A framework based on big data for intelligent monitoring of battery packs

Existing literature focus on the prediction of states of batteries are scattered and are individually studied based on several battery aspects such as: 1) Chemical (ionic concentration measurement or diffusion coefficient evaluation), 2) Electrochemical (capacity), 3) Electrical (internal resistance), 4) Thermal (temperature), 5) Mechanical (stack/enclosure stress) and 6) In-situ/ex-situ (characterization methods to measure porosity and grain size). Unfortunately, these studies have been done by experts of different fields and are yet to be combined in a common platform to predict the states of batteries in a comprehensive way. In this paper, the aim of this research is to propose a framework so as to establish a big database (from sources of literature, by performing real-time experiments and uncertainty studies) for batteries at all operating conditions by incorporating all aforesaid aspects. Once the data base is established, a suitable artifical intelligence approach such as artificial neural network will be applied to train and build the model for state of health prediction and physical evaluation that subsequently have the prime advantage of accurately predicting the battery capacity at system level as well as cell level based on all existing design parameters (diffusion coefficient, grain size, temperature, internal resistance, etc.) from the big database. Data collection will be processed on brand new batteries by repeating cycles of charge and discharge modes under dynamic current profiles at different temperatures for accuracy. The proposed battery model can be then integrated to the battery management system in the electric vehicle without any additional integration complexity.

[1]  Liang Gao,et al.  Electrochemical performance investigation of LiFePO4/C0.15-x (x=0.05, 0.1, 0.15 CNTs) electrodes at various calcination temperatures: Experimental and Intelligent Modelling approach , 2020 .

[2]  Liang Gao,et al.  Evaluation of batteries residual energy for battery pack recycling: Proposition of stack stress-coupled-AI approach , 2019 .

[3]  Ricky Wing-hong Lau,et al.  Extraction of Intrinsic Parameters of Lead–Acid Batteries Using Energy Recycling Technique , 2019, IEEE Transactions on Power Electronics.

[4]  Liang Gao,et al.  Multi‐objective design optimization for mini‐channel cooling battery thermal management system in an electric vehicle , 2019, International Journal of Energy Research.

[5]  Marco Breschi,et al.  Lead-Acid Battery Modeling Over Full State of Charge and Discharge Range , 2018, IEEE Transactions on Power Systems.

[6]  Catherine Rosenberg,et al.  Simple Spec-Based Modeling of Lithium-Ion Batteries , 2018, IEEE Transactions on Energy Conversion.

[7]  Maria Skyllas-Kazacos,et al.  Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery , 2016 .

[8]  Zonghai Chen,et al.  An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks , 2016 .

[9]  Amrane Oukaour,et al.  State-of-Charge and State-of-Health Lithium-Ion Batteries’ Diagnosis According to Surface Temperature Variation , 2016, IEEE Transactions on Industrial Electronics.

[10]  Dong Wang,et al.  Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter , 2016, IEEE Transactions on Instrumentation and Measurement.

[11]  Simona Onori,et al.  Electrochemical Model-Based State of Charge and Capacity Estimation for a Composite Electrode Lithium-Ion Battery , 2016, IEEE Transactions on Control Systems Technology.

[12]  Jianqiu Li,et al.  Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part II: Pseudo-two-dimensional model simplification and state of charge estimation , 2015 .

[13]  Jay Lee,et al.  Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle (EV) safety and mobility , 2014 .

[14]  Jun Xu,et al.  A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model , 2013 .

[15]  Christopher D. Rahn,et al.  Model-based electrochemical estimation of lithium-ion batteries , 2008, 2008 IEEE International Conference on Control Applications.

[16]  Henk Jan Bergveld,et al.  Battery Management Systems: Accurate State-of-Charge Indication for Battery-Powered Applications , 2008 .

[17]  Kaichao Wu,et al.  A probability and integrated learning based classification algorithm for high-level human emotion recognition problems , 2020 .

[18]  Hongwen He,et al.  Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles , 2018, IEEE Access.

[19]  Xuning Feng,et al.  Thermal runaway mechanism of lithium ion battery for electric vehicles: A review , 2018 .

[20]  Jaewook Lee,et al.  Robust and Efficient Capacity Estimation Using Data-Driven Metamodel Applicable to Battery Management System of Electric Vehicles , 2016 .