Why not use regression since the data is so simple? get something like percentage confidence levels

Like 95% confident that the beacon won't fail at certain millivolt level

80% confident that the beacon won't fail at certain millivolt level

Etc.

Calculate the average power usage for a beacon and then just do a prediction into the future to see when it's pretty confident that the battery fails

If I had more features in my dataset, will it be better then?

I have already used regression algorithms like random forest and svr. I just wanted to know if machine learning is required at all.

Ml is typically used when it's too difficult to make predictive models by hand, I think trying to make ml prediction model for this case would be harder and less accurate than just simple linear regression unless I'm missing something

I'm working with a non linear data therefore traditional linear regression isn't working with capturing the trend.

There is other regression types like x^2 regression, logarithmic regression and more

I mean you can try to use ml, I'm just trying to save you the trouble of having to implement a model

I tried exponential regression. Didn't fit well.

My only question is that is predictive modeling necessary in these cases like battery lifetime prediction? Won't they be just static values when working with non rechargeable battery with no cycle life?

There is some variation but mostly the mechanics shuts down when the battery gets below a certain voltage

If you want to predict when the voltage gets below that level, then you need the prediction model

Yes thank you so much for answering. May I DM you for further queries?

The rule of thumb in ML is that if you solve your problem algorithmically with correct equations and solid math you shouldn't go for estimators (Don't be lazy).

I'm not an expert, I'm sure you will be fine

If you need information about lithium cells and general statistical data, there is a huge database for this information. Unfortunately, it is a German site by a couple of smart German engineers who are working on batteries and solar cells and efficient storage of energy in different types of batteries.

This website basically does what you want to do, if I understand your use case correctly.

https://lili.fortyone.ai/

Maybe in the two forums down below users can give you their datasets to create your own model. One of the forums is a German one, but the people there should be able to answer you in English most of the time ;)

https://diysolarforum.com/resources/categories/battery-cell-data-sheets.3/

https://forum.drbacke.de/aboutus

Hi, I actually worked in battery testing about 2 years ago, and a colleague of mine was assigned to predict exactly that, only with simple regression function not full ML system. So first, good luck and PLEASE make an update post if you do find something intersting... 🙏🏾 Second, I don't know your background in electronics so I'll say that make sure you have all the data you need, mainly room temperature, battery temp., life cycles the battery has been through, estimated energy drawn from the battery at the moment etc.

Good luck!

I'm working currently working on a POC and since it's a non rechargeable battery, we do not have any information other than date and corresponding battery value. I don't know how to proceed further.

Thank you! One thing I understand from that website is that they have taken resistance and discharge current as input for cell prediction. Which I do not have in my dataset. I need to check further.

I'm pretty sure you have enough data for the training and testing of the neural network - whether from the datasheet, or the data set that someone here gave you in another comment. Another option is to conduct some battery testing to add to your data (discharge in various currents and temperatures). But you'll still need some sensors in your system- temprature and the current drawn- those are the main paramters in estimating battery performance. Then you can train and test the model.