It can be tough to know when a battery failure may be knocking at your door. But, what if we tell you that you can exactly know when to prevent any failure or a potential threat with the help of accurate predictive maintenance systems?
Through this blog, we will share the best practices on how you can predict the future of your batteries and make predictive maintenance systems for battery protection a viable reality. Let’s begin!
Did you know that in the next 3 years, the GOI (Government Of India) is planning to go all-electric? Well, we are talking about the government vehicles here. The execution of this plan will ramp up the roadmap to achieve net-zero emissions by 2070. Certainly, the subsidization and the battery swapping policies will supplement faster EV adoption.
The changes toward sustainable mobility solutions are beginning to strain the transmission and distribution of energy storage systems. Over the past couple of years, there has been an increase in Battery Energy Storage Systems (BESS) frequency to manage and effectively distribute the load of storing high-intensity batteries.
At the same time, the stakeholders in the EV ecosystem like the OEMs and battery manufacturers are considering putting their efforts into creating the safest standards and best practices for sustaining electric vehicles in the long run. One of the most promising ways to ensure that your battery is safe and secured is by utilizing the right predictive maintenance systems.
Let us explore in-depth all about predictive maintenance systems for battery protection and how they can assist a battery manufacturer or an EV owner know their battery’s health and ensuring the battery pack’s safety.
Predictive maintenance for battery protection precisely tells you the right timing for undergoing battery maintenance or troubleshooting operation. It collects multiple varying data point inputs obtained from the battery management system to deduce accurate predictions for the battery pack’s maintenance status.
You can either predict it manually or prevent any incident, catastrophe, or potential failure with the aid of IoT applications employing cloud technology and advanced machine learning algorithms.
The researchers at the University of Cambridge have analyzed that the power of AI/ML to predict battery health is 10x times more accurate than the industrial standards. It is interesting to note that predictive maintenance systems have reported savings ranging from 30-40% compared to reactive maintenance and 8-12% over preventive maintenance.
When you choose the advanced predictive maintenance systems for battery protection, you know exactly when the battery pack will reach its end of life, having enough time for a replacement.
Here are some of the top benefits of adopting this battery protection system:
1. Lesser downtime is required in equipment maintenance.
2. Effective and optimal battery use till the EOL (End-Of-Life)
3. Elimination of faulty cells.
4. Enhancing the overall battery progress and sustainability
5. Minimal loss in productive hours
6. Cost reduction in unwanted maintenance and spare part replacement.
Let’s briefly discuss how you can implement predictive maintenance systems for battery protection.
To use predictive maintenance, the analytics tools such as the IoT battery management systems with an interactive and user-friendly UI assists in ramping up the shift towards these advanced technologies enabling safer and more efficient battery life.
You should consider the steps below before performing a predictive maintenance run:
IONDASH: India’s most advanced cloud battery management platform detects any deviations from the normal functioning and operations of the most valuable asset of your electric car, the battery. IONDASH is equipped with the most advanced algorithms for battery storage risk assessment.
This cloud analytics platform uses data analysis and real-time normalization techniques to help you know your battery status. It facilitates remote mirroring and location tracking for your connected device(s) so that you can address any potential threats preventing thermal runaway or sometimes catastrophes.
IONDASH is at par with the battery storage regulations and anticipates even the smallest probabilities of situations requiring maintenance support. This reduces the overall cost of overhauling the operations, saving time, effort, and expenses in maintenance.
We can’t deny the fact that adopting predictive maintenance systems for battery protection requires an initial investment, training of the personnel, and a change in the ideology.
However, the value it brings to the table is unmatched by the reasons not to go for it. The future of battery management system for electric vehicles depends on predictive maintenance. And if you can predict your EV battery pack’s future, then why shouldn’t you?
1. Reduce the exposure to extremely high temperatures when your EV is parked.
2. Minimize the battery usage at 100% state of charge.
3. Control the optimal battery (SOC) state of charge during long storage.
It is necessary to monitor and maintain the batteries ensuring 100% safety and precaution. Due to abnormal charging-discharging and thermal runaway, short circuits in an EV can lead to catastrophes such as fire or battery blast. For many OEMs and battery manufacturing companies, such incidents could cost lakhs of rupees.
It is a piece of utility hardware for battery protection, preventing deterioration, degradation, or permanent damage.
The battery protection circuit monitors the Li-ion battery voltage and cuts off the load to prevent the battery discharge; during the incidents where the battery’s voltage goes down below the set threshold. If you store a battery-operated product in a complete discharge state, you are making it vulnerable to being completely discharged.
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