A Probabilistic Methodology to Quantify the Impacts of Cold Weather on Electric Vehicle Demand: A Case Study in the U.K.

Koncar, Ilija; Bayram, İslam Şafak

doi:10.1109/access.2021.3090534

Cited by 16 publications

(2 citation statements)

References 25 publications

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“…A probabilistic simulation study is presented in [219] to quantify the impacts of low temperatures charging in the UK. Different simulation scenarios are created based on different EV penetration levels, ambient temperature, and battery charge-discharge cycles using travel surveys in the UK.…”

Section: Increased Peak Demandmentioning

confidence: 99%

Electric Vehicles Under Low Temperatures: A Review on Battery Performance, Charging Needs, and Power Grid Impacts

et al. 2023

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Electric vehicles (EVs) are gaining mainstream adoption as more countries introduce netzero carbon targets for the near future. Lithium-ion (Li-ion) batteries, the most commonly used energy storage technology in EVs, are temperature sensitive, and their performance degradates at low operating temperatures due to increased internal resistance. The existing literature on EV-power grid studies assumes that EVs are used under "perfect temperatures" (e.g. 21 Celsius) and temperature-related issues are ignored. In addition, most of the countries/regions with high EV penetration (e.g. Norway, Canada, northern parts of the US and China, etc.) experience harsh cold months, making it extremely critical to understand EV performance and consequently their impacts on the electrical power networks. In this paper, we present a systematic review of the literature that considers the combined investigation of Li-ion battery technology and power networks, with a focus on their operation under suboptimal weather conditions. More specifically, we review: (i) the impact of low temperatures on the electrochemical performance of EV batteries in parking, charging and driving modes, (ii) the challenges experienced by EVs during charging and associated performance degradation, and (iii) the additional impacts of EV charging on the power networks. Our analysis shows that there are serious research gaps in literature and industry applications, which may hinder mass EV adoption and cause delays in charging station roll-out.INDEX TERMS Electric vehicles, Li-ion battery, low temperatures, power grid impacts, power quality. Nomenclature

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Section: Increased Peak Demandmentioning

confidence: 99%

Electric Vehicles Under Low Temperatures: A Review on Battery Performance, Charging Needs, and Power Grid Impacts

et al. 2023

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“…As mentioned above, external factors have an influence on the future usage conditions of EV stations. To integrate the information of a variety of external inputs, such as surrounding POIs [ 39 ] and weather conditions [ 40 ], previous studies have demonstrated great efforts, leveraging multi-source data to specifically design the model structure. In [ 41 ], the authors proposed an LSTM-based structure integrating an encoder to aggregate external information and treat multi-source data as the sequential inputs.…”

Section: Related Researchmentioning

confidence: 99%

AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting

Luo

Song

Huang

et al. 2023

Sensors

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Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With accurate EV station availability prediction, suitable charging behaviors can be scheduled in advance to relieve range anxiety. Many existing deep learning methods have been proposed to address this issue; however, due to the complex road network structure and complex external factors, such as points of interest (POIs) and weather effects, many commonly used algorithms can only extract the historical usage information and do not consider the comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatiotemporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both the external and internal spatiotemporal dependence of relevant transportation data. The external factors are modeled as dynamic attributes by the attributeaugmented encoder for training. The AST-GIN model was tested on the data collected in Dundee City, and the experimental results showed the effectiveness of our model considering external factors’ influence on various horizon settings compared with other baselines.

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Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach

Yang

Peng

Wang

et al. 2023

Energy

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A Probabilistic Methodology to Quantify the Impacts of Cold Weather on Electric Vehicle Demand: A Case Study in the U.K.

Cited by 16 publications

References 25 publications

Electric Vehicles Under Low Temperatures: A Review on Battery Performance, Charging Needs, and Power Grid Impacts

Electric Vehicles Under Low Temperatures: A Review on Battery Performance, Charging Needs, and Power Grid Impacts

AST-GIN: Attribute-Augmented Spatiotemporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting

Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach

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