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Currently, Hardware-In-the-Loop (HIL) testing is the defacto standard for ECU verification and validation at the majority of the Commercial Vehicle OEMs and Tier1 suppliers. HIL Testing is used to shorten development and testing time for both engine and machine control systems. In order to use this process, many of these companies have to develop and maintain expertise in the area of Model-based development (MBD). This paper introduces an approach which allows for the effective use of HIL systems without having to directly work in a MBD environment. Many HIL tests can be done with stimulus and response analysis of the ECUs, given core knowledge of the expected behavior of its control software and I/O subsystems. For hardware interface and diagnostics validation, this open-loop testing of the controller may suffice. It is important to provide the tester with capabilities to easily modify these stimuli and evaluate the responses.

Electric powertrains are quiet, efficient, and provide a better controllability over their conventional counterparts. There are also many other areas in off-highway and commercial vehicles that are beginning to apply Electric Drive technology. The heart of these systems is the Electric Drive control technology being done on ECUs. Due to the complexity of the systems and need for demanding control applications, these ECU systems require a level of closed-loop testing that previous standard bench test-methods cannot supply. The common approach to testing these systems is using Hardware-in-the-loop (HIL) systems designed for electric drive. This paper describes a typical HIL simulation platform for testing control systems for electric powertrain. The scope of this paper covers the standard practices in HIL simulation of electric drives, giving a general overview of the necessary interfaces and simulation technology.

Currently, hybrid and electric drive control systems are being developed for many types of platforms in the aerospace, automotive, and commercial vehicle industries. These systems also entail the use of Battery Management Systems (BMS) to handle their demanding power needs. However, the development of these technologies brings increased system complexity, evident in the platform variants and even more so in the control algorithms of various electronic control units (ECUs). There is also a greater need to handle system-level control strategies, via communication networks and command software. This increased system complexity poses new challenges for software design and ECU system validation, mandating the need for simulation tools that can easily handle the inherent system complexity, while providing cost-effective, industry-proven verification tools and processes.