Automated engine calibration of hybrid electric vehicles

We present a method for automated engine calibration, by optimizing engine management settings and power-split control of a hybrid electric vehicle. The problem, which concerns minimization of fuel consumption under a NOx constraint, is formulated as an optimal control problem. By applying Pontryagin's maximum principle, this study shows that the problem is separable in space. In the case where the limits of battery state of charge are not activated, we show that the optimization problem is also separable in time. The optimal solution is obtained by iteratively solving the power-split control problem using dynamic programming or the Equivalent Consumption Minimization Strategy. In addition, we present a computationally efficient suboptimal solution, which aims at reducing the number of power-split optimizations required. An example is provided concerning optimization of engine management settings and power-split control of a parallel hybrid electric vehicle

Drive-style emission testing on the latest two Honda hybrid technologies

Introduction Hybrid technology is seen by many as a potential solution to reduce vehicle emissions in cities. However type approval tests of hybrid vehicles measure emission levels comparable to those of conventional cars in the same market segment. It has been argued that type approval tests do not represent the reality of emission in cities therefore, to quantify the real emission of hybrids and to compare them with those of conventional vehicles in the same conditions, an emission measurement campaign was organised. Acquisition campaign Three Honda cars, one conventional (the Civic 2.0) and two hybrids (the Civic IMA and the Civic Hybrid), equipped to collect emissions as well as the engine and vehicle working parameters were driven three times by twenty drivers on the same urban route. Drivers were asked to drive normally and not requested to do anything special but to scrupulously follow the given itinerary. Results Two main results were obtained: average and maximum emission levels for the three cars are quantified; the effects of the drivers on such levels assessed. The conventional car (with two people and 250 kg of measurement tools onboard) consumes an average of 12.6 l/100 km, its CO2 emissions range between 200 g/km and 300 g/km with an average of 260 g/km. CO emissions range between 0.25 g/km and 6.25 g/km (Euro IV limit is 1 g/km) with an average of 2 g/km. The most recent of the two tested hybrids consume in average 8.23 l/100 km and emits between 150 and 230 g/km of CO2 with an average of about 180 g/km; it emits virtually no CO in the majority of cases but can reach up to 1.8 g/km and average CO emissions are about 0.2 g/km. The hybrid performs always better than the conventional; in terms of CO2 and consumption it can have up to a 30% reduction and in terms of CO up to 90% reduction. Conclusions The wideness of the measured ranges depends mostly on the drivers. Women tend to consume and emit less than men. The reason for this is the different way they use the accelerator pedal; they push it less and keep it steadier. In other word the standard deviation of the accelerator position (or throttle) is lower. It is here shown how a correlation exist between the throttle standard deviation and the emissions which justify using such parameter as the indicator of drive-style

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Automated model based engine calibration procedure using co-simulation

The final validation and sign-off of a production powertrain control module (PCM) calibration is a time-consuming and expensive task and requires a high degree of expertise. There are two main reasons for this; firstly, the validation test is an iterative process due to the fact that calibration changes may affect the true operating point of the engine at the desired test point. Secondly, modifications to the calibration require expert knowledge of the complete control strategy so as to improve the correlation to validation data without potentially negatively impacting the correlated mapping points. This paper describes the implementation of an optimisation routine on a virtual platform in order to both reduce the requirement for experimental testing during the validation procedure, and for development of the optimisation routine itself prior to execution on the engine dynamometer. It is shown that in simulation, the optimisation routine is capable of producing an acceptable calibration within just 5 iterations, reducing the 11-week process down to just a few days. It is also concluded that there are also a number of further improvements that could be made to further improve the efficiency of this process