Charge Events Research Articles

Hydrocarbon migration and accumulation in buried hills based on quantitative fluorescence techniques, fluid inclusion, and 1D basin modeling: Shulu sag, Bohai Bay basin, NE China

Interpreting the hydrocarbon generation, migration, and accumulation process is significant for understanding hydrocarbon evolution and finding exploration targets. The buried hills of Shulu Sag exhibit great potential for hydrocarbon exploration. However, the variations observed in different oil reservoirs highlight the complexity of hydrocarbon migration and accumulation in this region. In this study, 1D basin modeling, fluid inclusion analysis, and quantitative fluorescence techniques were used to investigate the thermal maturation history of the source rock, the timing of hydrocarbon charging, the pathway of hydrocarbon migration, and the oil-water contact. Hydrocarbons in the buried hills in the Western Slope and Paleo-uplift are derived from the source rocks at the edge and center of the sag, respectively, which have different thermal maturation histories. The source rocks in the center of the sag mainly supply hydrocarbons to the Paleo-uplift through faults, while the hydrocarbons generated from the source rocks in the edge of the sag can be rapidly transported to the Western Slope through clast-supported terrigenous carbonate rudstone. Differences in oil sources result in different hydrocarbon charging histories for the Western Slope and Paleo-uplift. The Western Slope experienced a single low-mature oil charging event at 5-0 Ma, while the Paleo-uplift encountered two hydrocarbon charging events, including low-mature oil charging at 35-25 Ma and high-mature oil charging at 5-0 Ma. As a result of secondary changes controlled by the hydrological environment, the oil reservoirs in the Western Slope have undergone varying degrees of adjustment or even destruction, resulting in variations in the oil quality of different buried hills. The results provide a detailed understanding of hydrocarbon migration and accumulation in the study area, which can be used as a reference for hydrocarbon exploration.

Modeling the Impact of Microstructure Variation on Charging Capability in Lithium-Ion Batteries

Battery models can be used to predict conditions ripe for lithium metal plating in the anode during rapid charging. One common procedure is to simulate a rapid charge using a pseudo-two-dimensional (P2D) model and determine if and when the minimum local potential reaches 0.0 V with respect to metallic lithium in the anode. This normally occurs at higher states of charge during a DC fast charging or rapid regenerative charging event. However, we have recently determined that three-dimensional (3D) models, while broadly agreeing with P2D results, demonstrate much variation in the in-plane and through plane direction, which P2D models cannot resolve. Because P2D models employ effective medium approximations, any “local” value in the P2D space along the through-plane coordinate represents an average across a slice of the domain. Therefore, it may be insufficient to use minimum anode potentials from P2D models as indicators of local plating and may only be representative of global lithium plating onset.The results of rapid-charge 3D microstructure simulations are compared to P2D results. This allows us to verify whether rapid charging induces negative local potentials at the anode active material surface that are left undetected in a P2D model. The expected result implies that the 3D microstructure model is necessary to predict the onset of lithium plating. We then extend our previous study to look at the impact of design changes on the microstructure performance, and then link the microstructure performance changes to performance metrics at the battery cell, module, pack, and vehicle level. Finally, the impact of variation in the microstructure domain considering particle size variation is quantified to understand tolerances that may be necessary when developing charging schemes.

Bill It Right: Evaluating Public Charging Station Usage Behavior under the Presence of Different Pricing Policies

This study investigates for the first time how public charging infrastructure usage differs under the presence of diverse pricing models. About 3 million charging events from different European countries were classified according to five different pricing models (cost-free, flat-rate, time-based, energy-based, and mixed) and evaluated using various performance indicators such as connection duration; transferred energy volumes; average power; achievable revenue; and the share of charging and idle time for AC, DC, and HPC charging infrastructure. The study results show that the performance indicators differed for the classified pricing models. In addition to the quantitative comparison of the performance indicators, a Kruskal–Wallis one-way analysis of variance and a pairwise comparison using the Mann–Whitney-U test were used to show that the data distributions of the defined pricing models were statistically significantly different. The results are discussed from various perspectives on the efficient design of public charging infrastructure. The results show that time-based pricing models can improve the availability of public charging infrastructure, as the connection duration per charging event can be roughly halved compared to other pricing models. Flat-rate pricing models and AC charging infrastructure can support the temporal shift of charging events, such as shifting demand peaks, as charging events usually have several hours of idle time per charging process. By quantifying various performance indicators for different charging technologies and pricing models, the study is relevant for stakeholders involved in the development and operation of public charging infrastructure.

