State of Charge Estimation for Lithium-Ion Batteries Under Wide Temperature Ranges Using Error Feedforward of Sparrow-Optimized Back Propagation Neural Network
- 1School of Information Engineering, Ordos Institute of Technology, Ordos 017000, China
- 2School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China
- 3School of Electrical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Abstract
Accurate state of charge (SOC) estimation for lithium-ion batteries (LIBs) is essential. To overcome the susceptibility of conventional data-driven models to local optima and terminal divergence under complex conditions, this paper proposes a novel joint estimation algorithm. Using a second-order resistor–capacitor (RC) model, the slime mould algorithm (SMA) identifies parameters offline at temperature chamber setpoints of 0 °C, 25 °C, and 45 °C for a modified extended Kalman filter (MEKF). Subsequently, a backpropagation (BP) neural network optimized by the sparrow search algorithm (SSA) is introduced to perform feedforward compensation for nonlinear residuals, addressing the error accumulation caused by the randomness of initial network weights, collectively constituting the proposed SSA-BP-MEKF algorithm. Validation at 0 °C, 25 °C, and 45 °C under the Dynamic Stress Test (DST), US06, Federal Urban Driving Schedule (FUDS), and Beijing Dynamic Stress Test (BJDST) demonstrates that the maximum absolute error (MaxAE) remains within 2.21% at 25 °C. At 0 °C and 45 °C, the algorithm effectively suppresses the local divergence of conventional BP compensation, restricting the MaxAE for the demanding BJDST cycle to 2.65% and 1.49%, respectively. Ultimately, the proposed approach significantly enhances estimation accuracy and robustness in complex operating environments.
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BibTeX
@article{wang2026state,
title = {{State of Charge Estimation for Lithium-Ion Batteries Under Wide Temperature Ranges Using Error Feedforward of Sparrow-Optimized Back Propagation Neural Network}},
author = {Wei Wang and Lei Zhang and Junlin Wang and Zhenchao Ren and Jinhao Meng},
journal = {Batteries},
year = {2026},
month = oct,
doi = {10.3390/batteries12100437},
url = {https://www.mdpi.com/2313-0105/12/10/437},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - State of Charge Estimation for Lithium-Ion Batteries Under Wide Temperature Ranges Using Error Feedforward of Sparrow-Optimized Back Propagation Neural Network
AU - Wei Wang
AU - Lei Zhang
AU - Junlin Wang
AU - Zhenchao Ren
AU - Jinhao Meng
PY - 2026
DA - 2026/10/19
JO - Batteries
DO - 10.3390/batteries12100437
UR - https://www.mdpi.com/2313-0105/12/10/437
AB - Accurate state of charge (SOC) estimation for lithium-ion batteries (LIBs) is essential. To overcome the susceptibility of conventional data-driven models to local optima and terminal divergence under complex conditions, this paper proposes a novel joint estimation algorithm. Using a second-order resistor–capacitor (RC) model, the slime mould algorithm (SMA) identifies parameters offline at temperature chamber setpoints of 0 °C, 25 °C, and 45 °C for a modified extended Kalman filter (MEKF). Subsequently, a backpropagation (BP) neural network optimized by the sparrow search algorithm (SSA) is introduced to perform feedforward compensation for nonlinear residuals, addressing the error accumulation caused by the randomness of initial network weights, collectively constituting the proposed SSA-BP-MEKF algorithm. Validation at 0 °C, 25 °C, and 45 °C under the Dynamic Stress Test (DST), US06, Federal Urban Driving Schedule (FUDS), and Beijing Dynamic Stress Test (BJDST) demonstrates that the maximum absolute error (MaxAE) remains within 2.21% at 25 °C. At 0 °C and 45 °C, the algorithm effectively suppresses the local divergence of conventional BP compensation, restricting the MaxAE for the demanding BJDST cycle to 2.65% and 1.49%, respectively. Ultimately, the proposed approach significantly enhances estimation accuracy and robustness in complex operating environments.
SN - 2313-0105
ER -