Autonomous Battery Management System (AutoBMS)

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Rechargeable batteries are an excellent form of energy storage. Particularly, Lithium based batteries have been widely adopted in electric vehicles, portable electronic equipment, household appliances, power tools, aerospace equipment and renewable energy storage systems. A battery management system (BMS), consisting of a battery fuel gauge, cell balancing circuitry, and optimal charging algorithm, is essential for the safe, reliable and efficient operation of a battery pack. The BMS uses three non-invasive measurements from the battery, voltage, current and temperature, to estimate the state of charge (SOC) and state of health (SOH); these estimates are used in BMS functions, such as the generation of optimal charging waveforms, cell balancing, and to activate safety protectors. Today's BMS technology is inadequate to accurately predict the SOH of a battery; as a result, the choices are either to prematurely replace the battery or to wait until a failure event occurs. Both of these choices have undesirable consequences: premature replacement will result in increased cost to the end user and excessive waste to the environment; waiting out will negatively impact the safety and quality of experience of the end user. Further, the state of the art BMS is constrained to particular chemistry, manufacturer, and size of the battery to which it is characterized for, i.e., the present-day BMS is not universal; this restricts battery selection and results in increased cost; also, such a restrictive BMS doesn't allow one to repurpose old/new battery packs. In addition, custom battery chargers generate excessive electronic clutter and environmental waste. The proposed research has two immediate goals. The first one is to discover a unique measurement index to accurately estimate SOH; for this, we will employ machine learning algorithms to study thousands of observations to identify succinct features that are accurate indicators of SOH. The second goal is to develop the necessary algorithmic foundations of a universal BMS that is independent of the chemical composition, manufacturer, size, and age of the battery; we will make use of the power of cloud computing and information fusion algorithms to achieve this goal. Some outcome of this research will help to improve optimal battery charging algorithms to reduce charging time without affecting SOH. The long-term objective of this research is to develop an autonomous BMS that provides the end user with efficiency, flexibility, and safety and enables them to use rechargeable batteries in uniquely creative ways to store and use renewable energy.