An On-Line Energy Management Strategy Based on Trip Condition Prediction for Commuter Plug-In Hybrid Electric Vehicles

Liu, Jichao; Chen, Yangzhou; Zhan, Jingyuan; Shang, Fei

doi:10.1109/tvt.2018.2815764

Cited by 49 publications

(28 citation statements)

References 28 publications

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“…In [42], a hybrid optimization based on GA and PSO was utilized to search for the optimal value of initial weights and thresholds for BPNN in order to improve the prediction accuracy. Moreover, traffic flow speed, even weather and workday/holiday conditions are also taken into consideration for training the multi-source power demand BPNN prediction models.…”

Section:  Prediction Model Distortionmentioning

confidence: 99%

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Zhou

Ravey

Péra

2019

Journal of Power Sources

189

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Driving prediction techniques (DPTs) are used to forecast the distributions of various future driving conditions (FDC), like velocity, acceleration, driver behaviors etc. and the quality of prediction results has great impacts on the performance of corresponding predictive energy management strategies (PEMSs), e.g., fuel economy (FE), lifetime of battery etc. This survey presents a comprehensive study on existing DPTs. Firstly, a review on prediction objectives and major types of prediction algorithms are presented. Then a comparative study on various prediction approaches is carried out and suitable application scenarios for each approach are provided according to their characteristics. Moreover, prediction accuracy-affecting factors are analyzed and corresponding approaches for dealing with mispredictions are discussed in detail. Finally, the bottlenecks of current researches and future developing trends of DPTs are given. In general, this paper not only gives a comprehensive analysis and review of existing DPTs but also indicates suitable application scenarios for each prediction algorithm and summarizes potential approaches for handling the prediction inaccuracies, which will help prospective designers to select proper DPTs according to different applications and contribute to the further performance enhancements of PEMSs for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs).

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Section:  Prediction Model Distortionmentioning

confidence: 99%

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Zhou

Ravey

Péra

2019

Journal of Power Sources

189

View full text Add to dashboard Cite

show abstract

“…To improve the prediction accuracy of the prediction model, the initial weights and thresholds can be optimized by some intelligent optimization algorithm. In [95], the focus was to provide an on-line energy management control strategy based on trip condition prediction to minimize fuel consumption for PHEVs. It predicts the vehicle speeds on-line by establishing the trip condition prediction model based on GA/PSOA-BPNN.…”

Section: Neural Network-dynamic Programming (Nn-dp)mentioning

confidence: 99%

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Kong

Liang

et al. 2019

Applied Sciences

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This paper presents a comprehensive review of energy management control strategies utilized in hybrid electric vehicles (HEVs). These can be categorized as rule-based strategies and optimization-based strategies. Rule-based strategies, as the most basic strategy, are widely used due to their simplicity and practical application. The focus of rule-based strategies is to determine and optimize the optimal threshold for mode switching; however, they fall into a local optimal solutions. To have better performance in energy management, optimization-based strategies were developed. The categories of the existing optimization-based strategies are identified from the latest literature, and a brief study of each strategy is discussed, which consists of the main research ideas, the research focus, advantages, disadvantages and improvements to ameliorate optimality and real-time performance. Deterministic dynamic programming strategy is regarded as a benchmark. Based on neural network and the large data processing technology, data-driven strategies are put forward due to their approximate optimality and high computational efficiency. Finally, the comprehensive performance of each control strategy is analyzed with respect to five aspects. This paper not only provides a comprehensive analysis of energy management control strategies for HEVs, but also presents the emphasis in the future.

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“…Alternative EMS approaches that exploit historic data to generate predictions include stochastic dynamic programming (SDP) based on stationary Markov chains [8]- [11], equivalent consumption minimization strategy (ECMS) [12] approaches based on data representations as normal distributions [13] and Markov chains [14], DP methods based on autoregressive integrated moving average (ARIMA) models [15], neural networks (NN) [16], [17], and databases storing recorded velocity profiles between stops [18]. However, position-independent data representations as in [8], [14]- [17] cannot offer any accurate predictions if the route consists of sections that may vary due to topographical characteristics, certain emissions regulations of city areas, speed limits, etc.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Graph Construction Algorithm for Probabilistic Predictions in Vehicular Applications

Ritter

Widmer

Niam

et al. 2021

IEEE Trans. Veh. Technol.

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Reducing the energy consumption of vehicles is one of the greatest challenges we are facing in the mobility sector. A major step in this direction has been taken with the introduction of hybrid electric vehicles. Their performance, however, depends strongly on the energy management strategy used, which exploits the additional degree of freedom of the propulsion system and is inevitably limited by the lack of knowledge about the exact future driving conditions. Various attempts are being made to offer predictions, one of which is to exploit recorded travel data. In this paper, we propose an incremental graph construction algorithm that encapsulates this data in a digital representation of the road network and captures the actual travel routes of the vehicle along with the sequences of the specified measurement signals. The algorithm processes each location estimate separately, together with any desired simultaneously recorded measurement signal such as the vehicle speed, and constructs a directed graph in whose vertices the measurement data is stored. The real-time capability of this algorithm allows an up-to-date representation of both the road network and the signals it contains at all times. Whenever the vehicle is driving on an already visited route, we can obtain distance-resolved predictions by traversing the graph in the direction of travel and querying the stored measurement data. We present two techniques to efficiently store and predict this data, i.e., by using frequentist prediction intervals and Gaussian process regression. Our algorithm runs in real time and without any manual initialization, pre-, or post-processing. Verifications both during real operation on a trolley bus in public transportation and by simulation on a publicly available dataset demonstrate that the algorithm is real-time capable, that it consistently captures and predicts the recorded signals, and that it works in practice.

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An On-Line Energy Management Strategy Based on Trip Condition Prediction for Commuter Plug-In Hybrid Electric Vehicles

Cited by 49 publications

References 28 publications

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

A survey on driving prediction techniques for predictive energy management of plug-in hybrid electric vehicles

Towards a Smarter Energy Management System for Hybrid Vehicles: A Comprehensive Review of Control Strategies

Real-Time Graph Construction Algorithm for Probabilistic Predictions in Vehicular Applications

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