Energy-Efficient Control Approach for Automated HEV and BEV With Short-Horizon Preview Information

Seok, Jinwoo; Wei, Yan; Filev, Dimitar; Kolmanovsky, Ilya; Girard, Anouck

doi:10.1115/dscc2018-8980

Cited by 7 publications

(15 citation statements)

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“…In [20][21][22][23], MPC is designed using short-term prediction and it has been confirmed that, to a certain degree, this method is effective to improve energy efficiency. However, the computational burdens depending on the several important factors in MPC such as sampling-time and length of the prediction time were not analyzed thoroughly.…”

Section: Literature Reviewmentioning

confidence: 95%

Battery Energy Management of Autonomous Electric Vehicles Using Computationally Inexpensive Model Predictive Control

2020

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With the emergence of vehicle-communication technologies, many researchers have strongly focused their interest in vehicle energy-efficiency control using this connectivity. For instance, the exploitation of preview traffic enables the vehicle to plan its speed and position trajectories given a prediction horizon so that energy consumption is minimized. To handle the strong uncertainties in the traffic model in the future, a constrained controller is generally employed in the existing researches. However, its expensive computational feature largely prevents its commercialization. This paper addresses computational burden of the constrained controller by proposing a computationally tractable model prediction control (MPC) for real-time implementation in autonomous electric vehicles. We present several remedies to achieve a computationally manageable constrained control, and analyze its real-time computation feasibility and effectiveness in various driving conditions. In particular, both warmstarting and move-blocking methods could relax the computations significantly. Through the validations, we confirm the effectiveness of the proposed approach while maintaining good performance compared to other alternative schemes.

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Section: Literature Reviewmentioning

confidence: 95%

Battery Energy Management of Autonomous Electric Vehicles Using Computationally Inexpensive Model Predictive Control

2020

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“…where t f corresponds to the entire duration of the trip. We remark that to compute such a globally optimal solution based on (20), the reference speed v r over the entire trip must be known a priori. This may not be as practical as the assumption of being able to predict v r for a short time horizon in the proposed receding-horizon optimization strategy (9).…”

Section: Alternative Energy Efficiency Optimization Approaches For Co...mentioning

confidence: 99%

“…We use a dynamic programming (DP) algorithm [32] to solve (20), where the constraints (9c)-(9e) are handled through penalties.…”

Section: Alternative Energy Efficiency Optimization Approaches For Co...mentioning

confidence: 99%

“…Using short-term preview information instead of prediction of the entire trip and optimizing vehicle speed based on a receding-horizon optimization/model predictive control framework has been considered in [17]- [20]. On the one hand, previewing road and traffic information over a short time horizon may be easier and also more accurate than prediction over a long horizon or over the entire trip [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

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Coordinated Receding-Horizon Control of Battery Electric Vehicle Speed and Gearshift Using Relaxed Mixed Integer Nonlinear Programming

Han

Kolmanovsky

et al. 2021

Preprint

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In this paper, we propose an approach to coordinated receding-horizon control of vehicle speed and transmission gearshift for automated battery electric vehicles (BEVs) to achieve improved energy efficiency. The introduction of multispeed transmissions in BEVs creates an opportunity to manipulate the operating point of electric motors under given vehicle speed and acceleration command, thus providing the potential to further improve the energy efficiency. However, cooptimization of vehicle speed and transmission gearshift leads to a mixed integer nonlinear program (MINLP), solving which can be computationally very challenging. In this paper, we propose a novel continuous relaxation technique to treat such MINLPs that makes it possible to compute solutions with conventional nonlinear programming solvers. After analyzing its theoretical properties, we use it to solve the optimization problem involved in coordinated receding-horizon control of BEV speed and gearshift. Through simulation studies, we show that co-optimizing vehicle speed and transmission gearshift can achieve considerably greater energy efficiency than optimizing them sequentially, and the proposed relaxation technique can reduce the online computational cost to a level that is comparable to the time available for real-time implementation.

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“…Short-range preview information and Model Predictive Control (MPC) are used in [12] to control the vehicle speed while satisfying following-distance and speed constraints. Also, the approach proposed in [13] also optimizes the speed trajectory over a short horizon, i.e., 7 sec, using short-range preview information to minimize the cumulative wheel power consumption. Parametric optimization of a feedback-based vehicle speed controller to improve fuel economy is presented in [14].…”

Section: A Literature Reviewmentioning

confidence: 99%

Hierarchical Optimization of Speed and Gearshift Control for Battery Electric Vehicles Using Preview Information

Han

Kolmanovsky

et al. 2020

2020 American Control Conference (ACC)

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This paper addresses the co-optimization of speed and gearshift control for battery electric vehicles using shortrange traffic information. To achieve greater electric motor efficiency, a multi-speed transmission is employed, whose control involves discrete-valued gearshift signals. To overcome the computational difficulties in solving the integrated speed-andgearshift optimal control problem that involves both continuous and discrete-valued optimization variables, we propose a hierarchical procedure to decompose the integrated hybrid problem into purely continuous and discrete sub-problems, each of which can be efficiently solved. We show, by simulations in various driving scenarios, that the co-optimization of speed and gearshift control using our proposed hierarchical procedure can achieve greater energy efficiency than other typical approaches.

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Energy-Efficient Control Approach for Automated HEV and BEV With Short-Horizon Preview Information

Cited by 7 publications

References 0 publications

Battery Energy Management of Autonomous Electric Vehicles Using Computationally Inexpensive Model Predictive Control

Battery Energy Management of Autonomous Electric Vehicles Using Computationally Inexpensive Model Predictive Control

Coordinated Receding-Horizon Control of Battery Electric Vehicle Speed and Gearshift Using Relaxed Mixed Integer Nonlinear Programming

Hierarchical Optimization of Speed and Gearshift Control for Battery Electric Vehicles Using Preview Information

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