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Volume 15
Issue 3
10.3390/electronics15030698
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Article

A Battery State-of-Charge Prediction Method Based on a Hammerstein Model Integrated with a Hippopotamus Optimization Algorithm and Neural Network

by
Liang Zhang
,
Bilong Yang
,
Ling Lyu
*,
Sihan Che
,
Haoqiang Li
and
Weifei Wang
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
*
Author to whom correspondence should be addressed.
Electronics 2026, 15(3), 698; https://doi.org/10.3390/electronics15030698
Submission received: 17 January 2026 / Revised: 1 February 2026 / Accepted: 3 February 2026 / Published: 5 February 2026
(This article belongs to the Topic Object Detection and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)
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Abstract

Accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical for assessing the safety and remaining range of electric vehicles. However, due to the complex and variable operating environment of batteries and their highly nonlinear internal mechanisms, achieving high-precision SOC prediction remains a central challenge in current research. To this end, this paper proposes a nonlinear Hammerstein model based on the Hippopotamus Optimization Algorithm (HO) to optimize the backpropagation neural network, thereby enhancing the accuracy of SOC prediction. The HO-BP-Hammerstein model optimizes the BP neural network architecture using the Hippopotamus Algorithm and conducts SOC prediction accuracy tests on real-world data. Experimental results demonstrate the superiority of the proposed method through comparative accuracy analysis of various SOC prediction approaches under different operating conditions, confirming its significant engineering application value.
Keywords: state of charge; Hippopotamus algorithm; Hammerstein model; prediction accuracy state of charge; Hippopotamus algorithm; Hammerstein model; prediction accuracy

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MDPI and ACS Style

Zhang, L.; Yang, B.; Lyu, L.; Che, S.; Li, H.; Wang, W. A Battery State-of-Charge Prediction Method Based on a Hammerstein Model Integrated with a Hippopotamus Optimization Algorithm and Neural Network. Electronics 2026, 15, 698. https://doi.org/10.3390/electronics15030698

AMA Style

Zhang L, Yang B, Lyu L, Che S, Li H, Wang W. A Battery State-of-Charge Prediction Method Based on a Hammerstein Model Integrated with a Hippopotamus Optimization Algorithm and Neural Network. Electronics. 2026; 15(3):698. https://doi.org/10.3390/electronics15030698

Chicago/Turabian Style

Zhang, Liang, Bilong Yang, Ling Lyu, Sihan Che, Haoqiang Li, and Weifei Wang. 2026. "A Battery State-of-Charge Prediction Method Based on a Hammerstein Model Integrated with a Hippopotamus Optimization Algorithm and Neural Network" Electronics 15, no. 3: 698. https://doi.org/10.3390/electronics15030698

APA Style

Zhang, L., Yang, B., Lyu, L., Che, S., Li, H., & Wang, W. (2026). A Battery State-of-Charge Prediction Method Based on a Hammerstein Model Integrated with a Hippopotamus Optimization Algorithm and Neural Network. Electronics, 15(3), 698. https://doi.org/10.3390/electronics15030698

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