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Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

The introduction of ISO 26262 concepts has brought important changes in the software development process for automotive software. While making the process more robust by introducing various additional methods of verification and validation, there has been a substantial increase in the development time. Thus, test automation and front loading approaches have become important to meet product timelines and quality. This paper proposes automated testing methods using formal analysis tools like Simulink Design Verifier™ (SLDV) for boundary value testing and interface testing to address the demands of ISO 26262 concepts at unit and component level. In addition, the method of automated boundary value testing proposed differs from the traditional methods and the authors offer an argument as to why the traditional boundary value testing is not required at unit (function) level. There are two aspects of the proposed method: automated test case generation and automated test case execution.

Many different control strategies have been developed to optimize a hybrid electric (HE) power train. Recently these have included the use of adaptive approaches. Such adaptive strategies attempt to alter the behavior of the HE power train based on the journey being undertaken. Unlike the journeys undertaken in personal vehicles, passenger buses often traverse the same unchanged route several times a day. If such a journey could be suitably modeled the model may then form part of an optimized control strategy. This paper describes the collection and analysis of a large number of bus journeys over a period of 10 months. Journey data was collected from a real route using a GPS receiver in conjunction with a handheld computer running bespoke logging software. A method has been developed to reduce each journey into a simple statistical representation based on power/energy use.

Automotive electronic control systems are expected to respond to input demands in real-time (circa: milliseconds) to ensure occupant and road user safety and comfort. System complexity and real-time computing requirements create significant challenges in proving the robustness of control systems; here robustness is the degree to which a system can function correctly in the presence of unexpected inputs. Evidence shows that faults still escape to customers incurring large warranty costs. Existing test methods can be ineffective in testing robustness with the primary focus being on requirements validation. Evidence from other industries such as IT and medical suggests faults that are difficult to find, manifest due to complex interactions and sequences of events. Research in model based software design, test optimization and formal methods - mathematical based approaches to prove robustness, is abundant in literature.