Cell & Module department is now recruiting an industrial PhD student with focus on Battery Cell Simulations and specifically on Machine learning assisted ageing prediction. Do you want to be a part of a company who is a key player within environmentally friendly transportation solutions? Take the opportunity to join Cell & Module Design and the Cell Simulation team within Electromobility in the exciting journey of this area which is changing rapidly. We are developing and creating future energy storage systems in our electric vehicles for our customers.In this position you will be part of an organization dedicated to technology where customer satisfaction is one of the vital performance measurements! Who are we?The Electromobility Department is responsible for developing electromobility solutions and to secure a stepwise implementation in different segments/regions. This organization develop and drives the electromobility product portfolio for Trucks & Buses as well as creating opportunities for all business areas within the Volvo Group. Electromobility is a growing organization in place to shape the future!The importance of electromobility is growing every day and a key component within the electrified powertrain is the Energy Storage System (ESS), especially the Cell. At our section, we are accountable for the lifecycle management of the ESS, from advanced engineering, throughout product development into the maintenance phase.The battery Cell Simulation team is one of the Battery Cell groups within the Cell & Module department and in the center of attention when it comes to development of new energy storage systems. We have the full responsibility of the battery cell in the system in close cooperation with the global teams around the world within Volvo as well as with our suppliers. Our working environment is highly creative with new technologies and inventions with a large network of suppliers and external partners, industrial or academic.What will you do?We are looking for a candidate to conduct an industrial PhD project in close collaboration with Chalmers and Uppsala University with focus on machine learning assisted ageing prediction. You will be part of a highly competent team of development and simulation engineers to take on advanced tasks in an efficient way.This requires a MSc in a relevant Engineering field in combination with excellent collaboration skills, strong player mind-set and a pragmatic approach to find balanced solutions. Description of research project:The performance and lifetime of battery cells are crucial for the economy of electric vehicles, and are determined not only by material properties but to a large extent also by how cells are used, which is managed by a battery management system (BMS). At present, cells operate through more or less fixed constraints on measurable cell-external variables, i.e. current, voltage and temperature, to avoid accelerated aging and risk of thermal runaway. However, the decisive causes of the limitations are intracellular conditions that cannot be measured. Due to limited computing capacity and poor coupling between the battery models used and the cell-internal states, this normally results in an underutilization of the cells at the same time as they age prematurely. Previous theoretical studies have shown that a transition to the use of cell-internal constraints provides significant improvements (tens of percent in performance and longevity).The next generation BMS, with greatly improved computational capabilities, opens for use of data-driven models and more computationally heavy physics-based models or combination of both. Data-driven methods, like machine learning models, enhance computationally heavy physics-based models, providing more physical insights about battery dynamics, performance, and aging. They also expedite calibration and parameterization through a hybrid modeling approach, combining data-driven and physical models. In this project, you will develop advanced hybrid electrochemical and machine learning models and use mathematical and statistical methods to further enhance fidelity and to reduce the computational cost of electrochemical as well as to enable applying physics-based models for online and onboard applications. The project is part of the Swedish Electromobility Centre, and is a collaboration between Chalmers, Uppsala University, and several industrial partners (Volvo AB, Volvo Cars, CEVT, Epiroc and ABB), with one PhD student employed at Chalmers and one industrial PhD student, funded by Volvo AB and under supervision of Uppsala University.Main Responsibilities:Your major responsibility is to pursue research in line with the project, publish and present scientific articles, and take part in technical discussions. As part of your PhD, you will receive further education by courses relevant for your research, research project, and your future career. You will also be expected to participate in teaching activities at the university.Education And Experience RequirementsMSc. Degree in Engineering Physics, Electrical Engineering, Chemical Engineering, Materials Science or similar.Mathematical talent and interest as well as previous experience in applied electrochemistry, battery modeling or machine learning are considered a merit for this position.Very good oral and written proficiency in English.Does this sound like your next challenge?Please apply here today as soon as possible as interviews will be held continuously.Further information, please