Report: Algorithm Analysis for Hybrid Electric Vehicle Systems

This report analyzes the algorithm used in Hybrid Electric Vehicles (HEVs) to optimize energy management and improve fuel efficiency. The core of the system lies in the Onboard Energy Management System (EMS), which balances the internal combustion engine and electric motor. The new algorithm, combining Discrete State Spatial Dynamic Programming (DP) and Pontreagin's Maximum Principle (PMP), calculates optimal energy usage up to 100,000 times faster than current systems, potentially increasing fuel efficiency by up to 50%. The report details the algorithm's steps, including inputs, variables, operations, and outputs, alongside a transition diagram. It also highlights the advantages of the modifications, such as improved acceleration, suspension, and cooling. A comparative analysis demonstrates significant fuel savings and overall cost benefits of hybrid cars over conventional vehicles. The report concludes with cited references supporting the findings.

Algorithm - Hybrid electric vehicles
Hybrid electric vehicles (HEVs) rely on the Onboard Energy Management System (EMS) to
maintain an adequate balance between internal combustion engines as well as electric motors
that reduce fuel consumption as well as CO2 emissions. The new algorithm can calculate the
optimal energy usage at up to 100,000 times the speed of the system currently in use.
Researchers say this method can increase fuel efficiency by up to 50%.
EMS must be able to shift the output very accurately between the internal combustion engine as
well as the electric motor. This shift is determined by an algorithm that takes into account the
state as well as environment of multiple vehicles, such as when the pedal is positioned or idle.
"Today's car has an adaptive cruise control system that keeps the speed set by the driver. If
you're driving at a speed lower than the set speed, follow the car in front.""Our research is
working on expanding these systems that change speed while maintaining the same travel time to
save fuel for hybrid electric cars. (Khanh & Gao, 2017) "
The new algorithm combines Discrete State Spatial Dynamic Programming (DP), one of the
most common EMS controls, as well as Pontreagin's Maximum Principle (PMP) to solve the
optimal control of HEV speed, battery energy, engine state, as well as gear at a calculation rate
of 100,000 times the speed of the benchmark technique. The combination of DP as well as PMP
overcomes the "dimensional curse" of DP. DP increases the amount of computation
exponentially with the number of state variables. PMP simplifies calculation overload problems
by taking battery energy limits, gears, as well as internal combustion engine on/off as control
signals, not state variables (NUR, 2017).
"The PMP has proven to be ideal for some kind of optimal control problem," explained Uebel.
"By adapting the optimal control problem solved by DP, our approach reduces the time to
compute the optimal control problem with similar solution quality."

Below are the Algorithm Steps –
Step 1 Input 4 stroke engine, new model AC filters, high bass sound system, disk brakes
and high suspension
Step 2 Variables Height and length of the vehicle during suspension
Step 3 Operations Conditional operations, iteration, sequential even branching
Step 4 Output Cooling, good acceleration, good sound and good suspension
Transition Diagram

Advantages of the improvements –
1. It accelerates fast due to 4 stroke engine.
2. Its suspension is high and balancing is good.
3. It cools the car immediately.
4. It doesn’t affect must because the modifications are suitable for all the users.
5. The hybrid car averages 45 miles/gallon. So, the oil requirement of the hybrid car in a
year is 20,000/45 = 444.44 gallon. The conventional car averages 25 miles/gallon. So, the
oil requirement of the conventional car in a year is 20,000/25 = 800 gallon. So, the hybrid
car costs $5000 more however the hybrid car saves fuel worth $20472.48 in 10 years.
Therefore, the hybrid car provides a net benefit = $20472.48 – $5000 = $15,472.48.
References
Khanh, D., & Gao, F. (2017). Multi-period Dynamic Optimization Model for Plug-in Hybrid
Electric Vehicles and Electric Vehicles Charging Service Station. International Journal
Of Hybrid Information Technology, 10(3), 67-82. doi: 10.14257/ijhit.2017.10.3.07
NUR, A. (2017). An overview of battery electric vehicles and plug-in hybrid electric vehicles.
International journal of advances on automotive and technology. doi:
10.15659/ijaat.17.04.527