Holger Schmidt

Predictive Energy Management on Multi-core Systems

To meet climate agreements, electric vehicles will become very important in the next few years. One class of electric vehicles are Fully Electric Vehicles (FEVs) which are purely powered by electric energy. At the moment the driving range of FEVs is very limited compared to vehicles driven by internal combustion engines. To contribute to increasing the driving range, a comprehensive energy management and enhanced control algorithms like the Model Predictive Controller (MPC) are introduced. The MPC presented in this book chapter is a first approach of solving a reference speed tracking problem on a multi-core platform in real-time. First a high performance off-board system generates an energy and time optimal reference speed profile. This optimization problem is solved by a dynamic programming approach with respect to speed limits, track profile and cloud-sourced data. Afterwards, the reference speed profile is provided to a MPC that is implemented on the vehicle’s on-board system. When utilizing a multi-core computing platform, the on-board system provides information about current speed, position and the driver’s torque demand, which is made available by the MPC. To enable the concept of a comprehensive energy management, a revised information and communications technology reference architecture is presented, as well as an overview about the available hardware and toolchain is given.

Model-Based Functional Safety Engineering

This chapter presents some aspects of model-based way of working applied in the EU funding projects called iCOMPOSE [1] and INCOBAT [2]. The presented results from INCOBAT project focus on basic structure that serves to organize/handle the technical complexity of the system under development. This approach was supported by the use case of INCOBAT battery system. The presented result from iCOMPOSE project focus on SysML model-based approach applied during the safety engineering development process and software integration of Integrated Comprehensive Energy Management System (iCEM). Some extensions to SysML profile are proposed and presented in this chapter and some findings on the data processing capabilities are discussed. The software development and integration of iCEM’s embedded software required also a dedicated toolchain which was established and deployed taking into account constraints typical for automotive domain like safety criticality, real-time applications, various communications interfaces, variety of tools involved in the development process.

Embedding Electrochemical Impedance Spectroscopy in Smart Battery Management Systems Using Multicore Technology

Improving the range of electric vehicles is a key requirement to achieve market acceptance—one important aspect is the efficient management of the electric energy stored into the cells. Within the INCOBAT (INnovative COst efficient management system for next generation high voltage BATteries, http://www.incobat-project.eu/. The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 608988.) project, we propose innovative and cost efficient battery management systems for next generation high voltage batteries targeting a more accurate estimation of the battery state over the lifetime. Target of this paper is to present the project’s achievements with respect to (a) deployment of safe and secure multicore computing platforms, (b) embedding of electrochemical impedance spectroscopy algorithms, (c) combined thermo-mechanical robustness tests, and finally (d) outlook towards integration of INCOBAT technology into a vehicle demonstrator.

A Smart Computing Platform for Dependable Battery Management Systems

An important acceptance criteria for electric mobility is the capability to efficiently use the energy stored into the battery cells over the vehicle lifetime. The battery management system (BMS) plays a central role by estimating the state of charge (current energy available) and state of health (degradation due to ageing effects) of the cells. Improvement of the estimation quality has a direct impact on the battery and thus vehicle range. It is the target of the INCOBAT project to improve the BMS by means of new electronic components, new control strategies and new development methods in order to achieve cost reduction and performance (driving range) increase. In this paper, the INCOBAT project is presented and important results with respect to computing platforms, control strategy and dependability analysis are discussed.