Abstract
Intelligent and pragmatic state-of-health (SOH) estimation is critical for the safe and reliable operation of Li-ion batteries, which recently have become ubiquitous for applications such as electrified vehicles, smart grids, smartphones, as well as manned and unmanned aerial vehicles. This paper introduces a convolutional neural network (CNN)-based framework for directly estimating SOH from voltage, current, and temperature measured while the battery is charging. The CNN is trained with data from as many as 28 cells, which were aged at two temperatures using randomized usage profiles. CNNs with between 1 and 6 layers and between 32 and 256 neurons were investigated, and the training data was augmented with noise and error as well to improve accuracy. Importantly, the algorithm was validated for partial charges, as would be common for many applications. Full charges starting between 0 and 95% SOC as well as for multiple ranges ending at less than 100% SOC were tested. The proposed CNN SOH estimation framework achieved a mean average error (MAE) as low as 0.8% over the life of the battery, and still achieved a reasonable MAE of 1.6% when a very small charge window of 85% to 97% SOC was used. While the CNN algorithm is shown to estimate SOH very accurately with partial charge data and two temperatures, further studies could also investigate a wider temperature range and multiple different charge currents or constant power charging.
Highlights
Received: 8 December 2021Like most things, Li-ion batteries age with time; a process underpinned by the degradation of electrode materials, loss of lithium in active carbon, lithium metal plating and chemical decomposition, to name a few
Once convolutional neural network (CNN) are trained offline, they can offer fast computational speeds on board a mobile device or vehicle, since they are formulated by a series of convolution and matrix multiplication operations which can be computed in parallel
The convolutional neural network is made more robust against noise, offsets and data impartiality; in other words, to avoid biasing model towards reference gains existent in real-world measurement devices bythe augmenting the training data.profiles
Summary
Li-ion batteries age with time; a process underpinned by the degradation of electrode materials, loss of lithium in active carbon, lithium metal plating and chemical decomposition, to name a few. A CNN is trained to estimate battery capacity from the measured impedance and state of charge values in [10]. In [18,19], incremental capacity analysis (ICA) profiles, described as dQ/dV, are used to detect small incremental changes in the charge curves To identify this ICA profile from noisy measurements and from partial reference profiles, a support vector machine showed the best results in [18]. To further increase the CNN’s practicality in real-world applications, it is trained to estimate SOH over partial charge profiles having varying ranges of state of charge (SOC). This is an important feature increasing the practicality of this method considerably.
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