show abstract

Vehicle Deceleration Prediction Model to Reflect Individual Driver Characteristics by Online Parameter Learning for Autonomous Regenerative Braking of Electric Vehicles

Cited by 11 publications

References 32 publications

Sensors and Sensing for Intelligent Vehicles

Sensors and Sensing for Intelligent Vehicles

An Adaptive Regenerative Braking Strategy Design Based on Naturalistic Regeneration Performance for Intelligent Vehicles

Smartphone Sensors in Motion: Advancing Traffic Safety with Mobile Technology

Contact Info

Product

Resources

About