Charging strategy design of lithium-ion batteries for energy loss minimization based on minimum principle

Energy loss during charging process for lithium­ion battery has become a main bottleneck for large-scale deployment of batteries in electric vehicles (EVs). This paper proposed a new energy loss minimization charging algorithm with satisfied accuracy and low complexity. To determine the charging current profile, an equivalent circuit model (ECM) is established to accurately characterize the dynamic and static performance of the battery. The battery parameters are identified based on genetic algorithm (GA), and Pontryagin's minimum principle (PMP) is employed to find the optimal charge current profile. The experiments prove that the proposed charge method can reduce the charge energy loss obviously, compared with constant current (CC) charge scheme.

[1]  Bing Xia,et al.  A novel state-of-charge estimation method for lithium-ion battery pack of electric vehicles , 2015, 2015 IEEE Transportation Electrification Conference and Expo (ITEC).

[2]  Shuo Zhang,et al.  Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus , 2015 .

[3]  Bo Lu,et al.  Selection of charge methods for lithium ion batteries by considering diffusion induced stress and charge time , 2016 .

[4]  Stephen Yurkovich,et al.  A technique for dynamic battery model identification in automotive applications using linear parameter varying structures , 2009 .

[5]  Zheng Chen,et al.  Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming , 2014 .

[6]  Wei Zhou,et al.  Study on the Optimal Charging Strategy for Lithium-Ion Batteries Used in Electric Vehicles , 2014 .

[7]  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 .

[8]  Yi-Jun He,et al.  Online state of charge estimation of lithium-ion batteries: A moving horizon estimation approach , 2016 .

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

[10]  Zheng Chen,et al.  Comparisons of Modeling and State of Charge Estimation for Lithium-Ion Battery Based on Fractional Order and Integral Order Methods , 2016 .

[11]  Giorgio Rizzoni,et al.  Battery Life Extending Charging Strategy for Plug-in Hybrid Electric Vehicles and Battery Electric Vehicles* , 2016 .

[12]  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 .

[13]  Cheng Zhang,et al.  An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries , 2015 .

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

[15]  Jun Zheng,et al.  Multi-objective optimization of lithium-ion battery model using genetic algorithm approach , 2014 .

[16]  Scott Dearborn.  Charging Li-ion Batteries for Maximum Run Times An understanding of battery-charging fundamentals and system requirements enable designers to choose a suitable linear or switch-mode charging topology and optimize battery performance in the application , 2022 .

[17]  Thanh Tu Vo,et al.  New charging strategy for lithium-ion batteries based on the integration of Taguchi method and state of charge estimation , 2015 .

[18]  Jing Deng,et al.  An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model , 2017 .

[19]  Lin Liu,et al.  Optimal power source sizing of fuel cell hybrid vehicles based on Pontryagin's minimum principle , 2015 .

[20]  Bing Xia,et al.  Energy management of power-split plug-in hybrid electric vehicles based on simulated annealing and Pontryagin's minimum principle , 2014 .

[21]  Simona Onori,et al.  Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt , 2015 .

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

[23]  Mark Sumner,et al.  Development of a battery energy loss observer based on improved equivalent circuit modelling , 2016, 2016 18th European Conference on Power Electronics and Applications (EPE'16 ECCE Europe).

[24]  Zonghai Chen,et al.  A novel state of health estimation method of Li-ion battery using group method of data handling , 2016 .