Introduction

The customer is one of the largest automotive manufacturing companies in India, by market share. The customer designs, builds and delivers electric vehicles (EVs). One well-publicized issue concerns the efficiency of the vehicle since the market in India is very sensitive to this information. The ability to control and monitor the power source of the vehicle is a critical component of achieving high efficiency of usage of electricity and other fuels.

Why We Chose Pluto7

Showing the amount of power remaining in the battery (i.e., its state of charge) is relatively simple. However, in order to facilitate consumer confidence, the accuracy of the amount of range a given increment of electricity provides (e.g., miles or kilometers per remaining charge) is a key enabler. By leveraging historical and current information, it is possible to compute and display much more reliable and valuable figures regarding the RUL (Remaining Useful Life) of a car. With these objectives in mind, the client needed help in analyzing the vast quantity of telemetry data collected over a period of time and in building a machine learning model (specifically, a sequence model) to predict the RUL of an EV at each point in time of the operation of said vehicle.

Solution

The Pluto7 team examined the data to first prepare it in a format that can be used by a machine learning model, then upon analysis, strove to engineer features whose addition could improve the performance of the machine learning model. The given data consists of a large number of observations, each of different vehicles on different trips at different points in time. The relevant data was uploaded to BigQuery so that our team could confirm findings and perform some preliminary data transformation. Doing this allowed to explore the data at scale and speed.

The team then used Google Cloud Dataprep for the purpose of feature manipulation and engineering. Once this was complete, we started building a machine learning model to predict the RUL of an EV.

Results

With the ease of scalability on  Google Cloud Platform, the time taken to train  Machine Learning model was brought down by a considerable amount of duration. The availability of the GCP components such as BigQuery enabled the team members to seamlessly pre-process a huge amount of raw datasets without depending on the IT team to upgrade the on-premise computing resources.

Several advanced machine learning models were tried to predict the Remaining State of Charge that resulted in various levels of forecast accuracy. The production-ready model (XGBoost) was finalized based on the best performing accuracy as compared to the existing rule-based approach.

Industry Manufacturing| Automotive

Solutions Preventive-maintenance-ml

Challenges

  • The client wanted a reliable estimate for the state of charge of the EV at all times that facilitates a better customer experience.
  • Incorporate maximum parameters sent from the EV to accurately predict the state of charge.
  • A complex process with many unpredictable variables, such as EV’s Torque, Rotor Temperature, etc that impact the state of charge were not included.

Results

  • Dependable accuracy for remaining useful lifetime (RUL) of the vehicle battery.
  • Increased consumer confidence because of a better user experience.
  • Competitive advantage for go-to-market.

Products Used

  • Google Cloud Platform
  • Google Cloud DataPrep
  • Google Cloud DataLab
  • Google BigQuery
  • Google Cloud Storage
  • Google Cloud Machine Learning Engine

Why Google? This client chose Google Cloud Platform because it:

  • Offers a flexible, scalable, ready-made infrastructure in the cloud.
  • Delivers powerful data processing, data warehousing, and state-of-the-art Machine Learning and Artificial Intelligence capabilities.
  • Provides a cost-effective platform that’s easy for business team members to use.
  • Delivers speed, security, reliability and flexible pricing.

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