Lithium-Ion Battery Pack Cycling Dataset with CC-CV Charging and WLTP/Constant Discharge Profiles.

Area of Science:

• Electrical Engineering
• Materials Science
• Automotive Engineering

Background:

• Lithium-ion batteries are critical for electric vehicles.
• Accurate battery performance data under dynamic conditions is essential for effective battery management system (BMS) development.
• Existing datasets may not fully capture real-world driving complexities and in-vehicle communication constraints.

Purpose of the Study:

• To create a comprehensive dataset of lithium-ion battery pack cycling tests.
• To simulate realistic dynamic driving conditions using the Worldwide Harmonised Light Vehicles Test Procedure (WLTP) cycle.
• To provide valuable data for BMS development and validation.

Main Methods:

• A custom test bench was developed to simulate dynamic driving conditions.
• Current profiles were derived from speed-time data using MATLAB/Simulink and a Tesla Model 3 vehicle model.
• Data acquisition included cell voltages, currents, surface temperatures, and pack resistance for up to 36 cells, utilizing a controller area network (CAN) bus architecture and commercial BMS units.

Main Results:

• A detailed dataset of lithium-ion battery pack cycling tests was generated.
• The dataset captures time-series data under controlled thermal conditions.
• The data reflects real-world in-vehicle communication constraints through CAN bus and commercial BMS integration.

Conclusions:

• The generated dataset offers a valuable resource for researchers and engineers.
• This dataset enhances the development and validation of battery management systems for electric vehicles.
• The inclusion of dynamic driving conditions and communication constraints increases the dataset's practical applicability.