Optimal charging strategy design for lithium‐ion batteries considering minimization of temperature rise and energy loss

Battery charging techniques are critical to enhance battery operation performance. Charging temperature rise, energy loss, and charging time are three key indicators to evaluate charging performance. It is imperative to decrease temperature rise and energy loss without extending the charging time during the charging process. To this end, an equivalent circuit electrical model, a power loss model, and a thermal model are built in this study for lithium‐ion batteries. Then, an integrated objective function is formulated to minimize energy loss and temperature increment during battery charging. To further validate the generality and feasibility of the proposed charging strategy, experiments are conducted with respect to different current, operating temperatures, battery types, and aging status. Comparison results demonstrate that the devised charging strategy is capable of achieving the intended effect under any operating temperature and with different aging status.

[1]  P. Kohl,et al.  The effects of pulse charging on cycling characteristics of commercial lithium-ion batteries , 2001 .

[2]  Y. Mita,et al.  Multi-step constant-current charging method for an electric vehicle nickel/metal hydride battery with high-energy efficiency and long cycle life , 2002 .

[3]  J.H.G. Op het Veld,et al.  Boostcharging Li-ion batteries: A challenging new charging concept , 2005 .

[4]  Jun Liu,et al.  Effect of entropy change of lithium intercalation in cathodes and anodes on Li-ion battery thermal management , 2010 .

[5]  Jin Wang,et al.  PHEV Charging Strategies for Maximized Energy Saving , 2011, IEEE Transactions on Vehicular Technology.

[6]  P. Novák,et al.  Memory effect in a lithium-ion battery. , 2013, Nature materials.

[7]  Tsair-Rong Chen,et al.  Sinusoidal-Ripple-Current Charging Strategy and Optimal Charging Frequency Study for Li-Ion Batteries , 2013, IEEE Transactions on Industrial Electronics.

[8]  Lin Ma,et al.  Thermal management of cylindrical batteries investigated using wind tunnel testing and computational fluid dynamics simulation , 2013 .

[9]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[10]  Jun Xu,et al.  Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications , 2013 .

[11]  F. Baronti,et al.  Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles , 2013, IEEE Industrial Electronics Magazine.

[12]  Wei He,et al.  State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures , 2014 .

[13]  Qiujiang Liu,et al.  An optimal charging strategy of lithium-ion batteries based on polarization and temperature rise , 2014, 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific).

[14]  Bing Xia,et al.  Loss-Minimization-Based Charging Strategy for Lithium-Ion Battery , 2015, IEEE Transactions on Industry Applications.

[15]  Huazhen Fang,et al.  Model-Based Condition Monitoring for Lithium-ion Batteries , 2015 .

[16]  Bor Yann Liaw,et al.  Optimal charging method for lithium ion batteries using a universal voltage protocol accommodating aging , 2015 .

[17]  Chenbin Zhang,et al.  A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter , 2015 .

[18]  Mario Cacciato,et al.  Real-time model-based estimation of SOC and SOH for energy storage systems , 2015, 2015 IEEE 6th International Symposium on Power Electronics for Distributed Generation Systems (PEDG).

[19]  Joeri Van Mierlo,et al.  Lithium-ion batteries: Evaluation study of different charging methodologies based on aging process , 2015 .

[20]  Guizhou Ren,et al.  Review of electrical energy storage system for vehicular applications , 2015 .

[21]  Tim Brown,et al.  The optimization of DC fast charging deployment in California , 2015 .

[22]  Angel Kirchev,et al.  Battery Management and Battery Diagnostics , 2015 .

[23]  Andreas Jossen,et al.  Charging protocols for lithium-ion batteries and their impact on cycle life—An experimental study with different 18650 high-power cells , 2016 .

[24]  Guojun Li,et al.  Investigation of the thermal performance of axial-flow air cooling for the lithium-ion battery pack , 2016 .

[25]  Pan Chaofeng,et al.  On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis , 2016 .

[26]  Kang Li,et al.  Real-time estimation of battery internal temperature based on a simplified thermoelectric model , 2016 .

[27]  G. V. Avvari,et al.  Optimal battery charging, Part I: Minimizing time-to-charge, energy loss, and temperature rise for OCV-resistance battery model , 2016 .

[28]  Zheng Chen,et al.  A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles , 2016 .

[29]  Gholamreza Karimi,et al.  Thermal management analysis of a Li-ion battery cell using phase change material loaded with carbon fibers , 2016 .

[30]  Le Yi Wang,et al.  Balanced Control Strategies for Interconnected Heterogeneous Battery Systems , 2016, IEEE Transactions on Sustainable Energy.

[31]  Xuning Feng,et al.  State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking , 2016 .

[32]  Xiaosong Hu,et al.  Charging optimization in lithium-ion batteries based on temperature rise and charge time , 2017 .

[33]  Yanhui Zhang,et al.  An SOE estimation model considering electrothermal effect for LiFePO4/C battery , 2017 .

[34]  Zheng Chen,et al.  Charging strategy design of lithium-ion batteries for energy loss minimization based on minimum principle , 2017, 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific).

[35]  Xiaoyu Li,et al.  LiFePO4 battery charging strategy design considering temperature rise minimization , 2017 .

[36]  Xiaoyu Li,et al.  An optimal charging algorithm for lithium-ion batteries considering temperature rise minimization , 2017, 2017 Chinese Automation Congress (CAC).

[37]  Yu Fang,et al.  A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach , 2018, Applied Energy.

[38]  Hao Yuan,et al.  Co-Estimation of State of Charge and State of Health for Lithium-Ion Batteries Based on Fractional-Order Calculus , 2018, IEEE Transactions on Vehicular Technology.

[39]  Torsten Wik,et al.  Electrochemical Estimation and Control for Lithium-Ion Battery Health-Aware Fast Charging , 2018, IEEE Transactions on Industrial Electronics.

[40]  Jiale Xie,et al.  State‐of‐charge estimators considering temperature effect, hysteresis potential, and thermal evolution for LiFePO4 batteries , 2018 .

[41]  Tao Sun,et al.  A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries , 2018 .

[42]  Jun Chen,et al.  Available power prediction limited by multiple constraints for LiFePO4 batteries based on central difference Kalman filter , 2018, International Journal of Energy Research.

[43]  Lei Zhang,et al.  State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis , 2019, Journal of Power Sources.

[44]  Lei Zhang,et al.  Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks , 2019, Journal of Energy Storage.