PloPdo
Location
Noida | India
Job description
Basic Qualification:
1. Ph.D. in Chemical/Energy/Electrical Engineering or Chemistry with a focus on
Battery/Chemical Energy Storage, modeling of Chemically Driven Energy Storage
Solutions, Polymer Chemistry (Energy systems), or related fields.
2. In-depth understanding and experience with ionically conducting polymers and their
application for energy storage.
3. Proven expertise in lithium-ion battery design, modeling, and practical application in
large-scale grid storage solutions.
4. Very detailed and comprehensive understanding of practical aspects of battery defects,
on-ground failures, and degradation.
5. Understanding of utility-scale Battery Management Systems (BMS) and their functioning.
6. Strong command and proven experience in electrochemical modeling (both aspects of
chemical science and electrical engineering of storage devices) for energy storage
solutions, preferably Li-ion and related battery chemistries.
7. Excellent data science skills and proven exposure to Machine Learning with Python.
8. Experience in leading multidisciplinary teams of research scientists and engineers.
9. Strong project development, management, and delivery skills.
Additional Qualifications:
1. Ability to work in a fast-paced, dynamic environment.
2. Effective communication skills for interaction with various stakeholders, including board
members, partners, and investors.
3. Familiarity with lithium extraction, separation, recovery, and refining processes is
advantageous.
4. Familiarity with the engineering of various large-scale energy storage solutions
(grid-based) and their interaction with renewable energy systems and the grid
Requirement:
1. Engineering Understanding : Gain a thorough understanding of utility-scale batteries
integrated with large-scale PV installations and grids. This includes the design, operation,
and maintenance aspects of these systems in an optimized and efficient manner.
2. Battery Chemistry Analysis : Investigate and interpret the battery chemistry and its
correlation with current (I), voltage (V), and temperature (T) patterns and trends. This
analysis is crucial for identifying battery health and performance.
3. Pattern Identification : Identify I, V, and T patterns during charging and discharging cycles.
Utilize this data to classify battery faults, and issues, and to quantify the state of health
and remaining life of the batteries.
4. Simulation and Digital Twin Development : Develop simulators for utility-scale batteries
and create digital twins for real-time application with PV systems.
5. Model Development : Develop models to analyze battery chemistry and the
charging/discharging cycle patterns (I, V, T). These models should be capable of detecting,
classifying, and predicting battery faults.
6. Optimization Models : Create optimization models for battery charging and dispatch
schedules. These models should consider solar irradiation predictions, current and future
battery health, energy demand, PV system performance, weather and energy demand
predictions, and asset value degradation.
7. Predictive Analytics : Develop models (AI and ML-based) based on available battery and
auxiliary system performance data to predict future potential failures and downtime of
the storage systems.
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