ACCURATE STATE OF CHARGE ESTIMATION OF LITHIUM-ION BATTERY USING RECURRENT AND NON-RECURRENT NEURAL NETWORKS FOR WLTP DRIVING PROFILES
DOI:
https://doi.org/10.11113/aej.v14.20676Keywords:
Battery, State of Charge (SoC), Neural Networks, Electric vehicle, WLTP, Data-drivenAbstract
Estimating the state of charge (SoC) of a battery is essential to maximize its performance and ensure reliable operation and battery life. Nowadays, many countries are increasingly adopting electric vehicles (EVs) with lithium-ion batteries due to their high specific energy and long service life. This paper presents a method for estimating the state of charge of lithium-ion batteries using artificial neural networks, specifically the Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM), through a data-driven approach. The training and testing of the networks are conducted using recorded datasets of the battery, based on the WLTP driving profiles class 2 and class 3. These driving profiles are specifically designed for testing electric vehicles, thereby enhancing the realism of the state of charge estimation by the network. In terms of the analytical aspect, the FNN was able to train the network faster due to its simpler structure, requiring less computation. On the other hand, the LSTM demonstrated more accurate SoC estimation with fewer response oscillations, thanks to its ability to learn and adapt network parameters internally.
References
H. Nazaripouya, B. Wang, and D. Black. 2019. Electric Vehicles and Climate Change: Additional Contribution and Improved Economic Justification. IEEE Electrification Magazine. 7(2): 33–39. DOI: https://doi.org/10.1109/MELE.2019.2908792.
E. Chemali, M. Preindl, P. Malysz, and A. Emadi. 2016. Electrochemical and electrostatic energy storage and management systems for electric drive vehicles: State-of-the-art review and future trends. IEEE Journal of Emerging and Selected Topics in Power Electronics. 4(3): 1117-1134. DOI: https://doi.org/10.1109/JESTPE.2016.2566583.
R. Ahmed, M. El Sayed, I. Arasaratnam, J. Tjong, and S. Habibi. 2014. Reduced-Order Electrochemical Model Parameters Identification and State of Charge Estimation for Healthy and Aged Li-Ion Batteries - Part II: Aged Battery Model and State of Charge Estimation. IEEE Journal of Emerging and Selected Topics in Power Electronics. 2(3): 678–690. DOI: https://doi.org/10.1109/JESTPE.2014.2331062
Z. Deng, L. Yang, Y. Cai, H. Deng, and L. Sun. 2016. Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery. Energy. 112: 469–480. DOI: https://doi.org/10.1016/j.energy.2016.06.130.
Bebis G., and M. Georgiopoulos. 1994. Feed-forward neural networks. IEEE Potentials. 13(4): 27–31. DOI: https://doi.org/10.1109/45.329294.
Dheeraj Kumar Dhaked, Sharad Dadhich, and Dinesh Birla. 2023. Power output forecasting of solar photovoltaic plant using LSTM. Green Energy and Intelligent Transportation. 2(5): 100113. DOI: https://doi.org/10.1016/j.geits.2023.100113.
Manjot Kaur and Aakash Mohta. 2019. A Review of Deep Learning with Recurrent Neural Network. International Conference on Smart Systems and Inventive Technology 2019 (ICSSIT). 460-465. DOI: https://doi.org/10.1109/ICSSIT46314.2019.8987837.
S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Computation. 9(8): 1735-1780.DOI: https://doi.org/10.1162/neco.1997.9.8.1735.
O. Kyung Su, K. Dong Myung. 2014. Product Specification Rechargeable Lithium Ion Battery Model: INR18650 MJ1. LG CHEM. SEOUL 150-721. KOREA.
Hyeonjik Lee and Kihyung Lee. 2020. Comparative Evaluation of the Effect of Vehicle Parameters on Fuel Consumption under NEDC and WLTP. Energies 2020. 13(16): 4245. DOI: https://doi.org/10.3390/en13164245.
S. Tsiakmakis, G. Fontaras, J. Pavlovic, and C. Cubito. 2017. From NEDC to WLTP: effect on the type-approval CO2 emissions of light-duty vehicles. JRC Science for Policy Report on European Commission. EUR 28724 EN.
Romulo N. Vieira, Phillip Kollmeyer and Mina Naguib. 2023. Feedforward and NARX Neural Network Battery State of Charge Estimation with Robustness to Current Sensor Error. 2023 IEEE Transportation Electrification Conference & Expo (ITEC). DOI: https://doi.org/10.1109/ITEC55900.2023.10187084.
Ephrem Chemali, Phillip J Kollmeyer and Ryan Ahmed. 2017. Long Short-Term Memory-Networks for Accurate State of Charge Estimation of Li-ion Batteries. Ieee Transactions On Industrial Electronics. 65: 6730 6739. DOI: https://doi.org/10.1109/TIE.2017.2787586.