Designing MnO2@amorphous carbon core-shell structure as a cathode to enhance rate performance and cycling stability of aqueous zinc-ion batteries

Manganese dioxide has received significant attention as a prospective cathode material in aqueous zinc-ion batteries (ZIBs) owing to its elevated working voltage and commendable energy density. Nevertheless, challenges such as structural instability, manganese dissolution, and poor electrical conductivity remain significant barriers to the practical application of research. Here, the MnO2 nanorods encapsulated in the amorphous carbon (MnO2@C) with core-shell structure are prepared by a special hard template method, and investigated as cathode materials for ZIBs. The amorphous carbon, characterized by its structural defects and disorder, manifests heightened active site density, thereby establishing an efficient conduit for charge transport. Concurrently, the nano-dimensional interstitial spaces augment the active sites available for zinc insertion reactions, thereby effectively curtailing the ion diffusion path and, consequently, engendering heightened electrochemical performance. Moreover, the distinctive core-shell architecture of MnO2@C not only furnishes substantial spatial accommodation but also serves to alleviate volume alterations throughout charge and discharge events. Consequently, MnO2@C demonstrates outstanding electrochemical performance, with reversible capacities of 210 mAh·g−1 and 50 mAh·g−1 at 0.1 A·g−1 and 1.6 A·g−1, respectively. Furthermore, even after 600 cycles at 0.8 A·g−1, it maintains exceptional cycling performance, reaching 102 mAh·g−1, endowing MnO2@C cathodes with robust competitiveness in aqueous ZIBs.

Discriminating Majorana and Dirac heavy neutrinos at lepton colliders

In this paper we investigate how well the nature of heavy neutral leptons can be determined at a future lepton collider, after its potential discovery. Considered in a simplified model are prompt decays of the neutrino in the mass range from 100 GeV to 10 TeV. We study event selection and application of multivariate analyses to determine whether such a newly discovered particle is of the Dirac or Majorana nature. Combining lepton charge and kinematic event variables, we find that the nature of a heavy neutrino, whether it is a Dirac or a Majorana particle, can be determined at 95% C.L. almost in the whole discovery range. We will briefly speculate about other than the studied channels and the robustness of this statement in more general models of heavy neutral leptons, particularly on the complementarity of high-energy electron-positron vs. muon colliders on resolving the flavor structure of heavy neutrinos.

Linking Microstructure Modeling of Lithium-Ion Batteries during Rapid Charging to System Performance

Battery models can be used to predict conditions ripe for lithium metal plating in the anode during rapid charging. One common procedure is to simulate a rapid charge using a pseudo-two-dimensional (P2D) model and determine if and when the minimum local potential reaches 0.0 V with respect to metallic lithium in the anode. This normally occurs at higher states of charge during a DC fast charging or rapid regenerative charging event. However, we have recently determined that three-dimensional (3D) models, while broadly agreeing with P2D results, demonstrate much variation in the in-plane and through plane direction, which P2D models cannot resolve. Because P2D models employ effective medium approximations, any “local” value in the P2D space along the through-plane coordinate represents an average across a slice of the domain. Therefore, it may be insufficient to use minimum anode potentials from P2D models as indicators of local plating and may only be representative of global lithium plating onset.The results of rapid-charge 3D microstructure simulations are compared to P2D results [1,2]. This allows us to verify whether rapid charging induces negative local potentials at the anode active material surface that are left undetected in a P2D model. The expected result implies that the 3D microstructure model is necessary to predict the onset of lithium plating. We then extend our previous study to look at the impact of design changes on the microstructure performance, and then link the microstructure performance changes to performance metrics at the battery cell, module, pack, and vehicle level.References J. S. Lopata, T. R. Garrick, F. Wang, H. Zhang, Y. Zeng and S. Shimpalee, ECSarXiv (2022).J. S. Lopata, T. R. Garrick, F. Wang, H. Zhang, Y. Zeng and S. Shimpalee, J Electrochem Soc, 170, 020530 (2023).

Photon counting x-ray detectors as scatter probes.

