Improve HMI for EV ride-hailing efficiency
Ruoxin Bai · UX Designer 2022.05 - 2022.11
Enhance the ride-sharing driver experience with electric vehicles through a comprehensive matching solution, optimizing everyone’s time from trip allocation to charging.
Efficiency Connection Future Ready
In this project, I design Didi D1 HMI, a complete solution for electric ride-hailing. This platform smoothly integrates intelligent range calculations, optimized batch matching, and charging station management, addressing challenges unique to full-time ride-sharing drivers using electric vehicles (EVs).
Balancing B2C and B2B Needs in Electric Ride-hailing
The D1 initiative, a collaboration between Didi and BYD, aims to revolutionize the ride-sharing industry by introducing a fleet of purpose-built electric vehicles, catering to both B2C and B2B segments. The design balances operational efficiency and user experience. For riders and owner-drivers (B2C), it offers a driver-centric seat with comfort features and a customizable passenger space. For driver-partners and fleet operators (B2B), the D1 provides a cost-effective solution with extended driving range, fast charging, and integration with Didi's platform.
Key Features and My Contributions
Dynamic Heat Maps
Dynamic heat maps with real-time data to optimize routes, reduce idle time, and increase passenger pickups by highlighting high-demand areas.
Intelligent Trip Assignment
Optimizes trip assignments based on battery levels, alerts drivers of low charge, and ensures smooth experiences by avoiding trips beyond the vehicle's range.
Connected Battery Solutions
Cloud-based services and advanced algorithms optimize battery performance, suggest optimal charging locations.
Our Research link of 500 Didi-registered EV drivers shows that range anxiety is cited as the primary concern, with 90% of respondents highlighting it as a significant issue preventing them from using EVs in ride-hailing operations.
The main concern for these drivers is the system's accuracy in planning trips based on battery range. EV ride-hailing drivers often receive trip assignments that exceed their range, causing anxiety and potential service disruptions. Additionally, the availability and wait times at charging stations are critical. Full-time drivers, working 8 to 13 hours a day, lose potential earnings while waiting in queues. Efficiently locating and using charging stations is crucial for them.
To address these issues, I provided accurate range calculations based on real-time battery levels, driving conditions, and charging station availability. This allows full-time ride-sharing drivers to confidently accept trips, knowing the system has accounted for their vehicle's range and charging needs. The platform plans routes with necessary charging stops, ensuring drivers can complete trips without depleting their batteries. By optimizing the charging schedule and location selection, the D1 platform minimizes detours and charging time, enhancing drivers' productivity and earnings.
Driving Efficiency in EV Ride Sharing
I led the development of our dispatch system, which revolutionizes EV ride-sharing by integrating vehicle, ride request, and traffic data to accurately predict battery levels for upcoming trips. This eliminates the need for drivers to manually search for charging stations and alleviates their anxiety about having sufficient range to complete ride requests. By continuously monitoring each vehicle's battery level and estimating the remaining range based on driving conditions, route elevation, and historical battery performance data, the system ensures that drivers are better prepared to meet passenger demands without the risk of mid-trip battery depletion, ultimately enhancing the reliability and efficiency of the service for both drivers and passengers.
Dynamic Heat Maps for Driver Efficiency
Heat maps, with their color gradients, highlight high-demand areas, helping drivers identify potential hotspots. This tool promotes route optimization, reduces idle time, and boosts passenger pickups by providing real-time demand information.
Intelligent Trip Matching and Battery Alerts
The system optimizes trip assignments based on driver's battery level and range. It calculates the remaining mileage, doesn't send requests beyond this distance, and alerts drivers of low battery levels. This design minimizes trip cancellations due to insufficient range, ensuring a smooth experience.
Battery in the Cloud
I developed a comprehensive, modular portfolio of cloud-based services that enhance battery performance and longevity. This approach improves electric vehicle usage by ensuring batteries operate efficiently throughout their extended lifespan. For drivers, this means reduced downtime and increased earning potential. They can have confidence that their vehicle's battery is managed optimally, lowering the likelihood of unexpected breakdowns or charging problems. Fleet operators can continuously monitor battery health and performance. Through proactive maintenance based on battery data analysis, they can predict when maintenance is required. This allows scheduling during off-peak hours, minimizing vehicle downtime and preventing battery-related issues from affecting service.
How the connected battery solutions work?
We use advanced algorithms to suggest optimal charging locations for rideshare drivers, taking into account charger availability, battery charging curves, and expected local ride demand. The system also calculates the battery's state of charge at the end of a trip when riders are dropped off, factoring in the current battery level, charge depletion rate, ambient temperature, speed, and route gradient. Additionally, it assists drivers in finding the best charging spots and effectively manages the charging network for the entire rideshare community.
Order-based Charging Recommendations
The proactive feature enhances driver experience and fleet management by alerting drivers of low battery levels upon ride completion and suggesting optimal charging stations based on cost, distance, and availability. This AI system minimizes driver downtime and ensures efficient use of charging stations, optimizing fleet performance.
Homescreen (old)
Display work-related information on widget on homepage.
Driver's Scene (new)
Hide work-related information on the homepage. Instead, separate ride-hailing information from daily use and display it only when the driver switches to the ride-hailing mode.
Driver's app (old)
Displays shortcuts that redirect to functions.
Driver's scene (new)
Displays an organized view of functions and maps in the ride-hailing mode. Tasks like taking orders, choosing assignments, viewing demands and deciding routes can all be completed on one screen without the need to switch between apps.
Arrive at Pickup Location (Old)
Displays direction, order, passenger information all at once.
Arrive at Pickup Location (New)
Prioritize passenger-related actions in the ride-hailing mode. Reduce distractions by preventing multiple threads of information and calls-to-action from appearing while driving.
Why is smart rider-driver matching crucial in the era of electric vehicles?
We believe that ultimate efficiency leads to the ultimate user experience. The smart design of batch-matching systems accelerates the transition to a superior user experience in the robotaxi era. As battery technology improves, enabling longer ranges and faster charging, advanced AI algorithms will efficiently match passengers with vehicles, optimize routes, and schedule predictive maintenance. This results in minimal wait times, reduced empty miles, and maximum fleet utilization. Leveraging vast data, AI models will continuously learn and adapt, delivering a seamless, on-demand transportation service that is both efficient and user-centric.
