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.
Highlights
Owing to the advantages of a high energy density, low self-discharge ratio and lack of memory effect, lithium-ion batteries are more and more commonly used in portable electronics, spacecrafts and electric vehicles [1]
We first explore health indicators (HIs) that can be obtained from the online condition-monitoring of the battery’s voltage and current, and we develop a model based on an elastic net to build the linkage between these HIs and the battery’s SOH, considering the usually existing redundancies between the HIs
Ztk ik (τ )dτ where ik (τ) is the current measured at the time τ in the kth cycle, which can be different at different τ according to the randomized loading condition; tk denotes the whole discharge time of the kth cycle
Summary
Owing to the advantages of a high energy density, low self-discharge ratio and lack of memory effect, lithium-ion batteries are more and more commonly used in portable electronics, spacecrafts and electric vehicles [1]. When lithium-ion batteries are used in electronic equipment, the battery’s capacity and internal resistance are very difficult to measure via existing sensors These quantities can only be used under constant loading conditions, which occur much less frequently in real practice. In this paper, we dedicate efforts to extracting some indirect HIs for batteries’ SOH estimation under randomized loading conditions For this purpose, we first explore HIs that can be obtained from the online condition-monitoring of the battery’s voltage and current, and we develop a model based on an elastic net to build the linkage between these HIs and the battery’s SOH, considering the usually existing redundancies between the HIs. To our knowledge, we are the first to successfully build up indirect HIs to represent the battery’s health status under randomized use.
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