Direct conversion x-ray Photon Counting Detectors (PCD) are posed to play a vital role in future medical imaging devices such as Computed Tomography (CT) scanners. PCD are expected to improve current CT technology on several fronts, such as resolution, dose utilization, and spectral performance. However, they are not readily expected to improve the handling of object scatter, one of the major sources of image artifacts in CT technology. We explore a potential method for obtaining in-situ object scatter estimation using the same PCD array used in the x-ray imaging system, such as in computed tomography. This unexpected benefit of using PCD has the potential to improve the image quality by providing better input into the scatter estimation and correction algorithms used in image reconstruction. In CT scanners the primary method for rejecting scatter signal originating from the scanned object relies on placing anti-scatter grids (ASG) close to the detector plane. This remains the case when transitioning to using PCD instead of energy integration detectors in CT. However, the combination of PCD and ASG opens a possibility to use some of the unique properties of PCD, namely, very low noise and coincidence counters to obtain, in addition to the attenuation data, a simultaneous and instantaneous estimate of the scatter signal reaching every detector element. When a small air gap is introduced between the ASG and the detector surface, the scatter radiation with large angular distribution has a greater probability of producing charge sharing events that can be detected by a coincidence counter. In this work we demonstrate the feasibility of such an approach in a tabletop experiment using PCD detector that lacks coincidence counting capability, instead we use the spectral signature of split charge events as proxy to coincidence counting. For this purpose, we first demonstrate the spectral impact of ASG misalignment using the same experimental setup. In addition, we perform a separate tabletop scattering experiment from a narrow column of water that demonstrates another potential use of the low noise capabilities of PCDs. We measured and quantified the high sensitivity of the spectral response to ASG alignment on the PCD detector pixel array, we found that the probability of energy misregistration of 60keV photons can increase by up to a factor of 3 when the ASG is poorly aligned. We then leveraged these results to obtain an estimate on the expected increase in coincidence counts for a wide range of scatter-to-primary (SPR) ratio and find a good match with expectations from a geometric modeling of the system, where the expected increase in coincidences was of the order of the SPR. Finally, the low noise detector also allowed us to measure the real space scatter signal associated with the coherent molecular form factor of water, revealing the ring-shaped scatter signal with an energy dependent distribution that was well captured by calculation. The advent of PCD detectors and their imminent use in commercial CT scanners opens new and exciting possibilities for utilizing PCD detectors in unexpected ways. In this proof-of-concept study, we showed how charge sharing, a spectral information degrading effect, can instead be used to obtain in-situ scatter estimation. We also demonstrated the PCD ability to perform extremely sensitive measurements using affordable benchtop setup for investigations normally reserved for synchrotron facilities.

In-situ calcite U-Pb ages and absolute timing of oil charge events: A case study of ultra-deep carbonate reservoirs in the Shunbei oilfield, Tarim basin, Northwest China

To accurately determine the timing of hydrocarbon charging events in petroleum systems is important but often challenging. Traditional methods for establishing the timing of these events in tectonic complex basins frequently suffer from unreliability due to uncertain burial and/or thermal histories, as well as the poor identification of inclusion assemblage. Recently developed calcite U-Pb dating has been proven to be a valuable tool for precisely determining the timing of these events, thus enhancing our understanding of the charging histories. We integrated oil-inclusion fluorescence analysis with in-situ calcite U-Pb dating, focusing on calcite fabrics hosting primary oil inclusions, to quantitatively constrain the oil charging timing of the SHB no.1 and no.5 fault zones in the Shunbei oilfield. The fluorescence spectra of oil inclusions suggested the presence of up to three discrete charging events in these fault zones. Calcite U-Pb ages provide constraints on the timing of the first charging episode in the two fault zones, at approximately 478–469.2 Ma and ca. 447.4–424 Ma, respectively. Comparing the fluorescence spectra of reservoir oils and oil inclusions reveals that the primary charging episodes in the SHB no.1 and no.5 fault zones correspond to the latest and the earliest charging episodes, respectively. Furthermore, through a comparison of the equivalent reflectance of reservoir oils with the thermal maturation history of the Cambrian source rock, the former age can be estimated at ca. 120–0 Ma. Regions characterized by weaker late-stage fault activity exhibit a higher potential for hydrocarbon preservation in deeper reservoirs.

Life cycle assessment of electrochemical and mechanical energy storage systems

The effect of the co-location of electrochemical and kinetic energy storage on the cradle-to-gate impacts of the storage system was studied using LCA methodology. The storage system was intended for use in the frequency containment reserve (FCR) application, considering a number of daily charge–discharge cycles in the range of 50–1000. The results show that a significant environmental benefit (up to a 96% decrease in cradle-to-gate global warming potential, from 1.65 ± 0.12 to 0.059 ± 0.004 kg CO2-eq./kWh) can be obtained by the co-location of battery and flywheel storage systems, owing to the ability of the flywheel component to preserve battery lifetime by delivering the frequent charge events required in the FCR application. A moderate saving of 24% in global warming potential (from 1.65 ± 0.12 to 1.26 ± 0.11 kg CO2-eq./kWh) could already be achieved by switching from a battery to flywheel storage system with repeated charge–discharge cycling (200 or more charge events per day). This study highlights the need to consider the intensity of charge–discharge cycling when choosing an environmentally preferable storage technology as well as introducing a methodology for incorporating the number of daily cycles in the analysis.

