Driving cycle prediction based on Markov chain combined with driving information mining

Abstract

Driving cycle prediction (DCP) is of great importance for vehicle’s awareness of surrounding environment and optimization of control strategy. It has won much attention around the world. However, the used driving database could not reflecting the great diversity in multi driving cycles in real world. The characteristic parameters could not represent the complex and diverse driving conditions. Therefore, DCP models’ adaptability and generalization ability are limited. Moreover, prediction methods in existing researches always take large computation burden, difficult to apply in practice. To tackle these issues, the present research focus on DCP based on Markov chain combined with driving information mining (DIM). Firstly, many standard test cycles and real cycles are collected to construct an database of great diversity. Secondly, DIM technologies are studied to extract the optimal parameter set reflecting driving characteristics and determine the driving cycle categories. Thirdly, prediction method based on recursive self-learning Markov chain is proposed. Recursive equation of Markov state transition probability matrix (TPM) is deduced to save computation time. The key parameter of prediction method is adjusted to achieve optimal prediction performance. Synthesized standard test driving cycles (SSTDC) and real driving cycles (RDC) in database, as well as a new real driving cycle (NRDC) are tested. Driving pattern prediction accuracy of the proposed method is 99.25% for SSTDC, 100.00% for RDC, and 99.84% for NRDC, respectively. Compared with the benchmark method, the prediction accuracy is improved by 18.87% for SSTDC, 10.07% for RDC and 23.51% for NRDC, respectively. In addition, processing time of the prediction program is significantly reduced by 2.23 s (36.14%) for SSTDC, 12.30 s (51.25%) for RDC and 3.99 s (52.16%) for NRDC, respectively. It can be verified that the proposed method has high precision, short response time, as well as good adaptability and charming generalization ability for new cycles.