Battery management strategies: An essential review for battery state of health monitoring techniques
Abstract
To prevent probable battery failures and ensure safety, battery state of health evaluation is a critical step. This study lays out a coherent literature review on battery health estimation techniques to assist the research community with helpful information. Various techniques are systematically classified into respective groups and subgroups for easier understanding and follow-up. This study addresses the advantages and limitations of those techniques, along with their precision and application complexity. Furthermore, the procedures are briefly discussed on the premise of cost, computational effort, the requirement of sophisticated equipment, and their adaptability to various battery chemistries. Lastly, it draws the reader's attention towards a probable futuristic battery management architecture that may dictate the next decade's research efforts.
- Publication:
-
Journal of Energy Storage
- Pub Date:
- July 2022
- DOI:
- Bibcode:
- 2022JEnSt..5104427P
- Keywords:
-
- 5G;
- Fifth-generation;
- AE;
- Average error;
- AEKF;
- Adaptive extended Kalman filter;
- AES;
- Auger electron spectroscopy;
- AFM;
- Atomic force microscopy;
- AF-RLS;
- Adaptive forgetting recursive least square;
- AF-RTLS;
- Adaptive forgetting recursive total least squares;
- ANN;
- Artificial neural network;
- BLCT;
- Battery life cycle tester;
- BMS;
- Battery management system;
- CALCE;
- Centre for Advanced Life cycle Engineering;
- CCCV;
- Constant current constant voltage;
- DKF;
- Dual Kalman filter;
- DOD;
- Depth of discharge;
- DVA;
- Differential voltage analysis;
- ECM;
- Equivalent circuit model;
- EIS;
- Electrochemical impedance spectroscopy;
- EKF;
- Extended Kalman filter;
- FBG;
- Fiber Bragg grating;
- FOI;
- Feature of interest;
- GA;
- Genetic Algorithm;
- GMA;
- Gaussian moving average;
- H∞;
- H-infinity;
- HPPC;
- Hybrid pulse power characterization;
- ICA;
- Incremental capacity analysis;
- KF;
- Kalman filter;
- LCO;
- Lithium cobalt oxide;
- LFP;
- Lithium iron phosphate;
- LIB;
- Lithium-ion battery;
- LMO;
- Lithium manganese oxide;
- LS;
- Least-squares;
- LTO;
- Lithium titanium oxide;
- MAE;
- Mean absolute error;
- Max;
- Maximum;
- ME;
- Maximum error;
- ML;
- Machine learning;
- NASA;
- National Aeronautics and Space Administration;
- NCA;
- Nickel Cobalt Aluminium;
- NLS;
- Nonlinear least squares;
- NMC;
- Nickel Manganese Cobalt;
- OCV;
- Open circuit voltage;
- P.E.;
- Prediction error;
- P2D;
- Pseudo-two dimensional;
- PF;
- Particle filter;
- RMSE;
- Root mean square error;
- RTD;
- Resistance temperature detector;
- SEI;
- Solid electrolyte interphase;
- SEM;
- Scanning electron microscope;
- SOC;
- State of charge;
- SOH;
- State of health;
- SPKF;
- Sigma point Kalman filter;
- SPM;
- Single particle model;
- SRMB;
- Self-reconfigurable multicell battery;
- SRSC;
- Self-regulated smart cells;
- STEM;
- Scanning transmission electron microscope;
- UKF;
- Unscented Kalman filter;
- VRB;
- Vanadium redox flow battery;
- VRLAB;
- Valve regulated lead-acid battery;
- VRLS;
- Vector-type recursive least squares;
- Battery management system;
- State of health (SOH);
- Battery health monitoring;
- Lithium-ion battery;
- Lead-acid battery