A multi-layer control hierarchy for heavy duty vehicles with off-line dual stage dynamic programming optimization

Donatantonio, Fabrizio; D’Amato, Antonio; Arsie, Ivan; Pianese, Cesare

doi:10.1016/j.trc.2018.05.006

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…All considered techniques are non-causal, except the ECMS-based one, which uses current and past information only. Both DP [13][14][15][16][17] and PMP [18,19] techniques are well established and have been extensively presented in the literature for hybrid powertrain optimization, and are here therefore used to evaluate the performance of the newly proposed FADP and ECMS-based approaches. In the following, a brief overview on optimal control problem formulation is given, followed by the introduction of the bases of all the considered methods (both conventional and novel) and the analysis of their performance for hybrid powertrain optimization.…”

Section: Supervisory Control Algorithmsmentioning

confidence: 99%

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Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

et al. 2022

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Well-designed energy management strategies are essential for the good operation of Hybrid Electric Vehicles (HEVs) in terms of fuel economy and pollutant emissions reduction, regardless of the specific powertrain architecture. The goal of this paper is to propose two innovative supervisory control strategies for HEVs derived from different optimization algorithms and to assess HEVs’ fuel consumption reduction (compared to conventional vehicles). These approaches are derived from the literature and modified by the authors to present novel algorithms for the optimization problem. One is based on Dynamic Programming (DP), here referred to as the Forward Approach to Dynamic Programming (FADP) and introduces a different implementation of the DP to achieve computational and accuracy benefits. The other is based on the Equivalent Consumption Minimization Strategy (ECMS) approach, and it adapts to the latest driving conditions using information gathered in a finite-length backward-looking horizon. These techniques are used to achieve the optimal power share between the thermal engine and the battery of a parallel HEV. Their performances are compared and analysed in terms of achieved fuel economy and computational time with respect to conventional DP and Pontryagin’s Minimum Principle (PMP) approaches.

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Section: Supervisory Control Algorithmsmentioning

confidence: 99%

“…Further detail on Dynamic Programming can be found in [21,22], whereas its application to the hybrid vehicle problem can be found, e.g., in [2,13,[15][16][17][23][24][25][26].…”

Section: Dynamic Programmingmentioning

confidence: 99%

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

et al. 2022

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“…Specifically, the upper level uses real-time traffic flow velocity to compute a global state of charge trajectory, and the lower level applies a MPC algorithm which takes advantage of short-term velocity prediction. To reduce the computational costs and time for on-board controller of the HDV, [25]- [27] decouple the whole optimization into the velocity profile and shifting schedule calculation, in which an off-line algorithm in the higher layer (can be deployed in cloud) is designed to optimize the vehicle velocity profile, while the on-line powertrain control is implemented in the lower layer based on the reference velocity profile. All these computation-intensive calculations can also be performed in the cloud [11] to generate the optimal speed profile which is provided as the reference for the driver via HMI.…”

Section: A Vehicle Eco-drivingmentioning

confidence: 99%

An Enhanced Predictive Cruise Control System Design With Data-Driven Traffic Prediction

Jia

Chen

Zheng

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The predictive cruise control (PCC) is a promising method to optimize energy consumption of vehicles, especially the heavy-duty vehicles (HDV). Due to the limited sensing range and computational capabilities available on-board, the conventional PCC system can only obtain a sub-optimal speed trajectory based on a shorter prediction horizon. The recently emerging information and communication technologies such as vehicular communication, cloud computing, and Internet of Things provide huge potentials to improve the traditional PCC system. In this paper, we propose a general framework for the enhanced cloud-based PCC system which integrates a data-driven traffic predictive model and the instantaneous control algorithms. Specifically, we introduce a novel multi-view CNN deep learning algorithm to predict traffic situation based on the historical and real-time traffic data collected from fields, and the time-varying adaptive model predictive control (MPC) to calculate the instantaneous optimal speed profile with the aim of minimizing energy consumption. We verified our approach via simulations in which the impact of various traffic condition on the PCC-enabled HDV has been fully evaluated.

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“…Utilizing the traffic information, an energy management strategy considering velocity optimization was proposed and near-optimality was achieved through updating the co-state values. 19 Donatantonio et al 20 proposed a comprehensive control strategy for a heavy-duty vehicle. The upper controller obtained the optimal velocity trajectory as well as the shift schedule utilizing the road information, which was based on dual step dynamic programming, the lower controller computed the control input for the powertrain considering the physical constraints of the engine.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective regenerative braking control strategy combining with velocity optimization for connected vehicles

Liu

Han

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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Deceleration is unavoidable owing to the traffic lights or obstacles during driving, which lead to great energy dissipation. To promote energy utilizing efficiency, regenerative braking is applied to convert kinetic energy into electricity. Previous researches about regenerative braking concentrate on the braking torque allocation optimization, which is undoubtedly important. Moreover, the control performance can be further improved combining with velocity optimization. With the emerging Connected Vehicle Technology, environmental information is available for vehicles. Therefore, the terminal braking distance and terminal velocity can be derived utilizing the information. Then a multi-objective regenerative braking control strategy based on the pesudospectral method is proposed with the terminal constraints. The control strategy optimizes both the velocity and braking torque allocation to reduce the energy dissipation and battery capacity loss simultaneously. Simulations are carried out under a speed bump scenario. Pareto front is obtained with different weights of the objectives and then analyzed to reveal the tradeoff between energy recovery and battery health, which assists in finding a desired balanced solution. Two representative Pareto optimal solutions are selected for comparison: the energy recovery priory strategy and the energy-life tradeoff strategy. Compared with the energy priory strategy, the energy-life tradeoff strategy decreases the battery capacity loss by 60.65%, but it leads to a 36.07% reduction in the energy recovery.

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A multi-layer control hierarchy for heavy duty vehicles with off-line dual stage dynamic programming optimization

Cited by 12 publications

References 24 publications

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

An Enhanced Predictive Cruise Control System Design With Data-Driven Traffic Prediction

A multi-objective regenerative braking control strategy combining with velocity optimization for connected vehicles

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