Improved LSTM-Based Battery SOH Estimation with Differential Evolution Hyperparameter Optimization
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Published
Jan 13, 2026
Karthickumar Ponnambalam
Sivaneasan Bala Krishnan
Anurag Sharma
Sze Sing Lee
Abstract
Reliable estimation of battery State of Health (SoH) is essential for the safety and longevity of lithium-ion battery systems. Previous work introduced a PSO-aided LSTM (PA-LSTM) model for SoH prediction using NASA’s battery degradation dataset. Building on this, our earlier GA-LSTM model utilized a Genetic Algorithm (GA) for LSTM hyperparameter tuning, achieving RMSE reductions of 12.4% to 76.79% using 70% of the discharge cycle data.
In this study, we propose a novel approach for SoH prediction by integrating Long Short-Term Memory (LSTM) networks with Differential Evolution (DE), a more efficient and scalable metaheuristic optimizer. Leveraging DE’s robust convergence behavior and global search capability, the proposed DE-LSTM model is evaluated on the same NASA dataset. Experimental results demonstrate that DE-LSTM outperforms our previous GA-LSTM model, achieving further RMSE reductions and highlighting DE’s effectiveness for hyperparameter optimization in data-driven battery health prognostics.
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Keywords
Battery State of Health (SOH),, Long Short-Term Memory (LSTM), Differential Evolution (DE), Hyperparameter Optimization, Battery Prognostics, NASA Battery Dataset, Metaheuristic Algorithms, State of Health (SOH), GA-LSTM, DE-LSTM
References
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Qianglong Li, Dezhi Li, Kun Zhao, Licheng Wang, and Kai Wang. State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression. Journal of Energy Storage, 50:104215, 6 2022.
Xiao Sheng Si, Wenbin Wang, Chang Hua Hu, and Dong Hua Zhou. Remaining useful life estimation - A review on the statistical data driven approaches. European Journal of Operational Research, 213(1):1–14, 8 2011.
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Lijun Qian, Liang Xuan, and Jian Chen. Battery soh estimation based on decision tree and improved support vector
machine regression algorithm. Frontiers in Energy Research, 11:1218580, 6 2023.
Kodjo S.R. Mawonou, Akram Eddahech, Didier Dumur, Dominique Beauvois, and Emmanuel Godoy. State-of-health estimators coupled to a random forest approach for lithiumion battery aging factor ranking. Journal of Power Sources, 484:229154, 2 2021.
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Lu Peng, Shan Liu, Rui Liu, and Lin Wang. Effective long short-term memory with differential evolution algorithm for electricity price prediction. Energy, 162:1301–1314, 2018.
Jianyong Sun, Xin Liu, Thomas Back, and Zongben Xu. Learning adaptive differential evolution algorithm from optimization experiences by policy gradient. IEEE Transactions on Evolutionary Computation, 25(4):666–680, 2021.
K Vijayaprabakaran and K Sathiyamurthy. Towards activation function search for long short-term model network: A differential evolution based approach. Journal of King Saud University-Computer and Information Sciences, 34(6):2637– 2650, 2022.
Swagatam Das, Sankha Subhra Mullick, and Ponnuthurai N. Suganthan. Recent advances in differential evolution – an updated survey. Swarm and Evolutionary Computation, 27:1–30, 2016.
Swagatam Das and Ponnuthurai N. Suganthan. Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1):4–31, 2011.
Gary Pampara, Andries P. Engelbrecht, and Nelis Franken. Binary differential evolution. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pages 1873–1879, 2006.
Lu Peng, Shan Liu, Rui Liu, and Lin Wang. Effective long short-term memory with differential evolution algorithm for electricity price prediction. Energy, 162:1301–1314, 2018.
Simone Disabato and Manuel Roveri. Incremental on-device tiny machine learning. pages 7–13, 11 2020.
Atallo Kassaw Takele and Balazs Vill ´ anyi. Resource aware ´ long short-term memory model (ralstmm) based on-device incremental learning for industrial internet of things. IEEE Access, 11:63107–63115, 2023.
