Driving cycles construction for electric vehicles considering road environment: A case study in Beijing

Zhang, Jin; Wang, Zhenpo; Liu, Peng; Zhang, Zhaosheng; Li, Xiaoyu; Qu, Changhui

doi:10.1016/j.apenergy.2019.113514

Cited by 40 publications

(28 citation statements)

References 33 publications

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“…Moreover, for vehicle dynamics, the Markov property has been validated [30] and Markov chains are applied to model driving cycles on empirical driving patterns [31,32]. However, using Markov chains for modeling driving patterns requires a fine temporal data resolution of speed and acceleration values.…”

Section: Impact Of Uncertain Energy Consumption and Available Charging Timesmentioning

confidence: 99%

Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets

Schücking

Jochem

2021

Applied Energy

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The possibility of electric vehicles to technically replace internal combustion engine vehicles and to deliver economic benefits mainly depends on the battery and the charging infrastructure as well as on annual mileage (utilizing the lower variable costs of electric vehicles). Current studies on electric vehicles' total cost of ownership often neglect two important factors that influence the investment decision and operational costs: firstly, the trade-off between battery and charging capacity; secondly the uncertainty in energy consumption. This paper proposes a two-stage stochastic program that minimizes the total cost of ownership of a commercial electric vehicle under uncertain energy consumption and available charging times induced by mobility patterns and outside temperature. The optimization program is solved by sample average approximation based on mobility and temperature scenarios. A hidden Markov model is introduced to predict mobility demand scenarios. Three scenario reduction heuristics are applied to reduce computational effort while keeping a high-quality approximation. The proposed framework is tested in a case study of the home nursing service. The results show the large influence of the uncertain mobility patterns on the optimal solution. In the case study, the total cost of ownership can be reduced by up to 3.9% by including the trade-off between battery and charging capacity. The introduction of variable energy prices can lower energy costs by 31.6% but does not influence the investment decision in this case study. Overall, this study provides valuable insights for real applications to determine the techno-economic optimal electric vehicle and charging infrastructure configuration.

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Section: Impact Of Uncertain Energy Consumption and Available Charging Timesmentioning

confidence: 99%

Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets

Schücking

Jochem

2021

Applied Energy

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“…First, the position x i and velocity v i of the particle i are initialized randomly as follows: The data set Y are clustered into k classes according to the Euclidean distance from each data y l to each cluster center c j of the particle i. The sum of distances is calculated to evaluate the fitness value of the particle i, which is shown in (13).…”

Section: Proposed K-mpso Clustering Algorithmmentioning

confidence: 99%

“…A common approach to constructing driving cycles is the Micro-trips method [13]. However, the k-means clustering analysis used in the Micro-trips method is easily trapped in local optima when selecting the clustering centers.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Zhong

et al. 2020

IEEE Access

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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.

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“…They used k-means clustering to group the kinematic fragments. In the [23] the authors present three methods to develop the driving cycle of Beijing based on the data of 40 electric taxis. These are the Markov Monte Carlo method, micro-trip (Random) method and the micro-trip (Sequence) method.…”

Section: Driving Cycles Representative For Special Purpose Vehiclesmentioning

confidence: 99%

Review on Construction Procedures of Driving Cycles

Czégé,

Vámosi,

Kocsis

2020

IJEMS

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The goal of this paper is to give an overview of the literature of construction techniques of driving cycles. Our motivation for the overview is the future goal of constructing our own driving cycles for various types of vehicles and routes. This activity is part of a larger project focusing on determination of fuel and energy consumption by dynamic simulation of vehicles. Accordingly, the papers dealing with sample route determination, data collection and processing, driving cycle construction procedures, statistical evaluation of data are in our focus.

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Driving cycles construction for electric vehicles considering road environment: A case study in Beijing

Cited by 40 publications

References 33 publications

Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets

Two-stage stochastic program optimizing the cost of electric vehicles in commercial fleets

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

Review on Construction Procedures of Driving Cycles

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