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Dynamic Programming

Dynamic programming is a power analytical tool which yields significant benefits when applied to dynamic simulation models

Dynamic system modeling has revolutionized the way in which novel system architectures are designed and evaluated. Yet a system’s design (architecture, component sizing, etc.) is only part of what determines a system’s ultimate performance. How the system is controlled is often of equal importance.

This is especially true with hybrid systems when power from multiple sources must be managed, though it applies to any system in which there are multiple state and control combinations that yield the desired output. Commonly a design team will be tasked with developing and employing a dynamic system model to both select a system architecture, and size components, with the goal of maximizing some performance metric. Too often the control system’s design is relegated to second place: a low importance task that receives only the minimal effort and attention required to achieve a working system model. 

Considering controls early in the design process

Once to the point of comparing multiple system architectures/component sizing, this lack of focus on the control systems begs the question as to whether one system design outperforms another due to an inherently better configuration, or a more effective control strategy. There is certainly a risk that a poor system design with an effective control strategy could be chosen over a superior system design with an ineffective control strategy. While a control strategy may be improved and refined in time to be implemented in the final system, selection of a poor system design can be difficult to rectify as a project progresses. The use of an ineffective control scheme should not be viewed as negligence on part of the design team, rather it must be understood that developing a highly effective control scheme is often a challenging task, especially when the development concerns novel system designs which are not well understood.

Dynamic programming removes the variable of controls strategy to ensure optimality of design choices

An effective solution to the problem of poor system control during the modeling and architecture selection phase is to apply a globally optimal controller directly to the dynamic simulation models. By using a globally optimal control scheme the influence of control on system performance can be eliminated, thereby enabling a fair system comparison. The most effective means of achieving optimal system control is through a technique known as dynamic programming. This white paper discusses the benefits of using dynamic programming as a design tool throughout a product’s development, using a hybrid powertrain as an illustrative example.

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