Passive Balancing Battery Management System for Cal Poly Racing\u27s Formula SAE Electric Vehicle

This senior project aims to replace the current battery management system (BMS) on Cal Poly’s Formula SAE electric vehicle with a more versatile, advanced, and reliable system. A BMS manages a rechargeable battery by ensuring the battery device operator’s safety, protecting battery cell integrity, prolonging battery lifetime, maintaining functional design requirements, and sending optimal usage information to the application controller. Passive balancing maximizes a battery pack’s capacity by dissipating excess energy through heat to regulate cell state of charge

Minimum Race-Time Planning-Strategy for an Autonomous Electric Racecar

Increasing attention to autonomous passenger vehicles has also attracted interest in an autonomous racing series. Because of this, platforms such as Roborace and the Indy Autonomous Challenge are currently evolving. Electric racecars face the challenge of a limited amount of stored energy within their batteries. Furthermore, the thermodynamical influence of an all-electric powertrain on the race performance is crucial. Severe damage can occur to the powertrain components when thermally overstressed. In this work we present a race-time minimal control strategy deduced from an Optimal Control Problem (OCP) that is transcribed into a Nonlinear Problem (NLP). Its optimization variables stem from the driving dynamics as well as from a thermodynamical description of the electric powertrain. We deduce the necessary first-order Ordinary Differential Equations (ODE)s and form simplified loss models for the implementation within the numerical optimization. The significant influence of the powertrain behavior on the race strategy is shown.Comment: Accepted at The 23rd IEEE International Conference on Intelligent Transportation Systems, September 20 - 23, 202

Firm technological responses to regulatory changes: A longitudinal study in the Le Mans Prototype racing

Despite the critical role of regulations on competition and innovation, little is known about firm responses and related effects on performance under regulatory contingencies that are permissive or restrictive. By longitudinally investigating hybrid cars competing in the Le Mans Prototype racing (LMP1), we counter-intuitively suggest that permissive regulations increase technological uncertainty and thus decrease the firms’ likelihood of shifting their technological trajectory, while restrictive regulations lead to the opposite outcome. Further, we suggest that permissive regulations favour firms that innovate their products by sequentially upgrading core and peripheral subsystems, while restrictive regulations—in the long term— favour firms upgrading them simultaneously. Implications for theory and practice are discussed

High Specific Power Electrical Machines: A System Perspective

There has been a growing need for high specific power electrical machines for a wide range of applications. These include hybrid/electric traction applications, aerospace applications and Oil and Gas applications. A lot of work has been done to accomplish significantly higher specific power electrical machines especially for aerospace applications. Several machine topologies as well as thermal management schemes have been proposed. Even though there has been a few publications that provided an overview of high-speed and high specific power electrical machines [1-3], the goal of this paper is to provide a more comprehensive review of high specific power electrical machines with special focus on machines that have been built and tested and are considered the leading candidates defining the state-of-the art. Another key objective of this paper is to highlight the key “system-level” tradeoffs involved in pushing electrical machines to higher specific power. Focusing solely on the machine specific power can lead to a sub-optimal solution at the system-level

Towards the design of robotic drivers for full-scale self-driving racing cars

Autonomous vehicles are undergoing a rapid development thanks to advances in perception, planning and control methods and technologies achieved in the last two decades. Moreover, the lowering costs of sensors and computing platforms are attracting industrial entities, empowering the integration and development of innovative solutions for civilian use. Still, the development of autonomous racing cars has been confined mainly to laboratory studies and small to middle scale vehicles. This paper tackles the development of a planning and control framework for an electric full scale autonomous racing car, which is an absolute novelty in the literature, upon which we report our preliminary experiments and perspectives on future work. Our system leverages real time Nonlinear Model Predictive Control to track a pre-planned racing line. We describe the whole control system architecture including the mapping and localization methods employed