Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine

Xiaopeng Tang, Ke Yao, Boyang Liu, Wengui Hu and Furong Gao
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Xiaopeng Tang: Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
Ke Yao: Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 510000, China
Boyang Liu: Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
Wengui Hu: Guangzhou HKUST Fok Ying Tung Research Institute, Guangzhou 510000, China
Furong Gao: Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China

Energies, 2018, vol. 11, issue 1, 1-16

Abstract: A battery’s state-of-power (SOP) refers to the maximum power that can be extracted from the battery within a short period of time (e.g., 10 s or 30 s). However, as its use in applications is growing, such as in automatic cars, the ability to predict a longer usage time is required. To be able to do this, two issues should be considered: (1) the influence of both the ambient temperature and the rise in temperature caused by Joule heat, and (2) the influence of changes in the state of charge (SOC). In response, we propose the use of a model-based extreme learning machine (Model-ELM, MELM) to predict the battery future voltage, power, and surface temperature for any given load current. The standard ELM is a kind of single-layer feedforward network (SLFN). We propose using a set of rough models to replace the active functions (such as logsig ()) in the ELM for better generalization performance. The model parameters and initial SOC in these “rough models” are randomly selected within a given range, so little prior knowledge about the battery is required. Moreover, the identification of the complex nonlinear system can be transferred into a standard least squares problem, which is suitable for online applications. The proposed method was tested and compared with RLS (Recursive Least Square)-based methods at different ambient temperatures to verify its superiority. The temperature prediction accuracy is higher than ±1.5 °C, and the RMSE (Root Mean Square Error) of the power prediction is less than 0.25 W. It should be noted that the accuracy of the proposed method does not rely on the accuracy of the state estimation such as SOC, thereby improving its robustness.

Keywords: state-of-power; electric vehicle; extreme learning machine; battery management system (BMS) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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