Battery (electricity)

Quantum mechanics

Algorithm

State of charge

Power (physics)

Computer science

Thermodynamics

Physics

Organic chemistry

Artificial intelligence

Charge (physics)

Chemistry

Artificial neural network

Machine learning

State (computer science)

Journal of Power Sources

Accurate State of Charge (SOC) estimation is crucial to ensure the safe and reliable operation of Li-ion batteries, which are increasingly being used in Electric Vehicles (EV), grid-tied load-leveling applications as well as manned and unmanned aerial vehicles to name a few applications. In this paper, a novel approach using Deep Feedforward Neural Networks (DNN) is used for battery SOC estimation where battery measurements are directly mapped to SOC. Training data is generated in the lab by applying drive cycle loads at various ambient temperatures to a Li-ion battery so that the battery is exposed to variable dynamics. The DNN's ability to encode the dependencies in time into the network weights and in the process provide accurate estimates of SOC is presented. Moreover, data recorded at ambient temperatures lying between −20 °C and 25 °C are fed into the DNN during training. Once trained, this single DNN is able to estimate SOC at various ambient temperature conditions. The DNN is validated over many different datasets and achieves a Mean Absolute Error (MAE) of 1.10% over a 25 °C dataset as well as an MAE of 2.17% over a −20 °C dataset.

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach