Energy-Saving Model Predictive Cruise Control Combined with Vehicle Driving Cycles

Xu, Zhihao; Li, Jianhua; Xiao, Feng; Zhang, Xu; Song, Shixin; Wang, Da; Qi, Chunyang; Wang, Jianfeng; Peng, SiLun

doi:10.1007/s12239-022-0040-z

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…The discrete state-space equations for the longitudinal kinematic model of the ACC system can be obtained by combining Equation ( 8) and (10).…”

Section: Longitudinal Kinematics Modelmentioning

confidence: 99%

“…Then Alomari [9] corrected the direction error obtained by the global localization system and a pre-built grid map to ensure the smooth speed and inter-distance adjustment of the ACC vehicle at high speed. Xu et al [10] and Zhang et al [11] researched the safety and economy of ACC systems of different vehicle models to improve the use scenarios of ACC systems. In the actual driving process, the control algorithm of ACC system needs to coordinate various performance indicators.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al. [ 10 ] and Zhang et al. [ 11 ] researched the safety and economy of ACC systems of different vehicle models to improve the use scenarios of ACC systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Curving Acc System for Electric Vehicle Driven by In‐wheel Motors Based on Fmpc And Smc

Yang,

Tian

2023

Advcd Theory and Sims

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In order to ensure the lateral stability of adaptive cruise control (ACC) vehicles during curve following, this paper proposes a hierarchical multi‐objective coordinated control ACC system structure for in‐wheel motor drive electric vehicles. Different control strategies are designed for the cruise mode and car‐following mode of the ACC system. A proportion integral differential (PID) controller is used to meet the need for speed control in cruise mode. For the car‐following mode, a multi‐objective ACC system distance control strategy is proposed considering the performance of car following, passenger comfort, driving safety and driving economy of ACC vehicles. The weight coefficients in the objective function are updated in real time by using the fuzzy model predictive control (FMPC) strategy to meet the needs of different road conditions and improve the driving economy. Then, the desired front wheel steering angle is obtained by preview control theory. According to the theory of sliding mode control (SMC), a lateral stability controller is designed to track the desired yaw rate and sideslip angle. Simulation results show that the electric vehicle driven by in‐wheel motors based on FMPC and SMC has better car‐following performance and lateral stability performance under variable curvature curved road conditions.

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“…The discrete state-space equations for the longitudinal kinematic model of the ACC system can be obtained by combining Equation ( 8) and (10).…”

Section: Longitudinal Kinematics Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research on Curving Acc System for Electric Vehicle Driven by In‐wheel Motors Based on Fmpc And Smc

Yang,

Tian

2023

Advcd Theory and Sims

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show abstract

“…The first strategy, known as efficiency ACC (EACC), consists of reducing the instantaneous acceleration or enhancing the energy efficiency of the driving system. This method can reduce the instantaneous energy consumption, but the energy economy on the entire route or in a section of the journey cannot be controlled [8,9]. The second strategy consists of programming and controlling the vehicle speed in a horizon based on the knowledge of future road conditions, such as the road slope, speed limit, and traffic signals; this strategy is usually known as predictive energy-saving control (PEC).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Predictive Energy-Saving Control for Electric Vehicle Based on Road Slope Prediction

Wu,

Lin,

et al. 2023

International Journal of Energy Research

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Predictive energy-saving control (PEC) is aimed at reducing energy consumption by designing the vehicle speed while considering future road and traffic information. In particular, the slope of the road ahead is necessary and critical for PEC. This paper proposes a road slope prediction method for production vehicles that uses the nonlinear autoregressive (NAR) neural network model based on road slope sensors. To adaptively balance the energy savings and trip time, this paper proposed a real-time variable weight PEC method for a four-wheel-drive (4WD) intelligent electric vehicle. The weight coefficients are automatically changed according to the characteristics of the road slope, where the vehicle energy-saving rate on the steep downhill road can be maximized. The results of real-time simulation on the dSPACE platform indicated that the road slope predictive model can be run in real time and adapted to changes in road slope and speed. The root mean square error (RMSE) of the predictive results is 0.3063. On a steep downhill road, the energy-saving rate of the proposed PEC method can reach 30.87% at a small expense of time of 3.75%. On uphill and flat roads, energy can be saved by 6.35% at a time cost of 3.0%. Compared with the PEC with constant weight factors, the two control objectives of energy savings and traveling time can be better balanced on various types of roads.

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Energy-efficient adaptive cruise control system with acceleration prediction via long short-term memory

Jiang,

Zheng,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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An energy-efficient adaptive cruise control (ACC) system that predicts the preceding state is proposed to meet the demands of driving in different states and working conditions. The long short-term memory (LSTM) network predicts the trajectory of the preceding vehicle’s future acceleration, allowing for an adaptive time headway that takes acceleration into account. The acceleration of the preceding vehicle is also incorporated as an augmented state in the deep deterministic policy gradient (DDPG) algorithm, resulting in a combined algorithm called prediction deep deterministic policy gradient (PDDPG). A multi-objective reward function is constructed based on human driving data to evaluate the performance indexes of vehicle longitudinal control, including efficiency, safety, and economy. The rules for changing the weight of each target performance under various typical cycle conditions are determined through experiments. The Carsim-based urban, suburban and highway conditions together with the Simulink vehicle dynamics model are compared with human driving data and ACC of conventional algorithms. Based on the test results, the proposed algorithm can improve fuel efficiency by 19.36% in urban driving conditions and reduce acceleration fluctuations.

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Energy-Saving Model Predictive Cruise Control Combined with Vehicle Driving Cycles

Cited by 4 publications

References 27 publications

Research on Curving Acc System for Electric Vehicle Driven by In‐wheel Motors Based on Fmpc And Smc

Research on Curving Acc System for Electric Vehicle Driven by In‐wheel Motors Based on Fmpc And Smc

Real-Time Predictive Energy-Saving Control for Electric Vehicle Based on Road Slope Prediction

Energy-efficient adaptive cruise control system with acceleration prediction via long short-term memory

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