In the town where I grew up, the local radio station - WJBCAM 1230, known for its slogan "Where Jazz Becomes Classic" - aired an afternoon show that, even today, sticks with me. Long before search engines and forums, this program provided a surprisingly elegant format for solving problems. Listeners would call in with specific, often quirky challenges - like “Where can I find a filter for a Bissell Model 8950 Vacuum?” - and fellow listeners would follow up with practical suggestions. It was communal, low-tech, and oddly effective.
What captivated me wasn't the vacuum filter. It was the structure: define the problem clearly, and then crowdsource a breadth of creative solutions. That simplicity has profound implications, especially in our highly technical world today.
Translating That Spirit to ESS Challenges
At TWAICE, where we specialize in BESS analytics software, this mindset is more relevant than ever. Energy Storage Systems (ESS) are complex, interdependent ecosystems. Failures or inefficiencies often don’t stem from one isolated issue but from a tangle of interconnected variables: cell performance, thermal anomalies, usage profiles, degradation patterns, and more.
Defining the right problem becomes the cornerstone of every meaningful solution. Just like those radio calls, the framing matters. Instead of asking, “Why is the battery system underperforming?” we dig deeper:
- Is it thermal runaway risk due to irregular heating patterns?
- Is the SoC estimation deviating under specific load conditions?
- Are there patterns of imbalance suggesting cell aging across certain modules?
Only with the right question can we surface the insights that our analytics platform is designed to uncover.
‍Crowdsourcing, Evolved
What once took a community of radio listeners now happens at a different scale - across data points, machine learning models, and domain expertise. TWAICE’s analytics platform is, in essence, a sophisticated version of that radio show’s call-in audience. Except here, the “callers” are ESS operators, OEMs, or fleet managers, and the “answers” come from historical performance data, simulations, predictive diagnostics, and our cross-platform experience with batteries in the field.
Each insight starts with the same humble principle: define the problem. That’s what turns raw data into meaningful diagnostics and, ultimately, reliable solutions.
Moving Forward: A Blog Series
This blog is the first in a series I’m calling Problems and Solutions. Each post will spotlight a real-world ESS problem - framed clearly - and walk through the pathways we explored to reach a solution. Sometimes, it will be a surprising insight from battery aging models. Other times, an issue with permitting. Always, it will be anchored in that same foundational idea: start with the right question.
Because the best solutions - whether it’s for a vacuum filter or a megawatt-scale energy storage system - start with understanding the problem.
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