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The analysis of nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. In this paper, a physics-based model of a Ni-MH battery will be presented. To analyze its performance, the efficiency of the battery is chosen as the performance measure, which is defined as the ratio of the energy output from the battery and the energy input to the battery while charging. Parametric sensitivity analysis will be used to generate sensitivity information for the state variables of the model. The generated information will be used to showcase how sensitivity information can be used to identify unique model behavior and how it can be used to optimize the capacity of the battery. The results will be validated using a finite difference formulation.

A predictive model of a specific vehicle was modeled in the system-level physical modeling tool, MapleSim, for performance and fuel consumption prediction of a full vehicle powertrain, driving a multi-body chassis model with tire models. The project also includes investigation into overall fuel efficiency and effect on vehicle handling for different drive cycles. The goals of this project were to investigate: 1) the relationships between the forces at tire/road interfaces during various drive cycles and the fuel efficiency of a vehicle, and 2) the interaction between the powertrain and the chassis of the vehicle. To accomplish these goals, a complete vehicle model was created in the lumped-parameter physical modeling tool, MapleSim. A great deal of effort has gone into using real parameters and to assure that some mathematical rigour has been employed in its development.

The “Holy Grail” of engineering education has been the reconciliation of theory with design. Why are the math and physics courses so disconnected from the application courses like control and mechatronics? Why are design concepts not directly integrated into all courses instead of just capstone courses or senior projects? The answers to these questions lies essentially in history and tradition but the reality of modern engineering in industry is now colliding with this legacy. This paper provides an overview of new collaborative initiatives among engineering colleges and vendors to increase design and real world dimensions into courses at large by using modern modeling technology. In particular a new generation of modeling tools rooted deeply in symbolic mathematics are quickly being adopted in industry due to their ability to develop higher fidelity models through rigorous mathematics while maintaining workflow efficiency.

Model Predictive Control (MPC) design methods are becoming popular among automotive control researchers because they explicitly address an important challenge faced by today’s control designers: How does one realize the full performance potential of complex multi-input, multi-output automotive systems while satisfying critical output, state and actuator constraints? Nonlinear MPC (NMPC) offers the potential to further improve performance and streamline the development for those systems in which the dynamics are strongly nonlinear. These benefits are achieved in the MPC framework by using an on-line model of the controlled system to generate the control sequence that is the solution of a constrained optimization problem over a receding horizon.

Mean value engine models (MVEMs) are intermediate-level internal combustion (IC) engine models which include more physical details than simplistic linear transfer function models, but significantly fewer details than large complex cylinder-by-cylinder models [1]. The MVEM is well-known as a suitable plant model for model-based control applications. The combinations of physics-based component models, which allow the physical parameter effects to be evaluated and controlled, and look-up table models, with fast response, make the MVEM suitable for control applications. A mean value engine model based on mathematical and parametric equations has recently been developed in the new MapleSim software. The model consists of three main components: the throttle body, the manifold, and the engine. The model is developed in the MapleSim environment which takes advantages from both Maple's powerful symbolic mathematical tool and Modelica's modern equation-based language.

As the demand for electric motors and drives grows, designers and manufacturers are faced with the challenge of understanding the effects of often non-deterministic duty cycles on their products. Too often, flaws in the design that can lead to failure only come to light when a prototype is built, or worse, after the product has been launched, leading to delays in product releases or costly recalls. To help mitigate these risks, designers are increasingly turning to simulation technologies that not only allow the engineer to implement the electric drives and motors but also all the various engineering factors, such as mechanical loads, vibrations and thermal effects, together in a single “virtual prototype” to get a clearer idea of how the whole system will behave over multiple duty cycles.

Due to rising fuel prices and environmental concerns, Electric Vehicles (EVs) and Hybrid Electric Vehicles (HEVs) have been gaining market share as fuel-efficient, environmentally friendly alternatives. Lithium-ion batteries are commonly used in EV and HEV applications because of their high power and energy densities. During controls development of HEVs and EVs, hardware-in-the-loop simulations involving real-time battery models are commonly used to simulate a battery response in place of a real battery. One physics-based model which solves in real-time is the reduced-order battery model developed by Dao et al. [1], which is based on the isothermal model by Newman [2] incorporating concentrated solution theory and porous electrode theory [3]. The battery models must be accurate for effective control; however, if the battery parameters are unknown or change due to degradation, a method for estimating the battery parameters to update the model is required.

The analysis of nickel metal hydride (Ni-MH) battery performance is very important for automotive researchers and manufacturers. The performance of a battery can be described as a direct consequence of various chemical and physical phenomena taking place inside the container. In this paper, a physics-based model of a Ni-MH battery will be presented. To analyze its performance, the efficiency of the battery is chosen as the performance measure, which is defined as the ratio of the energy output from the battery and the energy input to the battery while charging. Parametric sensitivity analysis will be used to generate sensitivity information for the state variables of the model. The generated information will be used to showcase how sensitivity information can be used to identify unique model behavior and how it can be used to optimize the capacity of the battery. The results will be validated using a finite difference formulation.