Battery Lifetime & Capacity Fade Prediction for Electric Vehicles Using Coupled Electro-Thermal Simulation Methodology

Global concerns over availability and environmental impact of conventional fuels in recent years have resulted in evolution of Electric Vehicles. Research and development focus has shifted towards one of its main components, Lithium-ion battery. Development of high performing, long lasting batteries within challenging timelines is the need of the industry. Lithium-ion batteries undergo “battery ageing”, limiting its energy storage and power output, affecting the EV performance, cost & life span. It is critical to be able to predict the rate of battery ageing & the impact of different environmental conditions on battery lifetime/capacity. Conventionally, extensive physical vehicle level testing is carried out on batteries to map the battery capacity in various conditions. This is a lengthy & expensive process affecting the product development cycle, paving the way for an alternative process. This paper proposes a quick and computationally feasible simulation process wherein battery life & capacity fade can be predicted based on in-house simulation of actual cell/battery pack models along with 24-hour temperature variation at different locations such as Pune, Delhi etc. A Coupled Electro-Thermal simulation methodology is explored using commercial thermal analysis tools which can extract battery capacity and Remaining Useful Life (RUL) data for different ambient temperatures & locations using cell life characteristics as input. It is possible to predict the individual cell temperatures, battery capacity and battery resistance. This method can also identify the critical point i.e. the instant at which battery performance drops below acceptable levels. Proposed methodology can help in early detection & resolution of possible bottlenecks due to battery life issues at the design stage, along with supporting the product by providing an accurate warranty period based on battery ageing. It also has applications in predictive product support by keeping the customer and manufacturer updated about battery health & replacement timelines.