State of Health Estimation of Lithium Batteries for Automotive Applications with Artificial Neural Networks

2019 ◽  
Author(s):  
Angelo Bonfitto ◽  
Ethelbert Ezemobi ◽  
Nicola Amati ◽  
Stefano Feraco ◽  
Andrea Tonoli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lithium Batteries ◽  
State Of Health ◽  
Automotive Applications ◽  
Artificial Neural

scholarly journals The Use of Artificial Neural Networks to Forecast the Behavior of Agent-Based Models of Pathophysiology: An Example Utilizing an Agent-Based Model of Sepsis

2021 ◽  
Vol 12 ◽  
Author(s):  
Dale Larie ◽  
Gary An ◽  
R. Chase Cockrell
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multi Layer Perceptron ◽  
State Of Health ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Clinical Sepsis ◽  
Agent Based ◽  
Artificial Neural

Introduction: Disease states are being characterized at finer and finer levels of resolution via biomarker or gene expression profiles, while at the same time. Machine learning (ML) is increasingly used to analyze and potentially classify or predict the behavior of biological systems based on such characterization. As ML applications are extremely data-intensive, given the relative sparsity of biomedical data sets ML training of artificial neural networks (ANNs) often require the use of synthetic training data. Agent-based models (ABMs) that incorporate known biological mechanisms and their associated stochastic properties are a potential means of generating synthetic data. Herein we present an example of ML used to train an artificial neural network (ANN) as a surrogate system used to predict the time evolution of an ABM focusing on the clinical condition of sepsis.Methods: The disease trajectories for clinical sepsis, in terms of temporal cytokine and phenotypic dynamics, can be interpreted as a random dynamical system. The Innate Immune Response Agent-based Model (IIRABM) is a well-established model that utilizes known cellular and molecular rules to simulate disease trajectories corresponding to clinical sepsis. We have utilized two distinct neural network architectures, Long Short-Term Memory and Multi-Layer Perceptron, to take a time sequence of five measurements of eleven IIRABM simulated serum cytokine concentrations as input and to return both the future cytokine trajectories as well as an aggregate metric representing the patient’s state of health.Results: The ANNs predicted model trajectories with the expected amount of error, due to stochasticity in the simulation, and recognizing that the mapping from a specific cytokine profile to a state-of-health is not unique. The Multi-Layer Perceptron neural network, generated predictions with a more accurate forecasted trajectory cone.Discussion: This work serves as a proof-of-concept for the use of ANNs to predict disease progression in sepsis as represented by an ABM. The findings demonstrate that multicellular systems with intrinsic stochasticity can be approximated with an ANN, but that forecasting a specific trajectory of the system requires sequential updating of the system state to provide a rolling forecast horizon.

scholarly journals Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

Batteries ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 47 ◽  
Author(s):  
Angelo Bonfitto ◽  
Stefano Feraco ◽  
Andrea Tonoli ◽  
Nicola Amati ◽  
Francesco Monti
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lithium Batteries ◽  
Hybrid Electric Vehicle ◽  
Electronic Control Unit ◽  
Computational Cost ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Soc Estimation ◽  
Artificial Neural

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.

scholarly journals A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2548
Author(s):  
Angelo Bonfitto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hybrid Electric Vehicle ◽  
Estimation Method ◽  
State Of Charge ◽  
Battery Management System ◽  
Battery Management ◽  
State Of Health ◽  
Combined Estimation ◽  
Artificial Neural

This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system.

Sign in / Sign up

Export Citation Format

Share Document