Implementation and evaluation of cooperative adaptive cruise control functionalities

Meier, Jan-Niklas; Kailas, Aravind; Adla, Rawa; Bitar, George; Moradi-Pari, Ehsan; Abuchaar, Oubada; Ali, Mahdi; Abubakr, M.; Deering, Richard K.; Ibrahim, Umair; Kelkar, Paritosh; Kumar, Vivek; Parikh, J.; Rajab, Samer; Sakakida, Masafumi; Yamamoto, Masao

doi:10.1049/iet-its.2018.5175

Cited by 7 publications

(5 citation statements)

References 2 publications

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“…To realize real-time parameter estimation, Formula (9) should be converted into the recursive form shown in Formulas (10) and (11).…”

Section: Recursive Least Squares Algorithm With Multiple Forgetting Factorsmentioning

confidence: 99%

“…In addition, because θ 1 (k) and θ 2 (k) are unknown in Formulas ( 10) and (11), the estimated values θ1 (k) and θ2 (k) are used instead, it can be obtained that:…”

Section: Recursive Least Squares Algorithm With Multiple Forgetting Factorsmentioning

confidence: 99%

“…Currently, safe car-following is the most important issue in the design of the ACC controller. Meanwhile, many manufacturers are also considering how to improve comfort and economy on the premise of ensuring safe car-following [9][10][11]. As safe car-following, comfort, and economy usually conflict with each other, coordinating them simultaneously creates great challenges for designers.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

Liang

Zhao

et al. 2021

Machines

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Conclusive evidence has demonstrated the critical importance of adaptive cruise control (ACC) in relieving traffic congestion. To improve the performance of the ACC system, this paper proposes a novel ACC strategy for electric vehicles based on a hierarchical framework. Three main efforts have been made to distinguish our work from the existing research. Firstly, a sliding acceleration identification model is established based on the recursive least squares algorithm with multiple forgetting factors (MFF-RLS). Secondly, with vehicle following, economy, and comfort as the optimization objectives, the upper-level controller is developed based on the model predictive control (MPC) algorithm. Benefit from the identification of the sliding acceleration, the MPC controller holds better capability in accommodating environmental changes. Thirdly, an iterative learning lower-level controller is designed to control the driving and braking systems. Considering the efficiency of regenerative braking, the braking force distribution strategy is also designed in the lower-level controller. Simulation results show that, compared with the conventional MPC-based ACC strategy, the proposed strategy has similar performance in vehicle following, but it makes great improvements in comfort and economy. The specific features are that the vehicle acceleration and speed fluctuation are significantly reduced, and the energy consumption is also reduced by 2.05%.

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“…To realize real-time parameter estimation, Formula (9) should be converted into the recursive form shown in Formulas (10) and (11).…”

Section: Recursive Least Squares Algorithm With Multiple Forgetting Factorsmentioning

confidence: 99%

“…In addition, because θ 1 (k) and θ 2 (k) are unknown in Formulas ( 10) and (11), the estimated values θ1 (k) and θ2 (k) are used instead, it can be obtained that:…”

Section: Recursive Least Squares Algorithm With Multiple Forgetting Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

Liang

Zhao

et al. 2021

Machines

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show abstract

“…Levels three to five are called the highly automated driving systems, where the driver does not need to be involved in driving. Vehicles equipped with level three to five intelligent driving systems are also called highly automated driving vehicles [7,8]. For the driving assistance system, it requires the regenerative braking system to have the functions of brake-by-wire and mechanical backup.…”

Section: Introductionmentioning

confidence: 99%

Research on Pressure Control Algorithm of Regenerative Braking System for Highly Automated Driving Vehicles

Liang

Zhao

et al. 2021

WEVJ

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Conclusive evidence has demonstrated the critical importance of highly automated driving systems and regenerative braking systems in improving driving safety and economy. However, the traditional regenerative braking system cannot be applied to highly automated driving vehicles. Therefore, this paper proposes a fully decoupled regenerative braking system for highly automated driving vehicles, which has two working modes: conventional braking and redundant braking. Aimed at the above two working modes, this paper respectively proposes the pressure control algorithm, based on P-V characteristics, and the pressure control algorithm, based on the overflow characteristics of the solenoid valve. AMESim is utilized as the simulation platform, and then is co-simulated with MATLAB/Simulink, which is embedded with the control algorithm. The simulation results show the feasibility and effectiveness of the regenerative braking system and the pressure control algorithm.

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“…Lu et al proposed a self-adaptive system for vehicle cruise control, and the dynamic model of a single lane vehicle was simulated under the hybrid traffic flow of smart cars [7][8][9]. Wang et al quantified the impacts on the behaviors of vehicles 2 of 15 following in a string (or string stability) to establish potential performance enhancements of automated vehicle following systems from the aspects of the CACC algorithm, software system architecture, and module implementation [10][11][12][13]. The work mentioned in [10][11][12][13] improved the stability of traffic flow from the intelligent driving model and the collaborative adaptive cruise model.…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

Lai

Huo

2019

Electronics

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This paper proposes a combination method of longitudinal control and fuel management for an intelligent Hybrid Electric Vehicle (HEV) fleet. This method can reduce the fuel consumption while maintaining the distance and speed for each vehicle in the fleet. An HEV system efficiency model was established to simulate the impact of different working modes. Based on the principle of optimal vehicle system efficiency, the energy management control strategy of HEV was designed. Then, the driver model of the piloting vehicle and the following vehicle was built by using an intelligent fuzzy control method. Finally, the intelligent fleet model and energy matching model of HEV were integrated with the simulation platform that was developed based on MATLAB/Simulink/Stateflow. The validity of the energy matching strategy of HEV under the principle of optimal system efficiency was verified by simulation results, and the purpose of improving the driving safety, traffic efficiency, and fuel economy of the fleet was achieved. Comparing with the conventional control strategy, the proposed method saved 7.79% of fuel for the HEV fleet. Meanwhile, the distance ranges between the vehicles were from 12 meters to 15 meters, which improved the driving safety, passing rate, and fuel economy.

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Implementation and evaluation of cooperative adaptive cruise control functionalities

Cited by 7 publications

References 2 publications

A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

A Novel Adaptive Cruise Control Strategy for Electric Vehicles Based on a Hierarchical Framework

Research on Pressure Control Algorithm of Regenerative Braking System for Highly Automated Driving Vehicles

Energy Management Strategy Design and Simulation Validation of Hybrid Electric Vehicle Driving in an Intelligent Fleet

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