RFID Data-Driven Vehicle Speed Prediction Using Adaptive Kalman Filter

Feng, Ao; Qian, Liping; Huang, Yupin

doi:10.1007/978-3-030-00557-3_5

Cited by 1 publication

(1 citation statement)

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“…A precise reliable vehicle speed prediction can pave the solid way and provide useful instructions for satisfactory energy management of PHEVs. Currently, a variety of algorithms have been spurred to achieve speed prediction, including Kalman filter [10], exponential method [11], autoregressive moving average (ARMA) methods [12], particle filter [13], stochastic forecast [14], and machine learning algorithms [15]. Amongst them, Markov chain (MC) based prediction algorithms and neural networks (NNs), belonging to stochastic forest and machine learning filed, are most attractive and widely exploited [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of vehicle driving conditions with incorporation of stochastic forecasting and machine learning and a case study in energy management of plug-in hybrid electric vehicles

Liu

Gao

et al. 2021

Mechanical Systems and Signal Processing

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Prediction of short-term future driving conditions can contribute to energy management of plug-in hybrid electric vehicles and subsequent improvement of their fuel economy. In this study, a fused short-term forecasting model for driving conditions is established by incorporating the stochastic forecasting and machine learning. The Markov chain is applied to calculate the transition probability of historical driving data, by which the stochastic prediction is conducted based on the Monte Carlo algorithm. Then, a neural network is employed to learn the current driving information and main knowledge after the simplified correlation of characteristic parameters, and meanwhile the genetic algorithm is adopted to optimize the initial weight and thresholds of networks. Finally, the short-term velocity prediction is achieved by combining them, and the overall performance is evaluated by four typical criteria. Simulation results indicate that the proposed fusion algorithm outperforms the single Markov model, the radial basis function neural network and the back propagation neural network with respect to the prediction precision and the difference distribution between expectation and prediction values. In addition, a case study is conducted by applying the built prediction algorithm in energy management of a plug-in hybrid electric vehicle, and simulation results highlight that the proposed algorithm can supply preferable velocity prediction, thereby facilitating improvement of the operating economy of the vehicle.

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