State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation

• Published in: Journal of power sources 2011-06, Vol.196 (12), p.5357-5363
• Main Authors: Remmlinger, Jürgen, Buchholz, Michael, Meiler, Markus, Bernreuter, Peter, Dietmayer, Klaus
• Format: Article
• Language: English
• Subjects: Applied sciences; Battery; Degradation; Direct energy conversion and energy accumulation; Electric batteries; Electrical engineering. Electrical power engineering; Electrical power engineering; Electrochemical conversion: primary and secondary batteries, fuel cells; Exact sciences and technology; Ground, air and sea transportation, marine construction; Health monitoring (engineering); Hybrid vehicles; Internal resistance; Lithium-ion batteries; Lithium-ion battery; Mathematical models; On-board diagnosis; Road transportation and traffic; State-of-health; Vehicles
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2010.08.035

For reliable and safe operation of lithium-ion batteries in electric or hybrid vehicles, diagnosis of the cell degradation is necessary. This can be achieved by monitoring the increase of the internal resistance of the battery cells over the whole lifetime of the battery. In this paper, a method to identify the internal resistance in a hybrid vehicle is presented. Therefore, a special purpose model deduced from an equivalent circuit is developed. This model contains parameters depending on the degradation of the battery cell. To achieve the required robustness and stable results under these conditions, the method uses specific signal intervals occurring during normal operation of the battery in a hybrid vehicle. This identification signal has a defined timespan and occurs regularly. The identification is done on vehicle measurement data of terminal cell voltage and current collected with a usual vehicle sampling rate. Using the adapted internal resistance value in the model, a degradation index is calculated by compensating other influences, e.g. battery temperature. This task is the main challenge, as the impact of the temperature on the resistance, for example, is one order of magnitude higher than the influence of the degradation for the investigated lithium-ion cell. The developed estimation and monitoring method is validated with measurement data from single cells and shows good results and very low computational effort.