System Identification I

Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration. Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a benchmark for evaluating the optimality of realizable control strategies. Real-time controllers must be simple in order to be implementable with limited computation and memory resources. Moreover, manual tuning of control parameters should be avoided. This article has analyzed two approaches, namely, feedback controllers and ECMS. Both of these approaches can lead to system behavior that is close to optimal, with feedback controllers based on dynamic programming. Additional challenges stem from the need to apply optimal energy-management controllers to advanced HEV architectures, such as combined and plug-in HEVs, as well as to optimization problems that include performance indices in addition to fuel economy, such as pollutant emissions, driveability, and thermal comfort

Control of hybrid electric vehicles

In this paper, a model-based strategy for the real-time load control of parallel hybrid vehicles is presented. The aim is to develop a fuel-optimal control which is not relying on the a priori knowledge of the future driving conditions (global optimal control), but only upon the current system operation. The methodology developed is valid for those problem that are characterized by hard constraints on the state-battery state-of-charge (SOC) in this application-and by an arc cost-fuel consumption rate-which is not an explicit function of the state. A suboptimal control is found with a proper definition of a cost function to be minimized at each time instant. The "instantaneous" cost function includes the fuel energy and the electrical energy, the latter related to the state constraints. In order to weight the two forms of energy, a new definition of the equivalence factors has been derived. The strategy has been applied to the "Hyper" prototype of DaimlerChrysler, obtained from the hybridization of the Mercedes A-Class. Simulation results illustrate the potential of the proposed control in terms of fuel economy and in keeping the deviations of SOC at a low level.

Optimal control of parallel hybrid electric vehicles

As hybrid electric vehicles (HEVs) are gaining more popularity in the market, the rule of the energy management system in the hybrid drivetrain is escalating. This paper classifies and extensively overviews the state-of-the-art control strategies for HEVs. The pros and cons of each approach are discussed. From different perspectives, real-time solutions are qualitatively compared. Finally, a couple of important issues that should be addressed in future development of control strategies are suggested. The benefits of this paper are the following: (1) laying down a foundation for future improvements, (2) establishing a basis for comparing available methods, and (3) helping devoted researchers choose the right track, while avoiding doing that which has already been done.

Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends

A number of strategies for the power management of hybrid electric vehicles (HEVs) are proposed in the literature. A key challenge is to achieve near-optimality while keeping the methodology simple. The Pontryagin's minimum principle (PMP) is suggested as a viable real-time strategy. In this brief, the global optimality of the principle under reasonable assumptions is described from a mathematical viewpoint. Instantaneous optimal control with an appropriate equivalent parameter for battery usage is shown to be possibly a global optimal solution under the assumption that the internal resistance and open-circuit voltage of a battery are independent of the state-of-charge (SOC). This brief also demonstrates that the optimality of the equivalent consumption minimization strategy (ECMS) results from the close relation of ECMS to the optimal-control-theoretic concept of PMP. In static simulation for a power-split hybrid vehicle, the fuel economy of the vehicle using the control algorithm proposed in this brief is found to be very close-typically within 1%-to the fuel economy through global optimal control that is based on dynamic programming (DP).

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Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle

Control strategies for hybrid powertrains are algorithms that choose the power split between the engine and motor of a hybrid vehicle in order to minimize the fuel consumption and/or emissions. The goal of this paper is to propose an efficient tool to evaluate minimal fuel consumption that is achievable in simulation. Several approaches have been proposed, using heuristics (Delprat et al. 1999) or dynamic programming (Brahma et al., 2000; Rimaux et al., 1999). One drawback of these approaches is the huge amount of time required to obtain solutions. The approach described here is based on optimal control theory (Lewis & Syrmos, 1995) and avoids this drawback. Moreover, it can be easily applied to a large family of parallel arrangements.

Control of a parallel hybrid powertrain: optimal control

This paper deals with the design of a new observer synthesis for discrete Takagi-Sugeno (T-S) fuzzy models. It is well established that quadratic synthesis for discrete T-S models and/or linear parameter-varying systems can be outperformed easily via nonquadratic syntheses. Several Lyapunov functions can be used. Nevertheless, this paper shows that with a “small” change in the initial Lyapunov function, a “better” (in the sense of solutions to the linear matrix inequality constraints problem) Lyapunov function can be reached. This one can introduce very important improvements.

An Efficient Lyapunov Function for Discrete T–S Models: Observer Design

http://lendek.net/files/papers/ad_jrnl.pdf

Jimmy Lauber

Papers

Control of a parallel hybrid powertrain: optimal control

An Efficient Lyapunov Function for Discrete T–S Models: Observer Design

Adaptive observers for TS fuzzy systems with unknown polynomial inputs

Discrete Tagaki-Sugeno models for control: Where are we?

Membership-function-dependent stability analysis of fuzzy-model-based control systems using fuzzy Lyapunov functions