A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle

Shi, Qin; Liu, Bingjiao; Guan, Qingsheng; He, Lei; Qiu, Duoyang

doi:10.1177/1687814019901054

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…In light of this, an appropriate number of characteristic parameters should be selected. Existing studies that have found that the appropriate number of driving cycle characteristic parameters is approximately 14 include [49] (Shi et al, 12 characteristic parameters), [18] (Kayma,et al,14 characteristic parameters), and [50] (Wang, et al, 14 characteristic parameters). However, the dimensionality of the 14 characteristic parameters is still high, and problems such as low computational efficiency and clustering difficulties exist in subsequent processing.…”

Section: Driving Cycle Processingmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

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Section: Driving Cycle Processingmentioning

confidence: 99%

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Wang,

Hao,

et al. 2023

Sensors

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“…The determination of cycle duration is an essential part of the construction driving cycles. Durations of cycles are different, for instance, the total duration of NEDC, WLTC, and CLTC-P are 1180s, 1800s, and 1800s, respectively, and the non-legislative cycles are mostly between 900-1800s [14], [23], [26]. The duration was determined to be 1200s for Xi'an BEVs urban cycle (XBUC).…”

Section: Determine the Duration Of Each Categorymentioning

confidence: 99%

“…Ample research has been conducted on developing non-legislative cycles in various regions for different aims. For instance, cycles of Dublin [4], Istanbul [12], Tianjin [13], Florence [5], and Hefei [14] were established to study energy consumption and driving range. Cycles of Mexico [15], Hanoi [16], Aleppo [17], and Taipei [18] were mainly used to research emissions.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhao

et al. 2021

IEEE Access

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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.INDEX TERMS Urban driving cycle, battery electric vehicles, random forest, kernel principal component analysis.

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“…The idling state, acceleration state, deceleration state, and cruising state are defined as follows 28 :…”

Section: Algorithm Designmentioning

confidence: 99%

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

Liu

Shi

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper proposes an adaptive control strategy of fuel consumption optimization for hybrid electric vehicles (HEVs). The strategy combines a moving-horizon-based nonlinear autoregressive (NAR) algorithm, a backpropagation (BP) neural network algorithm, and an equivalent consumption minimization strategy (ECMS) method to reduce energy consumption. The moving-horizon-based NAR algorithm is applied to predict the short future driving cycle. The BP neural network algorithm is employed to recognize the driving cycle types, which provides the basis for the adaptive ECMS. Based on the abovementioned approach, the power split of the fuel and electric system is determined in advance, and the optimal control of energy efficiency is achieved. A driving experiment platform is established, taking a synthetic driving cycle composed of several standard driving cycles as the target cycle, and the control strategy is tested by the driver’s real operation. The results indicate that, compared with the basic ECMS, the A-ECMS with moving-horizon-based driving cycle prediction and recognition has better SOC (state of charge) retention and reduces the fuel consumption of the engine by 3.31%, the equivalent fuel consumption of the electric system by 0.9 L/100 km and the total energy consumption by 1 L/100 km. Adaptive ECMS based on driving cycle prediction and recognition is an effective method for the energy management of HEVs.

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A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle

Cited by 7 publications

References 20 publications

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information

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

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

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