3D Microstructure Modeling of Lithium Ion Batteries during Rapid Charging

Battery models can be used to predict conditions ripe for lithium metal plating in the anode during rapid charging. One common procedure is to simulate a rapid charge using a pseudo-two-dimensional (P2D) model and determine if and when the minimum local potential reaches 0.0 V with respect to metallic lithium in the anode. This normally occurs at higher states of charge during a DC fast charging or rapid regenerative charging event. However, we have recently determined that three-dimensional (3D) models, while broadly agreeing with P2D results, demonstrate much variation in the in-plane and through plane direction, which P2D models cannot resolve. Because P2D models employ effective medium approximations, any “local” value in the P2D space along the through-plane coordinate represents an average across a slice of the domain. Therefore, it may be insufficient to use minimum anode potentials from P2D models as indicators of local plating and may only be representative of global lithium plating onset.In this work, the results of rapid-charge 3D microstructure simulations are compared to P2D results. This allows us to verify whether rapid charging induces negative local potentials at the anode active material surface that are left undetected in a P2D model. The expected result implies that the 3D microstructure model is necessary to predict the onset of lithium plating.

Self-supervised online learning algorithm for electric vehicle charging station demand and event prediction

With the increasing popularity of electric vehicles (EVs), countries are setting up new charging stations to meet up the rising demand. Therefore, accurately forecasting charging demand and charging events is highly significant. Historical data are crucial for developing a quality forecasting model, but countries or locations with recently installed EV stations suffer from data inadequacy. Delayed data accumulation for forecasting model creation impedes EV's optimal operation, and an offline or fixed-sized data-based learning model may not perform optimally due to the future uncertainties of input variables. Therefore, it is required to create an online forecasting model that can learn right from the beginning of the operation of charging stations, forecast, and relearn, when necessary, by considering the impact of input/external variables. For optimal model development, impactful input variables should be chosen online using appropriate feature engineering. In this research, a unique feature engineering considering multi-level correlation with multicollinearity and simultaneous online learning General Regression Neural Network (GRNN) based on has been suggested. Also due to the discrete and asynchronous nature of the charging event a detailed data handling method has been developed to create meaningful time series data. It is interestingly realized that the proposed model outperforms general Artificial Neural Networks (ANN), various sophisticated models, such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bi-LSTM, Gated Recurrent Unit (GRU), and the Deep Neural Network (DNN) model when the appropriate inputs and their delayed variables are used.

In situ calcite U–Pb geochronology of carbonate and clastic sedimentary rocks from the Canning Basin, Western Australia

In sedimentary basins, the determination of the absolute timing of deposition and diagenetic events is a challenging yet critical parameter necessary in the reconstruction of paleo-fluid evolution and burial histories. Here we demonstrate the practical application of in situ calcite U–Pb geochronology on core samples from the Olympic 1 well in the Canning Basin of Western Australia. Using quantitative mineralogy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) analytical techniques, we obtained an authigenic calcite U–Pb age of 469.7 ± 4.3 Ma for limestone in the Samphire Marsh Member of the Lower Ordovician Nambeet Formation. This precise depositional age can be independently verified using zircon thermal ionisation mass spectrometry U–Pb ages of 479.4 to 470.2 Ma determined on adjacent volcanic ash beds. Further geochronology studies on calcite cements from the Lower Ordovician Fly Flat Member sandstone from the Nambeet Formation returned a U–Pb age of 365.3 ± 5.8 Ma. This is the first study to place absolute age constraints on the diagenetic event that occluded the intergranular space in a sandstone reservoir. The calcite cementation age suggests that impairment of reservoir quality in the Fly Flat Member sandstone occurred in the Late Devonian, much earlier than major petroleum charge events in the Canning Basin. The calcite U–Pb geochronometer, when combined with complementary quantitative mineralogical analysis, can build direct temporal constraints on the depositional and diagenetic processes in both carbonate and clastic sedimentary rocks in basins worldwide.

Analysis of electric vehicle charging station usage and profitability in Germany based on empirical data.

