Battery Management System for Electric Garbage Compactor Trucks

Abstract

When garbage trucks perform garbage collection and compression operations, it is common to keep the engine idling and even increase the engine speed during garbage compression, which can lead to noise, air pollution, increased fuel consumption, and carbon emissions. Adopting an electric compression system can effectively reduce these issues. The battery pack used in electric garbage trucks is the core energy source of the vehicle, making proper battery management system crucial for the overall safety and performance of the vehicle. This study aims to utilize NUVOTON’s Cortex-M4 chip to develop a battery management system specifically designed for electric garbage trucks. By real-time online estimation of the battery state, optimal performance of the battery pack can be achieved. Battery health is assessed based on capacity cycle counting for parameter weighting evaluation of battery voltage drop. By comparing the capacities of battery modules to track and calibrate the open-circuit voltage, the capacity error is primarily estimated using a combination of Coulomb counting method and open-circuit voltage method to assess the battery’s state of charge and evaluate its lifespan. The proposed method is validated by integrating the battery state estimation technique into the microcontroller of the battery management system, and compared with the conventional Coulomb counting method. The real-time online battery estimation method adjusts the initial value check of SoC by tracking the variation of battery module capacity and adjusting the OCV lookup table, thereby enhancing the accuracy of SoC estimation and reducing errors. This method can be effectively applied to electric garbage compressors to improve battery utilization efficiency and maximize battery lifespan.

Keywords

• Battery management system (BMS)
• Coulomb counting method (CCM)
• state of charge (SoC)