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User-centred Design and Control of Electric Vehicles with Smart E-Corners

ULLC E-Lab > Courses > SmartCorners > User-centred Design and Control of Electric Vehicles with Smart E-Corners

User-centred Design and Control of Electric Vehicles with Smart E-Corners

Categories: SmartCorners
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What Will You Learn?

  • Explain how propulsion, braking, steering geometry, suspension and sensing can be integrated at the vehicle corner.
  • Relate SmartCorners’ five asset families to safety, efficiency, comfort, adaptability and development speed.
  • Distinguish a model, a digital twin, a virtual sensor and an XiL validation stage, and select an appropriate level of fidelity.
  • Compare rule-based, optimisation-based and learning-enabled control while preserving safety constraints and fallback behaviour.
  • Frame thermal comfort as a user-centred, multi-domain energy-management problem.
  • Evaluate a smart-corner concept across vehicle, passenger, operator, infrastructure, policy and business perspectives.
  • Communicate project targets, interim results and validated evidence without overstating maturity.
  • Design and defend an urban electric-vehicle concept using an evidence chain from need to architecture, control, validation and adoption.

Course Content

Module 1: The SmartCorners vision
Why treat the wheel corner as a system rather than a collection of separate components? By the end of this module, you can: 1. describe a smart e-corner and the functions brought together at each wheel; 2. map the five SmartCorners asset families to vehicle and urban-mobility outcomes; 3. identify the main stakeholder groups and three priority urban use-case families; 4. separate an expected impact or KPI target from an achieved result; and 5. write a concise mobility need statement that is specific enough to guide engineering choices.

  • Lesson 1.1. Start with the mobility need
  • Lesson 1.2. What is a smart e-corner?
  • Lesson 1.3. The five asset families
  • Lesson 1.4. Read the evidence correctly
  • Lesson 1.5. Stakeholders and urban use cases
  • Applied activity: Write the opportunity statement
  • Module 1: knowledge check

Module 2: E-corner design and digital twins
How can an integrated wheel-corner concept be designed, tested and trusted before every vehicle-level trial is possible? By the end of this module, you can: 1. Explain the main mechanical, electrical, thermal and software interfaces of an e-corner; 2. Analyse trade-offs created by in-wheel propulsion and active suspension; 3. Distinguish a simulation model, virtual sensor, digital twin and surrogate model; 4. Select suitable model fidelity and XiL stages for an engineering decision; and 5. Write a verification and validation claim with scope, metric and acceptance criterion.

Module 3: Thermal management and personalised cabin comfort
How can an electric vehicle keep people and components comfortable without spending energy blindly? By the end of this module, you can: 1. explain why cabin, battery and e-drive thermal management are coupled; 2. compare single-zone, multi-zone and detailed human-comfort models; 3. describe the roles of baseline logic, optimisation and reinforcement learning; 4. design a predictive preconditioning concept with user input and safety constraints; and 5. define comfort, energy and component-protection evidence for a cold-weather use case. Quick explainer: Comfort is a control objective, not a thermostat number An internal-combustion vehicle can use abundant waste heat to warm the cabin. In a battery-electric vehicle, heating may draw energy that could otherwise move the vehicle. Cooling, demisting and battery conditioning also consume power. The thermal controller is therefore negotiating between passenger comfort, visibility, component life, charging performance and route range. A single cabin-air temperature does not tell the whole comfort story. Solar radiation, surface temperatures, humidity, air speed, clothing, activity and individual preference all affect sensation. Two passengers in the same cabin may want different conditions. A user-centred system therefore estimates or receives comfort information, observes the thermal state of the cabin and components, and chooses actions that meet hard protection and visibility constraints while using energy efficiently. SmartCorners explores a hierarchy of models. A simple cabin model can run quickly for early control work. A multi-zone model can represent differences between seats or body regions. Detailed three-dimensional models can reveal local airflow and surface effects. Reduced models can then carry the useful behaviour into real-time control or large training campaigns. The control architecture also uses layers. A conventional rule-based and state-machine controller provides interpretable baseline behaviour and protected operating modes. Optimisation or reinforcement learning can search for better sequences, adapt to user preference or make use of route, weather and charging context. Learning does not remove engineering responsibility: its action space, reward, data and fallback must be designed so that safety, demisting and component limits are never treated as optional comfort preferences.

