Economic Adaptive Cruise Control for Electric Vehicles Based on ADHDP in a Car-Following Scenario

Chen, Xiyan; Yang, Jian; Zhai, Chunjie; Lou, Jiedong; Yan, Chenggang

doi:10.1109/access.2021.3081184

Cited by 18 publications

(14 citation statements)

References 31 publications

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“…In summary, to meet control requirements, an equivalent circuit model is adopted in this article. In references [11,38], the Rint model is used to account for the dynamic energy consumption of the batteries, and the verification results show that the model is accurate [4]. The Rint model has few parameters, which allows for faster controller calculations, as shown in Figure 2.…”

Section: Energy Loss Modelmentioning

confidence: 99%

“…The adaptive cruise control (ACC) system is one of the advanced driver assistance systems. As a key step in achieving autonomous vehicle driving [4], it can not only reduce the probability of accidents caused by driver misuse [5] and improve driving safety, but also design driving strategies that match the road conditions at any given time [6]. ACC-equipped vehicles use on-board sensors to understand the position and speed of surrounding vehicles and to adjust the vehicle's driving patterns, improving passenger comfort and battery energy utilization while ensuring safety.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics

et al. 2023

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This paper proposes an economic adaptive cruise controller (EACC) that considers battery aging characteristics based on adaptive model predictive control (AMPC). By establishing a battery capacity decay model based on experimental data, the capacity loss during vehicle operation is determined, and the parameters in the equivalent circuit model are updated according to the actual capacity of the battery. The controller uses indicators that characterize driving safety, tracking performance, comfort, and economy. The economic indicator is the decrease in the value of the battery capacity. Fuzzy weight allocation is designed based on the host vehicle’s speed and the workshop’s relative distance to adjust the weight between different indicators under different working conditions. Additionally, the proposed controller is compared with other traditional controllers under different working conditions, cycle times, and battery state of health (SOH). The simulation results indicate that, under various battery SOH conditions, the performance of the controller which considers battery capacity degradation characteristics is better than that of traditional controllers. Moreover, the fixed-weight controller performs better when following a vehicle at medium and low speeds. Finally, the proposed strategy was validated through hardware-in-the-loop testing, demonstrating its ability to meet the real-time requirements of the system.

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Section: Energy Loss Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics

et al. 2023

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“…Besides road efficiency, several existing works focused on energy consumption. Chen et al proposed model-free and real-time ecological ACC (eco-ACC) based on action-dependent heuristic dynamic programming (ADHDP) controller to accomplish safety, comfortable driving, improve long-life battery, and energy consumption for car-following scenario [24]. The other works of eco-ACC with objective energy consumption applications are presented in [25]- [32].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cruise Control With Gain Scheduling Technique Under Varying Vehicle Mass

Hidayatullah

Juang

2021

IEEE Access

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Today, advanced driver-assistance systems (ADAS) come up with different abilities. One of them is the adaptive cruise control (ACC) system. The ACC system is a continuation of research on cruise control (CC) system, which integrates spacing control with the existing velocity control on the CC system. The vehicles with an ACC system guarantee traffic safety while at the same time ensure a well-driving sense. Many studies have demonstrated numerous control techniques applied as ACC controllers to accomplish uncertainty and perturbation issues. Nevertheless, most of the existing papers assumed the model vehicle dynamics as a linear time-invariant (LTI) system while designing the ACC controller. This paper proposed an ACC controller using the gain scheduling technique to deal with the model vehicle dynamics as a linear parameter varying (LPV) system. The passenger vehicle's mass varies during ACC operation depending on how many passengers or loads on the vehicle's trunks. Later, the vehicle's mass is estimated by recursive least square (RLS) with a forgetting factor. Then, the disk margin is utilized to provide the high-level robustness at each operating or ''frozen'' point. The robustness performance will be analyzed using the worst-case gain metric while the uncertainty is modeled by integral quadratic constraints (IQC). The LPV system behavior, such as the rate vehicle's mass, is also considered in the analysis. The effectiveness algorithm is validated through joint simulation between Matlab/Simulink and PreScan. The last, the comparison performance between gain scheduling and fixed gain ACC controller is evaluated. INDEX TERMSAdaptive cruise control, advanced driver assistance systems, cruise control, gain scheduling controller, linear parameter varying system, spacing control. NOMENCLATURE B r,max Maximum brake pressure. T h,max Maximum throttle. m, m Actual and estimated mass of the vehicle. δ safe , δ rel , δ 0 Safe distance, relative distance, and minimum distance between vehicles. v ref , v x,h Longitudinal reference velocity and actual of velocity of host vehicle. t g Time gap between vehicles. X a Position of target or other vehicles in front of the host vehicle. X hPosition of the host vehicle.The associate editor coordinating the review of this manuscript and approving it for publication was Michail Makridis .

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“…At present, the research on adaptive cruise control mainly focuses on improving vehicle following performance, vehicle safety, and comfort by using algorithms, such as PID, sliding membrane variable structure control, and model predictive control [17][18][19][20][21][22][23][24]. Su et al [8] used throttle opening and brake cylinder pressure as input variables to establish fuzzy rules and designed a fuzzy PID algorithm to realize free switching between acceleration and braking.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on vehicle adaptive cruise control based on BP neural network working condition recognition

Wang

Guo

et al. 2021

The Journal of Engineering

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To solve the problems of the vehicle following model mismatch under different driving conditions and poor vehicle following performance, this paper proposes an adaptive cruise control system based on BP neural network to identify different driving conditions. Firstly, eight typical driving cycles are selected as samples, which are redivided into low-speed mode and medium high-speed mode. According to the selected characteristic parameters and re-division of driving mode, a new BP neural network structure is designed. Secondly, the characteristic parameter values are calculated by using the characteristic parameters, and the characteristic parameters are normalized. The working condition recognition model is obtained by training the normalized data values. Finally, according to the classification of working conditions, the fuzzy control upper control algorithm and the MPC upper control algorithm for medium-high speed conditions are designed respectively. Based on the above control algorithms, the joint simulation is carried out with Car-Sim and MAT-LAB/Simulink. The simulation results show that the low-speed fuzzy logic algorithm and the medium high-speed MPC algorithm proposed in this paper can make the vehicle have a better following performance and comfort.

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Economic Adaptive Cruise Control for Electric Vehicles Based on ADHDP in a Car-Following Scenario

Cited by 18 publications

References 31 publications

Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics

Adaptive Cruise Control Strategy for Electric Vehicles Considering Battery Degradation Characteristics

Adaptive Cruise Control With Gain Scheduling Technique Under Varying Vehicle Mass

Research on vehicle adaptive cruise control based on BP neural network working condition recognition

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