Common rail systems can be divided into two major groups with respect to high pressure control: one-actuator and two-actuator systems. The latter feature a fuel metering unit (FMU) as well as a pressure control valve (PCV). Most of the one- actuator systems use FMU only. PCV-only systems have been common in the past; however, because of severe drawbacks in terms of energy efficiency they disappeared from the main stream market segments. Both for one — and two- actuator systems FMU control mode with the PCV fully closed is preferred in most situations even though not in all. Using FMU control mode fuel delivery is limited to the quantity needed for a current operating point. In contrast the FMU is set to a specific flow (open loop control) in PCV control mode. Surplus high pressure fuel is returned through the PCV to the low pressure circuit and can be used to heat up the fuel circuit. This measure helps to prevent the emergence of paraffin wax that can clog the fuel filter under cold conditions.

This paper focuses on the FMU controller mode. Fig. 1 shows a typical Common Rail System topology. The control variable "rail pressure" is measured and fed back by the rail pressure sensor (RPS). The high pressure governor algorithm is based on PID control scheme with several add-ons like pre-control or windowing. A cascaded current controller helps to compensate additional disturbances.


Fig. 1: Common Rail System topology and sources of tolerances

Component and subcomponent tolerances e. g. RPS, FMU delivery characteristic, current sensing, high pressure leakage, pump inlet pressure (also shown in Fig. 1) limit the static as well as the dynamic pressure control quality. Depending on the applied governor calibration these tolerances lead to pressure over — and undershoots resulting from a poor disturbance response or a non-optimal response to set-point changes. Thus all components connected to the high pressure circuit face additional structural load. This additional load has to be considered during design especially for the latest generations of common rail systems featuring nominal pressures of up to 2500 bar. The provision of structural strength at an economic level of effort poses a major challenge. Therefore solutions to reduce or avoid additional structural load on the involved components have to be developed.

To improve the governor performance in the presence of component tolerances an adaptive learning function has been developed. "Adaptive Metering Unit Control" (AMC) determines characteristic learning values for the high pressure circuit during
normal system operation. Learning information is available out of the standard governor algorithm (2). AMC continually analyzes the delivery performance and stores learning values within the electronic control unit (ECU). The control performance can be brought back to nominal even in the presence of significant component tolerances by knowing the individual pump characteristic. Fig. 2 shows how control behaviour is optimized by AMC.

RAIL PRESSURE CONTROLRail pressure overshoot

Slow control

Nominal system ■ max. system min. system adapted system rail pressure set point





Fig. 2: AMC effect and quality attributes of dynamic control

Quality attributes of rail pressure control are shown on the right hand side of Fig. 2. The time to conquer 90% (t90) of a given set-point jump has to be as short as possible while pressure overshoots beyond the set-point level (pmax) are to be kept low. The conflict of targets is obvious. The main purposes of AMC are to identify valid learning conditions, evaluate signals and determine the learning value to guarantee optimum control behaviour. An associative learning algorithm provides a way to organise learning information in a very efficient way. Fig. 3 depicts the interface between rail pressure controller and AMC.


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Inverse pump curve

Associative correction curve


Rail pressure set point




Common rail system


Rail pressure sensor


Fig. 3: AMC input into controller structure

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