As more countries rely on renewable energy sources, battery systems must meet rising efficiency and longevity demands to stay relevant. Knowing key performance indicators of batteries, like Round Trip Efficiency (RTE) and State of Health (SOH), are critical to optimizing their operation and increasing overall performance.
Ideally, battery capacity is evaluated under a full low-current charge/discharge/charge cycle. However, for EVs in the field, it is impractical to subject the battery system to these ideal test conditions, making estimated capacity an unreliable health indicator, if used independently.
The vehicle manufacturers monitor the operation of the vehicle and track the performance of battery as it is charged and discharged. The detailed results are not currently shared with the vehicle owner unless it leads to the need for a recall to examine the battery.
Deploying battery state of health (SoH) estimation and forecasting algorithms are critical for ensuring the reliable performance of battery electric vehicles (EVs). SoH algorithms are designed and trained from data collected in the laboratory upon cycling cells under predefined loads and temperatures.
Conducting SOH assessments on lithium-ion batteries already in use can help assess their health status and decide whether replacement or maintenance is necessary. Round Trip Efficiency (RTE) and State of Health (SOH) are metrics used to assess battery performance and health.
The main goal of this study is to understand the importance of the proper battery operating control strategies, considering a wide range of SOC operation windows, on calendric and cyclic degradation rates, the lifetime longevity and consequently techno-economic profitability of the battery system for price arbitrage application in day-ahead market.