What does this research mean for the field?

Adaptive Neuro-Fuzzy Inference Systems (ANFIS) improve response time, adaptability, and energy efficiency compared to traditional Proportional-Integral (PI) controllers for battery management in plug-in electric vehicles. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to compare various battery management system (BMS) control strategies to enhance energy efficiency and safety.

February 8, 2026Open Access

Optimized Battery Management: A Comparative Analysis of Controller Systems for Enhanced Energy Efficiency

Q: What is the clinical evidence from this study?

Study design: Other. Population: Battery-powered systems including plug-in electric vehicles and renewable energy storage systems requiring battery management for safety and efficiency. Intervention: Advanced Battery Management Controllers including Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Fuzzy Logic Controllers (FLC) vs. Conventional Proportional-Integral (PI) Controllers. Primary outcome: Performance efficiency of battery management control systems measured by response time, adaptability, energy efficiency, and fault tolerance.

Key Result

Adaptive Neuro-Fuzzy Inference Systems (ANFIS) control strategies outperform conventional Proportional-Integral (PI) controllers by providing better response time, adaptability, and energy efficiency for battery management in electric vehicles.

Key Points

This research aims to compare various battery management system (BMS) control strategies to enhance energy efficiency and safety.
Comparison of centralized, distributed, and mixed control architectures
Evaluation of essential BMS operations like state of charge (SOC) and state of health (SOH) estimation
Analysis of fault detection and battery balancing techniques
Incorporation of emerging technologies such as IoT and AI
Identified differences in the complexity, cost, and scalability of various BMS control strategies
Demonstrated improvements in safety, efficiency, and battery lifespan with optimized control methods
Highlighted the potential of real-time data sharing through IoT and AI technologies

PICO

Population

Battery-powered systems including plug-in electric vehicles and renewable energy storage systems requiring battery management for safety and efficiency

Intervention / Comparator

Advanced Battery Management Controllers including Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Fuzzy Logic Controllers (FLC) vs Conventional Proportional-Integral (PI) Controllers

Primary Outcome

Performance efficiency of battery management control systems measured by response time, adaptability, energy efficiency, and fault tolerance

Limitations

No quantitative clinical trial data or exact effect size metrics provided
Study is a comparative analysis and review without randomized controlled trial design
Focus is on engineering system performance rather than clinical patient outcomes
No sample size or demographic specification for clinical population

Abstract

Battery management systems (BMS) are important feature and is compulsory unit for all battery-powered systems. It was known that that battery management system (BMS) has various strategies like centralized, distributed, and mixed architectures. The BMS is important to understand their working nature, performance, safety and effectiveness at various environmental conditions. This article is focused on essential operations of BMS like precise state of charge (SOC) and state of health (SOH) estimation, fault detection and battery balancing. For better understanding, it is important to compare control strategies interms of complexity, cost and scalability. Emerging technologies like IoT, cloud computing, machine learning, and artificial intelligence will help in improving BMS, so that the data can be shared and monitor batteries in real time. With this the life of battery can be predictable, faults can be detected, and safety and performance can be improved. This work highlights the importance of BMS in securing the safety, efficiency and to extend the battery life.

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Cite This Study

G et al. (Wed,) conducted a other in Battery-powered systems including plug-in electric vehicles and renewable energy storage systems requiring battery management for safety and efficiency. Advanced Battery Management Controllers including Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Fuzzy Logic Controllers (FLC) vs. Conventional Proportional-Integral (PI) Controllers was evaluated on Performance efficiency of battery management control systems measured by response time, adaptability, energy efficiency, and fault tolerance. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) control strategies outperform conventional Proportional-Integral (PI) controllers by providing better response time, adaptability, and energy efficiency for battery management in electric vehicles.

synapsesocial.com/papers/698828770fc35cd7a8847f70 https://doi.org/https://doi.org/10.1051/e3sconf/202669201017/pdf

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