When implementing impedance tracking for state-of-health (SOH) estimation, why do even advanced BMS solutions struggle to maintain errors below 5%? Recent data from Tesla's Q2 2024 battery report reveals a 3.8-4.2% margin of error persists in their flagship models, highlighting an industry-wide challenge we've yet to fully conquer.
Why do Kalman filter-based SOC estimations consistently outperform voltage-based methods by 3-5% in real-world applications? As battery systems evolve, the industry faces a critical crossroads: Should we prioritize mathematical modeling elegance or electrochemical fundamentals for state of charge determination?
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