Intelligent microprocessor system for diagnostics and balancing of rechargeable batteries of various electrochemical types

Contemporary energy storage deployments increasingly incorporate heterogeneous cell chemistries within a single installation, yet most commercial battery management circuits remain optimized for a single electrochemical type. This paper describes a microprocessor-controlled platform capable of autonomous diagnostics and charge equalization across four battery technologies: lithium-ion NMC, lithium iron phosphate (LiFePO₄), nickel-metal hydride (NiMH), and lead-acid. The measurement front-end is a 16-bit delta-sigma converter delivering 46 μV resolution and ±1.4 mV per-cell accuracy, interfaced to an STM32F446RE ARM Cortex-M4 processor at 180 MHz. Equalization combines resistive dissipation for voltage spreads below 50 mV with a switched-capacitor network achieving above 90 % energy-transfer efficiency for larger disparities. State-of-charge tracking couples’ coulomb integration with an adaptive extended Kalman observer, maintaining accuracy within ±3 % even from a 40 % seed-error initialization. Benchtop trials on four-cell series packs of each chemistry recorded inter-cell convergence to ±5 mV within 22 min, SOC root-mean-square error below 3 %, and correct automatic chemistry classification across all laboratory sessions. The novelty of the proposed system is not in the individual use of battery diagnostics or cell balancing, which are well-established topics, but in their integration into a single microprocessor-based multi-chemistry platform capable of automatic electrochemical-type identification, chemistry-specific parameter loading, adaptive SOC/SOH estimation, and hybrid passive-active balancing. This distinguishes the proposed approach from conventional BMS solutions optimized for only one battery chemistry.