Abstract

Energy shortage and environmental pollution issues can be reduced considerably with the development and usage of electric vehicles (EVs). However, electric vehicle performance and battery lifespan depend on a suitable battery arrangement to meet the various battery performance demands. The safety, reliability, and efficiency of EVs largely depends on the constant monitoring of the batteries and management of battery packs. This work comprehensively reviews different aspects of battery management systems (BMS), i.e., architecture, functions, requirements, topologies, fundamentals of battery modeling, different battery models, issues/challenges, recommendations, and active and passive cell balancing approaches, etc., as compared to the existing works which normally discuss one or two aspects only. The work describes BMS functions, battery models and their comparisons in detail for an efficient operation of the battery pack. Similarly, the work presents a comprehensive overview of issues and challenges faced by BMS and also provides recommendations to address these challenges. Cell balancing is very important for the battery performance and in this work various cell balancing methodologies and their comparisons are also presented in detail. Modeling of a cell balancer is presented and a comparative study is also carried out for active and passive cell balance technique in MATLAB/Simulink with an eight cell battery packcell balancing approach. The result shows that the active cell balancing technique is more advantageous than passive balancing for electrical vehicles using lithium-ion batteries.

Highlights

  • In the advanced automobile era, an enormous move towards drive train modification is just around the corner

  • For real time state of charge (SOC) estimations by simple open circuit voltage (OCV)-SOC models reduces accuracy, accumulates different errors from other estimated parameters, etc. These OCV models cover the estimated SOC range depending on the battery usage pattern and do not cover the entire SOC range which can only be achieved by a complete charge/discharge profile

  • Based on our comparison between active and passive cell balancing, we found out that active cell balancing is much better and efficient as compared to passive cell balancing because active cell balancing stores energy in the transformer during the ON time and transfer it to the secondary during the off-time and suspend the cell while in the passive cell balancing, the energy stored in the section is discharged until the cell is balanced

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Summary

Introduction

In the advanced automobile era, an enormous move towards drive train modification is just around the corner. Alongside the battery cells themselves, certain olfim38its must be continued to get the most life from the cells These limits include, for example, temperature limits, charging/discharging rate limits, release current cut-off points, and motesmt peexrtraetumre/lliemasitts,ccehllarvgoinltga/gdeislicmhaitrsg.inTghreastee lriemqiutsir,ermeleanstes caurrereonbtsceurtv-oefdf,pcoointsst,raninded, maminotsatienxetdre, maned/laedasmt icneilsltevroeldtagbey laimfriatsm. Direct estimation (Coulomb counting, open circuit voltage (OCV)-based, etc.), model-based (equivalent circuit and electrochemical, etc.) and data driven-based approaches have all been used to calculate OCV for the online estimation of SOC The precision of these models depends on the parameter tuning, training, state observers, and various practical conditions. Every cell has different internal resistance which changes with time/usage, temperature, chemical properties and other environmental conditions as well All of these parameters affect the power value of a cell. BMS should be able to assess the battery conditions regularly and should avoid EOD situations [18]

3.1.11. Thermal Management
3.1.16. Charging and Discharging of Cells
3.1.17. Communication
3.1.18. Computation
3.1.19. Data Monitoring and Storage
3.1.20. Miscellaneous BMS Functions
Contactors Requirement
Redundancy
Galvanic Isolation
Overall Protection
Other Requirements
Centralized Topology
Modularized Topology
Distributed Topology
Decentralized Topology
Polarizations
Charge Recovery Effect
Utilization Factor
Battery Models
Battery-Coupled Electro-Thermal Model
Real-Time SOH Estimation Issues
Optimal Charging Problem
Fast Characterization
Existing Battery Models Issues
Parameter Selection Issues for Intelligent Algorithms
Optimization Issues for Intelligent Algorithms
Thermal Management Issues
6.10. Thermal Runaway
6.11. RUL Prediction Issues
6.20. Self Evaluation Issues
6.21. Estimation of Maximum Capacity and Modeling under Different Conditions
6.22. Capacity and Power Fading Issues
6.24. Battery Recycling Issues
6.25. Battery Reuse Issues
6.26. Battery Disposal Issues
6.27. Batteries Discharging Issues
6.28. Battery Charger Issue
6.30. Communication Issues with Chargers
6.31. BMS Power Source and Power Consumption Issues
6.32. Miscellaneous Issues
Advanced Multi Scale and Co-Estimation Process Needed
Algorithm Hybridization
Development of Advanced Prognostic Approaches
Efficient Prototype Design and Training Performance Enhancement
7.22. Misc Recommendations
Commercially Available BMS
Active Cell Balancing Techniques
10. Comparison between Passive and Active Cell Balancingg
10.1. MMooddeelliinngg ooff aa CCeellll BBalancer
Findings
11. Conclusions

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