Ade Pitra Hermawan, Dong Seong Kim, and Jae Min Lee. Sensor failure recovery using multi look-back lstm algorithm in industrial internet of things. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2020-September:1363–1366, 9 2020.
Nikolaos Gorgolis, Ioannis Hatzilygeroudis, Zoltan Istenes, and Lazlo N.Grad Gyenne. Hyperparameter optimization of lstm network models through genetic algorithm. 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019, 7 2019.
D. Whitley, T. Starkweather, and C. Bogart. Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Computing, 14:347–361, 8 1990.
Li-ion Battery Aging Datasets — NASA Open Data Portal.
Nikolaos Gorgolis, Ioannis Hatzilygeroudis, Zoltan Istenes, and Lazlo – Grad Gyenne. Hyperparameter optimization of lstm network models through genetic algorithm. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pages 1–4, 2019.
Jia Jianfang, Keke Wang, Shi Yuanhao, Wen Jie, and Zeng Jianchao. Multi-Scale Prediction of RUL and SOH for Lithium-Ion Batteries Based on WNN-UPF Combined Model. Chinese Journal of Electronics, 30(1), 2021.
Eberhard Meissner and Gerolf Richter. The challenge to the automotive battery industry: the battery has to become an increasingly integrated component within the vehicle electric power system. Journal of Power Sources, 144(2):438–460, 6
2005.
Eduardo Redondo-Iglesias, Pascal Venet, and Serge Pelissier. Calendar and cycling ageing combination of batteries in electric vehicles. Microelectronics Reliability, 88-90:1212–1215, 9 2018.
Tan Afj, Tomacruz Jgt, Remolona Mfm, Guest Editors, Petar S Varbanov, Yee Van Fan, Jiˇr´ı J Klemes, Sandro Ni ˇ zeti ˇ c,´
Lerissah D Lim, Andrei J Felix Tan, Jan T Goran Tomacruz, Michael T Castro, Miguel M Francisco Remolona, and Joey D Ocon. CHEMICAL ENGINEERING TRANSACTIONS Cyclic Degradation Prediction of Lithium-Ion Batteries using Data-Driven Machine Learning.
Wasim Ahmad, Sheraz Ali Khan, M. M.Manjurul Islam, and Jong Myon Kim. A reliable technique for remaining useful
life estimation of rolling element bearings using dynamic regression models. Reliability Engineering & System Safety, 184:67–76, 4 2019.
Thanh Trung Le, Florent Chatelain, and Christophe Berenguer. Multi-branch hidden Markov models for remaining useful life estimation of systems under multiple deterioration modes. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 230(5):473–484, 10 2016.
S Saon, T Hiyama Computers Mathematics with Applications, and undefined 2010. Predicting remaining useful life of rotating machinery based artificial neural network. Elsevier.
Qianglong Li, Dezhi Li, Kun Zhao, Licheng Wang, and Kai Wang. State of health estimation of lithium-ion battery based on improved ant lion optimization and support vector regression. Journal of Energy Storage, 50:104215, 6 2022.
Xiao Sheng Si, Wenbin Wang, Chang Hua Hu, and Dong Hua Zhou. Remaining useful life estimation - A review on the statistical data driven approaches. European Journal of Operational Research, 213(1):1–14, 8 2011.
Søren B. Vilsen and Daniel Ioan Stroe. Battery state-ofhealth modelling by multiple linear regression. Journal of Cleaner Production, 290:125700, 3 2021.
Lijun Qian, Liang Xuan, and Jian Chen. Battery soh estimation based on decision tree and improved support vector
machine regression algorithm. Frontiers in Energy Research, 11:1218580, 6 2023.
Kodjo S.R. Mawonou, Akram Eddahech, Didier Dumur, Dominique Beauvois, and Emmanuel Godoy. State-of-health estimators coupled to a random forest approach for lithiumion battery aging factor ranking. Journal of Power Sources, 484:229154, 2 2021.
