Battery Engineering

Use smart sensors, battery models, and smart BMS algorithms to improve battery's operational performance safety and lifetime.

EVE-Ai Battery Fleet Analytics addresses key challenges across EV fleets and stationary storage, including limited battery visibility, unplanned downtime, uncertain lifetime, and fragmented data. Its unified cloud–edge intelligence layer converts raw streams into predictive, actionable insights for performance and safety. The platform delivers real-time SoH, SoC, RUL, early fault and anomaly detection, as well as prescriptive recommendations tied to business value. Powered by adaptive models, digital twins, and a learning AI agent, it scales across chemistries and asset types, supporting EV, BESS, grid, and renewable integration.

Nissan has introduced the all-new, 3rd generation LEAF to the Europian market, building on years of real-world EV experience. This presentation explores how the unique insights gained from previous LEAF models enabled breakthrough battery innovations, developed exclusively for the 3rd generation model. It also demonstrates how these advancements deliver a superior electric mobility experience that only the 3rd generation LEAF can provide.

This talk delves into the operational insights gained from Battery Management System (BMS) data. It highlights key metrics, data analysis techniques, and their role in monitoring battery performance, ensuring safety, and optimising system efficiency during real-world operation.

The market for utility scale battery energy storage systems (BESS) is growing rapidly and the numbers of planned BESS installations in Europe are skyrocketing. While this is great news for the energy transition, it creates challenges for operators as well as project developers and integrators. They have to be fast and deploy hundreds of MWh of batteries—at the same time they need to secure the invested assets by considering resilience against cyber attacks from an early project stage until end of life.

Use smart sensors, battery models, and smart BMS algorithms to improve batteries' operational performance safety and lifetime.

Modelling is an integral part of modern product development. For battery cells, electric circuit models are widely used, since they are simple, easy to understand and quick in the simulation. The talk will first highlight shortcomings of those models. Electrochemical models show main advantages in scalability, inter- and extrapolation, and above all, knowledge of inner states like the anode potential which limits fast charging. The models are then applied in a development process, especially in the early phase. Additionally, the talk gives insights in how electrochemical ageing models can be used to answer plating quick charging charging.

In this presentation recent changes in the regulatory requirements for safety testing of battery electric vehicles and, where applicable, their batteries are discussed.

This presentation examines the mechanisms and impacts of self-discharge in lithium-ion batteries, highlighting diagnostic methods to identify degradation pathways. Understanding self-discharge behavior supports improved battery health assessment, performance prediction, and safety management across diverse applications.

In the last years we performed numerous calendar and cyclic ageing studies to assess the safety level of significantly aged commercial cylindrical cells using the heat-wait-seek test in the ARC. The results reveal significant correlations between storage/operating temperatures, charge/discharge rates, and critical safety parameters, which are onset, venting and thermal runaway temperature. Insights from this study provide valuable guidance for optimizing the thermal and safety management strategies to enhance the longevity and safety of lithium-ion cells in first and second life applications.

Exposing Li-ion batteries to temperatures above 60°C leads to thermal degradation and potentially thermal runaway. This talk gives an insight into these processes including the underlying reaction network at the electrodes and in the electrolyte. Operando electrochemical mass spectrometry reveals a temperature-triggered complex sequence of various degradation gases during heating; the evolution pattern is reproduced by kinetic models, which give an unprecedented insight into the occurring reactions and crosstalk.

As the battery market rapidly evolves to serve diverse applications from mobility to energy storage, identifying the optimal cell chemistry is crucial. In this study, we present a comparative analysis of several major battery technologies using Batemo’s unique database of over 300 characterised cells. Through both experimental analysis and digital benchmarking, we isolate material performance from form factor effects to reveal true chemistry-level differences.

This talk outlines an advanced electrode engineering approach to architected and dry-processed battery electrodes, leveraging field- and laser-assisted strategies—micro-electric-field (μ-EF) casting and femtosecond-laser structuring—together with dry-processing routes. These methods control particle alignment, porosity, and ion-transport pathways to realize hyper-thick electrodes up to ~1000 µm with high areal capacity and stable cycling. Case studies will show how the resulting architectures improve rate capability and durability while enabling scalable, solvent-lean manufacturing.

High-voltage battery systems are at the heart of electrified transportation, yet ensuring their safety requires a holistic approach spanning every level of design - from cell chemistry and design to full vehicle integration. This presentation introduces a systems engineering methodology for battery safety design, emphasising structured requirements flowdown, interface management, and trade-off analysis across the entire hierarchy. Beginning at the chemistry level, we examine material choices and failure modes that influence thermal stability and thermal runaway risk. At the cell group and pack levels, we explore strategies for thermal runaway propagation prevention, and mitigation of secondary failures such as electrical arcing. Finally, we address vehicle-level integration, including failure detection, warning, and response mechanisms. By applying a systems engineering approach, we demonstrate how safety can be embedded throughout the system levels, reducing risk while enabling high-performance, cost-effective electrification.