Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles

Liu, Hui; Li, Xunming; Wang, Weida; Han, Lijin; Chen, Xiang

doi:10.1016/j.energy.2018.03.148

Cited by 51 publications

(27 citation statements)

References 37 publications

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“…For the frequencies vector the result is the vector, with a length "xTL" expressed by equation (10) that contains all the sinusoid frequencies in descending order without any repetition. Moreover aj and φj vectors were joined in the Axk and Φxk matrix respectively and so the arithmetic average value of the rows was calculated for each matrix using the equation (11) and (12).…”

Section: Application Of Inverse Fast Fourier Transformmentioning

confidence: 99%

“…Regardless of the type of management, it is agreed that in order to achieve optimal management of the powertrain, it is necessary to have the entire driving cycle that the car has to drive through [7][8] along with a forecast of energy expenditure. Several methods have been proposed for the prediction of the driving cycle considering sometimes a higher number and sometimes a lower number of characteristic parameters of a driving cycle [9], data then processed through the use of neural networks, Markov Chain, Exponentially Decreasing Model (EDM) and others [10][11][12]. This study proposes to assign a "speed-time" curve not to a specific route but to a specific traffic condition that is generally shown by Global Positioning System (GPS) and Intelligent Transportation System (ITS) software.…”

Section: Introductionmentioning

confidence: 99%

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Passenger Car Energy Demand Assessment: a New Approach Based on Road Traffic Data

2020

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Nowadays the automotive market is oriented to the production of hybrid or electric propulsion vehicle equipped with Energy Management System that aims to minimize the consumption of fossil fuel. The EMS, generally, performs a local and not global optimization of energy management due to the impossibility of predicting the user’s energy demand and driving conditions. The aim of this research is to define a driving cycle (speed time) knowing only the starting and the arrival point defined by the driver, considering satellite data and previous experiences. To achieve this goal, the data relating to the energy expenditure of a car (e.g. speed, acceleration, road inclination) will be acquired, using on-board acquisition system, during road sections in the city of Messina. At the same time, the traffic level counterplot and others information provided, for these specific sections, from GPS acquisition software will be collected. On-board and GPS data will be compared and, after considering an adequate number of acquisitions, each value of the traffic level will be associated with a driving cycle obtained by processing the acquired data. After that, the numerical model of a car will be created which will be used to compare the energy demand of two driving cycles. The first one acquired on a section with a random starting and destination point inside the historic city centre of Messina. The second is the one assigned, for that same section, considering only the value of the traffic level counterplot.

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Section: Application Of Inverse Fast Fourier Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Passenger Car Energy Demand Assessment: a New Approach Based on Road Traffic Data

2020

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“…Ref. [22] designed a highorder Markov velocity predictor combined with a linear programming algorithm, and the speed prediction accuracy was significantly improved compared with the firstorder Markov. Although the predicting vehicle speed within the training speed can ensure high accuracy, the predicted vehicle speed outside the training speed cannot be followed well.…”

Section: Introductionmentioning

confidence: 99%

Combined Prediction for Vehicle Speed with Fixed Route

Zhang

Liu

2020

Chin. J. Mech. Eng.

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Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles. Nowadays, people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning, but the prediction accuracy still needs to be improved. The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy; problems, such as over fitting, occur in the process of improving prediction accuracy. The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction. By combining the two prediction algorithms, the fusion of prediction performance is achieved, the limit of the single prediction performance is crossed, and the goal of improving vehicle speed prediction performance is achieved. In this paper, an extraction method suitable for fixed route vehicle speed is designed. The application of Markov and back propagation (BP) neural network in predictions is introduced. Three new combined prediction methods, all named Markov and BP Neural Network (MBNN) combined prediction algorithm, are proposed, which make full use of the advantages of Markov and BP neural network algorithms. Finally, the comparison among the prediction methods has been carried out. The results show that the three MBNN models have improved by about 19%, 28%, and 29% compared with the Markov prediction model, which has better performance in the single prediction models. Overall, the MBNN combined prediction models can improve the prediction accuracy by 25.3% on average, which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

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“…Parameter optimization of PHEVs is related to the energy management strategy, and energy management strategies need to be developed before parameter optimization. At present, the research on the energy management strategy for PHEVs mainly focuses on the development of the advanced optimization algorithm, such as the algorithm based on the minimum equivalent fuel consumption [26][27][28][29], the dynamic programming algorithm [30][31][32], stochastic dynamic programming [33], the algorithm based on convex optimization [2,34] and the model predictive control algorithm [35][36][37][38]. Although the above-mentioned optimization algorithm can obtain the local or global optimum, it is difficult to apply to real vehicle control for hardly knowing the driving cycles beforehand or the large amount of calculation.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain

et al. 2019

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This paper focuses on the parameter optimization for the CVT (a continuously variable transmission) based plug-in 4WD (4-wheel drive) hybrid electric vehicle powertrain. First, the plug-in 4WD hybrid electric vehicle (plug-in 4WD HEV)’s energy management strategy based on the CD (charge depleting) and CS (charge sustain) mode is developed. Then, the multi-objective optimization’s mathematical model, which aims at minimizing the electric energy consumption under the CD stage, the fuel consumption under the CS stage and the acceleration time from 0–120 km/h, is established. Finally, the multi-objective parameter optimization problem is solved using an evolutionary based non-dominated sorting genetic algorithms-II (NSGA-II) approach. Some of the results are compared with the original scheme and the classical weight approach. Compared with the original scheme, the best compromise solution (i.e., electric energy consumption, fuel consumption and acceleration time) obtained using the NSGA-II approach are reduced by 1.21%, 6.18% and 5.49%, respectively. Compared with the weight approach, the Pareto optimal solutions obtained using NSGA-II approach are better distributed over the entire Pareto optimal front, as well as the best compromise solution is also better.

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Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles

Cited by 51 publications

References 37 publications

Passenger Car Energy Demand Assessment: a New Approach Based on Road Traffic Data

Passenger Car Energy Demand Assessment: a New Approach Based on Road Traffic Data

Combined Prediction for Vehicle Speed with Fixed Route

Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain

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