A Consensus Algorithm for Multi-Objective Battery Balancing

Batteries stacks are made of cells in certain series-parallel arrangements. Unfortunately, cell performance degrades over time terms capacity, internal resistance, or self-discharge rate. In addition, degradation rates heterogeneous, leading to cell-to-cell variations. Balancing systems can be used equalize those differences. Dissipative non-dissipative systems, so-called passive active balancing, either voltage at end-of-charge, state-of-charge (SOC) all times. While balancing is broadly adopted by industry, has been mostly studied academia. Beyond that, an emerging research field multi-functional i.e., that pursue additional goals on top SOC equalization, such as equalization temperature, power capability, rates, losses minimization. Regardless their functionality, circuits based centralized decentralized control systems. Centralized entails difficult expandability and single point failure issues, while severe controllability limitations. As a shift this paradigm, here we present for the first distributed multi-objective algorithm, multi-agent consensus algorithm. We implement validate simulations, considering electro-thermal lithium-ion battery model electric vehicle parameterized with experimental data. Our results show our novel enhance batteries substantial differences under most demanding operating conditions, aggressive driving DC fast charging (2C). Driving times extended (>10%), reduced (>20%), maximum temperatures decreased (>10 °C), temperature lowered (~3 °C rms), occurrence low violations during (>5×), minimizing need derating enhancing user experience. The algorithm effective, scalable, flexible, requires implementation tuning effort, resulting ideal candidate industry adoption.