A Novel k-MPSO Clustering Algorithm for the Construction of Typical Driving Cycles

Abstract

The practical driving cycle is of great significance in studying the control strategy of vehicles, and effective clustering of micro-trips is the key to obtaining the typical driving cycle. A novel and efficient method for constructing typical driving cycles is presented in this paper. First, by combining the preying behavior and random behavior of the artificial fish swarm algorithm (AFSA) with particle swarm optimization (PSO), a modified particle swarm optimization (MPSO) is proposed. By comparing the means and standard deviations of the optima, MPSO is verified as much more accurate and stable than PSO, AFSA, select particle swarm optimization (SPSO) and cross particle swarm optimization (CPSO) in the optimization calculation of four typical multi-modal benchmark functions. Second, by applying MPSO to optimize the k-means algorithm, the k-MPSO clustering algorithm is obtained. In the case of clustering the Iris standard data set, the average error rates of the k-means algorithm and k-MPSO clustering algorithm are 11.6% and 7.8%, respectively, which means that the k-MPSO clustering algorithm has a stronger searching ability. Finally, with the ECAN Tools software, real-world driving data that include thousands of micro-trips in Jinan are collected, and 19 representative characteristic parameters are selected to fully describe the driving conditions. After principal component analysis (PCA), the k-MPSO clustering algorithm method is applied to cluster the micro-trips into three classes and construct the typical driving cycle in Jinan. INDEX TERMS Artificial fish swarm algorithm, driving cycle, k-MPSO clustering algorithm, particle swarm optimization.

Parameter identification of direct‐drive permanent magnet synchronous generator based on EDMPSO‐EKF

“…The average best position PSO (MPSO) algorithm was proposed in [30], the PSO was added the average amount of the individual best position $$P_{best - md}$$: $$\begin{equation}P_{best\hbox{-}md} = \frac{1}{m}\sum_{i = 1}^m P_{best\hbox{-}id} \end{equation}$$…”

“…The average best position PSO (MPSO) algorithm was proposed in [30], the PSO was added the average amount of the individual best position P best −md :…”

Dynamic Modified Chaotic Particle Swarm Optimization for Radar Signal Sorting

“…In the literature [15] the clustering centroid is improved by changing the fitness function. In the literature [16], the inertia weight setting and updating strategy are improved to enhance the ability of local search and global search. However, they are not fully adaptive and the clustering results do not well meet the requirements of radar system for signal sorting accuracy and real-time performance.…”

Development of a Typical Urban Driving Cycle for Battery Electric Vehicles Based on Kernel Principal Component Analysis and Random Forest

“…Although the PCA and K-means algorithm is effective, only one candidate cycle can be obtained from the same original driving data. Besides, PCA can only extract linear features of characteristics, and K-means is too sensitive to the initial cluster center to achieve global optimization [24].…”