Dr Johannes Rohwer

Johannes’s Journey in AAPS: An Alumni Profile

Johannes joined the AAPS CDT after completing a pre-doctoral research programme at Argonne National Laboratory (USA). He also built a broad technical foundation through industry roles spanning a heat exchanger manufacturer in Germany and a robotics start-up in London. This combination of applied engineering experience and research exposure shaped interests that cut across propulsion systems, machine learning, and lithium-ion battery state-of-health (SoH) estimation—making the AAPS CDT a natural progression.

A key attraction of the programme was the opportunity to collaborate with AVL, alongside access to the advanced experimental and innovation infrastructure at the £70 million IAAPS R&I Centre. For Johannes, the CDT environment offered both a rigorous academic framework and a sector-connected pathway to ensure that research questions remain grounded in real operational constraints and deployment needs. Outside of research, he maintains an active routine and particularly enjoys tennis, valuing the balance between technically demanding work and physical activity.

PhD Focus

Johannes’s PhD, supervised by Chris Vagg and sponsored by AVL, focused on lithium-ion battery state-of-health estimation, an enabling capability for reducing cost, improving performance, and supporting more sustainable electric vehicles. While electrification is central to lowering transport-related greenhouse gas emissions, adoption remains constrained by factors including vehicle cost, range limitations, and charging time. Because battery degradation directly influences all three, improved SoH estimation is important not only for reliability and safety, but also for optimising usable capacity, supporting warranty strategies, and enabling more efficient battery utilisation over the asset lifetime.

His research addressed these challenges by developing modelling and testing approaches that link battery behaviour to ageing mechanisms, while remaining practical for real-world use. Specifically, his work aimed to:

• Develop hybrid physics-informed machine learning models for lithium-ion battery SoH estimation and prediction, combining data-driven flexibility with the interpretability and generalisability offered by physical structure and constraints.

• Design optimal testing strategies using advanced design of experiments (DoE) to maximise information gain from laboratory campaigns while reducing time and cost, with careful attention to identifiability, uncertainty, and transferability across operating conditions.

Looking Forward

Over the next 12 months, Johannes plans to further strengthen the machine learning dimension of his research, with an emphasis on improving model robustness, uncertainty quantification, and performance under variable usage profiles. A particular priority is progressing the work towards industry-ready maturity, ensuring that methods are not only effective in controlled settings, but also dependable when confronted with sensor noise, heterogeneous cell histories, and practical constraints on onboard computation and available measurements.

Looking further ahead, Johannes aims to contribute to engineering solutions that measurably improve battery longevity and resource efficiency, thereby helping make sustainable transport more accessible and affordable. He is especially motivated by work that translates research outcomes into tools and processes that can be adopted by industry, accelerating impact across mobility and energy storage.

Reflection on AAPS

A standout experience during his time in the CDT was a research visit to the Polytechnic of Turin, supported through the AAPS travel scheme. The visit provided exposure to real-time applications of SoH models within electric-vehicle battery management contexts, reinforcing the importance of deployability considerations such as computation time, signal availability, and operational variability.

Reflecting on the broader AAPS experience, Johannes highlights the value of the integrated master’s programme in strengthening systems thinking and expanding his perspective beyond a single technical domain. He credits the CDT’s transdisciplinary approach with helping him develop a more holistic understanding of the sector—spanning engineering, policy, and user experience—while maintaining a clear focus on solving industry-relevant problems.