Energy Optimization in Electric Vehicles Dr. Kumeresan A. Danapalasingam Department of Control & Mechatronics Eng. Faculty of Electrical Engineering Universiti Teknologi Malaysia

Electric Vehicles Why energy optimization in an EV?

Electric Vehicles Electric Vehicles Hybrid Electric Vehicles (HEVs) Plug-in Hybrid Electric Vehicles (PHEVS) All-Electric Vehicles (EVs)

Electric Vehicles Powered by an Electric Motor Driving Range Battery stores electrical energy that powers the motor Battery charged by plugging into outside electric power source Zero tailpipe emissions, but air pollution may be produced through electricity generation All-Electric Vehicles (EVs) Driving Range EVs can travel 160-350 km per charge, depending on the model. A 160-km range is sufficient for more than 90% of all U.S. household vehicle trips.

Electric Vehicles To keep EVs running, they need to be charged Level 1: 120 V, alternating current (AC) plug; dedicated circuit, full charge takes 8-20 hours   Level 2: 240 V, AC plug and uses the same connector on the vehicle as Level 1, full charge takes 3-8 hours Level 3: In development; faster AC charging, full charge could take less than 30 minutes DC Fast Charging: Equipment (480 V) provides 50 kW to the battery and can take less than 30 minutes to fully charge a battery Inductive Charging: Installed for early EVs and is still in use in certain areas—possible method of charging for future EVs

Save electrical energy Electric Vehicles Save electrical energy Save cost Save environment Increase range Save time

Electric Power Steering Subsystems in an EV VCM Entertainment, Lighting, Radiator Fan, Display Panel, door, safety unit, wiper, etc DISPLAY BECU Research focus AUX BMCU SAT-NAV MCU Brake Vacuum Pump EPSU E-ACU OBCU DC-DC Converter WPU HV Battery Traction Motor Electric Power Steering AC On-Board Charger E-Water Pump PROPULSION LOAD NON-PROPULSION LOAD

Stage 1 – Non-propulsion load Title: Energy Optimization of Electric Power-Assisted Steering System Abstract Electric power-assisted steering (EPS) is a control system where an electric motor is used to provide assistance in vehicle steering. In this work controllers are designed for a column-type EPS equipped with brushed DC and brushless DC (BLDC) motors to enable energy optimization. Using a mathematical model of EPS a controller is developed based on nonlinear adaptive regulation method to generate driver torque. PID control is then applied to produce assistance torque in accordance to desired energy saving. Simulation results using Matlab show the trade-off between driver’s comfort and energy consumption. The control paradigm introduced here fits appropriately in electric vehicles (EVs) where electrical energy is scarce.

Stage 1 – Non-propulsion load Proposed features: Motor brushed DC motor brushless DC motor Nonlinear tire-road dynamics and friction To produce simulation results that reflect real world scenario To design a feasible controller Nonlinear controller To be able to achieve control objectives in a robustly stabilizing manner Energy optimization Eco factor, E

Stage 1 – Non-propulsion load Mathematical model:

Stage 1 – Non-propulsion load

Stage 1 – Non-propulsion load List of symbols:

Stage 1 – Non-propulsion load Results – Brushed DC

Stage 1 – Non-propulsion load Results – Brushed DC

Stage 2 – Propulsion load Title: Electric Vehicle Traction Control for Energy Optimization Abstract An electric vehicle (EV) with four in-wheel motors offers several advantages over other types of EVs. Due to a limited electrical energy source and a long battery charging time, any ways to minimize energy consumption in an EV has to be fully utilized. Undoubtedly one of the systems in an EV that drains the most amount of energy is the propulsion system. In this work the existence of an optimal slip ratio that enables energy saving in an EV propulsion system is investigated. A controller is designed to ensure the slip ratio of each wheel is limited by the optimal value. Simulation results demonstrate the effectiveness of the proposed traction control scheme in energy optimization in an EV.

Stage 2 – Propulsion load

Stage 2 – Propulsion load Vehicle Dynamics Mathematical model:

Stage 2 – Propulsion load Driver Controller

Stage 2 – Propulsion load Driver Controller Hard suspension

Stage 2 – Propulsion load Driver Controller Soft suspension

Stage 2 – Propulsion load Optimal Slip Ratio

Stage 2 – Propulsion load Simulation Results

Stage 2 – Propulsion load Simulation Results

Stage 2 – Propulsion load Simulation Results

Conclusion Conclusion Electrical energy optimization for a non-propulsion load (EPS) and a propulsion load (propulsion system) are considered. Conventional method of determining target assist motor current using a lookup table is completely eliminated to give way to an approach enabling battery energy saving. Rather that using fixed values of reference assist motor current to generate required assistance torque here an option is enabled to set the level of steering comfort as desired, by means of the eco factor E. For the propulsion load the existence of an optimal value of slip ratio of each wheel of a four-wheel drive electric vehicle (EV) with in-wheel motors is shown. Since exceeding the optimal slip ratio only results in a reduction of longitudinal tire force, it is considered as a waste of the electrical energy. A controller is developed to keep the slip ratio of each wheel below the optimal value.

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