Predictive planning of optimal velocity and state of charge trajectories for hybrid electric vehicles

Heppeler, Gunter; Sonntag, Marcus; Wohlhaupter, Uli; Sawodny, Oliver

doi:10.1016/j.conengprac.2016.07.003

Cited by 49 publications

(10 citation statements)

References 23 publications

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“…It is noteworthy that in this research, the eco-driving control is optimized in spatial domain, since the speed limit information is typically provided by intelligent traffic system (ITS) according to the current driving position. For actual applications, the speed constraints can be constructed in terms of legal limit, road grade and traffic lights [33,34]. For the studied PHEV, the total energy consumption cost of powertrain is defined as the objective function for eco-driving control.…”

Section: Eco-driving Control Problem Formulationmentioning

confidence: 99%

Data-driven based eco-driving control for plug-in hybrid electric vehicles

Liu

Zhang

et al. 2021

Journal of Power Sources

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With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control strategy with efficient computation capacity is proposed for plug-in hybrid electric vehicles to achieve approximate optimal energy economy. An efficient hierarchical optimal control scheme is designed to mitigate the massive computational cost during velocity optimization and powertrain control. A data-driven optimal energy consumption cost model and an optimal battery current model are respectively constructed via two neural networks and served as the critic model and the system model during velocity optimization. Furthermore, the neural networkbased dynamic programming is exploited to optimize the vehicle velocity by merging the data-driven models and Bellman optimality principle. The simulation results demonstrate that the proposed method can remarkably improve fuel economy by up to 16.7% in complicated driving conditions, compared with conventional sequential optimization methods. Furthermore, the data-driven control scheme can drastically improve the computational efficiency with slight sacrifice on fuel economy, compared with the optimum benchmark.

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Section: Eco-driving Control Problem Formulationmentioning

confidence: 99%

Data-driven based eco-driving control for plug-in hybrid electric vehicles

Liu

Zhang

et al. 2021

Journal of Power Sources

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“…M f and T have different dimensions, and a normalization factor is used in this case. If the predicted distance h is divided into N segments, then equations (11) and (12) can be written in discrete forms as…”

Section: Look-ahead Gear-shifting Strategy On Rampsmentioning

confidence: 99%

“…In the literature of speed adaptation, an optimal control algorithm based on dynamic programming (DP) algorithm is commonly used. [9][10][11][12] DP algorithm can also be used for optimal gear shift sequence with look-ahead information in ordinary driving mode where the driver controls the throttle. For different vehicle types, the specific application of DP algorithm is not identical due to different optimization objectives and vehicle structures.…”

Section: Introductionmentioning

confidence: 99%

Look-ahead gear-shifting strategy on ramps for heavy trucks with automated mechanical transmission

Cong

Zhang

Wang

et al. 2019

Advances in Mechanical Engineering

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Look-ahead information has been applied to vehicle shift systems and has led to the development of innovative changes in shifting strategies. A shifting strategy significantly influences the dynamic and economic performance of a truck. If a shift control system can predict the information of the road ahead, then the dynamic programming method can be used to obtain an optimal shift schedule, thereby achieving the best balance between the dynamic and the economic performance of a truck. Determining the weights of the economic and dynamic performance of a truck is critical in dynamic programming. Weight is related to road slope value; hence, this study uses different weights on 3%, 5%, and 10% ramp simulation experiments to investigate the relationship between fuel consumption and running time and to determine the optimum weight for different slopes. On the basis of the optimal shift schedule obtained via dynamic programming, the ramp information of the road ahead can be utilized to adjust the shift point to avoid unnecessary shifting before entering the slope and reduce shift times on a ramp. An actual truck test shows that the anthropomorphic shifting strategy proposed in this study meets driver expectations and is conducive to the ramp driving of trucks with automated manual transmission.

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“…[37] optimizes the power split ratio and speed trajectory simultaneously based on PMP. To further improve the fuel efficiency, the sequential quadratic programming (SQP) [38] and MPC are applied synthetically to plan the speed trajectory and energy allocation according to the rolling updated traffic information [39].…”

Section: Introductionmentioning

confidence: 99%

Cooperative optimization of velocity planning and energy management for connected plug-in hybrid electric vehicles

Liu

Huang

et al. 2021

Applied Mathematical Modelling

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In this paper, a cooperative optimization strategy is proposed for velocity planning and energy management of intelligent connected plug-in hybrid electric vehicles. Based on the established vehicle model, a mathematical analytical method is investigated to convert the driving cycles from the original time based profiles to the driving distance based speed values. Then, the iterative dynamic programming is exploited to achieve the synergistic optimization in terms of speed planning and power allocation of the vehicle with the consideration of gear shifting limits and speed fluctuation. To meet the requirement of trip duration limitation which may be violated due to autonomous speed planning, the terminal driving time is constrained by adding a time adjustment factor to the cost function. The simulation results suggest that the proposed strategy attains the collaborative optimization with high efficiency in terms of speed planning and driving power distribution. In addition, the proposed strategy leads to significant reduction of the energy consumption cost under the constraints of allowed speed variation ranges.

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Predictive planning of optimal velocity and state of charge trajectories for hybrid electric vehicles

Cited by 49 publications

References 23 publications

Data-driven based eco-driving control for plug-in hybrid electric vehicles

Data-driven based eco-driving control for plug-in hybrid electric vehicles

Look-ahead gear-shifting strategy on ramps for heavy trucks with automated mechanical transmission

Cooperative optimization of velocity planning and energy management for connected plug-in hybrid electric vehicles

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