Battery Power - Fall 2015 - (Page 6)

Feature Real-Time Battery Modeling in HiL Testing of Battery Management Systems Paul Goossens, Vice President, Engineering Solutions Maplesoft Demand growth for rechargeable batteries in products such as portable electronic devices, electrified vehicles (EVs) and decentralized power utilities (smart grids) is nothing short of robust. With forecasted annual growth pegged at 8 percent by the Freedonia Group, the global battery market will reach almost $74 billion this year. Rechargeable batteries will account for about 82 percent of that, or $60 billion, according to market researcher Frost & Sullivan. This strong growth has brought numerous major electronics companies into the market, offering products that target a range of applications: from small and light-weight power for hand-held devices to batteries the size of shipping containers for utilities. It has also led to a significant increase in research investment into battery technologies that address many of the technical challenges facing the industry, ranging from increasing specific energy (capacity of a cell in watt-hours per kilogram, W/Kg), to thermal stability, battery life extension through Battery Management Systems (BMS), and on to final disposal of spent cells. Technical Challenges in Large Systems As battery systems get larger, monitoring and controlling them becomes increasingly complex. Issues such as charging and discharging the cell array in a way that minimizes charge times while maximizing energy efficiency and battery life must all be considered when developing Battery Management Systems (BMS) for these products. In particular, testing the BMS poses major challenges: they are costly to put together, any faults in the BMS design can cause catastrophic damage to very expensive test units, and often it may not be possible to test for all scenarios, such as balancing across the cells, leading to uncertainty around the test results. An attractive solution to these challenges is to use virtual batteries, mathematical models of battery cells that are capable of displaying the same dynamic behavior as real ones, for earlystage testing of the BMS. Not only have these models proven to be highly accurate, they are computationally efficient and are able to achieve the execution implementation required to deliver real-time performance for batteries containing hundreds of cells on real-time platforms. Maplesoft, a developer of battery modeling technology, recently partnered with ControlWorks, Inc. of South Korea to develop a turn-key BMS system for one of the largest producers of consumer electronics products in the world. This project focused on a large electrical storage system for the Smart Grid and UPS markets. The end result was a battery model capable of being configured to represent a stack of up to 144 cells that can be connected in any combination of parallel and series networks. Fault modes were also required, such as individual cells shorting or opening, as well incorporating variations in charge capacity from cell to cell, and degradation of capacity over the life of the cells. 6 Battery Power * Fall 2015 Approaches to Developing High-Fidelity Battery Models There are two main approaches to battery modeling. The first is to use equivalent electrical circuits that reflect charge capacity and internal resistance through the use of standard electrical components to represent these properties. These equivalent-circuit models are conceptually simple and computationally light, while capable of capturing many of the non-linear behaviors within the cell. However, their scope of operation is somewhat limited and it is not easy to map the components of the model to the physical aspects of the real cell. The other method is to use electrochemistry-based battery models, those that include the details of the underlying physics within the reaction between the electrodes and electrolytes. These have been shown to be highly accurate predictors of the overall charge/discharge characteristics of a cell. This physical behavior can be represented by a system of well-documented partial differential equations (PDE), such as those developed by Newman. Solving these PDEs, however, can only be achieved through the use of computationally intensive techniques such as finite-element (FE) and computational fluid dynamics (CFD). These approaches make them unsuitable for system-level modeling since they can take hours to compute behavior over a few seconds. In the last few years a new technique has been developed. This compromise approach provides real-time performance while almost completely maintaining the accuracy of the full physics models. This uses a rigorous PDE discretization technique to simplify the model to a set of ordinary differential equations (ODE) that can be readily solved by system-level tools like MapleSim. The model optimization techniques employed in MapleSim also allow the resulting model code to be fast and capable of running in real-time. Using these physics-based models, it is possible to implement battery models that predict the charge/discharge rates, state of charge (SoC), heat generation and state of health (SoH) through a wide range of loading cycles within complex, multi-domain system models. This approach provides the performance needed for system-level studies, with minimal loss in model fidelity. Furthermore, the underlying foundation for model formulation and integration in MapleSim is based on the conservation of energy. Where there are inherent inefficiencies in the model, lost energy is computed as well as the effective (or "useful") energy. This means that the user can allow for energy loss through heat (in large applications that proportion is very close to 100 percent), making these models useful for performing thermal studies to determine component sizes in cooling systems required to control battery temperature. This is critically important when battery SoH must be considered, as the temperature of operation of each cell has a very significant effect on the degradation of its performance. If not carefully controlled, this can lead to reduced operational life or, in extreme cases, destruction through thermal runaway, a common problem in many battery-powered systems. This can be caused by unforeseen loading cycles, or a compromised cooling system www.BatteryPowerOnline.com http://www.BatteryPowerOnline.com

Table of Contents for the Digital Edition of Battery Power - Fall 2015

Real-Time Battery Modeling in HiL Testing of Battery Management Systems
Battery Second Use Offsets Electric Vehicle Expenses, Improves Grid Stability
2015 Battery Power Resource Guide
Batteries
Converters & Inverters
ICs & Semiconductors
Industry News
Calendar of Events
Marketplace

Battery Power - Fall 2015

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