Battery

1. Machine learning based battery failure
prediction along with regenerative
power.

2. • Abstract:
• Lithium-ion batteries (LIBs) play a significant role in our highly electrified world and will continue to
lead technology innovations. Millions of vehicles are equipped with or directly powered by LIBs, mitigating
environmental pollution and reducing energy use. This rapidly increasing use of LIBs in vehicles will introduce
a large quantity of spent LIBs within an 8–10-year span. Proper handling of end-of-life (EOL) vehicle LIBs is
required, and multiple options should be considered. This project involves the prediction of lifetime of the
lithium batteries in vehicles using machine learning. Initially dataset in csv form is collected by selecting the
parameters. The collected dataset is processed to drop the null values. The cleaned dataset is split into test and
train and training is done. The trained model is used for prediction of life time of lithium batteries. rom a life-
cycle perspective, remanufacturing and repurposing extend the life of LIBs, and industrial demonstrations
indicate that this is feasible. Recycling is the ultimate option for handling EOL LIBs. Thus this project also aims
in the development of regenerative power in the batteries. AC to DC converters are the key component in battery
chargers of dynamo motors in hybrid electric vehicle. DC to DC converter in the above mentioned charger will
step up or step down the voltage according to the requirement of the battery. This energy will be utilized to
operate other electrical appliances . In this project the vehicle running motion is used to rotate spur gear
mechanism which generates power and is stored in the lithium batteries.