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The 12 V advanced start stop systems can offer 5-8% fuel economy improvement over a conventional vehicle. Although the fuel economy is not as high as those of mild to full hybrids, its low implementation cost makes it an attractive electrification solutions for vehicles. As a result, the 12 V advanced start stop technology has been evolving fast in recent years. On one hand, battery suppliers are offering a variety of energy storage solutions such as stand-alone lead acid, stand-alone LFP/Graphite, dual batteries of lead acid parallel with NMC/LTO, LMO/LTO, NMC/Graphite, and capacitors, etc. For dual battery solutions, the architecture also varies from passive parallel connection to active switching. On the other hand, OEM are considering to leverage a lot more use out of traditional 12 V SLI (start, light, and ignition) for functions such as power steering, air conditioning, heater, etc.

Lithium plating is an important failure factor for lithium ion battery with carbon-based anodes and therefore preventing lithium plating has been a critical consideration in designs of lithium ion battery and battery management system. The challenges are: How to determine the charging current limits which may vary with temperature, state of charge, state of health, and battery operations? Where are the optimization rooms in battery design and management system without raising plating risks? Due to the complex nature of lithium plating dynamics it is hard to detect and measure the plating by any of experimental means. In this work we developed an electrochemical model that explicitly includes lithium plating reaction. It enables both determination of plating onset and quantification of plated lithium. We have studied the effects of charging pulses on homogenous plating in order to provide guidance for lithium ion battery design in hybrid applications.

Power limit estimation of a lithium-ion battery system plays an important balancing role of optimizing the battery design cost, maximizing for power and energy, and protecting the battery from abusive usage to achieve the intended life. The power capability estimation of any given lithium-ion battery system is impacted by the variability of many sources, such as cell and system components resistance, temperature, cell capacity, and real time state of charge and state of health estimation errors. This causes a distribution of power capability among battery packs that are built to the same design specification. We demonstrated that real time power limit estimation can only partially address the system variability due to the errors introduced by itself. Integrating feedback control algorithms with the lithium-ion battery model maximizes the battery power capability, improves the battery robustness to variabilities, and reduces the real time estimation errors.

Validation of the State-Of-Function (SOF) algorithm and associated cell models are critical for battery management as they are responsible for optimal pack power utilization as well as safety protection and life. The SOF accomplishes this optimization task by communicating pack level operation limits related to power, current, voltage and temperature. These operation limits are, in some cases, estimated via parameters and equations derived from cell models. Correspondingly, any errors within the cell models will propagate into the model-dependent SOF limits. Understanding the source of errors and thus finding areas for improvement requires a visualization-based SOF validation strategy.

Passively parallelizing two energy storage systems, one is energy type and the other is power type, requires minimal modifications of auto makers and thus a cost-effective method to enable advanced start stop technology. Traditional lead acid battery, lithium-ion battery, capacitor, are all candidate chemistries for dual energy storage solutions. However due to the dual nature of the technology the open circuit potential, resistance, and some other control variables should match in order to achieve optimal performance. In this work we use coupled equivalent circuit model and electrochemical model to study a few options of dual systems, namely the lead acid with NMC/LTO, lead acid with LFP-Graphite, and lead acid with capacitor. A few charging and discharging pulses are designed and simulated to evaluate the regen receiving capability and cranking capability of different chemistries.

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.

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.