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Real-Time Estimation of Model Parameters and State-of-Charge of Li-Ion Batteries in Electric Vehicles Using a New Mixed Estimation Model

Conference Paper


Abstract


  • A precise estimation of the state of charge (SoC) of the lithium-ion battery is crucial for reducing range-Anxiety and improving the performance of the electric vehicle (EV) battery management system. An accurate estimation of the SoC, however, has remained elusive due to the complex and nonlinear behavior of the battery. In this article, a new mixed estimation model (MEM) for the battery parameters and the SoC estimation is proposed, where the route is specified before the travel. The new MEM uses a combination of a battery power-based method (BPBM), a combined model, and a partial adaptive forgetting factor recursive least-square (PAFF-RLS) SoC calibration algorithm to make use of the best characteristics of each model to determine a better and more accurate SoC estimation. The partial adaptive forgetting factors solves the issue of the different rate changes in the battery parameters and reduces the complexity of the algorithm compared to the fully adaptive recursive models. The BPBM allows various traveling factors to be included in the model, such as the environmental conditions, the effect of auxiliary loads, and the traffic congestion. To verify the validity of the PAFF-RLS algorithm, two laboratory tests using real-Time driving cycles have been conducted on a 2012 Nissan Leaf 31.1 Ah Manganese-oxide Li-ion battery cell. The effectiveness of the MEM model has been demonstrated by driving the Nissan Leaf along two selected routes in Australia. The results demonstrate the great accuracy of the proposed method for the SoC estimation, when compared with those from the previous models.

Publication Date


  • 2020

Citation


  • Sarrafan, K., Muttaqi, K. M., & Sutanto, D. (2020). Real-Time Estimation of Model Parameters and State-of-Charge of Li-Ion Batteries in Electric Vehicles Using a New Mixed Estimation Model. In IEEE Transactions on Industry Applications Vol. 56 (pp. 5417-5428). doi:10.1109/TIA.2020.3002977

Scopus Eid


  • 2-s2.0-85091744459

Start Page


  • 5417

End Page


  • 5428

Volume


  • 56

Issue


  • 5

Abstract


  • A precise estimation of the state of charge (SoC) of the lithium-ion battery is crucial for reducing range-Anxiety and improving the performance of the electric vehicle (EV) battery management system. An accurate estimation of the SoC, however, has remained elusive due to the complex and nonlinear behavior of the battery. In this article, a new mixed estimation model (MEM) for the battery parameters and the SoC estimation is proposed, where the route is specified before the travel. The new MEM uses a combination of a battery power-based method (BPBM), a combined model, and a partial adaptive forgetting factor recursive least-square (PAFF-RLS) SoC calibration algorithm to make use of the best characteristics of each model to determine a better and more accurate SoC estimation. The partial adaptive forgetting factors solves the issue of the different rate changes in the battery parameters and reduces the complexity of the algorithm compared to the fully adaptive recursive models. The BPBM allows various traveling factors to be included in the model, such as the environmental conditions, the effect of auxiliary loads, and the traffic congestion. To verify the validity of the PAFF-RLS algorithm, two laboratory tests using real-Time driving cycles have been conducted on a 2012 Nissan Leaf 31.1 Ah Manganese-oxide Li-ion battery cell. The effectiveness of the MEM model has been demonstrated by driving the Nissan Leaf along two selected routes in Australia. The results demonstrate the great accuracy of the proposed method for the SoC estimation, when compared with those from the previous models.

Publication Date


  • 2020

Citation


  • Sarrafan, K., Muttaqi, K. M., & Sutanto, D. (2020). Real-Time Estimation of Model Parameters and State-of-Charge of Li-Ion Batteries in Electric Vehicles Using a New Mixed Estimation Model. In IEEE Transactions on Industry Applications Vol. 56 (pp. 5417-5428). doi:10.1109/TIA.2020.3002977

Scopus Eid


  • 2-s2.0-85091744459

Start Page


  • 5417

End Page


  • 5428

Volume


  • 56

Issue


  • 5