This paper provides empirical data and a profitability estimation of public charging infrastructure usage in Germany. Given that, in Germany, there are now 2.5 times as many vehicles per charging station compared with 2017, the system needs to allocate charging points efficiently. To this end, this paper presents representative data on energy consumption, arrival times, occupation, and estimated profitability of 22,200 charging stations in Germany. The observed patterns are translated into compact empirical models that allow working with the results without the burden of the large-scale datasets. Charging happens mainly during the day and on weekdays for AC charging stations, whereas DC fast-charging stations are more popular on the weekend. Fast-chargers service approximately three times as many vehicles per charge point at higher profits because of better margins. For AC chargers, up to 20kWh of energy are charged in an average charge event, whereas fast-chargers supply approximately 40kWh. Energy transfer typically terminates after 4h for AC chargers and 45min for fast-chargers. The power rates are significantly below the rated station power and rarely exceed 11kW for AC charging. This paper allows fellow researchers to build simulation and test scenarios using presented data or to verify models.

Observational Evaluation of the Maximum Practical Utilization of Electric Vehicle DCFC Infrastructure

Central to the design of a direct current fast charging (DCFC) network is the question of how much energy a DCFC of a given power can supply to vehicles without users being forced to queue to charge. We define ‘utilization factor’ as the ratio of the energy delivered by a DCFC in a multi-day period to the maximum amount of energy it could deliver in period. Three and a half years of data from 12 DCFCs are examined, characterizing each charging event by both the utilization factor and the time lag since the termination of the previous charging event. Short lags between events are inferred to indicate queuing. To keep the fraction of would-be users who have to queue below 10%, the overall utilization of the DCFC must likewise be limited to 10% (or 7–17% in exceptionally heterogeneous or exceptionally homogeneous traffic patterns, respectively). E.g., a 100 kW DCFC should not be expected to deliver more than 240 kWh per day (100 kW × 24 h × 10%).

Modeling Electrochemical Transport within a Three-Electrode System Towards Fast Charge Applications

In support of GM's traction battery efforts, we derive and implement a method to describe the electrochemical performance of a battery cell through the combination of a modified Newman Pseudo 2-Dimensional model and a three-electrode experimental apparatus. To assess the capability of the method, we compare model results with experimental data for a lithiated graphite and lithium nickel manganese cobalt oxide system. The model is applied to simulate the electrochemical and transport processes within the battery cell to predict the negative electrode potential and positive electrode potential with respect to a lithium iron phosphate reference electrode, as well as the terminal voltage. We extend this discussion to cover applicability to fast charge considerations to avoid lithium plating at the anode during a fast charge event.

EVStationSIM: An end-to-end platform to identify and interpret similar clustering patterns of EV charging stations across multiple time slices

Transport electrification introduces new opportunities in supporting sustainable mobility. Fostering Electric Vehicle (EV) adoption integrates vehicle range and infrastructure deployment concerns. An understanding of EV charging patterns is crucial for optimizing charging infrastructure placement and managing costs. Clustering EV charging events has been useful for ensuring service consistency and increasing EV adoption. However, clustering presents challenges for practitioners when first selecting the appropriate hyper-parameter combination for an algorithm and later when assessing the quality of clustering results. In a clustering process, the ground truth data is normally not available for practitioners to validate different modeling decisions. Consequently, it is difficult to judge the effectiveness of the discovered patterns because there is no objective method to compare them. This work proposes an end-to-end platform prototype named “EVStationSIM” that allows for the creation of relative rankings of similar clustering results. The ultimate goal is to support users/practitioners by allowing them to identify and interpret similar clustering patterns of EV charging stations using multiple time slices. The performance of this proposed platform is demonstrated with a case study using real-world EV charging event data from charging station operators in New Brunswick, Canada. The case study illustrates how generated results can assist in downstream analytical tasks such as planning infrastructure allocation expansions.

