678 IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL. 2, NO. 3, SEPTEMBER 2014 Reduced-Order Electrochemical Model Parameters Identification and State of Charge Estimation for Healthy and Aged Li-Ion Batteries—Part II: Aged Battery Model and State of Charge Estimation Ryan Ahmed, Member, IEEE, Mohammed El Sayed, Ienkaran Arasaratnam, Jimi Tjong, and Saeid Habibi, Member, IEEE Abstract—Recently, extensive research has been conducted in the field of battery management systems due to increased interest in vehicles electrification. Parameters, such as battery state of charge (SOC) and state of health, are of critical importance to ensure safety, reliability, and prolong battery life. This paper includes the following contributions: 1) tracking reduced-order electrochemical battery model parameters variations as battery ages, using noninvasive genetic algorithm optimization technique; 2) the development of a battery aging model capable of capturing battery degradation by varying the effective electrode volume; and 3) estimation of the battery critical SOC using a new estimation strategy known as the smooth variable structure filter based on reduced-order electrochemical model. The proposed filter is used for SOC estimation and demonstrates strong robustness to modeling uncertainties, which is relatively high in case of reduced-order electrochemical models. Batteries used in this research are lithium-iron phosphate cells widely used in automotive applications. Extensive testing using real-world driving cycles is used for estimation strategy application and for conducting the aging test. Limitations of the proposed strategy are also highlighted. Index Terms— Electrochemical battery model, lithium-ion batteries, smooth variable structure filter (SVSF), state of charge (SOC) estimation, state of health (SOH) estimation. I. I NTRODUCTION T HIS paper involves the identification of the reduced- order electrochemical model parameters based on aged batteries, in addition to the application of a battery state of charge (SOC) estimation strategy. The paper presents an extension to [1], in which battery model parameters for fresh batteries are obtained using genetic algorithm. Furthermore, an aging model has been developed by changing the effective Manuscript received October 28, 2013; revised February 4, 2014; accepted April 30, 2014. Date of publication June 26, 2014; date of current version July 30, 2014. This work was supported in part by Ford Motor Company of Canada, Windsor, ON, Canada, and in part by the National Sciences and Engineering Research Council of Canada. Recommended for publication by Associate Editor S. Lukic. R. Ahmed, M. El Sayed, I. Arasaratnam, and S. Habibi are with the Department of Mechanical Engineering, McMaster University, Hamilton, ON L8S4L7 Canada (e-mail: ryan.ahmed@mcmaster.ca; abugabma@mcmaster.ca; haran@ieee.org; habibi@mcmaster.ca). J. Tjong is with the Powertrain Engineering Research and Development Center, Windsor, ON N8Y 1W2 Canada (e-mail: jtjong@ford.com). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JESTPE.2014.2331062 volume of the electrode to accommodate for battery aging. This section includes research motivation, peak oil concept, literature review of battery SOC estimation techniques, signif- icant paper research contributions, and the paper outline. A. Motivation and Technical Challenges Battery SOC and state of health (SOH) estimation rep- resent a challenging task, since the traction battery exhibits fast changing dynamics due to acceleration and deceleration depending on the driving cycle. To ensure a reliable electric vehicle performance, precise estimation of lithium-ion battery SOC and SOH is crucial [2]. SOC is defined as the remaining pack capacity thus provides an indication of the vehicle remaining range [3]. SOH is a measure of the irreversible degradation that occurs in the battery performance due to cycling [3]. The current state of the battery is compared with that of the fresh battery (before cycling) [3]. SOH is a measure of the battery capability to respond to the required power demand and alarm if maintenance is required. Accordingly, an accurate estimation of the battery SOH is crucial to the battery operation [3]. In general, two main critical factors are considered when addressing the battery SOH, namely: capacity fade and power capability. The battery capacity fade has a huge impact on the vehicle range associated with customer range anxiety. The second factor is the power capability, which impact the vehicle performance and drivability. The remaining useful life (RUL) is used to predict the battery remaining useful time during its life time thus it represents a proactive act for battery maintenance [3]. Battery SOC and SOH are highly correlated, a trade-off exists between extending the life-time of the battery and extending the range of the vehicle [2]. Discharging the battery to a high level of depth of discharge (DOD) (i.e., low SOC) is generally not recommended as it will significantly shorten the lifetime of the battery. However, this will lead to shorter driving range as only partial charge is being utilized from the entire stored charge. In contrast, charging the battery beyond the acceptable range of operation results in high temperature and shortens the battery life [2]. Consequently, an accurate SOC estimate is of utmost importance in electric vehicles; any deviation in SOC estimation might result in an irreversible loss of capacity or even battery permanent damage [2]. 2168-6777 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.