EV Battery Longevity Optimisation
Finding and prioritizing next-generation Battery Management System (BMS) hardware-software stacks to extend EV pack life by ≥20% within tight cost and safety constraints.
Client
Global EV Manufacturer
Objective
Extend Pack Life by ≥20%
Timeline
10-Week Sprint
Key Focus
ASIL-C Compliance & Cost
The Challenge: Managing Degradation in the Real World
Traction-battery packs account for up to 40% of an EV’s cost, making their longevity critical to resale value and warranty exposure. While "adaptive" BMS algorithms that dynamically tune battery parameters are emerging, they must overcome three significant real-world challenges to be effective and certifiable.
Our Approach: A 5-Phase Sprint from Scan to Solution
Our 10-week sprint was designed to rapidly move from a broad landscape scan to a prioritized list of pilot-ready solutions, incorporating techno-economic modeling and hardware-in-the-loop (HIL) testing to validate performance and safety.
- Phase 1: Landscape Scan (Weeks 1-2): Catalogued 60 sensor-algorithm platforms.
- Phase 2: Data & Bench Analysis (Weeks 3-4): Benchmarked SoH-prediction errors of candidate models.
- Phase 3: Techno-Economic & Safety Modelling (Weeks 5-6): Simulated pack life over 12 real-world duty profiles.
- Phase 4: Prototype Scoring (HIL) (Weeks 7-8): Ran hardware-in-the-loop tests for top six finalists.
- Phase 5: Prioritisation & Roadmap (Weeks 9-10): Delivered top-five ranked solutions with an 18-month integration timeline.