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Cell balancing is a key function of battery management system (BMS) that is implemented to maximize the battery's available capacity and service life. The increasing demand of larger and better performance pack has raised the need to investigate the various cell balancing techniques so that the energy of the battery can be fully realized. In this work we develop a phenomenological model in order to quantify the benefits of passive balancing and active balancing. The electrical response of a model pack consisting of serially connected lithium ion cells is simulated with Matlab. The effects of the variance of cell capacity, internal resistance, self-discharge rates, pack configuration and size are studied. The possible optimization rooms for implementing passive and active balancing are suggested.

The microhybrid electric vehicle (MHEV) has increasingly received attention since it holds promise for significant increases in fuel economy vs. traditional gasoline vehicles at a lower price point than hybrid vehicles. Passive parallel connection of the traditional 12V lead acid battery and a high power lithium ion battery has been identified as a potential architecture that will facilitate fuel economy improvements with minimal changes to the electrical network. Enabling a passive dual-battery connection requires a design match between the two batteries, including characteristics such as battery size and resistance, so that the performance can be optimized. In this work we have developed a hybrid model that couples electrochemical model of lithium ion battery (NMC-Graphite as an example) and an equivalent circuit model of lead acid battery in order to study the behavior of 12V dual-battery microhybrid architectures.

Competitive engineering of battery packs for vehicle applications requires a careful alignment of function against vehicle manufacturer requirements. Traditional battery engineering practices focus on flow down of requirements from the top-level system requirements through to low-level components, meeting or exceeding each requirement at every level. This process can easily produce an over-engineered, cost-uncompetitive product. By integrating the key limiting factors of battery performance, we can directly compare battery capability to requirements. Here, we consider a power-oriented microhybrid battery system using coupled thermal and electrochemical modeling. We demonstrate that using dynamic resistance acquired from drive cycle characteristics can reduce the total size of the pack compared to typical static, fixed-duration resistance values.