Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack

Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles.

[1]  Andrew McGordon,et al.  A study of the open circuit voltage characterization technique and hysteresis assessment of lithium-ion cells , 2015 .

[2]  Najoua Essoukri Ben Amara,et al.  Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter , 2017 .

[3]  Gregory L. Plett,et al.  Recursive approximate weighted total least squares estimation of battery cell total capacity , 2011 .

[4]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .

[5]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[6]  Yujie Wang,et al.  Model migration based battery power capability evaluation considering uncertainties of temperature and aging , 2019, Journal of Power Sources.

[7]  Hongwen He,et al.  Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles , 2013 .

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

[9]  Binggang Cao,et al.  A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter , 2017 .

[10]  King Jet Tseng,et al.  A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model , 2017 .

[11]  Hongwen He,et al.  A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .

[12]  Zhongbao Wei,et al.  Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery , 2018, Journal of Power Sources.

[13]  Andrea Marongiu,et al.  Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles , 2015 .

[14]  Roberto Saletti,et al.  Hysteresis Modeling in Li-Ion Batteries , 2014, IEEE Transactions on Magnetics.

[15]  Dirk Uwe Sauer,et al.  Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application , 2013 .

[16]  Shi Li,et al.  A comparative study of model-based capacity estimation algorithms in dual estimation frameworks for lithium-ion batteries under an accelerated aging test , 2018 .

[17]  Ruixin Yang,et al.  A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles , 2019 .

[18]  Kristen A. Severson,et al.  Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.

[19]  G. Plett,et al.  Controls-oriented models of lithium-ion cells having blend electrodes. Part 2: Physics-based reduced-order models , 2017 .

[20]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[21]  Furong Gao,et al.  A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging , 2019, Energy Conversion and Management.

[22]  Christian Fleischer,et al.  On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation , 2014 .

[23]  Hongwen He,et al.  A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles , 2017 .