10180241Q-BATT: Quantum-Enhanced Battery Management for Optimised Electric Vehicle Fleet OperationsClosedCollaborative R&DInnovate UKThe transition to electric mobility is accelerating across the UK, but **fleet operators continue to face major challenges in managing battery performance, reliability, and lifespan**. Current battery management systems rely on conventional electrical and thermal sensors that only provide indirect data on battery condition. These systems often fail to detect early-stage cell degradation, leading to unexpected breakdowns, inefficient charging, and costly replacements. The limited accuracy of existing Battery Management Systems (BMS) also constrains predictive maintenance and undermines confidence in large-scale EV adoption for commercial fleets such as buses, vans, and logistics vehicles. The **Q-BATT** project addresses these challenges by exploring how **quantum sensing** and **artificial intelligence (AI)** can transform the way electric-vehicle fleets are monitored and maintained. Led by **Incode Technology UK Ltd**, this three-month feasibility study investigates the potential of a **quantum-enhanced Battery Management System (BMS)** that can provide real-time, high-precision insights into EV battery health and performance. Q-BATT combines **diamond-based quantum magnetometry** with **Incode's causal-AI analytics platform** to deliver a next-generation monitoring solution. The **quantum sensor** offers exceptional magnetic-field sensitivity, enabling precise mapping of current flow within the battery. The **AI engine**, operating at the edge within the vehicle, interprets this data to estimate the **State-of-Health (SoH)** and **State-of-Charge (SoC)** of the battery with greater accuracy than existing systems. Together, these technologies provide a **hybrid quantum-classical solution** that supports predictive maintenance, optimised charging, and extended battery life, improving reliability and reducing costs for fleet operators. **Q-BATT** directly supports the UK's **Net Zero** and **transport-decarbonisation goals** by extending the usable life of **EV batteries**, reducing replacement frequency, and enabling **second-life applications** in energy storage. These outcomes contribute to a more **sustainable circular economy** for EV batteries and strengthen the UK's position in the growing **quantum-technology supply chain**. During **Phase 1**, the project will carry out **simulation**, **use-case definition**, and **business-case development** to validate the **technical and commercial feasibility** of the **Quantum-Enhanced BMS**. It will also assess **scalability** across multiple EV platforms and identify opportunities for **Phase 2 prototype testing** with UK fleet operators, building confidence in its **operational readiness** and **real-world applicability**. By combining **advanced quantum sensing** with **explainable AI**, **Q-BATT** aims to deliver a **practical, scalable route** to **smarter, safer, and more sustainable electric-vehicle operations**, driving **innovation**, **reducing maintenance costs**, and ensuring **long-term resilience** within the UK transport ecosystem.