Room: E-2024, Bldg: E, 1100, Notre-dame ouest, Ecole de Technologie Superieure, Montreal, Quebec, Canada, H3C 1K3, Virtual: https://events.vtools.ieee.org/m/356310

Enhancing the life of Lithium-ion (Li-ion) battery packs has been the topic of much interest. In this framework, the role of on-board cell voltage balancing of Li-ion batteries will be highlighted in this talk. This is a very important topic in the context of battery energy storage cost and life/state-of-charge/state-of-health (SOC/SOH) monitoring. This talk will also introduce a first-of-its-kind closed-loop cell charge (voltage) balancing and extreme fast charging technique. The technique uses instantaneous cell voltage and/or temperature rise (ΔT) as a control parameter. Existing charging techniques for Li-ion batteries use a largely open-loop approach, where the charge profile is pre-decided, based on apriori knowledge of cell parameters. There is a need for closed-loop charging techniques that use instantaneous cell voltage and/or temperature to modulate the charging current magnitude. This talk addresses this gap by proposing for the first time ever a constant-temperature constant-voltage (CT-CV) charging technique, considering cell temperature as a key degradation metric. Furthermore, continuous monitoring of SOC/SOH of a Li-ion battery is essential to avoid over-charging, over-discharging, and ensure smart battery management. It also ensures overall safe operation, increase in calendar life, and reduction of average life-cycle cost. However, accurate SOC/SOH estimation has become a major challenge, since these studies need large amounts of experimental data, and adopt standard charge/discharge patterns that do not reflect real world driving loads. In this talk, advanced machine learning (ML) techniques will be introduced to estimate battery SOC/SOH based on measured critical battery parameters. The effectiveness of the proposed ML techniques are verified using experimental data of the Li-ion battery operating under varied driving schedules and temperatures. Experimental test results show that the proposed ML approaches outperform other conventional approaches with much greater accuracy. This presentation will also highlight the current status and future opportunities within Ontario Tech University’s research program on transportation electrification and electric energy storage systems. The above-mentioned research initiatives will be described in the presentation and industry-specific projects within the STEER group will be highlighted. The NSERC Canada Research Chair (CRC) program includes several novel initiatives in the areas of transportation electrification and is built upon the expertise and knowledge of the STEER group in a number of promising interdisciplinary areas related to power electronics and motor drives. Speaker(s): Prof. Sheldon Williamson, Room: E-2024, Bldg: E, 1100, Notre-dame ouest, Ecole de Technologie Superieure, Montreal, Quebec, Canada, H3C 1K3, Virtual: https://events.vtools.ieee.org/m/356310