Version 1.0 : Re-write the Electric vehicles and batteries simulation model, build a CNN-LSTM model for SOC prediction, the SOH is also considered.

• Xin Chen: Supervisor
• Yuchen Zhang: SOC Prediction Model
• Siyuan Liang, Peiyuan Sun: Electric Vehicles Model
• Jiali Lu: Investigation

• Python: 3.x
• Pytorch: 0.4+
• Matlab: R2019a
• Simulink: 10.0

Improve prediction accuracy and avoid the high errors near zero point by cutting off the parameter transfers between different batches of data

1. Download smallNewModelData dataset from here
2. Sturcture:

Prediction_Model_v1/
├── smallNewModelData
	├── soc01.csv  
	├── soc01.csv 
	...
├── main.py
    ├── SOC_v1.py
    ├── SOC_test_v1.py
    ├── Data.py
└── net_params.pkl

4. Modify the path to your actual data path
5. Run main.py

EPOCHES     = 1500
RATE        = 8e-3
HIDDEN_SIZE = 48
Optimizer   = Adam

• SOC=83% RMSE=1.76%
• SOC=90% RMSE=1.88%

1. Run main.py to train the model
2. CNN-LSTM model is defined in Soc_v1.py
3. Soc_test_v1.py is for testing
4. Data.py is for data processing, but you don't need to run it
5. Parameter is in net_params.pkl

1. Determine the weight, windward area, internal drive efficiency and the numbers of batteries of the simulation objects
2. Fill them into the corresponding position of the model
3. The speed and slope is also needed

1. Operating input module
2. Automotive speed control module
3. Torque conversion module
4. Vehicle kinect module
5. Power conversion module
6. Thermodynamics module

• Get access to the simulation of the vehicle's inner situation under different working states which involve the variation of slope and velocity

• Figure out the specific power needed from the battery to support the vehicle's work and the

• State of Charge

• Velocity

• Current

• Terminal voltage

Use the PNGV model to descibe the port of the battery

• Lithium battery SOC calculation block diagram based on PNGV model
• PNGV equivalent model
• OCV-SOC

• State of Charge
• Temperature
• Terminal Voltage