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Hybrid electric powertrain can be a promising and cost-effective technology to meet forthcoming emissions regulations. However, the hybridization of a conventional powertrain remains a complex task. According to their functions, hybrid powertrains can be classified into full-hybrid, mild-hybrid and micro-hybrid, of which micro-hybrid system is regarded as the most cost-effective solution for current regulations. Although a micro-hybrid system employs relatively simple new functions, such as stop/start and regenerative braking, to achieve the target fuel economy with reasonable cost, the engineer must consider many practical aspects in order to deliver a solution which is robust, effective and easy to understand for customers. In this paper the development of a belt-driven integrated starter generator (BISG) micro-hybrid system for a light-duty commercial vehicle application is presented.

This paper outlines the procedure used to assess the performance of a Lane Keeping Assistance System (LKAS) in a virtual test environment using the newly developed Euro NCAP Lane Support Systems (LSS) Test Protocol, version 1.0, November 2015 [1]. A tool has also been developed to automate the testing and analysis of this test. The Euro NCAP LSS Test defines ten test paths for left lane departures and ten for right lane departures that must be followed by the vehicle before the LKAS activates. Each path must be followed to within a specific tolerance. The vehicle control inputs required to follow the test path are calculated. These tests are then run concurrently in the virtual environment by combining two different software packages. Important vehicle variables are recorded and processed, and a pass/fail status is assigned to each test based on these values automatically.

New environmental legislation on emission and fuel efficiency targets increasingly requires good transient engine performance and this in turn means that the previously acceptable static engine calibration and control methodologies based on steady-state testing must be re-placed by dynamical optimization using dynamical models. Although many advances have been made in predictive models for internal combustion engines, the phenomena involved are so many, complex and nonlinear that dynamical black-box models typically employing neural network structures must be determined from system identification through experimental testing. Such identified dynamical models are required to provide high accuracy multiple step-ahead predictions of emissions but must accordingly also be compactly implementable for speed and memory to allow for the required large scale optimization involving possibly many thousands of iterations.