HARDWARE-INTHE-LOOP SIMULATION OF A HYBRID ELECTRIC DIESEL AND ELECTRIC ONLY LOCOMOTIVE

Hardware-in-the-Loop (HIL) testing allows for scaled to full-scale evaluation of components without constructing fullscale vehicle prototypes. HIL is also a rapid means of performance evaluation of difficult to model components, in real-time simulation with up to full-scale components providing their own response in place of a model. HIL also provides a working tool for control algorithm refinements prior to full system integration. In hybrid and electric locomotive applications this is particularly important while choosing the battery chemistry and sizing the energy storage system. Energy storage HIL testing permits optimal sizing of battery power and energy density along with management algorithm tuning. In general, HIL provides insight into optimal locomotive powertrain design. After the development of full EV and HEV locomotive models, battery component models were replaced with real-time interfaces to lead carbon and lithium cobalt batteries, which validated the battery models and helped tune vehicle controllers for direct application onto the vehicle platform. INTRODUCTION This work is intended to support the design and operation of an all-electric switcher and hybrid-electric, long-haul locomotives. Both would rely on large electrical energy storage devices to store some of the otherwise wasted electrical energy that is converted to heat by large power resistors during dynamic braking. The research group focused its efforts on battery systems, specifically two large-format battery modules, first a lead-acid battery with carbon-negative electrodes (LAC), then a Lithium Nickel Manganese Cobalt Oxide (NMC) module. Initial system-level testing started with the procurement of a 650V battery module string to represent one full voltage string, but only one of many parallel strings required to contain a reasonable amount of energy relative to its use as a sole or even hybrid power source. Following integration of this large system in the laboratory, the team began fixed-profile load cycling of the LAC batteries. This cycling served several purposes. It helped to gather the data which would be used later in battery modeling characterization, evaluate the associated Battery Management System (BMS), and understand thermal behavior as well as characterize the decrease in State of Health (SOH) as the kilowatt throughput increased over time. Following the fixed-profile load cycling, a means of controlling the battery’s environment was developed. A finite element analysis (FEA) was performed to develop the battery enclosure parameters and understand cooling air flow around the battery modules. An investigation into the effects of solar exposure on the enclosure was performed and replicated using heat sources for indoor testing. Fixed cycling continued while HIL experiments into the optimal cooling algorithm were performed, resulting in an algorithm and set of parameters that reduces energy consumption and maximizes effective cooling. Using load profiles recorded from long-haul locomotives battery performance was analyzed. Energy and component models were developed for real-time simulation of energy storage devices in both a switcher and long haul locomotive loading profiles. HIL simulations are often used as part of a validation process of either software models running in real time or laboratory testing of components while outside of their typical system. One example is an electric drive motor or battery pack being tested in the laboratory loaded with external devices and not the system for which it was intended or designed. Often scaling is performed to simplify the testing and HIL simulation. Battery systems are a good example, frequently simulated by loading of only one cell or battery module and estimating fullpack responses by adding voltage or dividing current, depending on the number of cells in series or parallel, respectively. Little work has been published utilizing HIL for locomotive powertrain development. Yet, many groups have relied on HIL in the automotive industry for powertrain development with countless examples, such as Argonne National Laboratories’ work Proceedings of the 2015 Joint Rail Conference JRC2015 March 23-26, 2015, San Jose, CA, USA 1 Copyright © 2015 by ASME JRC2015-5755 Downloaded From: http://proceedings.asmedigitalcollection. asme.org/ on 09/03/2016 Terms of Use: http://www.asme.org/about-asme/terms-of-use resulting in a software support package, Autonomie/PSAT [1]. This research team pulled from the previous work completed in the automotive industry and applied it to the locomotive battery HIL simulations. Here a full battery string with active thermal management as cycled under HIL testing scenarios to evaluate its expected performance in both a switcher and long-haul platform application. These systems represent the full DC bus voltage but are only loaded with a fraction of the total system current, as the vehicle is assumed to contain many parallel strings. NOMENCLATURE HIL Hardware-in-the-Loop SOC State of Charge SOH State of Health LAC Lead-Acid w/Carbon NCA Lithium Nickel Cobalt Oxide ESR Effective Series Resistance COMPONENT MODELING & CHARACTERIZATION To replicate the behavior of each locomotive component without the real device, first-order computer models were built using either experimentally measured or manufacturer data. Many models were borrowed from the automotive tool kit, PSAT [1]. The following sections discuss the details of each component. Diesel Engine Several assumptions were made to simplify the modeling effort of the diesel compression ignition engine. First, only hot temperature engine maps for maximum torque and efficiency were used. This is based on the assumption that engine temperature is kept near running and neglects the effects warmup has on power and efficiency. The model’s inputs are state, engine throttle, and speed while its outputs are torque, fuel consumption, and emissions, as shown in Figure 1. Figure 1. Engine Model Inputs/Outputs The higher-order dynamics of a diesel, compression ignition engine were ignored while concentrating only on system response, power, and efficiency. To do so, the following equation, in concert with torque and efficiency maps, as used to represent the engine component of the simulation. Tout = [(1-TClosed Throttle) + (Throttle x T Maximum)] (1) Equation 1 uses a combination of wide-open throttle torque map, closed throttle torque map, and throttle position to calculate engine torque at any given speed. Note that T indicates torque and, as used in the above equation, is a function of speed. Figure 2. Engine Maximum Torque Map Typically, torque is represented as a map based on data, as seen in Figure 2. Fuel economy and efficiency are represented in a similar fashion yet are functions of not only speed but also torque, requiring the use of a three-dimensional map. The control of the engine model is executed though a PID speed controller. The engineer throttle notch, which is mapped to a particular engine speed, sets speed, thus the PID error inputs are mapped throttle notch and actual speed and its output throttle command. Induction Generator / Drive Motor Similar to the engine model, a first-order electric motor model was used for both drive and generator components in the locomotive system model, with maps to reference only maximum torque available at any given speed. It should be noted that it was assumed here that the equal and opposite torque was available for regeneration. The equation for motor torque is the maximum available at the given speed times the command; in other words, the percentage of maximum torque available. Figure 3 shows the inputs/outputs of the motor/generator models. 0 200 400 600 80