Intelligent management techniques for reconfigurable battery systems

Abstract

In a reconfigurable battery pack, the connections among cells can be changed during operation, to form different configurations. This can lead a battery, a passive two-terminal device, to a smart battery which can reconfigure itself according to the requirement to enhance operational performance. Several hardware architectures with different levels of complexities have been proposed. Some researchers have used existing hardware and demonstrated improved performance on the basis of novel optimization and scheduling algorithms. The possibility of software techniques to benefit the energy-storage systems is exciting and it is the perfect time for such methods as the need of high performance and long lasting batteries is on the rise. This novel field requires new understanding, principles and evaluation metrics of proposed schemes. Traditionally used methods, in battery management system for measuring State of Charge (SoC) of a battery cell are Coulomb counting and Extended Kalman Filter (EKF) which suffer from the accumulation of noise and common phenomenon of biased noise, respectively. The noise in sensor readings makes the estimation even more challenging, especially in battery-operated systems where the supply voltage of sensor keeps changing. The traditional approach of dealing with ever-increasing demand for accuracy is to develop more complicated and sophisticated solutions which generally require special models. A key challenge in the adoption of such systems is the inherent requirement of specialized knowledge and hit-and-trial based tuning. In this study, we explore a new dimension from the perspective of a self-tuning algorithm which can provide accurate SoC estimation without error accumulation by creating a negative feedback loop and enhancing its strength to penalize the estimation error. Specifically, we propose a novel method which uses battery model and a conservative filter with a strong feedback which guarantees that worst-case amplification of noise is minimized. We capitalize on battery model for data fusion of current and voltage signals for SoC estimation. To compute the best parameters, we formulate the Linear Matrix Inequality (LMI) conditions which are optimally solved using open-source tools. This approach also features a low computational expense during estimation which can be used in real-time applications. Thorough mathematical proofs, as well as detailed experimental results, are provided which highlight the advantages of the proposed method over traditional techniques. Charging a battery pack with variable power supply e.g. solar panels or air driven generators can cause voltage mismatch which may result in damage to the battery pack. Conventionally, DC-DC converters are used to solve this problem but these converters can cause charging inefficiency. In this work, instead of DC-DC converters, battery pack with reconfigurable architecture is used to solve this voltage mismatch problem. We develop algorithms to dynamically decide the battery connections of reconfigurable battery pack, to both minimize the voltage mismatch and maintain SoC balancing among difference batteries. Moreover, we develop techniques to integrate the solar panel into the reconfigurable circuit of the batteries to further improve the charging efficiency when the power output condition of different panels are different (e.g., when some of the panels are shaded).