Driving conditions-driven energy management strategies for hybrid electric vehicles: A review

Li, Teng; Tan, Wenhao; Tang, Xiaolin; Zhang, Jinwei; Xing, Yang; Cao, Dongpu

doi:10.1016/j.rser.2021.111521

Cited by 98 publications

(38 citation statements)

References 148 publications

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“…The affiliation matrix U ¼ [u ij ] denotes the affiliation function of the i-th sample for the j-th cluster. The clustering loss function defined by the affiliation function can be written as Equation (6).…”

Section: Fcm-based Driver Experience Knowledgementioning

confidence: 99%

“…The adopted dataset was divided into training and test part, as described in Section 3.1, for offline training and online operation. By taking the vehicle speed and acceleration per step within the driving cycle as feature vectors, the clustering centers and affiliation functions are iterated continuously according to Equation ( 7) and ( 8) before satisfying the objective function shown in Equation (6). The objective function J m tends to be minimized and finally reaches a steady state; then, the final affiliation matrix and clustering centers are obtained.…”

Section: Fcm-based Driver Experience Knowledgementioning

confidence: 99%

“…EMS of HEV can be broadly classified into three categories: rule based, optimization based, and learning based. [ 6 ] Rule‐based control strategy has been widely used in HEV control systems because of its reliable and efficient, [ 7 ] but it is difficult to ensure the optimality under variable driving cycles. Optimization‐based control strategies are divided into global and real‐time optimization.…”

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning Based on Driver Experience Embedding for Energy Management Strategies in Hybrid Electric Vehicles

Zhang

2022

Energy Tech

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Reinforcement learning (RL) is a solution with great potential for hybrid electric vehicle (HEV) energy management strategies (EMS). However, traditional deep reinforcement learning (DRL) suffers from inefficiency and poor stability during random exploration in action space, so it is necessary to model some advanced driver experience knowledge and combine it with DRL. Herein, an advanced driver experience (DE) model of traffic congestion level and power matching is constructed based on fuzzy clustering and embedded into DRL. The results show that the DE embedding improves the training convergence efficiency of DRL on a power‐split HEV model, where it improves the convergence of the deep deterministic policy gradient (DDPG) by 46.2%. As DE can better adjust engine operating points and vehicle drive modes under various driving cycles, it enables DDPG to improve fuel economy by ≈6.29% while maintaining the terminal state of charge. This study aims to improve the efficiency of action space exploration and optimize the DRL learning strategy, so as to provide a theoretical basis for the design and development of EMS.

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Section: Fcm-based Driver Experience Knowledgementioning

confidence: 99%

Section: Fcm-based Driver Experience Knowledgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Reinforcement Learning Based on Driver Experience Embedding for Energy Management Strategies in Hybrid Electric Vehicles

Zhang

2022

Energy Tech

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“…Hybrid electric vehicles (HEVs) are currently important carriers of self-driving technology [1]. HEVs involve two or more energy sources, which realize efficient power distribution between the motor and engine.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles

Xu¹,

Li²,

Li³

et al. 2022

Preprint

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The deep reinforcement learning-based energy management strategies (EMS) has become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are changed, the network will be retrained, which is a timeconsuming and laborious task. A more efficient way of choosing EMS is to combine deep reinforcement learning (DRL) with transfer learning, which can transfer knowledge of one domain to the other new domain, making the network of the new domain reach convergence values quickly. Different exploration methods of RL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work. Results indicate that the network added parameter space noise is more stable and faster convergent than the others. In conclusion, the best exploration method for transferable EMS is to add noise in the parameter space, while the combination of action space noise and parameter space noise generally performs poorly. Our code is available at https://github.com/BIT-XJY/ RL-based-Transferable-EMS.git.

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“…The complexity and variability of real-world driving conditions will cause the established control strategy to fail to achieve optimal performance [19,20]. To realize the real-time application based on global optimization, it is a feasible solution to switch the corresponding offline model or calculation results through the driving condition identification methods based on machine learning (ML) [21,22]. Comparing the performance of different methods to choose the better identification algorithm has a great impact on improving the performance of an energy management strategy.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers

Huang

Jiang

Xie

2021

WEVJ

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The new energy of concrete truck mixers is of great significance to achieve energy conservation and emission reduction. Unlike general-purpose vehicles, in addition to driving conditions, upper-mixing system conditions, operation scenarios, and variable loads are the key factors to be considered during the new energy of concrete truck mixers. This study focuses on the machine-learning-based approximate optimal energy management design for a concrete truck mixer equipped with a novel extended-range powertrain from two aspects: trip information and energy management strategy. Firstly, an optimal control database is constructed, which benefits from a global optimization algorithm with dimension reduction for the constrained time-varying two-point boundary value problems with two control variables, and the driving data analysis through machine learning and data-driven methods. Then, different machine-learning-based driving condition identifiers are constructed and compared. Finally, a vehicle mass and power demand of an upper-part system based novel neural network energy management strategy is designed based on a constructed optimal control database. Simulation results show that the intelligent optimization algorithm based on the ML of trip information and energy management is an appropriate way to solve the online energy management problem of the concrete truck mixer equipped with the proposed novel powertrain.

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Driving conditions-driven energy management strategies for hybrid electric vehicles: A review

Cited by 98 publications

References 148 publications

Deep Reinforcement Learning Based on Driver Experience Embedding for Energy Management Strategies in Hybrid Electric Vehicles

Deep Reinforcement Learning Based on Driver Experience Embedding for Energy Management Strategies in Hybrid Electric Vehicles

A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles

Machine-Learning-Based Optimization of Energy Management in a Novel Hybrid Powertrain of Concrete Truck Mixers

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