Robust real-time control of a parallel hybrid electric vehicle

  • Wisdom Enang

Student thesis: Doctoral ThesisPhD


The gradual decline in global oil reserves and the presence of ever so stringent emissions rules around the world have created an urgent need for the production of automobiles with improved fuel economy. HEVs (hybrid electric vehicles) have proved a viable option to guaranteeing improved fuel economy and reduced emissions. The fuel consumption benefits which can be realised when utilising HEV architecture are dependent on how much braking energy is regenerated, and how well the regenerated energy is utilised. The challenge in developing a real-time HEV control strategy lies in the satisfaction of often conflicting control constraints involving fuel consumption, emissions and driveability without over-depleting the battery state of charge at the end of the defined driving cycle.Reviewed literature indicates some research gaps and hence exploitable study areas for which this thesis intends to address. For example, despite the research advances made, HEV energy management is still lacking in several key areas: optimisation of braking energy regeneration; real-time sub-optimal control of HEV for robustness, charge sustenance and fuel reduction; and real-time vehicle speed control. Consequently, this thesis aims to primarily develop novel real-time near-optimal control strategies for a parallel HEV, with a view to achieving robustness, fuel savings and charge sustenance simultaneously, under various levels of obtainable driving information (no route preview information, partial route preview information).Using a validated HEV dynamic simulation model, the following novel formulations are proposed in this thesis and subsequently evaluated in real time:1. A simple grouping system useful for classifying standard and real-world driving cycles on the basis of aggressivity and road type.2. A simple and effective near-optimal heuristic control strategy with no access to route preview information.3. A dynamic programming-inspired real-time near-optimal control strategy with no access to route preview information.4. An ECMS (Equivalent Consumption Minimisation Strategy) inspired real-time near-optimal control strategy with no access to route preview information.5. An ECMS-inspired real-time near-optimal control strategy with partial access to route preview information.6. A dynamic programming based route-optimal vehicle speed control strategy which accounts for real-time dynamic effects like engine braking, while solving an optimisation problem involving the maximisation of fuel savings with little or no penalty to trip time.7. A real-time vehicle speed control approach, which is based on smoothing the speed trajectory of the lead vehicle, consequently reducing the acceleration and deceleration events that the intelligent vehicle (follower vehicle) will undergo. This smoothing effect translates into reduced fuel consumption, which tends to increase with increasing traffic preview window.Among other studies performed in this thesis, the fuel savings potential of the proposed near-optimal controllers was investigated in real time over standard driving cycles and real-world driving profiles. Results from these analyses show that, over standard driving cycles, properly formulated near-optimal real-time controllers are able to achieve a fuel savings potential within 0.03% to 3.71% of the global optimal performance, without requiring any access to route preview information. It was also shown that as much as 2.44% extra fuel savings could be achieved over a driving route, through the incorporation of route preview information into a real-time controller.Investigations were also made into the real-time fuel savings that could be realised over a driving route, through vehicle speed control. Results from these analyses show that, compared to an HEV technology which comes at a bigger cost, far higher fuel savings, as much as 45.96%, could be achieved through a simple real-time vehicle speed control approach.
Date of Award27 Jun 2017
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorChristopher Bannister (Supervisor) & Chris Brace (Supervisor)


  • Hybrid vehicle
  • Control
  • Electric vehicle
  • automotive engineering

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