Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach

Battery peak power capability estimations play an important theoretical role for the proper use of the battery in electric vehicles. To address the failures in relaxation effects and real-time ability performance, neglecting the battery’s design limits and other issues of the traditional peak power capability calculation methods, a new approach based on the dynamic electrochemical-polarization (EP) battery model, taking into consideration constraints of current, voltage, state of charge (SoC) and power is proposed. A hardware-in-the-loop (HIL) system is built for validating the online model-based peak power capability estimation approach of batteries used in hybrid electric vehicles (HEVs) and a HIL test based on the Federal Urban Driving Schedules (FUDS) is used to verify and evaluate its real-time computation performance, reliability and robustness. The results show the proposed approach gives a more accurate estimate compared with the hybrid pulse power characterization (HPPC) method, avoiding over-charging or over-discharging and providing a powerful guarantee for the optimization of HEVs power systems. Furthermore, the HIL test provides valuable data and critical guidance to evaluate the accuracy of the developed battery algorithms.

[1]  Seongjun Lee,et al.  Discrimination of Li-ion batteries based on Hamming network using discharging–charging voltage pattern recognition for improved state-of-charge estimation , 2011 .

[2]  Song Yan,et al.  Integrated control method for a fuel cell hybrid system , 2009 .

[3]  Gregory L. Plett,et al.  High-performance battery-pack power estimation using a dynamic cell model , 2004, IEEE Transactions on Vehicular Technology.

[4]  Cheng-ning Zhang,et al.  Estimation of Real-Time Peak Power Capability of a Traction Battery Pack Used in an HEV , 2010, 2010 Asia-Pacific Power and Energy Engineering Conference.

[5]  Jeffrey Belt.  Battery Test Manual For Plug-In Hybrid Electric Ve , 2008 .

[6]  Farouq S. Mjalli,et al.  Recursive Least Squares-Based Adaptive Control of a Biodiesel Transesterification Reactor , 2010 .

[7]  Wei Liu,et al.  Battery algorithm verification and development using hardware-in-the-loop testing , 2010 .

[8]  Hongwen He,et al.  Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach , 2011 .

[9]  Hongwen He,et al.  Energy management strategy research on a hybrid power system by hardware-in-loop experiments , 2013 .

[10]  Hongwen He,et al.  A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles , 2013 .

[11]  Thomas A. Huls,et al.  Development of the Federal Urban Driving Schedule , 1973 .

[12]  Tsung-Jen Ho,et al.  Extended Ponchon−Savarit Method for Graphically Analyzing and Designing Internally Heat-Integrated Distillation Columns , 2010 .

[13]  Hongwen He,et al.  Dynamic Modeling and Simulation on a Hybrid Power System for Electric Vehicle Applications , 2010 .

[14]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[15]  Hongwen He,et al.  Modeling for Lithium-Ion Battery used in Electric Vehicles , 2011 .

[16]  D. Piyabongkarn,et al.  On the development of a real-time digital control system using xPC-Target and a magnetic levitation device , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[17]  Yi-Hsien Chiang,et al.  Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electr , 2011 .

[18]  Hongwen He,et al.  Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles , 2012 .

[19]  Hongwen He,et al.  Model-based dynamic multi-parameter method for peak power estimation of lithium-ion batteries , 2012 .

[20]  Michael Yu Wang,et al.  On the development of a real time control system by using xPC Target: solution to robotic system control , 2005, IEEE International Conference on Automation Science and Engineering, 2005..

[21]  Suleiman M. Sharkh,et al.  Estimation of State of Charge of Lithium-Ion Batteries Used in HEV Using Robust Extended Kalman Filtering , 2012 .

[22]  Bin Chen,et al.  Low-Carbon Development Patterns: Observations of Typical Chinese Cities , 2012 .