Service-Oriented Real-Time Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles

Kim, Do-Hee; Eo, Jeong Soo; Kim, Kwang-Ki K.

doi:10.1109/tits.2021.3099812

Cited by 18 publications

(6 citation statements)

References 40 publications

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“…The Previous work has predominantly focused on long-term deceleration planning over a residual distance of 200 meters, with the horizon determined by the vehicle's installed sensor [40]. These strategies have proven effective for driving behaviors characterized by a uniform maximum speed (90 km/h) and under specific experimental conditions.…”

Section: Comparative Studymentioning

confidence: 99%

“…Some works in the literature regarding the maximization of regeneration respect the motor's physical limit [39]. In [40], real driving test data characterizing the physical limits of regenerative braking are used to propose an energy-optimal deceleration planning system and improve energy-recuperation efficiency gains. Such strategies are employed in formulating ADAS, mainly used by autonomous vehicles of level 5.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ziadia,

Kelouwani,

Amamou

et al. 2023

IEEE Access

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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.

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Section: Comparative Studymentioning

confidence: 99%

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

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show abstract

“…The road gradient information is a fixed value, and it can be obtained from navigation systems. Using this predefined road gradient, the electrified CAV can plan its speed profile in a way that minimize the battery output power, resulting in increased energy efficiency [29], [30].…”

Section: B Maximization Of the Amount Of Energy Recuperationmentioning

confidence: 99%

Energy-Efficient Speed Planner for Connected and Automated Electric Vehicles on Sloped Roads

2022

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This paper proposes an energy-efficient speed planning strategy for a connected and automated vehicle (CAV) considering the upcoming traffic and road gradient information, which can be provided by the vehicle-to-everything communication systems. Unlike human drivers, CAV that receives long and short sighted traffic and road geometry information can optimize their speed profile to increase energy efficiency, depending on the powertrain types. In particular, the developed speed planner reducing the battery output power through the energy-efficiency improvement systems in electrified vehicles. Consequently, the CAV that is aware of the existence of the upcoming road gradient increases the speed on the uphill, and decreases the speed on the downhill to minimize the battery output power, which is different from the natural behaviors of human-driven vehicles on sloped roads. To consider the constraints, the model predictive control-based speed planner is developed, and its effectiveness is verified under various driving conditions. Simulation results show that our approach significantly outperforms the alternative speed profiles in terms of battery energy-saving, achieving about 27.21% of the energy efficiency improvement.INDEX TERMS Connected and automated vehicle, model predictive control, electric vehicle, energyefficiency improvement, energy-efficient driving.

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“…By planning their speed profiles, it is possible to improve the efficiency of braking energy recovery while considering traffic efficiency, thereby further reducing the overall energy consumption of the vehicle. Regarding related research, Kim et al [26][27] introduced an Energy Optimal Deceleration Planning System (EDPS) with the objective of maximizing the regenerative braking energy recovery for CAVs. Building upon this, they presented a Real-Time Energy Optimal Deceleration Planning System (RT-EDPS).…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-Objective Optimization Models for Intersection Crossing of Connected Autonomous Vehicles With Traffic Signals

Ning,

Tian,

Lin

et al. 2024

IEEE Access

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This research delves deeply into the traffic light intersection control issue for connected autonomous vehicles (CAVs). Firstly, we employed the vehicle dynamics simulation software, CarSim, to model the vehicle, and utilized the intelligent driving simulation software, PreScan, to establish a road environment model. Subsequently, we designed a rule-based traffic light crossing controller (RB-TLCC) for CAVs in Matlab/Simulink and implemented a co-simulation with CarSim and PreScan. Furthermore, we conducted driver-in-the-loop studies using the Logitech G27 driving simulator kit and compared the experimental results with those from RB-TLCC. Our findings indicate that the RB-TLCC enhances regenerative braking energy by 23.5%, improves traffic efficiency by 17.9%, and increases driving smoothness by 50.7%. Additionally, based on Markov Chain theory, we proposed a multi-objective optimization model (MO-OM) for CAVs traffic light intersection crossing using the Proximal Policy Optimization algorithm (PPO). A comparison was conducted under two different operating conditions between RB-TLCC and dynamic programming algorithms (DP). The results indicate that the MO-OM proposed in this paper exhibits the best comprehensive control performance among the three control methods. While adhering to traffic regulations, it enhances the recuperation of braking energy, the efficiency of vehicle passage, and the smoothness of travel at traffic light intersections. This study offers effective methodologies for enhancing the performance of CAVs in traffic light intersection control and holds significant reference value for the advancement of future intelligent transportation systems (ITS).INDEX TERMS Connected autonomous vehicles, intelligent traffic system, multi-objective optimization, optimal speed planning, PPO algorithm, traffic light intersection control.

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Service-Oriented Real-Time Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles

Cited by 18 publications

References 40 publications

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

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

Energy-Efficient Speed Planner for Connected and Automated Electric Vehicles on Sloped Roads

Research on Multi-Objective Optimization Models for Intersection Crossing of Connected Autonomous Vehicles With Traffic Signals

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