Fangdan Zhenga,b,c, Jiuchun Jianga,b, Bingxiang Suna,b, Weige Zhanga,b, and Michael Pecht c
a National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing, 100044, China
b Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Jiaotong University, Beijing, 100044, China
c CALCE, Center for Advanced Life Cycle Engineering, Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20740, USA
The power capability of lithium-ion batteries affects the safety and reliability of hybrid electric vehicles and the estimate of power by battery management systems provides operating information for drivers. In this paper, lithium ion manganese oxide batteries are studied to illustrate the temperature dependency of power capability and an operating map of power capability is presented. Both parametric and non-parametric models are established in conditions of temperature, state of charge, and cell resistance to estimate the power capability. Six cells were tested and used for model development, training, and validation. Three samples underwent hybrid pulse power characterization tests at varied temperatures and were used for model parameter identification and model training. The other three were used for model validation. By comparison, the mean absolute error of the parametric model is about 29 W, and that of the non-parametric model is around 20 W. The mean relative errors of two models are 0.076 and 0.397, respectively. The parametric model has a higher accuracy in low temperature and state of charge conditions, while the non-parametric model has better estimation result in high temperature and state of charge conditions. Thus, two models can be utilized together to achieve a higher accuracy of power capability estimation.
This article is available online here and to CALCE Consortium Members for personal review.