State of charge estimation of an electric vehicle's battery using tiny neural network embedded on small microcontroller units
Abstract
The latest breakthroughs in artificial intelligence resulted in their adoption in the electric vehicle's battery management systems. Simultaneously, the remarkable increase in the number of embedded systems in electric vehicles (EVs) has led to the search for new optimized state of charge (SOC) estimation strategies suitable for implementation on small and limited MCUs. In this regard, this paper provides a design and quantization processes of the 1D convolutional neural network (1D CNN) and the GRU-recurrent neural network (GRU-RNN), devoted for SOC estimation. Both the NN algorithms are designed and trained using data collected from an 18650 Li-ion battery. Furthermore, to account for the limited computational resources of the EV MCUs, the pre-trained models are then converted into highly optimized C-codes using the latest model quantification techniques. In this regard, this paper investigates, the performances of the STM32Cube.AI and the TensorFlow Lite for microcontroller (TFLite Micro), in quantizing the activation functions and weights of the proposed NN models. This is achieved by converting these latter from 32-bits floating-point to 8-bit integer precision. The obtained experimental results, under different profiles, indicate that the 1D CNN is more accurate than the GRU-based model with root mean squared error (RMSE) of 2.33% and mean absolute error (MAE) of 1.62%. Furthermore, the impact of the quantization using STM32Cube.AI is compared with that using the TFLite Micro. The obtained results, demonstrated the superiority of the 1D CNN model quantized using the STM32Cube.AI. This latter reduces the flash memory size of the 1D CNN model from 10.82 to 2.89 KB and the RAM size from 2.49 to 1.04 KB, compared to the TFLite Micro, which reduces the RAM size from 7.0107 to 4.23 KB, and the flash from 43.361 to 15.88 KB.
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Using the STM32Cube AI framework, the 1D-CNN quantification model achieved an inference speed of 0.971 ms.
“Using the STM32Cube AI framework, the 1D-CNN quantification model achieved an inference speed of 0.971 ms.”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [73]
Mazzi et al. compared CNNs and GRUs, deploying a more accurate and less complex 1D-CNN on an STM32 embedded chip.
“Mazzi et al. compared CNNs and GRUs, deploying a more accurate and less complex 1D-CNN on an STM32 embedded chip[73].”
From Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches (2025) · cites this paper as [73]
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- Authors
- Mazzi, Y., Ben Sassi, H., Gaga, A., & Errahimi, F.
- Affiliations
- Sidi Mohamed Ben Abdellah University (MA)
- LSED Private University of Fez Fez Morocco
- Université Sultan Moulay Slimane (MA)
- Corresponding author
- Mazzi, Yahia
- Venue
- International Journal of Energy Research
- Publisher
- Wiley
- Published
- 2022-02
- Volume
- 46
- Issue
- 6
- Pages
- 8102-8119
- ISSN
- 0363-907X (ISSN-L)
- 1099-114X
- OpenAlex
- W4210291182
- Cited by
- 53
- References
- 60
- Citations by year
- 2026: 12 · 2025: 13 · 2024: 13 · 2023: 9 · 2022: 6
- FWCI
- 2.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Journal of Energy Research (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Advanced Battery Technologies Research
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- state of charge estimation, electric vehicle batteries, battery management systems, 1D convolutional neural network, model quantization, microcontroller implementation, TensorFlow Lite Micro, lithium-ion batteries, 18650 cells, embedded machine learning
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