Abstract:
This paper presents a nonlinear autoregressive moving average with exogenous variables (NARMAX) method to estimate the residual capacity of high-capacity Ni/MH battery pa...Show MoreMetadata
Abstract:
This paper presents a nonlinear autoregressive moving average with exogenous variables (NARMAX) method to estimate the residual capacity of high-capacity Ni/MH battery pack in electric vehicles. The state of charge (SOC) represents the battery residual capacity. The SOC of battery cannot be measured directly and estimated from measurable battery parameters such as current and voltage. The proposed NARMAX produces accurate SOC estimate, using industry standard Federal Urban Driving Schedule (FUDS) aggressive driving cycle test procedures. The results indicate that the NARMAX can provide an accurate and effective estimation of the SOC, resulting in minimal computation load and suitable for real-time embedded system application. The maximum average relative error of the estimating results is 0.02%.
Date of Conference: 03-05 June 2008
Date Added to IEEE Xplore: 01 August 2008
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Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Electric Vehicles ,
- Residual Capacity ,
- NiMH Batteries ,
- State Of Charge ,
- Exogenous Variables ,
- Average Relative Error ,
- Maximum Relative Error ,
- Battery State Of Charge ,
- Degrees Of Freedom ,
- Artificial Neural Network ,
- Plant Cells ,
- Prediction Error ,
- Nonlinear Systems ,
- Kalman Filter ,
- Battery Capacity ,
- Recursive Least Squares ,
- Recursive Least Squares Algorithm
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Electric Vehicles ,
- Residual Capacity ,
- NiMH Batteries ,
- State Of Charge ,
- Exogenous Variables ,
- Average Relative Error ,
- Maximum Relative Error ,
- Battery State Of Charge ,
- Degrees Of Freedom ,
- Artificial Neural Network ,
- Plant Cells ,
- Prediction Error ,
- Nonlinear Systems ,
- Kalman Filter ,
- Battery Capacity ,
- Recursive Least Squares ,
- Recursive Least Squares Algorithm