Yuxuan Zheng, Jiaxiang Hu, Jianjun Chen, Huiwen Deng, and Weihao Hu. State of health estimation for lithium battery
random charging process based on cnn-gru method. Energy Reports, 9:1–10, 5 2023.
Hanlei Sun, Jianrui Sun, Kun Zhao, Licheng Wang, and Kai Wang. Data-driven ica-bi-lstm-combined lithium battery soh estimation. Mathematical Problems in Engineering, 2022, 2022.
Jiantao Qu, Feng Liu, Yuxiang Ma, and Jiaming Fan. A neural-network-based method for rul prediction and soh monitoring of lithium-ion battery. IEEE Access, 7:87178–87191, 2019.
Riccardo Poli, James Kennedy, and Tim Blackwell. Particle swarm optimization: An overview. Swarm Intelligence, 1, 10 2007.
Mujahid Tabassum. A genetic algorithm analysis towards optimization solutions. International Journal of Digital Information and Wireless Communications, 4:124–142, 2014.
Mohammed Amine Janati Idrissi, Hassan Ramchoun, Youssef Ghanou, and Mohamed Ettaouil. Genetic algorithm
for neural network architecture optimization. In 2016 3rd International Conference on Logistics Operations Management (GOL), pages 1–4, 2016.
Karthickumar Ponnambalam, B Sivaneasan, A Sharma, SS Lee, and Prasun Chakrabarti. Battery soh estimation using lstm with genetic algorithm-based hyperparameter optimization. In TENCON 2024-2024 IEEE Region 10 Conference (TENCON), pages 1632–1635. IEEE, 2024.
Rainer Storn. Differrential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report, International Computer Science Institute, 11, 1995.
Lu Peng, Shan Liu, Rui Liu, and Lin Wang. Effective long short-term memory with differential evolution algorithm for electricity price prediction. Energy, 162:1301–1314, 2018.
Jianyong Sun, Xin Liu, Thomas Back, and Zongben Xu. Learning adaptive differential evolution algorithm from optimization experiences by policy gradient. IEEE Transactions on Evolutionary Computation, 25(4):666–680, 2021.
K Vijayaprabakaran and K Sathiyamurthy. Towards activation function search for long short-term model network: A differential evolution based approach. Journal of King Saud University-Computer and Information Sciences, 34(6):2637– 2650, 2022.
Swagatam Das, Sankha Subhra Mullick, and Ponnuthurai N. Suganthan. Recent advances in differential evolution – an updated survey. Swarm and Evolutionary Computation, 27:1–30, 2016.
Swagatam Das and Ponnuthurai N. Suganthan. Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1):4–31, 2011.
Gary Pampara, Andries P. Engelbrecht, and Nelis Franken. Binary differential evolution. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pages 1873–1879, 2006.
Lu Peng, Shan Liu, Rui Liu, and Lin Wang. Effective long short-term memory with differential evolution algorithm for electricity price prediction. Energy, 162:1301–1314, 2018.
Simone Disabato and Manuel Roveri. Incremental on-device tiny machine learning. pages 7–13, 11 2020.
Atallo Kassaw Takele and Balazs Vill ´ anyi. Resource aware ´ long short-term memory model (ralstmm) based on-device incremental learning for industrial internet of things. IEEE Access, 11:63107–63115, 2023.
Ade Pitra Hermawan, Dong Seong Kim, and Jae Min Lee. Sensor failure recovery using multi look-back lstm algorithm in industrial internet of things. IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, 2020-September:1363–1366, 9 2020.
Nikolaos Gorgolis, Ioannis Hatzilygeroudis, Zoltan Istenes, and Lazlo N.Grad Gyenne. Hyperparameter optimization of lstm network models through genetic algorithm. 10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019, 7 2019.
D. Whitley, T. Starkweather, and C. Bogart. Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Computing, 14:347–361, 8 1990.
Li-ion Battery Aging Datasets — NASA Open Data Portal.
Nikolaos Gorgolis, Ioannis Hatzilygeroudis, Zoltan Istenes, and Lazlo – Grad Gyenne. Hyperparameter optimization of lstm network models through genetic algorithm. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pages 1–4, 2019.
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