Abstract
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%.
Highlights
In recent years, the rapid development of the economy has resulted in a great increase in car ownership, leading to a series of social problems such as environmental pollution, traffic congestion, and traffic accidents [1,2]
Many researchers have been working on designing adaptive cruise control (ACC) controllers based on different control algorithms, such as 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]
Is the rolling resistance coefficient, g is the gravity acceleration, α is the road slope, When the host vehicle is in the sliding state, neither the driving system nor the braking ρ is the mass density of air, Cd is the aerodynamic drag coefficient, S is the vehicle system is in operation
Summary
The rapid development of the economy has resulted in a great increase in car ownership, leading to a series of social problems such as environmental pollution, traffic congestion, and traffic accidents [1,2]. Zhang et al designed a multi-objective coordinated ACC controller based on an MPC framework, which could simultaneously handle issues regarding vehicle safety, lateral stability, and longitudinal tracking performance. Driving and brakingfrom for feedback correction, which will lead to mechanical damage of the switching between drivingofand critical todiscomfort, the vehicle’s vehicleAs components, theperformance loss of energy, fluctuations thebraking vehicle is dynamics, comfort and economy, a novel ACC strategy that considers the switching performance is and dissatisfaction from passengers proposed this paper. Considering the influ(2) With following, economy, comfort the optimization objectives, the ence vehicle of switching performance on and economy andas comfort, the identified sliding accelMPC upper-level controller is designed in the ACC.
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