Battery Health Monitoring System
In recent times there has been a hysteria surrounding the future of energy as fossil fuels continue to deplete at an alarming rate from the face of the earth. Moreover the environmental impact it has had has also begun to unleash its wrath upon the world. Therefore there has been a need to prolifera
2025-06-28 16:30:36 - Adil Khan
Battery Health Monitoring System
Project Area of Specialization Electrical/Electronic EngineeringProject SummaryIn recent times there has been a hysteria surrounding the future of energy as fossil fuels continue to deplete at an alarming rate from the face of the earth. Moreover the environmental impact it has had has also begun to unleash its wrath upon the world. Therefore there has been a need to proliferate the development of alternative energy sources. A major component in the renewable energy realm is energy storage. With their longstanding history, batteries are the go-to choice for this purpose since they are portable and energy efficient.
Electro-chemical batteries are an integral part of most electrical systems as they are able to convert stored chemical energy to electrical energy on demand. Despite the ubiquity of portable electronic devices like digital cameras, mobile phones, laptops having proliferated battery technology, it has yet not reached the paramount efficiency that it is capable of achieving. Most big electrical systems make use of lead-acid batteries which have over time continued to be a reliable and cost effective solution to battery needs. Moreover another major player emerging in the battery paradigm is a lithium based battery, which has significantly high energy density.
But current inefficiencies in battery technology leave battery systems in a predicament. Therefore there is a need to evaluate battery parameters and make informed judgment on the current state of the battery ensuring both safety and optimum use of the battery. This is important to ensure safety in high stake situations like aero planes and sensitive industrial loads. This will prevent costly maintenance and replacements.Therefore the aim of this project is to monitor those essetial parameters and make informed judgements based on them.
Project ObjectivesThe objective of this project is to esure that batteries run smoothly and if there is any eminent failure then a warning be generated beforehand.This is an important safety tool in critical systems where the system is battery powered.This will ensure the robustness of the system.
Project Implementation MethodThe voltage and current sensor co variances are found. The battery is charged to 100%.The voltage and current of the battery are monitored and stored and state space matrices At, Bt, Ct and Dt are calculated using the historical data collected previously. EKF algorithm is utilized and the three steps with three sub steps each are used to find SOC estimates.These SOC estimates are used to predict SOH of the battery later.
SOC estimated value and the current sensor readings are read and stored in memory. They are used to derive the AWTLS polynomial, the roots of which are calculated. Complex and negative are ignored and the remaining roots are substituted in the cost function. The root that gives the lowest value is considered the capacity of the battery.
This value is divided with the rated maximum capacity of
the battery as given by the battery manufacturer.
An ARM 64-bit microprocessor was used to run the algorithms, an ACS-712 current sensor was utilized in the experiment and an ADS1115 analog to digital converter (ADC) was incorporated into the setup. The Li-ion cell is connected to voltage sensor and current sensor, which are connected via an ADC to the micro processor on which the entire system algorithm is loaded. SOH is estimated using the uploaded algorithms and is displayed on the attached monitor screen
Benefits of the ProjectThe system will ensure that batteries run optimally. Another important benefit it provides is in the predictive maintenance sector.Since battery failure can be predicted beforehand, batteries can be replaced beforehand preventing an unexpected downtime from happening and thus preventing revenue losses.
The system will also improve the life of the battery.It will also improve safety in critical systems as they are battery powered, it is imperative that there would be safety checks in place to ensure smooth functioning and robustness.
Technical Details of Final DeliverableThe end product will be able to tell the state of health of a lithium ion battery. This will be done by finding the SOC first.An ARM 64-bit microprocessor was used to run the algorithms,
an ACS-712 current sensor was utilized in the experiment
and an ADS1115 analog to digital converter (ADC) was
incorporated into the setup. The Li-ion cell is connected to
voltage sensor and current sensor, which are connected via
an ADC to the micro processor on which the entire system
algorithm is loaded. SOH is estimated using the uploaded
algorithms and is displayed on the attached monitor screen
The above mentioned experiments were simulated on octave
and the simulations were validated using the datasets of
the same battery and the battery model. The graphs were
generated. The first graph illustrates the SOC estimation results using EKF algorithm. The estimation results were compared with the actual dataset values. The error bounds were also plotted along with the error bounds. The capacity estimation through approximate total least square is also plotted with error bounds along with the actual capacity of the cell.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 79050 | |||
| Raspberry pi 3b | Equipment | 4 | 6000 | 24000 |
| ACS 712 current sensor | Equipment | 6 | 3000 | 18000 |
| ADS 1115 | Equipment | 6 | 300 | 1800 |
| Battery test pack | Equipment | 6 | 3000 | 18000 |
| Balance charger | Equipment | 5 | 1000 | 5000 |
| connectors | Equipment | 5 | 450 | 2250 |
| Vero board | Miscellaneous | 10 | 1000 | 10000 |