CALCE Battery Group
Battery Management System Research

    CALCE is focused on the development of state of the art battery management systems (BMS) 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 best reliability and operational health information to the user.

    Fig. 1 PHM-based Advanced BMS

    A BMS is an electronic device that manages a rechargeable battery in order to protect the battery from damage, prolong the life of the battery, maintain the battery in a healthy state and provide the user with the operational status of the battery. A BMS consists of a number of sensors measuring the battery parameters (current, voltage, impedance and temperature). The central unit of a BMS is comprised of a set of models and algorithms to estimate the battery SOC and SOH, and then based on the state estimation to make control strategies. Another essential function of BMS is cell balancing, which is vital to maximize the usable battery capacity and lifetime. A typical BMS for EVs should contain the following functions, including data acquisition, cell protection, charge/discharge control, SOC estimation, 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 electric vehicle can rely on frequent discharge data in order to make SOC estimations, where as a BMS in a standby power supply will be required to make state estimations off-line 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 which outlines some of the interactions between subsystems of a BMS is shown below

    Fig. 2 A high level Schematic of a BMS

    Real time data processing algorithms are key components in battery management systems. These algorithms evaluate inputs such as current, voltage, and temperature, in order to estimate the remaining charge in a battery or the state of charge (SOC), the amount of degradation that has occurred in a battery or the state of health (SOH), and the remaining time the battery can operate before it must be replaced or the remaining useful performance (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 in the figure 3 below, A state of the art filtering technique that CALCE has developed is being applied to estimate and predict battery SOH and RUP. As shown in the figure 4 below, A temperature-based model is being used to estimate battery SOC taking into account different ambient temperatures.

    Fig. 3 Battery Remaining Useful Life Prediction using a Bayesian Monte Carlo Method
    A state of the art filtering technique
    Fig. 4 Battery State of Charge Estimation based on a Temperature-based Model
    A temperature-based model