Online State-of-health Estimation Of Li-ion Batteries Using Dynamic Bayesian Network

With the development of electronic science and improvement of people’s living standards,more and more lithium-ion batteries are used in mobile devices and electro mobiles. Safety issuesmust be concerned when we use Li-ion batteries, so battery management system (BMS) is of greatimportance. The state of health (SOH) is one of the key parameters of batteries that a BMS shouldmonitor. Accurate estimation of SOH can help us to get better balance between safety of system andeconomic benefits.The SOH cannot be measured by any direct method, at present; the main estimation methodsinvented by researchers all over the world include completely discharge method, the method basedon neural network and the method based on Kalman filter. Firstly, these three methods areintroduced in detail and compared in this thesis, and some deficiencies of them are pointed out.Then, according to the actual situation, SOH is originally discretized using five intervals(brand-new, new, ok, old and very-old). On this basis, this thesis presents a novel online method forthe estimation of the SOH of Li-ion. The proposed method is based on Dynamic Bayesian Network(DBN). The structure of the model is created according to the experience of experts, and theparameters of the model is learned based on data collected in a Li-ion battery aging experiment.Forward algorithm is used here to estimate SOH in real-time.Finally, an online SOH estimation system based on STM32F103VCT6is designed to validatethe SOH prediction method presented, and experimental results show that the proposed method isaccurate.