# Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated
## 2 Related Works
### Artificial Neural Networks  
Due to high non-linear relationship between input data and output data, numerous research works introduced ANN as they have an inherent ability to learn non-linear input-output mappings [42]. [PERSON] et al. [43] decided to use ANN in addition to other traditional ML models. ANN recorded the best performance in terms of MSE and Root MSE concluding that deep learning approaches were appropriate for accurate SoC estimation. In the study proposed by [PERSON] et al. [44] several ML algorithms were developed to estimate the SoC through the relationship with open circuit voltage. ANNs achieved the best results for each operating temperature in comparison with decision tree (DT), ensemble boosting, ensemble bagging, and support vector machines. [PERSON] et al. [45] tested the performance of deep NN using data from batteries exposed to varying dynamics and presented a comparative study between the experimentally measured SoC and the SoC obtained in simulations at different temperatures and under different profiles. [PERSON] et al. compared multiple state-of-the-art DL methods, and the results revealed that the FC model was the worst in terms of accuracy [46]. [PERSON] et al. [47] designed a SoC estimator based on EKF combined with a multi-layer perceptron (MLP), whose input data were only current, voltage and temperature. By combining the two approaches, the number of network weights and the required computation could be reduced, and by including EKF in the ANN it was possible to achieve the model adaptability to the dynamics of the battery system. The combined EKF-MLP outperformed approaches using EKF and MLP used independently, the maximum SoC error in charge and discharge was 2.3% and 2.6%. [PERSON] et al. [48] merged a back propagation (BP) neural network with an EKF algorithm, enhancing the model accuracy. [PERSON] et al. proposed a transfer learning strategy to improve the monitoring of battery states under different application scenarios [49]. [PERSON] et al. developed a convolutional ANN (CNN) fed with partial charging data. The model included \(n\) CNN blocks, where each CNN block was formed by a 1D convolution layer, followed by a batch normalisation and a ReLu activation layer; the architecture ended with a global averaging pooling and a dense layer. The CNN model was the most accurate in comparison to non ANN approaches [50]. [PERSON] et al. tested the ability of a deep fully convolutional (FCN) model to estimate the SoC of a Panasonic 18650 lithium battery cell at constant ambient temperature and varying temperature over different drive cycles. The proposed architecture recorded lower RMSE and fewer floating-point operations per second (FLOPs) than recurrent models (RNN) such as long short-term memory (LSTM) and gated recurrent units (GRU). The experiment was conducted on a GTX1080 Ti GPU [51]. [PERSON] developed a RNN architecture for estimating SoC of electric air vehicles. The model consisted of two LSTM layers with 100 and 60 neurons, two Dropout and two Dense layers; this network had the Nash-Sutcliffe model efficiency coefficient (NSE) of 0.023 [52]. [PERSON] et al. [53] introduced GRU into their RNN topologies to capture the dependency between past input and present output. In particular, the GRU-RNN network was built with a minimum number of GRUs so as to make the computation more efficient. The number of fp32 operation was reduced by 56 times compared to [54]. The ANN was subsequently deployed on a Teensy MCU and estimated SoC was achieved in 0.215 s. [PERSON] et al. proposed a mixed CNN-GRU model trained on FUDS data. In the proposed neural architecture a 1-D convolutional layer was followed by two GRUs layers then connected to a fully connected layer linked to the output corresponding to the battery SoC [55]. [PERSON] et al. also developed a data-driven model by combining CNN and LSTM, the results indicated an accurate estimate of the SoC with RMSE below 0.31% [56].