Estimating of energy consumption of electric vehicles under different road characteristics: a case study for Nanjing, China

Pang, Qianqian; Chen, Shuyan; Ma, Yongfeng; Qiao, Fengxiang; Zhang, Zhiyu; Liu, Hongru

doi:10.1080/15435075.2024.2319287

Cited by 1 publication

(2 citation statements)

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“…However, when the values of battery power and instantaneous fuel consumption of the engine were higher, GRU's predictions deviated significantly, while LSTM's predictions deviated slightly less. In order to compare the accuracy of the energy consumption of different models, the difference between the predicted and measured fuel consumption of the vehicle per 100 km is usually expressed as shown in Equation (11):…”

Section: Test Resultsmentioning

confidence: 99%

“…This method is suitable for estimating the overall energy consumption of the traffic flow, but it cannot predict the energy consumption of individual vehicles in detail or study the instantaneous changes in vehicle energy consumption. Other factors that influence the energy consumption of pure electric vehicles include trip conditions, weather, driving behavior, and road characteristics [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network

Zhang,

Chen,

Wang

et al. 2024

Energies

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In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model’s prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions.

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Section: Test Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%