Framework for Co-estimation of State of Charge and State of Available Power for Battery Management Systems
2022-01-0859
03/29/2022
- Event
- Content
- The battery management system (BMS) ensures safe operation of cells defining a safe operation zone for charge/discharge currents, temperatures, provides cell balancing and fault management. The BMS is also responsible for defining limiting currents during acceleration, regenerative braking and gradient climb. Accurate state of charge (SOC) and state of available power (SoAP) estimation is required for these tasks. This enables energy management system to affect regulation over power flow in the vehicle thus improving battery performance and lifetime. The goal is to develop a computationally inexpensive algorithm for Soc and SoAP estimation which can be implemented in BMS for onboard state estimation tasks. In this work, a framework for co-estimation of SOC and SoAP is proposed. The algorithm is developed considering a series – parallel connected battery pack for light electric vehicle (electric scooter or bicycle) application. An equivalent circuit model (ECM) is formulated for the battery from the hybrid pulse power characterization (HPPC) tests carried out at different ambient temperatures. An iterated extended Kalman filter (iEKF) based estimation algorithm is proposed for state of charge estimation of the cells. In battery packs with series - parallel connected cells, because of multiple factors such as local temperature distribution and busbar resistances, cell to cell deviation is observed in the impedance values and state of charge. Thus, weakest cell methodology is used for pack available power prediction. The battery parameters for SoAP are identified online using the Recursive Least Square (RLS) algorithm with a forgetting factor. The power limits are calculated based on current limits, peak SOC limits and SOC limits. The simulation results are compared with cycler experimental data for multiple drive cycles. The results show good real time performance and acceptable estimation accuracy.
- Citation
- Aphale, S., "Framework for Co-estimation of State of Charge and State of Available Power for Battery Management Systems," SAE Technical Paper 2022-01-0859, 2022, .