An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures

Abstract

The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system.

Accurate Remaining Available Energy Estimation of LiFePO4 Battery in Dynamic Frequency Regulation for EVs with Thermal-Electric-Hysteresis Model

“…However, Huria et al [16] accurately described the hysteresis voltage using a hysteresis voltage reconstruction equation. The NNM is a black-box model that typically takes features such as current, temperature, and polarization as inputs [17]. It trains the neural network node parameters to construct a non-Energies 2023, 16, 5239 3 of 28 linear mapping between the input features and the terminal voltage output.…”

“…The experimental environment of this study is the same as that of reference [17], and the convective heat transfer coefficient W is set as 5 according to the setting of the reference.…”

Improved Digital Twin of Li-Ion Battery Based on Generic MATLAB Model

“…The existing methods for SoC estimation can be divided into three categories: direct, indirect, and adaptive [4]. Direct methods are based on measuring the physically accessible battery properties such as the battery voltage, or battery current [6][7][8]. Indirect methods are data-based and use a database of measured battery properties within a cycle [9,10].…”

“…Finally, adaptive methods use adaptive filters formed from Fuzzy logic [11], artificial neural networks [12], and the Kalman filter [12], which are based on adaptive algorithms for adjusting parameters. The direct methods combining coulomb counting and open circuit voltage measurements are the easiest to implement and provide reliable results in periodic battery cycling [6][7][8][9][10][11][12][13].…”

High-Precision and Robust SOC Estimation of LiFePO4 Blade Batteries Based on the BPNN-EKF Algorithm

“…Accurate SOC estimation can enable drivers to use battery power reasonably, avoid overcharging or over-discharging the battery system, and mitigate safety risks [6]. On the other hand, accurate SOC estimation can fully utilize the capacity performance of batteries, reducing user anxiety for drivers [7,8]. Therefore, accurate SOC estimation is crucial for the performance of the battery system.…”