Lithium-Ion Batteries: Modelling and State of Charge Estimation

Abstract

<p>Lithium-ion (Li-ion) cells are increasingly used in many applications affecting our</p> <p>daily life, such as laptops computers, cell phones, digital cameras, and other portable</p> <p>electronic devices. Lithium-ion batteries are increasingly being considered for their use in Electric Vehicles (EV), Hybrid Electric Vehicles (HEV) and Plug-in Hybrid Electric Vehicles (PHEV) due to their high energy density, slow loss of charge when not in use, and for lack of hysteresis effect. New application domains for these batteries has placed greater emphasis on their energy management, monitoring and control strategies.</p> <p>In this thesis, a comparative study between different models and state of charge (SOC) estimation strategies is performed. Battery models range from black-box representation to detailed electrochemical reaction models that consider the underlying physics. The state of charge is estimated using the Extended Kalman filter (EKF) and the Smooth Variable Structure Filter (SVSF). The models and SOC estimation strategies are applied to experimental results from BMW Electrical and Hybrid Research and Development center and validated using a simulation model from AVL CRUISE software.</p> <p>Overall, different models and SOC estimation scenarios were studied. An average improvement of 30% in the estimation accuracy was shown by the SVSF SOC method when compared with the EKF SOC strategy. In general, the SVSF SOC estimation technique demonstrates excellent capability and a fast speed of convergence.</p>