Module 4: Predictive motion control and fleet intelligence
How can a vehicle coordinate many wheel-level actuators, anticipate the road, and remain safe when data or components fail? By the end of this module, you can: 1. explain over-actuation and control allocation in an independent-corner vehicle; 2. describe the hierarchy from local actuator loops to vehicle and mission control; 3. compare rule-based, optimisation-based and learned control strategies; 4. design the split between on-board safety and fleet/cloud intelligence; and 5. define evidence for a low-grip, rough-road and fault scenario. Quick explainer: More actuators create choices A driver asks the vehicle to accelerate, brake or turn. An automated-driving system issues similar motion requests. In a conventional architecture, the mapping from request to actuator may be largely fixed. With four independently driven corners, active suspension and adaptable steering geometry, several combinations of commands can produce a similar overall force or moment. The vehicle is over-actuated: it has more control inputs than strictly necessary for the requested motion. Redundancy can improve performance and fault tolerance. Torque can be shifted to a wheel with more available grip; regenerative braking can be balanced with stability and battery limits; suspension forces can reduce body motion; rear steering or wheel geometry can support path tracking. But the controller must decide who does what, how quickly and within which constraints. SmartCorners uses a hierarchy. Fast actuator loops close near the hardware. A coordinated layer allocates wheel torque, braking, suspension force and geometry. A vehicle supervisor interprets motion intent, checks limits and manages degraded modes. Mission or fleet services may add route, roughness, grip or maintenance context. The essential principle is local autonomy plus remote intelligence: cloud information may improve anticipation, but the vehicle’s safe stabilising behaviour remains on board. Predictive control uses a model to evaluate future consequences over a horizon. Nonlinear model-predictive control can coordinate nonlinear tyre and actuator behaviour but may be computationally demanding. Learned surrogates or imitation learning can approximate a high-quality controller; reinforcement learning can adapt weights or policies; online learning can infer intent. Each technique introduces an assurance question. The evidence chain must include constraints, scenario coverage, timing, fallback and error outside the training envelope.

Module 5: From vehicle innovation to the urban mobility ecosystem
What must align for smart e-corners to create durable public and business value? By the end of this module, you can: 1. map stakeholders by need, influence, risk and contribution; 2. translate technical features into user, operator, city and business outcomes; 3. identify adoption barriers involving cost, charging, trust, data and compatibility; 4. apply a co-creation and evidence loop to refine a use case; and 5. outline a responsible replication, standardisation and exploitation pathway. Quick explainer: Adoption is a system design problem A technically successful corner module can still fail to create value. An operator may not accept long repair times. A city may need accessible service outcomes rather than a specific mechanism. A supplier may face uncertain volume and interface standards. Passengers may distrust data collection or automated decisions. Charging and depot infrastructure may not support the promised duty cycle. SmartCorners’ ecosystem work therefore treats stakeholder engagement as an iterative design input. Identify actors and needs; co-create use cases and value propositions; evaluate evidence, risks and feasibility; then adapt the design, business model and communication. The process is not a final dissemination step. It can change technical requirements. Feature-to-value translation is essential. ‘Independent wheel torque’ is a feature. ‘More stable low-grip path tracking under a validated scenario’ is a performance claim. ‘Fewer service interruptions or safer accessible transport’ is an outcome that still requires operational evidence. Each arrow in that chain carries assumptions. Course learners will make those assumptions visible. Adoption also depends on rules and shared infrastructure. Functional safety, cybersecurity, software-update governance, SOTIF, AI governance, braking and steering requirements, electromagnetic compatibility and environmental durability all shape development. Modular wheel corners may benefit from clearer mechanical, electrical and software interfaces. The Advanced Wheel Control Community provides a possible post-project forum for research, standardisation and ecosystem continuity.

Final Assessment

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