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Optimized Estimation of Battery ParametersCapacity, Resistances and SOC for ElectricVehicles: A Hybrid Approach Based onGenetic Algorithm and Artificial Intelligence
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| Author(s) |
Jamila HEMDANI, Laid DEGAA, Nassim RIZOUG, Moez SOLTANI, Achraf TELMOUDI, Abdelkader CHAARI |
| Abstract |
Index Terms\_AI-Enhanced Battery Modeling forElectric Vehicles: Real-Time Parameter Optimization andState EstimationAbstract\_In this paper, we present a method foroptimizing the key parameters of an equivalent electricalmodel of a battery (Rohmic, R1, C1) and its capacity (Cbat)using advanced artificial intelligence techniques. The goalis to improve the accuracy of the estimation of the State ofCharge (SOC) and State of Health (SOH). We introducea dynamic optimization approach where resistances andcapacitances are adjusted at each time step, while Cbatis optimized over a full cycle. The results demonstrate asignificant improvement in terms of reducing predictionerrors. |
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| Filename: | 0330-epe2025-full-10300426.pdf |
| Filesize: | 338.7 KB |
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| Type |
Members Only |
| Date |
Last modified 2025-08-31 by System |
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