Intelligent battery management system with AI-based performance monitoring

Abstract

A battery system consisting of a multitude of replaceable cells forming a battery, an inspection unit connected to the cells to monitor the parameters of each individual cell, and a processing unit configured to receive and analyze the monitored parameters in order to calculate a health value for each cell, compare the health value with a threshold, flag a cell whose health value is below the threshold, and transmit data of the flagged cell to a computer dashboard, with only degraded or defective cells being replaced.

Description

Application area of ​​the invention

[0001] The invention relates to battery management systems for rechargeable multi-cell batteries with individual cell sensors and artificial intelligence for assessing the cell condition, marking degraded cells and enabling selective replacement without having to dispose of the entire battery. Background of the invention

[0002] Conventional battery management systems (BMS) monitor parameters at the pack level and perform charge balancing. However, maintenance often requires replacing entire modules or packs when some cells degrade, increasing lifecycle costs and waste. Modular designs with cell-specific sensors and diagnostics can identify underperforming cells, while data-driven analytics more accurately estimate remaining lifespan. A system that continuously collects cell-specific measurements, calculates an AI-powered health score, flags cells exceeding thresholds, and displays actionable recommendations on a maintenance dashboard enables targeted cell replacement and cost-effective maintenance of batteries in electric vehicles, stationary storage systems, or power tools. Summary of the invention

[0003] The invention relates to a battery system comprising: a plurality of replaceable cells forming a battery pack; an inspection unit connected to the cells for monitoring parameters such as voltage, temperature, impedance, and current; and a processing unit for analysis. The system monitors parameters to calculate a state value for each cell, compares this value with a threshold, marks cells below the threshold, and transmits the data of the marked cells to a computer dashboard. Thanks to this design and workflow, only defective or damaged cells need to be replaced, instead of discarding the entire battery.

[0004] In embodiments, the processing unit uses machine learning models trained on historical cycle and impedance data to estimate health status and remaining service life; a service mode isolates and safely unlocks marked cells electrically for replacement; and the dashboard provides alerts, diagnostics, and inventory guidance for field service technicians. Detailed description

[0005] The battery pack consists of a mechanical frame and a bus architecture that accommodates multiple cells in interchangeable carriers with locking connectors to prevent incorrect installation. The inspection unit includes cell-specific measuring leads and temperature sensors, as well as optional AC excitation paths for impedance measurement. A measurement front end multiplexes the cell-specific channels into an analog-to-digital converter (ADC) with calibration for offset and gain.

[0006] The processing unit receives time-series measurements, including open-circuit and load voltages, temperatures, charge / discharge currents, coulomb numbers, and derived impedance characteristics per cell. It calculates a state index that combines indicators such as capacity loss, increase in internal resistance, voltage recovery, temperature rise under load, and balancing effort. Thresholds can be set statically or adaptively depending on the cell chemistry and usage profile.

[0007] An AI model, such as a gradient-boosting ensemble or a resource-efficient neural network, processes current features (e.g., differential voltage, incremental capacitance, impedance spectra) to refine the condition assessment and predict the remaining service life. The model runs locally on the device to generate real-time alerts and optionally synchronizes anonymized summaries with a cloud service to enable model updates via signed firmware.

[0008] When a cell's health falls below a threshold, the system flags it, logs this, and highlights the cell's slot ID. A service interlock mode opens semiconductor switches or contactors to electrically isolate the flagged cell, enabling safe replacement. The dashboard, accessible via a connected device, displays the pack layout, flagged cells, trend charts, and recommended actions. Role-based access rights restrict service functions.

[0009] The balancing circuits operate independently to ensure the uniformity of the battery pack; the thermal management is coordinated with the inspection unit to prevent hotspots. All communication with the dashboard is encrypted; test logs record replacements and parameter history for traceability.

[0010] During operation, the battery management system (BMS) continuously monitors each cell, calculates a status value, flags cells that exceed thresholds, and reports the location and reason on the dashboard. For scheduled maintenance, a technician activates service mode via the dashboard, isolates the flagged cell, replaces it, and initiates a re-verification and recalibration. The battery system then resumes normal operation without a complete cell replacement.

Claims

[1] A battery system consisting of a plurality of replaceable cells forming a battery, an inspection unit connected to the cells to monitor the parameters of each individual cell, and a processing unit configured to receive and analyze the monitored parameters to calculate a health value for each cell, compare the health value with a threshold, flag a cell whose health value is below the threshold, and transmit data of the flagged cell to a computer dashboard, with only degraded or defective cells being replaced. [2] System according to claim 1, wherein the inspection unit measures at least cell-specific voltage, temperature and impedance parameters and the processing unit uses a machine learning model trained on historical cycle data to estimate the health status and remaining service life for evaluation. [3] System according to claim 1, further comprising an isolation mechanism configured to electrically isolate a marked cell position in response to a service command, wherein the dashboard is configured to display a pack layout that identifies the marked cell and enables safe replacement. [4] System according to claim 1, wherein the processing unit maintains test logs of marked events and exchange operations, performs role-based access control for service functions, and coordinates cell balancing and thermal management independently of the marking function.