Optimal Scheduling of Movable Electric Vehicle Loads Using Generation of Charging Event Matrices, Queuing Management, and Genetic Algorithm

The extensive adoption of electric vehicles (EVs) can introduce negative impacts on electric infrastructure in the form of sporadic and excessive charging demands, line overload, and voltage quality. Because EV loads can be movable around the system and time-dependent due to human daily activities, it is therefore proposed in this research to investigate the spatial effects of EV loads and their impacts on a power system. We developed a behavior-based charging profile simulation for daily load profiles of uncontrolled and controlled charging simulations. To mitigate the impact of increased peak demand, we proposed an optimal scheduling method by genetic algorithm (GA) using charging event matrices and EV queuing management. The charging event matrices are generated by capturing charging events and serve as an input of the GA-based scheduling, which optimally defines available charging slots while maximizing the system load factor while maintaining user satisfaction, depending on the weight coefficients prioritized by the system operator. The EV queuing management strategically selects EVs to be filled in the available slots based on two qualification indicators: previous charging duration and remaining state of charge (SoC). The proposed methodology was tested on a modified IEEE-14 bus system with 3 generators and 20 transmission lines. The simulation results show that the developed methodology can efficiently manage the peak demand while respecting the system’s operational constraints and the user satisfaction level.

Calcite U-Pb ages constrain petroleum migration pathways in tectonic complex basins

Abstract Tracing secondary oil migration pathways is critical for understanding petroleum system evolution histories. Traditional tools (e.g., molecular indicators and numerical modeling) utilized for evaluating oil migration processes either lead to ambiguous interpretations or only provide qualitative estimates. We quantitatively constrain secondary oil migration processes under an absolute time frame by integrating oil-inclusion fluorescence and in situ calcite U-Pb dating on calcite veins and cements hosting primary oil inclusions. Fluorescence spectra of oil inclusions and U-Pb ages were obtained on samples from ultra-deep Ordovician reservoirs along two major faults in the Halahatang oilfield, Tarim Basin (northwestern China). Absolute U-Pb ages suggest two main oil charge events during 475–433 Ma and 294–262 Ma, respectively, and revealed a northward-decreasing trend for oil maturity during single charge events. Vertical migration of oil from in situ source rock through active (or reactivated) faults is believed to be the key process inducing the spatial maturity variation in charged oils and considered as the main mechanism of secondary migration, with brecciated fault zones and dilatant fractures along faults acting as major vertical oil-migration pathways. The successful application of this approach has wider implications for elucidating petroleum migration processes in tectonic complex basins worldwide.

Geochemical rationalisation for the variable oil quality in the Orcutt reservoir, California, USA

The relationship between fluid physical properties and hydrocarbon compositions was investigated on a suite of eleven heavy oil samples from the Orcutt Field, onshore Santa Maria Basin in California. The physical properties of oils display a considerable range across the reservoir, with viscosity varying from 46.3 to 611,150 centipoises at 20 °C and API gravity from 16.3 to 25°. Not surprisingly, the molecular composition of the Orcutt Field oils is also highly variable.Present-day fluid physical properties in the field are determined by the mixing of multiple petroleum charges. The bulk of the oil volume in place was generated by source rocks of the Monterey Formation in the early oil window stage of petroleum generation. The mixing of biodegraded and non-biodegraded oils indicates at least two oil charge events, punctuated by episodes of biodegradation. Oil viscosity decreases towards the Orcutt Frontal fault and Solomon Hills fault, which bound the Orcutt anticlinal structure to the northeast. These faults appear to be conduits for a separate diamondoid-rich light oil or condensate charge responsible for localised improvements in oil viscosity towards the NE and in the crest of the structure.Data from this study show the profound impact of charge mixing and in-reservoir alteration on physical properties of oils within a single accumulation.

Learning electric vehicle driver range anxiety with an initial state of charge-oriented gradient boosting approach

This manuscript focuses on the modeling of electric vehicle (EV) driver’s range anxiety, a fear that a vehicle does not have sufficient range, or state of charge (SOC) of the battery pack, to reach its destination and would strand its occupants. Despite numerous research studies on the modeling of charging behaviors, modeling efforts to understand at what battery percentages do EV drivers charge their vehicles, and what are the associated contributing factors, are rather limited. To this end, an ensemble learning model based on gradient boosting is developed. The model sequentially fits new predictors to new residuals of the previous prediction and, then, minimizes the loss when adding the latest prediction. A total of 18 features are defined and extracted from the multisource data, which cover information on driver, vehicles, stations, traffic conditions, as well as spatial-temporal context information of the charging events. The analyzed dataset includes 4.5-year’s charging event log data from 3,096 users and 468 public charging stations in Kansas City Missouri, and the macroscopic travel demand model maintained by the metropolitan planning organization. The result shows the proposed model achieved a satisfactory result with a R square value of 0.54 and root mean square error of 0.14, both better than multiple linear regression model and random forest model. To reduce range anxiety, it is suggested that the priorities of deploying new charging facilities should be given to the areas with higher daily traffic prediction, with more conservative EV users or that are further from residential areas.