SOC Estimation-Based Battery Management System for Electric Bicycles: Design and Implementation

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation due to their energy efficiency and zero-emission operation.However, challenges such as battery overcharging, overheating, and degradation from improper use can reduce battery lifespan and increase maintenance costs.To address these issues, this paper presents the design and implementation of a Battery Management System (BMS) tailored for E-Bike applications, with a focus on enhancing safety, reliability, and performance.The proposed BMS includes core functionalities such as State of Charge (SOC) estimation, temperature monitoring, and under-voltage and overcharge protection.Different approaches, including open-circuit voltage (OCV), Coulomb counting (CC), and Kalman filter techniques are employed to improve SOC estimation accuracy.The circuit for CC-based BMS was first simulated using Proteus, and system behavior was modeled in MATLAB Simulink is used to validate design assumptions before hardware implementation.An Arduino Uno microcontroller was used to control the system, interfacing with an LM35 temperature sensor, a voltage divider, and an ACS712 current sensor.The BMS controls battery charging based on SOC levels and activates a cooling fan when the battery temperature exceeds 45 C.It disconnects the charger at 100% SOC and triggers a beep alarm when the SOC falls below 40%.An external charger and regenerative charging from four electrodynamometers on the bicycle chain recharge the battery when the SOC drops below 20%, provided the load is disconnected.Measurement results closely matched simulation data, with the MATLAB model showing 44% SOC after 3 h, compared to the actual real-time 45.85%.The system accurately tracked charging/discharging patterns, validating its effectiveness.This compact and cost-effective BMS design ensures safe operation, improves battery longevity, and supports broader adoption of E-Bikes as an eco-friendly transportation solution.

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Published
2025-11-07
DOI
https://doi.org/10.3390/ecsa-12-26513
Citations
1
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
0.68
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article

SOC Estimation-Based Battery Management System for Electric Bicycles: Design and Implementation

Bhanu Pratap Soni, P. Ram Kishore Kumar Reddy, Satyanand Singh
1 citations
Advanced Battery Technologies Research
0.68
article

SOC Estimation-Based Battery Management System for Electric Bicycles: Design and Implementation

Bhanu Pratap Soni, P. Ram Kishore Kumar Reddy, Satyanand Singh
article en
1 citations

Abstract

Electric bicycles (E-Bikes) are gaining popularity as a sustainable mode of transportation due to their energy efficiency and zero-emission operation.However, challenges such as battery overcharging, overheating, and degradation from improper use can reduce battery lifespan and increase maintenance costs.To address these issues, this paper presents the design and implementation of a Battery Management System (BMS) tailored for E-Bike applications, with a focus on enhancing safety, reliability, and performance.The proposed BMS includes core functionalities such as State of Charge (SOC) estimation, temperature monitoring, and under-voltage and overcharge protection.Different approaches, including open-circuit voltage (OCV), Coulomb counting (CC), and Kalman filter techniques are employed to improve SOC estimation accuracy.The circuit for CC-based BMS was first simulated using Proteus, and system behavior was modeled in MATLAB Simulink is used to validate design assumptions before hardware implementation.An Arduino Uno microcontroller was used to control the system, interfacing with an LM35 temperature sensor, a voltage divider, and an ACS712 current sensor.The BMS controls battery charging based on SOC levels and activates a cooling fan when the battery temperature exceeds 45 C.It disconnects the charger at 100% SOC and triggers a beep alarm when the SOC falls below 40%.An external charger and regenerative charging from four electrodynamometers on the bicycle chain recharge the battery when the SOC drops below 20%, provided the load is disconnected.Measurement results closely matched simulation data, with the MATLAB model showing 44% SOC after 3 h, compared to the actual real-time 45.85%.The system accurately tracked charging/discharging patterns, validating its effectiveness.This compact and cost-effective BMS design ensures safe operation, improves battery longevity, and supports broader adoption of E-Bikes as an eco-friendly transportation solution.

Fiji National University (FJ)
Openalex Percentile: Top 23%
Advanced Battery Technologies Research
0.68
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