Machine Learning Algorithms-based Methods for Monitoring Performance and Aging in Electric Vehicle Batteries

Summary

This study introduces the Artificial Intelligence-powered Lifelong Estimation Learning Framework, an artificial intelligence-based framework for adaptive monitoring, charging strategy optimization, and state estimation in electric vehicle batteries. The approach is experimentally evaluated under controlled conditions to assess feasibility and predictive capability.

Abstract

This protocol describes the Artificial Intelligence–Powered Lifelong Estimation Learning Framework (AI-PLELF) for adaptive battery monitoring and management in electric vehicle battery systems. The framework integrates real-time battery sensor data, including current, voltage, and temperature, with historical usage information and environmental parameters to support structured state estimation and charging strategy evaluation within a Battery Management System (BMS).

The protocol outlines sequential procedures for sensor data acquisition, preprocessing, feature organization, machine learning–based prediction of State of Charge (SOC) and State of Health (SOH), and formulation of adaptive control responses. The workflow is implemented in a controlled computational environment using publicly available real-world driving cycle datasets. Data are partitioned into training, validation, and testing subsets to evaluate prediction stability and generalization under varying operating conditions.

Simulation-based trials are conducted across different cycling and environmental scenarios to assess the consistency of SOC and SOH estimations and corresponding control suggestions. The described methodology provides a reproducible framework for developing and evaluating AI-assisted battery monitoring strategies. Although the present study focuses on simulation-based validation, the protocol establishes a structured foundation that may be extended to laboratory and hardware-integrated testing in future applications.

Introduction

Background and context

Electric vehicles (EVs) depend on reliable battery performance for safety, efficiency, and longevity. Battery Management Systems (BMS) monitor battery statuses, regulate charging and discharging, and prevent harmful operation. Traditional BMS systems use physics-based models and rule-based logic, which may lose accuracy as batteries age or duty cycles change¹. AI and ML technologies are being used to overcome restrictions by analyzing data-driven correlations between sensor readings and battery states². AI-enabled BMS techniques use real-time measu....

Protocol

Battery life and performance are crucial as EV adoption grows. Battery management systems improve charging cycles, forecast degradation, and regulate thermal effects. AI-PLELF enhances energy management, charging control, and EV battery SOC/SOH prediction. AI-PLELF uses real-time sensor data, usage patterns, and ambient conditions to improve battery performance and longevity.

The battery was cycled using an LBT cycler with ±0.05% full-scale accuracy. A temperature-controlled chamber was used to mount the cells at 25.0 ± 0.5 °C. A high-temperature polyimide adhesive tape was used to secure the thermocouples to the cell surface to ensure stab....

Results

This study shows that AI can improve the performance and longevity of electric car batteries. BMSs using AI increase battery diagnostics, efficiency, predictive maintenance, scalability, and grid integration. These systems optimize charging cycles, anticipate battery health, and improve the reliability of EV applications by leveraging real-time data and ML algorithms. AI boosts sustainable transportation by improving EV energy efficiency, reliability, and eco-friendliness. Dataset description: Driver behavior heavily str.......

Discussion

AI-PLELF can renovate EV BMS. AI-PLELF optimizes charging cycles, prevents heat, and predicts battery health using powerful ML algorithms. In real time, sensor data, historical usage patterns, and environmental variables enable AI-PLELF to predict battery SOH and SOC, improving performance and lifespan. AI-PLELF surpasses BMS in battery performance and durability in massive simulations. Character EV owners save money and get more reliable motors with this new method. The scaled fleet management allows more individuals to.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Item	Specification	Manufacturer / Model	Purpose
Battery Cycler	±0.05% full-scale accuracy	LBT21084 Battery Test System, Arbin Instruments, USA	Precision charge–discharge cycling (battery testing system)
Battery Testing Control Software (MITS Pro)	Version 5.3.2	Arbin Instruments, USA	Configuration of CC–CV charging/discharge protocols (control software)
Polyimide Adhesive Tape	High-temperature resistant (up to 260 °C), electrically insulating	Kapton Polyimide Tape, DuPont, USA	Securing thermocouples to battery surface (adhesive material)
Precision Shunt Resistor	0.1% tolerance	Vishay Precision Group, USA	Current measurement calibration
Reference Voltage Cell	3.000 ± 0.001 V accuracy	Fluke Calibration, USA	Channel calibration (voltage calibration)
Temperature-Controlled Chamber	Stability: 25.0 ± 0.5 °C	SH-241 Environmental Chamber, ESPEC Corp., Japan	Thermal regulation during cycling (environmental control)
Thermocouples (Type-K)	Surface-mount, ±0.5 °C accuracy	Omega Engineering, USA	Surface temperature monitoring (temperature measurement)