Artificial Neural Network Based Energy Storage System Modeling for Hybrid Electric Vehicles

2000 ◽  
Author(s):  
Sanjay. R. Bhatikar ◽  
Roop. L. Mahajan ◽  
Keith Wipke ◽  
Valerie Johnson
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
System Modeling ◽  
Storage System ◽  
Energy Storage System ◽  
Artificial Neural ◽  
Hybrid Electric

An artificial neural network module for simulation of the energy storage system of a hybrid electric vehicle

2001 ◽  
Vol 215 (8) ◽  
pp. 919-932 ◽  
Author(s):  
S. R. Bhatikar ◽  
R. L. Mahajan ◽  
K Wipke ◽  
V Johnson
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Storage ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Storage System ◽  
Energy Storage System ◽  
Ann Model ◽  
Artificial Neural ◽  
Hybrid Electric

A hybrid electric vehicle (HEV) is a complex system integrating interactive subsystems of disparate degrees of complexity. The simulation of an HEV thus poses a challenge. An accurate simulation requires highly accurate models of each subsystem. Without these, the system has a poor overall performance. Typically, modelling problems are not amenable to physical solutions without simplifying assumptions that impair their accuracy. Conventional empirical models, on the other hand, are time consuming and data intensive and falter where extensive non-linearity is encountered. An artificial neural network (ANN) approach to simulation of an HEV is presented in this paper. An ANN model of the energy storage system (ESS) of an HEV was deployed in the ADVISOR simulation software developed by the National Renewable Energy Laboratories (NREL) of the US Department of Energy. The ANN model mapped the state of charge (SOC) and the power requirement of the vehicle to the voltage and current at the ESS output An ANN model was able accurately to capture the complex, non-linear phenomena underlying the ESS. A novel performance-enhancing technique for design of ANN training data, Smart Select, is described here. It resulted in a model of 0.9978 correlation (R2 error) with data. ANNs can be data hungry. The issue of knowledge sharing between ANN models to save development time and effort is also addressed in this paper. The model transfer technique presents a way of levering the expertise of one ANN into the development of another for a similar modelling task. Lastly, integration of the ANN model of the ESS into the ADVISOR software, on the MATLAB software platform, is described.

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