Paper Citation Record · LEDGER

On-Device Adaptive Battery Power Prediction for Electric Vehicles

As of 8 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2607.09400.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.09400 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T03:18:04.504155Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

28 of 28 outbound references displayed

verified exact0
verified fuzzy0
unresolved28
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 305622d0-dbf4-4b1f-9379-ba7925b39d47 · outbound

This paper cites Deep Learning for Time Series Forecasting: The Electric Load Case.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Deep Learning for Time Series Forecasting: The Electric Load Case

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation d88a9f8f-0911-453a-b70c-338db9b966da · outbound

This paper cites Deep Learning for Time Series Forecasting: A Survey,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Deep Learning for Time Series Forecasting: A Survey,

Reference 2

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Observation 6c039a03-4480-49cc-9f3e-a570f2866593 · outbound

This paper cites Time Series Forecasting With Deep Learning: A Survey,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Time Series Forecasting With Deep Learning: A Survey,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 660c92ed-95e0-4595-9223-3eeea58356bf · outbound

This paper cites Time Series Forecasting With Deep Learning: A Survey.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Time Series Forecasting With Deep Learning: A Survey

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 8dc65a83-3a1f-477f-91d1-beffcf6c36f8 · outbound

This paper cites Electric vehicle charging demand forecasting using deep learning model,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Electric vehicle charging demand forecasting using deep learning model,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 08a280c2-2b20-4718-891c-922e6199865b · outbound

This paper cites Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 647d4b03-ac0a-4255-909c-6de01f35181f · outbound

This paper cites Prediction of Electric Vehicles Charging Demand: A Transformer-Based Deep Learning Ap- proach,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Prediction of Electric Vehicles Charging Demand: A Transformer-Based Deep Learning Ap- proach,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 2907a494-408b-4c69-82ef-c39d57187e82 · outbound

This paper cites State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 4057d1ad-d3b5-4cc6-91d4-fe8a5e2f0403 · outbound

This paper cites Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 3b276612-ac49-40fc-94f5-3c562ac126b2 · outbound

This paper cites Mitigating Power Peaks in Automotive Power Networks by Exploitation of Flex- ible Loads,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Mitigating Power Peaks in Automotive Power Networks by Exploitation of Flex- ible Loads,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation f6ceaf9a-6632-417c-ae80-ef8a9af42256 · outbound

This paper cites Effect of Driving Behavior and Vehicle Characteristics on Energy Consumption of Road Vehicles Running on Alternative Energy Sources,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Effect of Driving Behavior and Vehicle Characteristics on Energy Consumption of Road Vehicles Running on Alternative Energy Sources,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation b0e45146-7284-45b9-88cb-5c421f95bdd8 · outbound

This paper cites Learning Fast and Slow for Online Time Series Forecasting.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Learning Fast and Slow for Online Time Series Forecasting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 1734d5c6-19da-45a0-8ff5-1f4cb39ff2b5 · outbound

This paper cites Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 57050b55-9bdc-4704-830a-28905d42e462 · outbound

This paper cites OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling.

On-Device Adaptive Battery Power Prediction for Electric Vehicles OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation ef74ec7b-30d6-498b-84c7-860ff2072bf0 · outbound

This paper cites NeuralForecast: User friendly state-of-the-art neural forecasting mod- els.

On-Device Adaptive Battery Power Prediction for Electric Vehicles NeuralForecast: User friendly state-of-the-art neural forecasting mod- els

Reference 15

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Observation b0fe770e-b1a4-4bd0-b0e7-4fbf5972fb0f · outbound

This paper cites Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx

Reference 16

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Observation 0ca9caf4-3604-4b53-87ca-55bfac9c5c43 · outbound

This paper cites Deep Non-Parametric Time Series Forecaster.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Deep Non-Parametric Time Series Forecaster

Reference 17

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Observation af422228-a726-4c72-8a6b-52343edce0bd · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 18

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Observation 3d37a5e5-6c8a-475c-9022-6445aaf1dc63 · outbound

This paper cites N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting.

On-Device Adaptive Battery Power Prediction for Electric Vehicles N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 56a3beca-5057-4615-a02f-b367fd9422c3 · outbound

This paper cites Parameter-efficient deep probabilistic forecasting,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Parameter-efficient deep probabilistic forecasting,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation a5e376a1-6ab4-4830-8dcc-ffb85a29beb9 · outbound

This paper cites Temporal Fusion Transformers for interpretable multi-horizon time series forecasting,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Temporal Fusion Transformers for interpretable multi-horizon time series forecasting,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 1adde716-1b1f-420f-95b6-891efbc5b59a · outbound

This paper cites Kan: Kolmogorov-arnold networks,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Kan: Kolmogorov-arnold networks,

Reference 22

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Observation 3760e522-676b-4429-95dd-124c0d91a7c6 · outbound

This paper cites Tune: A Research Platform for Distributed Model Selection and Training.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Tune: A Research Platform for Distributed Model Selection and Training

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 968a39ff-951c-4d97-9d17-b0643ee001fe · outbound

This paper cites Tvm: An automated end-to-end optimizing compiler for deep learning,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Tvm: An automated end-to-end optimizing compiler for deep learning,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation b5274ac5-ed51-430e-9a62-b3d51e1f8d50 · outbound

This paper cites Zephyr project rtos.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Zephyr project rtos

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 0ef8e547-db38-4dd2-b447-419457b90790 · outbound

This paper cites Efficient edge ai: Deploying convolutional neural networks on fpga with the gemmini accelerator,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Efficient edge ai: Deploying convolutional neural networks on fpga with the gemmini accelerator,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Observation 24f56b85-4d45-4ac4-8f5e-bc43263f84ab · outbound

This paper cites Onnx runtime.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Onnx runtime

Reference 27

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Observation e24e9f18-858f-4575-bbda-85c1752c1daf · outbound

This paper cites Battery and heating data in real driving cycles,.

On-Device Adaptive Battery Power Prediction for Electric Vehicles Battery and heating data in real driving cycles,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-13T03:18:04.504155Z

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Pith citing papers

No inbound Pith citation observations are available.