Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: State-of-energy☆

State-of-energy (SoE) is an important index for batteries in electric vehicles and it provides the essential basis of energy application, load equilibrium and security of electricity. To improve the estimation accuracy and reliability of SoE, a novel multi-model fusion estimation approach is proposed against uncertain dynamic load and different temperatures. The main contributions of this work can be summarized as follows: (1) Through analyzing the impact on the estimation accuracy of SoE due to the complexity of models, the necessity of redundant modeling is elaborated. (2) Three equivalent circuit models are selected and their parameters are identified by genetic algorithm offline. Linear matrix inequality (LMI) based H-infinity state observer technique is applied to estimate SoEs on aforementioned models. (3) The concept of fusion estimation is introduced. The estimation results derived by different models are merged under certain weights which are determined by Bayes theorem. (4) Batteries are tested with dynamic load cycles under different temperatures to validate the effectiveness of this method. The results indicate the estimation accuracy and reliability on SoE are elevated after fusion.

[1]  Binggang Cao,et al.  The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer , 2014, IEEE Transactions on Vehicular Technology.

[2]  Huei Peng,et al.  A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring , 2014 .

[3]  Andrew McGordon,et al.  The effect of average cycling current on total energy of lithium-ion batteries for electric vehicles , 2016 .

[4]  Seongjun Lee,et al.  Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles , 2015 .

[5]  Jian Xiao,et al.  Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine , 2015 .

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

[7]  Hui Li,et al.  An SOC estimation approach based on adaptive sliding mode observer and fractional order equivalent circuit model for lithium-ion batteries , 2015, Commun. Nonlinear Sci. Numer. Simul..

[8]  Zonghai Chen,et al.  A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures , 2014 .

[9]  Stephen B. Bayne,et al.  Overview of grid connected renewable energy based battery projects in USA , 2015 .

[10]  Huazhen Fang,et al.  Improved adaptive state-of-charge estimation for batteries using a multi-model approach , 2014 .

[11]  Hongwen He,et al.  A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique , 2016 .

[12]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

[13]  Zhenwei Cao,et al.  Adaptive gain sliding mode observer for state of charge estimation based on combined battery equivalent circuit model in electric vehicles , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[14]  Zonghai Chen,et al.  A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation , 2014 .

[15]  Unai Viscarret,et al.  Enhanced closed loop State of Charge estimator for lithium-ion batteries based on Extended Kalman Filter , 2015 .

[16]  Krishna R. Pattipati,et al.  A battery chemistry-adaptive fuel gauge using probabilistic data association , 2015 .

[17]  Wei He,et al.  State of charge estimation for electric vehicle batteries using unscented kalman filtering , 2013, Microelectron. Reliab..

[18]  Zechang Sun,et al.  ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries , 2015 .

[19]  Jiahao Li,et al.  A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems , 2015 .

[20]  Hanxu Sun,et al.  Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion Battery Using Dual Neural Network Fusion Battery Model , 2016 .

[21]  Cecilio Blanco Viejo,et al.  Support Vector Machines Used to Estimate the Battery State of Charge , 2013 .

[22]  Xiaoping Li,et al.  State-of-charge estimation for battery management system using optimized support vector machine for regression , 2014 .

[23]  Dan Simon.  Kalman filter generalizations , 2006 .

[24]  Baojin Wang,et al.  State-space model with non-integer order derivatives for lithium-ion battery , 2016 .

[25]  Wei Shi,et al.  Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery , 2015 .

[26]  Hao Mu,et al.  A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm , 2016 .

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

[28]  Guangzhong Dong,et al.  An online model-based method for state of energy estimation of lithium-ion batteries using dual filters , 2016 .

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

[30]  Van-Huan Duong,et al.  Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares , 2015 .

[31]  Chenbin Zhang,et al.  An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles , 2016 .

[32]  Wei Sun,et al.  A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter , 2014 .

[33]  Guojun Li,et al.  State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting , 2015 .

[34]  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 .

[35]  Fengchun Sun,et al.  A novel dual-scale cell state-of-charge estimation approach for series-connected battery pack used in electric vehicles , 2015 .

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

[37]  Hongwen He,et al.  A novel Gaussian model based battery state estimation approach: State-of-Energy , 2015 .

[38]  Dong Wang,et al.  A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile , 2016 .

[39]  Chenbin Zhang,et al.  A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries , 2014 .

[40]  J. Doyle,et al.  Essentials of Robust Control , 1997 .