Cell to Pack Modelling of Li-Ion Batteries for Electric Vehicle Applications: Development and Potential Challenges

Abstract

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) which are powered by Li-ion batteries (LIBs) have the potential to address the challenges of reducing the dependence on fossil fuels and environmental pollution caused by the transportation sector. However, for the reliable and safe operation of the EVs and HEVs, an accurate estimation of the state of charge (SOC) and state-of-health (SOH) of the battery pack is required. The battery capacity degrades with cycling and calendar ageing over time and usage of the vehicles, which reduces the power capability and driving range of the vehicle. An accurate prediction of the battery SOC and SOH can assist the vehicle user in planning to reach the next charging station or the destination with enough range left before the battery gets fully discharged, hence avoiding annoying circumstances.Battery models have been widely employed in battery health and SOC prediction and can successfully predict the SOC and SOH of individual cells. However, the translation of these models from cell to pack levels adds several unknown critical factors which affect the model prediction capability significantly.Our proposed ECM model is based on the electrical and thermal behavior of individual cells, and it accounts for critical factors such as cell-to-cell variations, temperature gradients, and aging effects. We also incorporate a series-parallel configuration to simulate the behavior of multiple cells at the module and pack levels. By upscaling the model in this way, we can accurately predict the SOC and SOH of the entire battery pack, and provide reliable estimates of the remaining driving range and charging requirements.We anticipate that our proposed ECM model will significantly improve the accuracy of SOC and SOH predictions, which will enable EV/HEV users to better plan their trips, avoid unexpected battery failures, and improve the safety of EV/HEV batteries. Furthermore, our study has the potential to inform the design of future battery technologies and contribute to the transition toward sustainable transportation powered by clean energy sources.