Center for Advanced Life
Cycle Engineering

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CALCE  Battery  Research  Group

    Battery Management System Research

    CALCE is focused on the development of state-of-the-art battery management systems (BMSs) for single- and multi-cell systems to provide the most accurate state-of-charge (SOC) and state-of-health (SOH) metrics. CALCE is dedicated to developing a BMS that not only assures safe usage, but also provides the most reliable performance and operational battery health information.

    PHM-based Advanced BMS

    A BMS is an electronic device that manages a rechargeable battery in order to protect it from damage, prolong its life, maintain it in a healthy state, and provide the user with its operational status. A BMS consists of a number of sensors that measure the battery parameters (current, voltage, impedance, and temperature). The central unit of a BMS is comprised of a set of models and algorithms that estimate the battery SOC and SOH and then, based on the state estimation, make control strategies.

    Another essential function of a BMS is cell balancing, which is vital for maximizing the usable battery capacity and lifetime. A typical BMS for electric vehicles (EVs) should contain the following functions: data acquisition, cell protection, charge/discharge control, SOC and SOH estimation, cell balancing, thermal management, and communication. The problem of state estimation must be considered in the context of the entire BMS. Certain applications may have restrictions on the types of data that can be collected. For example, a BMS in an EV can rely on frequent discharge data in order to make SOC estimations, whereas a BMS in a standby power supply must make state estimations offline due to infrequent use.

    Therefore, the type of sensors available to a BMS must be considered when developing a state estimation algorithm. In order for these algorithms to be used effectively, they must interact with other subsystems of the BMS. If SOH monitoring is applied to individual cells in a multi-cell battery pack, then SOH can be used to determine when to perform cell balancing. If a voltage measurement largely disagrees with the modeled voltage in the SOC algorithm, then a fault condition could be triggered and the BMS should stop current flow through the battery. A high-level schematic that outlines some of the interactions between subsystems of a BMS is shown below.

    A high level Schematic of a BMS

    Real-time data processing algorithms are key components in BMSs. These algorithms evaluate inputs such as current, voltage, and temperature in order to estimate the remaining charge in a battery (the SOC), the amount of degradation that has occurred in a battery (the SOH), and the remaining time the battery can operate before it must be replaced (the remaining useful performance, or RUP). SOC is necessary to ensure that a battery can perform a given task before it requires a recharge while SOH and RUP are used for planning maintenance and battery replacement. As shown below, A state of the art filtering technique that CALCE has developed is being applied to estimate and predict battery SOH and RUP and A temperature-based model is being used to estimate battery SOC taking into account different ambient temperatures.

    Battery Remaining Useful Life Prediction using a Bayesian Monte Carlo Method
    A state of the art filtering technique
    Battery State of Charge Estimation based on a Temperature-based Model
    A temperature-based model