Energy Systems & Storage

Highly specialised requests are first checked for feasibility from this information. Please do not send confidential data through the public form; describe it first and agree secure transfer afterwards.

1. 01Enquiry and requirement assessment
2. 02Technical scoping and proposal
3. 03Execution
4. 04Quality review and delivery, with an agreed round of revisions

Read the full project workflow.

In depth: how the work is done

1. Battery performance modelling

   Equivalent-circuit models represent voltage response with resistances and capacitances whose parameters depend on state of charge, temperature and current, and are identified from pulse or drive-cycle tests. Physics-based models, such as single-particle or pseudo-two-dimensional models, give insight into internal processes with more parameters. We help choose the model, identify parameters with uncertainty and validate on tests not used for fitting.

2. Thermal and electrochemical analysis

   Heat generation couples electrochemistry and temperature, affecting performance and safety. Thermal models of cells and packs predict temperature distribution under cooling strategies. Thermal energy storage with phase-change materials or sensible heat involves heat-transfer analysis, charging and discharging rates and losses. Results are presented as research findings with measurement-based validation.

3. Dispatch and sizing optimisation

   Storage dispatch optimisation decides when to charge and discharge given prices, forecasts and constraints. Sizing couples capacity, power rating and operation over the lifetime. Formulations include linear and mixed-integer programmes, and results depend on assumed efficiency, degradation and perfect or imperfect foresight. We analyse sensitivity to these and distinguish upper bounds from achievable operation.

4. Degradation data analysis

   Capacity fade and resistance growth depend on temperature, state of charge, rate and cycling depth, with calendar and cycle components. Statistical analysis must handle cell-to-cell variation, censored tests and non-linear fade including knee points. Extrapolating beyond tested duration is uncertain, and we present projections with intervals and the model assumptions.

5. Hydrogen and other storage pathways

   Power-to-hydrogen-to-power chains have low round-trip efficiency but large capacity and duration. Comparisons across storage types should use consistent boundaries, including conversion losses, degradation and cost. We help structure comparisons and avoid conclusions that depend on a single metric.

6. Safety and standards awareness

   Storage involves energy hazards: thermal runaway in lithium-ion cells, hydrogen flammability, high pressures. Research reports should not suggest that modelling alone establishes safety. Relevant standards guide testing and installation, and compliance rests with qualified professionals and accredited tests.

7. Test protocols for cells and stores

   Characterisation tests should follow documented protocols: temperature control, rest periods, current profiles and cut-off criteria. Reference performance tests repeated periodically show capacity and resistance changes. For thermal stores, charging and discharging rates, inlet temperatures and heat losses need recording. Providing test protocols and raw data with a publication allows models to be reproduced and compared, which the storage field needs because results from different protocols are hard to compare.

8. Operation strategies and real-world constraints

   Real storage operation faces forecast error, communication delays, ramp limits and maintenance outages. Studies that include these constraints give more realistic value estimates. Rolling-horizon operation with imperfect forecasts is a better proxy than perfect foresight. Reporting results for both cases brackets what can be achieved and shows how much is lost to uncertainty.

9. Comparing storage technologies fairly

   Comparisons across storage technologies often mix different durations, power ratings and applications. A fair comparison defines the service required, such as daily cycling for eight hours or seasonal storage, then evaluates each technology on the same service with realistic efficiency, degradation and cost. Results are presented for several service definitions, showing where each technology is competitive, instead of a single ranking that depends on one assumed use case.

Common pitfalls this service helps avoid

• Fitting an equivalent-circuit model on one test. Parameters may not transfer to other conditions.
• Ignoring degradation in sizing. Capacity declines over life, changing economic results.
• Assuming perfect foresight in dispatch. This overstates achievable value.
• Extrapolating short ageing tests. Fade rates can change abruptly after a knee point.

What good reporting looks like

A storage report describes the technology and test data, the model and parameter identification, the validation, the thermal analysis, the optimisation formulation and assumptions, the degradation analysis with intervals, the sensitivity studies and the limitations. It makes no safety or compliance claims, which depend on standards-based testing by accredited organisations.

An illustrative scenario

Hypothetical example written for explanation. It is not a client case, a result or a testimonial.

Imagine a researcher sizing a battery for peak shaving. An optimisation with perfect foresight shows large savings, but with a simple forecast the savings fall by a third. Including degradation reduces the optimal size. The report presents the three cases, notes that the degradation model is based on a manufacturer's cycle-life data for a single temperature, and recommends testing at the actual operating temperature.

Questions to settle before you begin

• Which temperatures and rates must my model cover?
• Do I have separate data to validate parameters?
• How good are my forecasts relative to perfect foresight?
• What degradation mechanisms matter for my duty?
• Which safety standards apply to my application?

Key terms

State of health

A measure of present capacity relative to new.

Equivalent-circuit model

A model using resistors and capacitors to represent cell behaviour.

Round-trip efficiency

Energy out divided by energy in over a storage cycle.

Phase-change material

A material storing heat during phase transitions.

Knee point

Where capacity fade accelerates.

Working together on Environmental, Energy & Sustainability Research projects

Environmental, energy and sustainability engagements begin with the system boundary and the question: emissions, efficiency, resource use, performance of a technology or the effect of a policy or design choice. We ask for data, assumptions and the standards or frameworks you intend to follow. Analyses document boundaries, data sources and uncertainty, and they avoid presenting model outputs as measured facts. Reports state clearly which results depend on assumptions you supplied, and they do not constitute regulatory submissions or formal environmental assessments.

Few projects need a single service in isolation. These are the services most often combined with this one.

• Renewable Energy Research

  Research support on solar, wind, bioenergy and other renewables: resource assessment, performance modelling, system sizing and techno-economic and environmental evaluation.

• Heat Transfer & Thermal Simulation

  Conduction, convection and radiation analysis of components and systems, including conjugate heat transfer, transient heating and cooling, and thermal management studies.

• Electrical Power Systems & Smart Grids

  Modelling and data analysis for power systems and smart grids: load flow and stability studies, renewable integration, and analytics on grid or consumption data.

• Automotive & Electric Vehicle Engineering

  Research support for conventional and electric vehicles: vehicle dynamics, structures, thermal management, energy consumption and battery or powertrain analysis.