Passive Cell Balancing of Li-Ion batteries used for Automotive Applications

Samaddar, Neil; Kumar, Naresh; Jayapragash, R.

doi:10.1088/1742-6596/1716/1/012005

Cited by 18 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the doubled setup for index 14, this results in a total number of resistor setups of 30. As described by Samaddar et al [77], a passive balancing circuit for n cells usually requires n + 1 resistors. Assuming that both setups contain a separate balancing circuit for n A (respectively, n B ) cells, the total number of resistors n resistors for a total of n A + n B = 25 cells is computed to be n resistors = (n A + 1) + (n B + 1) = 27, which does not match the 30-resistor setups found on the PCB.…”

Section: Data Availability Statementmentioning

confidence: 99%

Quantifying the State of the Art of Electric Powertrains in Battery Electric Vehicles: Comprehensive Analysis of the Tesla Model 3 on the Vehicle Level

Rosenberger,

Rosner,

Bilfinger

et al. 2024

WEVJ

View full text Add to dashboard Cite

Data on state-of-the-art battery electric vehicles are crucial to academia; however, these data are not published due to non-disclosure policies in the industry. As a result, simulation models and their analyses are based on assumptions or insider information. To fill this information gap, we present a comprehensive analysis of the electric powertrain of a Tesla Model 3 Standard Range Plus (SR+) from 2020 with lithium iron phosphate (LFP) cells, focusing on the overall range. On the vehicle level, we observe the resulting range in multiple test scenarios, tracing the energy path from source to sink by conducting different test series on the vehicle dynamometer and through alternating current (AC) and direct current (DC) charging measurements. In addition to absolute electric range tests in different operating scenarios and electric and thermal operation strategies on the vehicle level, we analyze the energy density and the power unit’s efficiency on the component level. These tests are performed through procedures on the chassis dynamometer as well as efficiency analysis and electric characterization tests in charge/discharge scenarios. This study includes over 1 GB of attached measurement data on the battery pack and vehicle level from the lab to the real-world environment available as open-source data.

show abstract

Section: Data Availability Statementmentioning

confidence: 99%

Quantifying the State of the Art of Electric Powertrains in Battery Electric Vehicles: Comprehensive Analysis of the Tesla Model 3 on the Vehicle Level

Rosenberger,

Rosner,

Bilfinger

et al. 2024

WEVJ

View full text Add to dashboard Cite

show abstract

“…Nevertheless, these are of conflicting nature, which leads to increase in demand for power and energy from the battery pack. The battery modelling and the important design factors are significant while designing a BMS for EVs, are discussed in [27]. It clearly highlights the reason for selecting the passive balancing system by discussing the trade-offs of using active balancing system.…”

Section: A Passive Cell Balancing Approachesmentioning

confidence: 99%

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Duraisamy¹,

Deepa

2021

IEEE Access

View full text Add to dashboard Cite

Cell balancing is a vital function of battery management system (BMS), which is implemented to extend the battery run time and service life. Various cell balancing techniques are being focused due to the growing requirements of larger and superior performance battery packs. The passive balancing approach is the most popular because of its low cost and easy implementation. As the balancing energy is dissipated as heat by the balancing resistors, an appropriate thermal scheme of the balancing system is necessary, to keep the BMS board temperature under a tolerable limit. In this paper, optimum selection of balancing resistor with respect to degree of cell imbalance, balancing time, C-rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management. Variable resistors are utilised in the passive balancing system, in order to optimize the power loss and to obtain optimal thermal characterization. The performance of the proposed system is evaluated using back propagation neural network (BPNN), radial basis neural network (RBNN), and long short term memory (LSTM). Error analysis of the balancing system is done to optimize balancing parameters and the proposed algorithms are compared using performance indices such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to validate the balancing model performance. The possible optimization scope for implementing passive balancing using machine learning algorithms are experimented in the Matlab-Simscape environment.

show abstract

“…When examining the characteristics of various approaches, factors such as the Li-ion battery's balancing speed, cost, capacity to charge and discharge, application, and complexity are all taken into account. Cell balancing is a key component of the BMS that enhances battery performance [6]. The 440 V/800 V battery BMS solution, lithium ion batteries, and lithium-titanate batteries are all included in the system architecture of a 48 Volt battery system.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective and Scalable Approach to EV Battery Management: MOSFET-Based Passive Cell Balancing

Sunil Varma L S,

Sathish Kumar V,

Sathish Rao R

et al. 2024

Int Res J Adv Engg Hub

View full text Add to dashboard Cite

Cell balancing is crucial for preserving battery life and protecting battery cells in order to guarantee the safe and dependable functioning of LiFePo4 batteries on electric vehicles. Because passive cell balancing is inexpensive and simple to perform, BMS routinely uses it. In the passive cell balancing procedure, resistors are used as the balancing element to eliminate excess charge in the battery. Although resistors are inefficient when used with little cells, they are necessary when using large capacity batteries. The internal resistance of MOSFETs, which is utilized as a balancing element, allows for efficient cell balancing. The need for extra balancing components is eliminated by utilizing the MOSFET's intrinsic resistance. The current through the resistance is decreased hence the power losses are minimized and the heat dissipation is also reduced thus, the battery pack temperature is also maintained in the nominal value which decreases the danger of battery damage due to temperature.

show abstract

Passive Cell Balancing of Li-Ion batteries used for Automotive Applications

Cited by 18 publications

References 6 publications

Quantifying the State of the Art of Electric Powertrains in Battery Electric Vehicles: Comprehensive Analysis of the Tesla Model 3 on the Vehicle Level

Quantifying the State of the Art of Electric Powertrains in Battery Electric Vehicles: Comprehensive Analysis of the Tesla Model 3 on the Vehicle Level

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Cost-Effective and Scalable Approach to EV Battery Management: MOSFET-Based Passive Cell Balancing

Contact Info

Product

Resources

About