Abstract
This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.
Highlights
Increasing concerns about global warming as well as oil and resource depletion have led to more stringent regulations linked to fuel economy, emissions, and energy conservation
The aim is to evaluate the method performance in terms of duration and precision of the training process, estimation accuracy, and computational cost. The latter is analyzed by measuring the memory and processor occupation, when the designed algorithms are deployed on an electronic control unit
The estimation accuracy and robustness of the Artificial Neural Networks (ANNs) is evaluated with an additional profile obtained from a real
Summary
Increasing concerns about global warming as well as oil and resource depletion have led to more stringent regulations linked to fuel economy, emissions, and energy conservation. This has created an incentive to focus efforts on alternative powertrain technologies [1]. In this context, the development of battery systems has gained a sizable momentum [2,3] due to their fundamental role in fully electric, hybrid and plug-in hybrid electric vehicles (BEVs, HEVs, PHEVs). Lithium batteries require constant and accurate monitoring to check their condition, the level of the remaining available energy, indicated by the SOC
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