Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric Vehicles Article Swipe
David Jiménez
,
Jesús Fraile-Ardanuy
,
Sandra Castaño-Solis
,
Julia Merino
,
Roberto Álvaro-Hermana
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2018.04.077
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2018.04.077
Due to urban pollution, transport electrification is being currently promoted in different countries. Electric Vehicles (EVs) sales are growing all over the world, but there are still some challenges to be solved before a mass adoption of this type of vehicles occurs. One of the main drawbacks of EVs are their limited range, for that reason an accurate estimation of the state-of-charge (SOC) is required. The main contribution of this work is the design of a Nonlinear Autoregressive with External Input (NARX) artificial neural network to estimate the SOC of an EV using real data extracted from the car during its daily trips. The network is trained using voltage, current and four different battery pack temperatures as input and SOC as output. This network has been tested using 54 different real driving cycles, obtaining highly accurate results, with a mean squared error lower than 1e-6 in all situations
Related Topics
Concepts
Computer science
Nonlinear autoregressive exogenous model
State of charge
Electrification
Artificial neural network
Battery (electricity)
Electric vehicle
Battery pack
Range (aeronautics)
Autoregressive model
Voltage
Nonlinear system
Automotive engineering
Mean squared error
Work (physics)
Driving range
Artificial intelligence
Electrical engineering
Power (physics)
Electricity
Econometrics
Engineering
Physics
Aerospace engineering
Mathematics
Statistics
Economics
Mechanical engineering
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2018.04.077
- OA Status
- diamond
- Cited By
- 70
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2801753412
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2801753412Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.procs.2018.04.077Digital Object Identifier
- Title
-
Using Dynamic Neural Networks for Battery State of Charge Estimation in Electric VehiclesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
David Jiménez, Jesús Fraile-Ardanuy, Sandra Castaño-Solis, Julia Merino, Roberto Álvaro-HermanaList of authors in order
- Landing page
-
https://doi.org/10.1016/j.procs.2018.04.077Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.procs.2018.04.077Direct OA link when available
- Concepts
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Computer science, Nonlinear autoregressive exogenous model, State of charge, Electrification, Artificial neural network, Battery (electricity), Electric vehicle, Battery pack, Range (aeronautics), Autoregressive model, Voltage, Nonlinear system, Automotive engineering, Mean squared error, Work (physics), Driving range, Artificial intelligence, Electrical engineering, Power (physics), Electricity, Econometrics, Engineering, Physics, Aerospace engineering, Mathematics, Statistics, Economics, Mechanical engineering, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
70Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 7, 2023: 13, 2022: 12, 2021: 11Per-year citation counts (last 5 years)
- References (count)
-
15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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