Multiple-grained velocity prediction and energy management strategy for hybrid propulsion systems

Wang, Yujie; Li, Xiyun; Wang, Li; Sun, Zhen‐Dong

doi:10.1016/j.est.2019.100950

Cited by 36 publications

(19 citation statements)

References 35 publications

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“…The experimental results indicate the rule-based strategy is more efficient than the PID-based strategy. Again, an improved power splitting strategy is proposed in [25] for the hybrid propulsion system that utilizes DP and multiplegrained velocity prediction to verify the proposed scheme for different hybrid energy resources. The method forms a semiphysical platform to maintain simulation activities in hardware-in-the-loop simulation.…”

Section: Introductionmentioning

confidence: 99%

Control Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

et al. 2021

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Choice of hybrid electric vehicles (HEVs) in transportation systems is becoming more prominent for optimized energy consumption. HEVs are attaining tremendous appreciation due to their ecofriendly performance and assistance in smart grid notion. The variation of energy storage systems in HEV (such as batteries, supercapacitors or ultracapacitors, fuel cells, and so on) with numerous control strategies create variation in HEV types. Therefore, choosing an appropriate control strategy for HEV applications becomes complicated. This paper reflects a comprehensive review of the imperative information of energy storage systems related to HEVs and procurable optimization topologies based on various control strategies and vehicle technologies. The research work classifies different control strategies considering four configurations: fuel cell-battery, battery-ultracapacitor, fuel cell-ultracapacitor, and battery-fuel cellultracapacitor. Relative analysis among different control techniques is carried out based on the control aspects and operating conditions to illustrate these techniques' pros and cons. A parametric comparison and a crosscomparison are provided for different hybrid configurations to present a comparative study based on dynamic performance, battery lifetime, energy efficiency, fuel consumption, emission, robustness, and so on. The study also analyzes the experimental platform, the amelioration of driving cycles, mathematical models of each control technique to demonstrate the reliability in practical applications. The presented recapitulation is believed to be a reliable base for the researchers, policymakers, and influencers who continuously develop HEVs with energy-efficient control strategies.

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Section: Introductionmentioning

confidence: 99%

Control Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

et al. 2021

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“…Although near-optimal fuel economy has been achieved by their rule-based strategy in the two load profiles under investigation, the performance of the strategy in other profiles which can vary significantly is not clear. Wang et al [8] proposed an energy management strategy based on velocity prediction for three typical non-plug in hybrid electric propulsion structures. An urban driving cycle was used to calculate the state transition probabilities; subsequently, dynamic programming was employed to generate the strategy.…”

Section: Introductionmentioning

confidence: 99%

Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

Partridge

Bucknall

2020

Applied Energy

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Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for coastal ships with access to H 2 replenishment and battery charging infrastructures in ports. However, such systems could be constrained by high power source degradation and energy costs. Cost-effective energy management strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double Q reinforcement learning based energy management system for such systems to achieve near-optimal average voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management strategies generated by the agent were validated using another test dataset collected over a different period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion systems for ships. The results show that without prior knowledge of future power demands, the strategies can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming with the equivalent state space resolution.

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“…Markov chain is a stochastic process prediction approach, which can forecast the velocity via transition probability 25,26 . In Reference 27, a multiple‐grained velocity prediction method using the Markov chain is proposed to improve the prediction accuracy. Reference 28 combines model predictive control with the Markov chain predictor and achieves almost optimal control performance.…”

Section: Introductionmentioning

confidence: 99%

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

Lin

Wang

2020

Int J Energy Res

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Summary The stochastic driving cycles pose a challenge to the actual implementation of the control strategy for fuel cell electric vehicles (FCEVs). Considering the problem, this research proposes a predictive control strategy based on velocity prediction. Firstly, a novel velocity prediction method is developed, which considers the prediction error of back propagation neural network (BPNN)‐based method. Then, it is incorporated into the predictive control strategy for a plug‐in FCEV. Finally, simulation studies are conducted to verify the effectiveness and superiority of the proposed predictive control strategy. Simulation results show that the proposed velocity prediction method can adaptive to different driving cycles with high accuracy. In another case, with the velocity prediction used in the predictive control strategy, hydrogen consumption reduces by 17.07% when compared with the traditional rule‐based strategy. All the results indicate that the designed velocity prediction approach made it possible to forecast vehicle velocity with relatively high precision and promising to promote the energy management strategy (EMS) to reduce the hydrogen consumption of the plug‐in FCEV.

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Multiple-grained velocity prediction and energy management strategy for hybrid propulsion systems

Cited by 36 publications

References 35 publications

Control Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

Control Strategies of Different Hybrid Energy Storage Systems for Electric Vehicles Applications

Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

Energy management strategy based on velocity prediction using back propagation neural network for a plug‐in fuel cell electric vehicle

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