Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

Li, Teng; Zou, Yuan; Liu, Dexing; Sun, Fuchun

doi:10.3390/en8077243

Cited by 81 publications

(35 citation statements)

References 27 publications

(13 reference statements)

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“…Furthermore, the RL technique framework is constructed [32], and the Qlearning algorithm is harnessed to rapidly search the optimal control based on the online TPM of predicted power demand. The elementary parameters of the HTV powertrain are listed in Table I [33].…”

Section: Lower-level: Htv Powertrain and Rlmentioning

confidence: 99%

“…i e−g is the gear ratio between the engine and generator, and 0.1047 is the transformation factor that denotes 1 r/min = 0.1047 rad/s. The torque and output voltage of the generator can be derived as follows [33]:…”

Section: A Vehicle Powertrain Modelmentioning

confidence: 99%

“…Since the core of this article focuses on discussing the control performance of the proposed real-time control strategy, both traction motors, as power conversion devices, are assumed to have an identical efficiency, and the battery aging is neglected in this study [32], [33].…”

Section: A Vehicle Powertrain Modelmentioning

confidence: 99%

“…To remedy this deficiency, reinforcement learning (RL) [23], [24] has been recently considered as a model-free method to search optimal control in energy management problem. Liu et al [25], [32], [33] proposed a RLbased energy management strategy using Q-learning algorithm and MC models. The results indicated that fuel efficiency could be significantly improved by using the proposed RL-based energy management strategy.…”

Section: Introductionmentioning

confidence: 99%

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A Bi-Level Control for Energy Efficiency Improvement of a Hybrid Tracked Vehicle

2018

IEEE Trans. Ind. Inf.

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In this paper, a bi-level control framework is proposed to improve the energy efficiency for a hybrid tracked vehicle. The higher-level discusses how to accurately predict power demand based on the Markov Chain. Specially, fuzzy encoding predictor is used for power demand prediction, and a real-time recursive algorithm is applied to fuse the future power demand information into transition probability matrix (TPM) computation. Furthermore, the Kullback-Leibler (KL) divergence rate is employed to decide the alteration of control strategy. The lower-level computes the relevant energy management strategy, based on the updated TPM and a model-free reinforcement learning (RL) technique. Simulation results illustrate that the vehicular energy efficiency in the proposed scheme exceeds the common RL control by tuning the KL divergence value. Comparative results also show that the developed control strategy outperforms the common RL one, in terms of energy efficiency and computational speed.Index Terms-Energy management, hybrid tracked vehicle (HTV), Kullback-Leibler (KL) divergence rate, power demand prediction, reinforcement learning (RL).

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Section: Lower-level: Htv Powertrain and Rlmentioning

confidence: 99%

Section: A Vehicle Powertrain Modelmentioning

confidence: 99%

Section: A Vehicle Powertrain Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Bi-Level Control for Energy Efficiency Improvement of a Hybrid Tracked Vehicle

2018

IEEE Trans. Ind. Inf.

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“…HEVs coordinate both electric machines and internal combustion engines (ICEs) to deliver propulsion power. Many previous studies have focused on the energy management and optimal power flow of HEV dynamics [1][2][3][4]. However, HEV speed control techniques are drastically different than those of a conventional vehicle, because HEVs typically move in electric mode, and ICEs can be operated at higher speeds.…”

Section: Introductionmentioning

confidence: 99%

Robust Longitudinal Speed Control of Hybrid Electric Vehicles with a Two-Degree-of-Freedom Fuzzy Logic Controller

Perng

Lai

2016

Energies

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This paper proposes a new robust two-degree-of-freedom (DoF) design method for controlling the nonlinear longitudinal speed problem of hybrid electric vehicles (HEVs). First, the uncertain parameters of the HEV model are described by fuzzy α-cut representation, in which the interval uncertainty and the possibility can be simultaneously indicated by the fuzzy membership function. For the fuzzy parametric uncertain system, the maximum uncertainty interval can be translated into the weighting matrix Q of the linear quadratic tracking problem to guarantee that the designed feedback controller is robust. Second, the fuzzy forward compensator is incorporated with a robust feedback controller to enhance the system tracking response. The simulation results demonstrate that the proposed controller has higher tracking performance compared to the single-DoF self-tuning fuzzy logic controller or conventional optimal H 8 controller.

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A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Wang

Tang

et al. 2023

Circuit Theory & Apps

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SummaryThe design of energy management strategy (EMS) plays a vital role in the power performance and economy of battery–ultracapacitor for electric vehicles. A reinforcement learning (RL)‐based EMS is proposed to obtain an optimal power allocation strategy for battery–ultracapacitor electric vehicle, and its robustness is verified at different temperatures. First of all, the dynamic characteristic experiments of the battery and ultracapacitor were performed at 10°C, 25°C, and 40°C to obtain mechanism characteristics at different temperatures. Secondly, a genetic algorithm is selected to identify the parameters of the battery and ultracapacitor model. Next, the RL‐based strategy takes the minimum energy loss of the hybrid energy storage system as the reward function and solves the optimal policy based on Markov theory. The simulation results show that the economy of the RL‐based strategy correspondingly improved by 3.05%, 3.20%, and 3.15% at different temperatures in comparison with the fuzzy‐based strategy, and the economic gap between the RL‐based strategy and the DP‐based strategy is further narrowed down to 7.30%, 3.88%, and 8.40% at different temperatures, respectively. Finally, the proposed strategy is validated under different driving conditions, which indicate that the RL‐based strategy can effectively reduce energy consumption and has good robustness at different temperatures.

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Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle

Cited by 81 publications

References 27 publications

A Bi-Level Control for Energy Efficiency Improvement of a Hybrid Tracked Vehicle

A Bi-Level Control for Energy Efficiency Improvement of a Hybrid Tracked Vehicle

Robust Longitudinal Speed Control of Hybrid Electric Vehicles with a Two-Degree-of-Freedom Fuzzy Logic Controller

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

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