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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.

Design of batteries for automotive applications requires a careful balance between vehicle requirements - as driven by automakers - and cost. Typically, for batteries, the goal is to meet the most stringent requirement at a competitive cost. The real challenge in doing so is understanding how the battery-level requirements vary with changes in the vehicle, powertrain, and drive cycle. In this work, we consider the relationship between vehicle-level and battery-level requirements of microhybrid vehicles and their linkage with battery design. These vehicle platforms demand high-power pulses for impractical durations - over 60 seconds on some drive cycles. We demonstrate a method for optimizing the battery design for fuel economy against any specific drive cycle, whether regulatory, consumer, or otherwise. This method allows for a high degree of customization against manufacturer or consumer value.

Start-stop, aka engine-stop or idle-stop, technologies are increasingly being applied to automotive vehicles to increase fuel economy. Start-stop vehicles turn off the engine during periods of zero speed and/or during prolonged coast down. During engine-stop, the vehicle electronics are powered solely by the battery. To replenish the battery, the battery needs to be recharged. In typical ICE vehicles, the battery is continuously charged. However, fuel economies can be improved if strategic charging of the battery can be achieved through selective charging through the alternator or through regenerative braking. To optimize fuel economy, an accurate estimation of the battery state of charge (SOC) during vehicle operation is required. Although state of charge estimation has mainly focused on Li-ion batteries, lead-acid batteries may be used successfully in start-stop applications.

48V battery packs, with rated power capabilities on the order of 8-16kW, are rapidly becoming a new standard in the automotive industry. Improving on their 12V counterparts (2-5kW), 48V Mild Hybrid Electric Vehicles (MHEV) allow for extended start-stop and regenerative braking functionalities, providing fuel economy benefits of up to 10-15% in standard passenger vehicles. New and relatively unexplored opportunities exist to further increase the fuel economy and performance of 48V systems. Improvement in battery power (to ~25kW) would further enable hybridization to near-HEV levels as well as engine downsizing, thus paving the way to fuel economy improvements beyond the current 10-15% MHEV limit. Additionally, new electrified features may be added, such as electric turbo/supercharging, electric traction, electric power steering, electric suspension and electric air conditioning.

Traditional testing approaches for fundamental battery materials focus on highly artificial test profiles, for example constant current (CC) or constant voltage (CV) testing. Additionally, the currents used for capacity and cycle tests are often very low. These profiles are not indicative of the types of current/voltage profiles that the battery will experience during actual vehicle operation. As a result, these simple tests may fail to sufficiently elicit the reduction in performance and failure modes that occur during more dynamic cycling. In this paper, we outline an approach in which vehicle-level modeling is applied to regulatory drive cycles in order to derive power vs. time requirements for an energy storage system. These requirements are used to identify segments of the regulatory drive cycles that present significant challenges to the battery. Finally, the most stressing portions of the drive cycle are used to determine limiting physical characteristics of batteries.

Power limit estimation of a lithium-ion battery pack can be employed by a battery management system (BMS) to balance a variety of operational considerations, including optimization of pulse capability while avoiding damage and minimizing aging. Consideration of cell-to-cell performance variability of lithium-ion batteries is critical to correct estimation of the battery pack power limit as well as proper sizing of the individual cells in the battery. Further, understanding of cell variability is necessary to protect the cell and other system components (e.g., fuse and contactor, from over-current damage). In this work, we present the use of an equivalent circuit model for estimation of the power limit of lithium-ion battery packs by considering the individual cell variability under current or voltage constraints. We compare the power limit estimation by using individual cell characteristics compared to the estimate found using only max/min values of cell characteristics.

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.