Configuration and Sizing of Machine Learning Algorithms for Battery SOC, SOH, and Temperature Estimation

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While there is increasing interest in neural network machine learning algorithms for battery state estimation and modeling, the process of determining what type of network to use and how to size and configure the networks remains difficult. Battery applications typically utilize networks which fall into a few broad categories, including (1) non-recurrent networks with no memory of past time steps, (2) recurrent networks with inherent memory of past time steps, and (3) networks which take a large matrix of data as an input and have a single output, such as convolutional networks for state of health (SOH) estimation. For these network types there are many considerations around the input features, data filtering, data augmentation, amount of training data, number of layers, number of learnable parameters, computational complexity, training time, and other aspects. To give insight on how to best design a neural network for a battery application, the results of three case studies on battery SOC, SOH, and temperature estimation will be presented.

For the battery SOC estimation case study, a non-recurrent, feedforward neural network (FNN) and a recurrent, long short-term memory (LSTM) neural network are trained and tested with automotive drive cycle data for two different cells, with temperature ranging from -10 ⁰C to 25 ⁰C. To give insight into another application type, a convolutional neural network was utilized to estimate battery state of health from the measured voltage, current, and temperature during charging. In the final case study, the temperature of a lithium-ion cell is estimated based on the measured voltage, current, ambient temperature, and state of charge. The study focuses on feature selection, the process of selecting the neural network inputs. Inputs including unfiltered and filtered voltage and current, SOC, and ambient temperature are compared via eight different cases.

A series of recommendations and best practices can be drawn from these cases studies, helping practitioners to design and apply neural networks for their battery applications more quickly and effectively.

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