Abstract
Great concerns have been raised on the driving cycle due to its critical importance in vehicle design, energy management strategy, and energy consumption forecast of new energy vehicles. Taking Xi'an city as a case, a novel method of driving cycle development for battery electric vehicles is proposed in this paper. First, the chase car method and on-board measurement method are combined to collect sufficient real driving data, which are randomly divided into two parts for developing and validating the target cycle. Then the nonlinear dimension reduction of characteristic parameters with respect to the micro-trips is achieved by employing kernel principal component analysis, and an improved clustering method is developed for constructing candidate cycles, in which the K-means clustering algorithm is applied in the training of random forest. The target cycle is selected from the candidate cycles by determining the assessment criteria with consideration of the characteristic parameters and the speed-acceleration distribution probability. Finally, a comparative study of different methods is implemented to illustrate the effectiveness of the proposed method. The typicality of the target cycle is revealed by analyzing the discrepancies between the target cycle and other legislative cycles.
Highlights
Owing to the advantages of environmental friendliness, simple structure, and high energy conversion efficiency, new energy vehicles have great development prospects with strong policy support [1], [2]
Quantitative analyses for parameters show that operating characteristics of Xi’an BEVs urban cycle (XBUC) are lower speed, more violent acceleration and deceleration, and higher positive kinetic energy (PKE) and relative positive acceleration (RPA), From Fig. 10(a), the minimum values of speed are equal to 0 for all cycles
Based on kernel principal component analysis and random forest algorithm, we proposed a novel method to develop an urban driving cycle for battery electric vehicles, which provides an accurate input for estimating energy consumption and driving range
Summary
Owing to the advantages of environmental friendliness, simple structure, and high energy conversion efficiency, new energy vehicles have great development prospects with strong policy support [1], [2]. China has been adopting NEDC as the standard cycle for vehicle design and test, but NEDC cannot represent the real driving behavior of the vehicle in China since the test procedure of NEDC is based on the actual road operation in Europe. The micro-trip-based method divides the driving data into many micro-trips, the specified duration or mileage driving cycles are obtained by connecting several micro-trips according to different criteria. Markov analysis-based method divides the driving data into kinematic fragments and develops the cycles based on Markov analysis, which is increasingly used in combination with Monte-Carlo simulation [15], [22]. The PCA and K-means algorithm is effective, only one candidate cycle can be obtained from the same original driving data.
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