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Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

Battery Management System (BMS) design is a complex task requiring sophisticated models that mimic the electrochemical behavior of the battery cell under a variety of operating conditions. Equivalent circuits are well-suited for this task because they offer a balance between fidelity and simulation speed, their parameters reflect direct experimental observations, and they are scalable. Scalability is particularly important at the real time simulation stage, where a model of the battery pack runs on a real-time simulator that is physically connected to the peripheral hardware in charge of monitoring and control. With modern battery systems comprising hundreds of cells, it is important to employ a modeling and simulation approach that is capable of handling numerous simultaneous instances of the basic unit cell while maintaining real time performance.

Lithium battery cells are commonly modeled using an equivalent circuit with large lookup tables for each circuit element, allowing flexibility for the model to closely match measured data. Pulse discharge curves and charge curves are collected experimentally to characterize the battery performance at various operating points. It can be extremely difficult to fit the simulation model to the experimental data using optimization algorithms, due to the number of values in the lookup tables. This challenge is addressed using a layered approach to break the parameter estimation problem into smaller tasks. The size of each estimation task is reduced to a small subset of data and parameter values, so that the optimizer can better focus on a specific problem. The layered approach was successful in fitting an equivalent circuit model to a lithium iron phosphate (LFP) cell data set to within a mean of 0.7mV residual error, and max of 9.2mV error at a transient.

The lithium iron phosphate (LFP) cell chemistry is finding wide acceptance for energy storage on-board hybrid electric vehicles (HEVs) and electric vehicles (EVs), due to its high intrinsic safety, fast charging, and long cycle life. However, three main challenges need to be addressed for the accurate estimation of their state of charge (SOC) at runtime: Long voltage relaxation time to reach its open circuit voltage (OCV) after a current pulse Time-, temperature- and SOC-dependent hysteresis Very flat OCV-SOC curve for most of the SOC range In view of these problems, traditional SOC estimation techniques such as coulomb counting with error correction using the SOC-OCV correlation curve are not suitable for this chemistry. This work addressed these challenges with a novel combination of the extended Kalman filter (EKF) algorithm, a two-RC-block equivalent circuit and the traditional coulomb counting method.

Typically, battery models are complex and difficult to parameterize to match real-world data. Achieving a good generalized fit between measured and simulated results should be done using a variety of laboratory data. Numerical optimizations can ensure the best possible fit between a simulation model and measured data, given a set of constraints. In this paper, we propose a semi-automated process for parameterizing a lithium polymer battery (LiPB) cell simulation model that is able to satisfy constraints on the optimized parameters. This process uses a number of measured data sets under a variety of conditions. An iterative numerical optimization algorithm using Simulink Parameter Estimation was implemented to estimate parameter values by minimizing error between measured and simulated results.

Traditionally, code generated from Simulink models has been incorporated into production applications in a manner similar to hand-written code. As the size of the content created in Simulink has grown, so has the desire to do more integration in Simulink. Integrating content from C/C++ calling environments directly into Simulink blocks rather than just calling external legacy code prevents errors and preserves signal flow visibility in the Simulink models. Although much of the application content has transitioned to Simulink models, most of the Common Utility Services (e.g., communications, diagnostics, and nonvolatile memory) still exist in C/C++ libraries. While application content changes frequently, Common Utility Service content changes infrequently and is heavily leveraged across many applications. Therefore, it is often desirable to call these Common Utility Services from their existing C/C++ libraries rather than porting them to be generated directly from Simulink models.

The alternator provides power to vehicle electrical loads with the battery, and its maximum current depends on various factors such as electrical load, engine speed, thermal condition, and other variables. Above all, thermal effects make alternator simulations more complicated. For example statically similar conditions may show different results according to the temperature variation for each alternator operation. This paper proposes a two-stage statistically-based model structure which separates dynamic thermal effects from steady state performance. The method was validated by experiments and shows good predictive performance, suitable for use in test reduction.

Electrical system capacity determination for conventional vehicles can be expensive due to repetitive empirical vehicle-level testing. Electrical system modeling and simulation have been proposed to reduce the amount of physical testing necessary for component selection [1, 2]. To add value to electrical system component selection, the electrical system simulation models must regard the electrical system as a whole [1]. Electrical system simulations are heavily dependent on the battery sub-model, which is the most complex component to simulate. Methods for modeling the battery are typically unclear, difficult, time consuming, and expensive. A simple, fast, and effective equivalent circuit model structure for lead-acid batteries was implemented to facilitate the battery model part of the system model. The equivalent circuit model has been described in detail. Additionally, tools and processes for estimating the battery parameters from laboratory data were implemented.

The chemical storage battery is currently the primary choice of automotive powertrain designers for hybrid-electric vehicles. This design suffers from complexity, manufacturing, cost, durability, poor performance predictability and other problems. Additionally, the trend in hybrid powertrain design is to move from high energy density to high power density. A proposed alternative to chemical batteries for some hybrid vehicle applications is an electro-mechanical battery (EMB) that combines an electric machine with hydro-pneumatics to provide energy capture, storage, and propulsion assistance. An initial multi-domain physical system model of an EMB-based hybrid powertrain has been developed in the Simulink environment to show the behavior of the EMB design in a midsize hybrid passenger vehicle application.