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For modern vehicle development, on-board vehicle Mass and road Gradient Estimation (MGE) can offer great benefit to many sub-systems on the vehicle, such as vehicle control system, transmission control system, and active safety system etc. However, there are still several challenges that need to be solved. Firstly, thanks to good accuracy, reliability, and robustness, regression analysis-based approaches: Recursive Least Squares (RLS) and Kalman Filter (KF) are very popular for MGE, but the trade-off between estimator’s accuracy and converge time is challenging. Furthermore, depending on vehicle and powertrain types, the implementation of MGE function could be very different. It is desired to have a structured approach for various vehicle applications’ MGE development. Lastly, good reliability of MGE does not always satisfy for complicated real-world driving maneuvers and road conditions.

The development of control strategies for new Engine Management Systems requires a simulation tool to represent a model of the engine such that these strategies can be tested and verified. Current simulation tools for hardware-in-the loop (HiL) testing are limited to mean-value engine models. Although these lumped parameter type models are sufficient for current production Engine Management Systems, new technologies are emerging that would require sophisticated modeling tools to support the development of more complex systems such as closed-loop combustion control and for more thorough testing of onboard diagnostics. However, such hi-fidelity models have traditionally been distributed parameter typed models (e.g. WAVE), reserved for engine design purposes and therefore inappropriate for real-time testing. A new piece of software has been developed to bridge the gap between distributed and lumped parameter models.

Future demands for very low emissions from diesel engines, without compromising fuel economy or driveability, require Engine Management Systems (EMS) capable of compensating for emissions dispersion caused by production tolerances and component ageing. The Advanced Diesel Engine Control (ADEC) Project, a collaboration between Ricardo and General Motors, is aimed at reducing engine-out emissions dispersion and enabling alternative combustion modes, such as Highly Premixed Cool Combustion (HPCC), in real-world scenarios. This is being achieved by high-level co-ordination of fuel, air and EGR in order to meet the conflicting performance requirements of current and future diesel engines. A sensor feasibility study was undertaken which included a number of new sensing technologies appropriate for future mass production. Two sensor types, namely cylinder pressure and accelerometer sensors, were then selected to demonstrate varying degrees of benefits versus sensor technology cost.

Conventional engine HiL simulators use Mean Value (MV) modelling techniques to represent the plant and provide closed loop feedback parameters to the ECU. Once configured, these models require parameterising with the engine specific data. This data can be obtained from two primary sources: Test bed data - running a range of steady state/dynamic speed/load points. Engine required. WAVE* simulation model - Physical sizes of engine required for simulation. No Engine required. The accuracy of the MV model once fully parameterised is in the region of 70 - 80% assuming accurate test data. Another limitation of current techniques is that differences between individual cylinders, for example due to intake system geometrical arrangement, are ignored.

Future emission regulations and customer needs require revolutionary new approaches to engine management systems. In the EC part-funded AENEAS program the partners Ricardo, Kistler and DaimlerChrysler formed a consortium to investigate the application of a new combustion pressure sensor concept and innovative algorithms for engine management systems. This paper describes the general scope and the basic concepts of the system.