Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

Jinsong Yu, Baohua Mo, Diyin Tang, Jie Yang, Jiuqing Wan and Jingjing Liu
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Jinsong Yu: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Baohua Mo: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Diyin Tang: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Jie Yang: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Jiuqing Wan: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Jingjing Liu: National Key Laboratory of Science and Technology on Aerospace Intelligence Control, Beijing 100854, China

Energies, 2017, vol. 10, issue 12, 1-19

Abstract: Lithium-ion batteries are widely used in many systems. Because they provide a power source to the whole system, their state-of-health (SOH) is very important for a system’s proper operation. A direct way to estimate the SOH is through the measurement of the battery’s capacity; however, this measurement during the battery’s operation is not that easy in practice. Moreover, the battery is always running under randomized loading conditions, which makes the SOH estimation even more difficult. Therefore, this paper proposes an indirect SOH estimation method that relies on indirect health indicators (HIs) that can be measured easily during the battery’s operation. These indicators are extracted from the battery’s voltage and current and the number of cycles the battery has been through, which are far easier to measure than the battery’s capacity. An empirical model based on an elastic net is developed to build the quantitative relationship between the SOH and these indirect HIs, considering the possible multi-collinearity between these HIs. To further improve the accuracy of SOH estimation, we introduce a particle filter to automatically update the model when capacity data are obtained occasionally. We use a real dataset to demonstrate our proposed method, showing quite a good performance of the SOH estimation. The results of the SOH estimation in the experiment are quite satisfactory, which indicates that the method is effective and accurate enough to be used in real practice.

Keywords: lithium-ion battery; indirect state-of-health (SOH) estimation; randomized loading condition; elastic net; particle filter (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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