Nominal capacity calculation for lithium-ion batteries with advanced algorithms

Abstract

The speed of global warming and demand for energy increased rapidly after Industrial Revolution by high energy usage for the current high-level technologies. The automotive industry is one of main industries demanding high energy resources to grant quality transportation. However, due to this high usage of energy, the Carbon emissions rapidly increased due to fossil-fuel usage. Many countries adjusted their emission regulations to decrease the devastating effects of global warming. Therefore, many automotive producers started to search for technologies to comply with these strong regulations. Electric vehicles emerged from these regulations. In addition to that, the comfort and the decreased complexity of the design in the electric vehicles are also very appealing for the people which means lower effort on the service needs. Therefore, it is expected that the number of electric vehicles (EVs) will increase on the way. The main components for the electric vehicle are the propulsion and energy storage components. As a driver, there are 2 main parameters selecting a vehicle: Propulsion Power and Range. Propulsion power could be granted with a good electric motor and inverter design. On the other hand, EV batteries play a key role in increased range. The majority of the people generally focus on the range and usage instead of high-powers. Therefore, the point of focus in this study is the EV batteries. EV batteries are one of the main and most expensive components in the vehicle. The drivers mainly tend to use the vehicle as much as they can without any component change. However, as time goes on, the battery gets older and its performance would be decreased. Furthermore, an aged battery could harm the driver and the passengers. As battery developers tend to avoid any unwanted situation, the batteries are restricted to be used for a limited time. This limited-time is mainly dependent on battery State-of-Health (SOH). The SOH is the main indicator in a Battery Management System (BMS) to monitor the battery. The developers target to ensure a safe range for battery usage. On the other hand, the driver would like to challenge the limits to use the battery longer to avoid frequent battery-related service costs. State-of-Health of the battery determines the battery life cycle. If a battery is aged due to driving, temperature and cycle conditions, then, the usage of that battery is restricted by the Battery Management System (BMS). To avoid failure due to ageing, there are some mechanisms to warn the driver before the battery fails so that any kind of incident is prevented. This restriction relies on the accuracy of the SOH calculation. In the automotive industry, the battery manufacturers' warranty period mainly depends on the battery SOH. In other words, if the actual SOH of the battery is lower than the guaranteed value, then, the warranty period is over. This SOH value is defined under the worst-case scenario. In other words, the calculation accuracy for SOH is added as an offset to the target value of the manufacturer. The worse value of SOH calculation accuracy would result in an earlier finish of the warranty period. In other words, better accuracy would result in a longer battery usage period. Better accuracy results in a longer warranty period. For example, the state-of-art batteries are generally guaranteed for 5 years under the condition SOH is calculated with %5 accuracy. If this accuracy is increased by ~% 3, then the battery life would be guaranteed for 1 more year. Since the battery technologies are growing, the warranty period could be more than 5 years which results in a higher increase based on SOH accuracy improvement. There are 2 main indicators for SOH: Capacity and Internal Resistance.