Modeling and Simulink of Optimizing SOC and SOH Estimation Algorithms for Enhanced Electric Vehicle Efficiency
Implementation plan:
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Step 1: Initially, we will construct the Battery Energy Storage Simulink model for electric vehicles.
Step 2: Then, we collect the data and preprocess it using PCA with Wavelet Transform filtering filters to increase SOC and SOH estimation accuracy.
Step 3: Next, we estimate SOC using Kalman Filter (EKF) and Particle Filter (PF) and train the estimated values using (Hybrid DNN-LSTM).
Step 4: Next, we implement SVR with RFR to predict SOC and use RNN-Bi-LSTM for battery deterioration.
Step 5: Next, we optimize dynamic power allocation using PPO-DQN to increase energy efficiency
Step 6: Finally, we plot performance for the following metrics:
6.1: Time Vs SOC (%)
6.2: Time Vs Voltage (V)
6.3: Time Vs Current (A)
6.4: Number of Epochs Vs Accuracy (%)
6.5: Number of Epochs Vs. Mean Absolute Error (%)
Software Requirements:
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1. Development Tool: Matlab-R2024a/Simulink
2. Operating System: Windows-11 (64-bit)
Note:
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[1] If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
[2] Please note that this implementation plan does not include any further steps after it is put into implementation.
[3] Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.
[4] If the above plan satisfies your requirement please confirm with us.
We perform with an Existing Approach Reference 1 :Title:- An Improved Collaborative Estimation Method for Determining The SOC and SOH of Lithium-Ion Power Batteries for Electric Vehicles
