Research on adaptive cruise control strategy of pure electric vehicle with braking energy recovery

Sun, Cheng; Liang, Chao; Guo, Jianhua; Shi, Dapai; Li, Tianjiao; Jiang, Yunsong

doi:10.1177/1687814017734994

Cited by 16 publications

(11 citation statements)

References 30 publications

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“…Additionally, a well-designed ACC controller should not only satisfy the control objectives and constraints but also perform robustly under system disturbances and model mismatches. Nowadays, many researchers have been working on designing ACC controllers based on different control algorithms, such as Machines 2021, 9, 263 2 of 26 the optimal control algorithm [12], fuzzy control algorithm [13], sliding mode control algorithm [14], neural network learning algorithm [15], proportional-integral-derivative (PID) control algorithm [16], and the model predictive control (MPC) algorithm [17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Sun et al designed an ACC controller based on the MPC theory with a particle swarm optimization algorithm. With braking energy, tracking, comfort, and safety as the control objectives, the vehicle could recover braking energy as much as possible while satisfying the requirements of tracking, safety, and comfort [24]. Sakhdari and Nasser L proposed an adaptive tube-based nonlinear model predictive control (AT-NMPC) method to design the ACC system.…”

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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Liang

Zhao

et al. 2021

Machines

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“…Applying the intelligent driving system and regenerative braking system to new energy vehicles can not only improve driving safety, but also further enhance the economy [5,6]. As the regenerative braking system is a subsystem of the intelligent driving system, it should meet the performance requirements of it.…”

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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“…The objective was to reduce energy consumption for four-wheel independent drive PEV. In [17], a control algorithm combining with regenerative braking function for ACC system is presented. Some variables like state of charge (SOC) for battery were considered in modeling.…”

Section: Introductionmentioning

confidence: 99%

Model‐Predictive Optimization for Pure Electric Vehicle during a Vehicle‐Following Process

Zhang

Zhuan

2019

Mathematical Problems in Engineering

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In this paper, a pure electric vehicle (PEV) equipped with adaptive cruise control (ACC) system is studied for a vehicle-following process. And a multiobjective optimization algorithm for ACC system is proposed in a model-predictive control (MPC) framework for optimizing safety, tracking capability, driving comfortability and energy consumption. The longitudinal dynamics of the ACC system are modeled, which not only considers the vehicle spacing and speed, but also introduces the acceleration and the change rate of acceleration (jerk) for the host vehicle and fully considers the influence of the acceleration of the leading vehicle. The improvement of driving comfortability and the reduction of energy consumption are achieved mainly by optimizing the acceleration and jerk of host vehicle. Some optimized reference trajectories are introduced to MPC for improving driving comfortability of host vehicle. The performances of the multiobjective upper level algorithm combined with the PEV model are evaluated for three representative scenarios. The results demonstrate the effectiveness of the proposed algorithm.

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Research on adaptive cruise control strategy of pure electric vehicle with braking energy recovery

Cited by 16 publications

References 30 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

Model‐Predictive Optimization for Pure Electric Vehicle during a Vehicle‐Following Process

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