To achieve a reliable and high-performance charging control for Li-ion batteries, the following three tasks were accomplished.
Firstly, we have proposed an enhanced hybrid stacking-boosting ensemble model for EV charging duration prediction. The time index features, EV information features, and historical statistical features are selected as the prediction model input. Then, an ensemble model is designed by combining multiple linear regression, support vector regression, random forest, XGBoost, and artificial neural network. Experimental results show that better prediction performance (less root mean square of prediction error) can be achieved compared with the commonly used single machine learning model. At last, an optimal charging algorithm is proposed for EV battery charging to meet the EV user charging demand that can extend the lifetime of the battery.
Secondly, we have conducted a comprehensive review of the state of charge estimation techniques. Over the last decade, numerous attempts have been made to effectively analyze and compare the state of charge estimation methods for commercial Li-ion batteries. However, they seldom reflect on the state of charge estimation techniques based on a control-oriented viewpoint for a Li-ion battery system. To fill this gap, we have reviewed the most up-to-date battery state of charge estimation methods applied to lithium-ion battery systems. They are broadly classified as open-loop-based, closed-loop-based, and hybrid approaches. Finally, we provide an analysis of the positive and negative aspects of the reviewed techniques and some suggestions for future research.
Thirdly, we introduce a novel, hypergraph-based approach to establish the first unified model for various active battery equalization systems. This model reveals the intrinsic relationship between battery cells and equalizers by representing them as the vertices and hyperedges of hypergraphs, respectively. With the developed model, we identify the necessary conditions for all equalization systems to achieve balance through controllability analysis, offering valuable insights for selecting the number of equalizers. It can be used to solve the cell imbalance problem in the battery pack charging process.
